The Market Mind Hypothesis: Understanding Markets and Minds Through Cognitive Economics 9783111215051, 9783111211619

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Patrick Schotanus The Market Mind Hypothesis

Patrick Schotanus

The Market Mind Hypothesis Understanding Markets and Minds Through Cognitive Economics

ISBN 978-3-11-121161-9 e-ISBN (PDF) 978-3-11-121505-1 e-ISBN (EPUB) 978-3-11-121561-7 Library of Congress Control Number: 2023940885 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the internet at http://dnb.dnb.de. © 2023 Walter de Gruyter GmbH, Berlin/Boston Cover image: Hybert Design Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Advance Praise for The Market Mind Hypothesis: Understanding Markets and Minds Through Cognitive Economics Patrick Schotanus is a deep thinker and should be listened to. Kiril Sokoloff Founder and CEO 13D Research & Strategy This book is a fascinating analysis of an area ignored in traditional economics: the interaction between the market and how people collectively learn and change their mind. Mervyn King Former Governor Bank of England Author of The End of Alchemy and co-author of Radical Uncertainty Do markets really have a mind? Do they have moods? What can this mean, and how might it impact the way we behave and manage risk? In this unique and compelling treatment, Patrick Schotanus weaves together theoretical economics and the science and philosophy of extended cognitive systems. Essential reading for anyone interested in markets - or minds! Andy Clark FBA, FRSE Professor of Cognitive Philosophy, University of Sussex Author of Supersizing the Mind: Embodiment, Action, and Cognitive Extension, and Surfing Uncertainty: Prediction, Action, and the Embodied Mind This is a visionary book that sets the scene for a paradigm shift in economic thinking: namely, a shift from behavioural economics to cognitive economics. This move is motivated by an eclectic appeal to crosscutting themes in economics and cognitive (neuro) science. For example, the dialectic between brain and mind — and ensuing considerations of consciousness — and how this could frame a new way of thinking about the Market. Cognition and consciousness get into the game at two levels; namely, the fact that decisions and choices are made by conscious people, on the one hand. On the other hand, there is the intriguing notion of a collective consciousness that supervenes on individual decisions and transactions. This kind of collective (planning as) inference is now an important theme in many areas of neuroscience and ethology; for example, the notion of federated inference or distributed cognition and how it manifests in terms of (e.g., cultural) niche construction. This book offers a truly novel and compelling view of the Market — a view not only of how the Market works but a vision of how it could and should work in the future. Karl Friston Professor of Neuroscience, University College London Co-author of Principles of Brain Dynamics

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The research agenda suggested by this book provides a perspective to integrate the science of heuristics with economic theory. That is, to move from the bias-centered interpretation of behavioral economics to a more cognitive-oriented economics. For instance, agent-based models already assume that agents interact via adaptive rules, and this perspective can provide a formal basis for a revision of macroeconomic models that deal with heterogeneous behavior and uncertain environments. It is highly recommended reading. Gerd Gigerenzer Director of the Harding Center for Risk Literacy, University of Potsdam Author of Gut Feelings and Reckoning With Risk In this book, Patrick provides a deep and deeply challenging philosophical dive into the interaction of economics, markets and the human mind. The book is breathtakingly broad in the material and fields covered, a rarity in today’s world of micro-orientated specialization. It is this breath that provides the power of the central concept — the market mind hypothesis — which is essentially an emergent property of a complex adaptive system. I urge you to read this book, expand your understanding and let Patrick be your guide on a fascinating journey of discovery. James Montier Partner GMO and member of its Asset Allocation team Author of Behavioural Investing The “physics-envy” of academic economics has led the discipline down an intellectual cul-de-sac. Messier but more accurate frameworks have been subordinated to cleaner but less accurate ones on the spurious grounds of mathematical tractability. Patrick Schotanus’ ‘big idea’—that markets exhibit characteristics and properties more similar to the human mind than to Newton’s solar system—is an impressive and refreshing attempt to get things back on track. Inspiring stuff. Dylan Grice Co-founder Calderwood Capital The Market Mind Hypothesis (MMH) from Patrick Schotanus helps us move financial market analysis from the simply mechanistic approaches such as CAPM, to well beyond the developments already established through the behavioural economics approach that has gained ground in recent years. It has long been recognised that ‘Mr Market’ has a mind of its own—and one that often runs counter to/or in advance of what traditional economic and valuation analysis might suggest. This work begins to formalize a framework for thinking about how ‘Mr Market’ works. Patrick Schotanus builds on his many years of practical investing with leading institutions to interweave a novel theoretical model of how markets function, combining the physical aka ‘the economy’ with the psychological financial economy aka ‘the market’, to show how we can start to better understand this ‘market mind’.

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Patrick helps us understand how the ‘market mind’ balances the well-recognized conflict between ‘fear’ and ‘greed’. He shows how rather than markets being ‘neutral observers’ of the dramatic economic market events that have shaped the last three decades such as the LTCM crisis of 1998, and the subsequent blow-up of the Tech bubble, the GFC and the COVID crises, Mr Market is an active participant responding to, and often amplifying, both the risks and returns with its various ‘market moods’, at times shifting rapidly from despair to exuberance. This is a valuable new insight into the way markets behave and an exciting new lens for both academics and practitioners to view markets and market developments. Ian Harnett Co-founder and Chief Investment Strategist Absolute Strategy Research Every investor is the fruit of his or her own experiences; experiences that create habits, biases and blind-spots. And very rarely, a book comes around to challenge established patterns and encourages the reader to look at markets, the broader economy and even one’s own life through a new lens. This is such a book. The Market Mind Hypothesis offers a different approach to financial markets and economics, one that goes beyond the breakthroughs accomplished by behavioural economists over recent decades, and fully embraces cognitive science, a new multidisciplinary field that includes Artificial Intelligence (AI), neuroscience, philosophy, psychology, and sociology. A must read! Louis-Vincent Gave Founding Partner and Chief Executive Officer Gavekal In this interesting and original book, Dr. Patrick Schotanus takes dead seriously the everyday anthropomorphizing talk about what the market thinks and develops the idea that the market does have a mind, a personality, moods, that emerge collectively from its participants. And after all, what are markets, stock prices, interest rates, volatility, and default rates if not mental rather than physical phenomena? Emanuel Derman Professor, Columbia University, and Director of its Financial Engineering Program Author of My Life as a Quant and Models. Behaving. Badly. Patrick is an innovative and courageous thinker, rarely swayed by the ‘received wisdom’. In this simultaneously well-researched and unorthodox book, he exposes the fault lines of classical decision-making theory and implores the profession to move beyond even behavioural economics and to consider how cognitive sciences can improve our understanding of economic decision-making. Larry Hatheway Co-founder Jackson Hole Economics and former Chief Economist UBS

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A must read and a fascinating new way of understanding how economies – and markets – work. If the last few decades have taught us anything, it is that we don’t really understand them. This important book offers new thinking that might help us do better. A deeply necessary addition to the debate. The economy is all too often treated as a machine. But if all the actors in it have a consciousness, might it not have one of its own? This intriguing new book explains how that mind rules our world. Merryn Somerset-Webb Senior Columnist Bloomberg and former Editor in Chief of Moneyweek As someone who has written about the need for new theories and understandings of cognitive economics I was delighted to read this book, which introduces the Market Mind Hypothesis and sets out an ambitious research agenda. I’m hopeful that we will see a stream of significant work in cognitive economics, not only from economics, but also drawing on insights from other fields, varying from biology and neuroscience to psychology and computer science. The aim should be to show that the workings of ‘invisible hands’ are not something magical but are rather amenable to rigorous analysis. Sir Geoff Mulgan Professor, University College London, and Editor in Chief, Collective Intelligence (Sage/ACM) Author of Big Mind; How Collective Intelligence Can Change Our World

To Marilyn, Nimeesha, and Jazzlyn

Acknowledgements You have no responsibility to live up to what other people think you ought to accomplish. Richard Feynman

This book is the culmination of many years of research, study, and work, including almost 30 years in investment management.1 It’s impossible to retrace the exact origins of its ideas as most of the major philosophies and theories of both cognitive science and economics have left their mark. The numerous quotes and references give you some clues. At the same time, as a Jack of all trades but master of none, I like to invoke Deirdre McCloskey’s excuse: “I must apologize for my amateurish understanding of what is happening in philosophy, mathematics, literary criticism, rhetorical studies, and other places beyond my competence, and ask that practitioners in these fields assist in my further education”. Below I want to express my gratitude more personally to those who did assist and helped shape this book in one way or another.2 My parents—Jan and Diet, who I miss every day—provided the physical and mental foundation for all my creations. My sister Joyce is my pivotal ‘backstop’. I could not have asked for a more sound and stable family grounding, being raised in that unremarkable Dutch city of Almelo, of which local comedian Herman Finkers famously stated: Een stoplicht springt op rood, een ander weer op groen. In Almelo is altijd wat te doen.3

Everything has built up from there, peaking with the joy and pride of my own family —Marilyn, Nimeesha, and Jazzlyn—who supported me during this arduous chase of a dream (despite suffering its consequences). This book is to you, girls. I also cherish the memories of my ‘American family’ in Mill Valley—Alan, Miriam, and the rest of the Burdick-clan—during my extended and transformative stays in the United States. My teachers—from primary to university—prepared me for my eventual career. A few stood out. Meneer Bonekamp, head-teacher at the St. Stephanus School, and Meneer Welling, math teacher at the Pius X College, were exemplary in being ‘oldschool’: clear, fair, and frank. As part of my Dutch MBA I spent my internship in the San Francisco Bay Area where I had the pleasure and privilege to work with Willis Harman. Willis was professor emeritus at Stanford University, a cognitive pioneer, and a visionary futurist. He kindly introduced me in person to numerous other thought leaders—from corporate trailblazers to mind explorers—many of whom I ad-

 Some sections have appeared previously elsewhere and have been reprinted, often in revised form.  Some of whom are, unfortunately, no longer with us.  Freely translated (to make it rhyme): A traffic light turns red, another turns green. In Almelo is always something to be seen. https://doi.org/10.1515/9783111215051-202

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mired as a student, like Fritjof Capra (The Turning Point),4 Gary Zukav (The Dancing Wu Li Masters), and Robert Waterman (co-author of In Search of Excellence). Crucially, Willis influenced my early thinking on consciousness and encouraged me to do my bit in understanding its role in the economy and society at large. I’m also grateful to my University of Groningen academic supervisor Caroline Quispel, a leading expert in business and personal transformation.5 She guided me in my early explorations and helped to arrange this internship. Those first years of my academic life were also greatly shaped by (often sharp-tongued, regularly booze fuelled) discussions with fellow students. I particularly valued the company of Rob ter Brugge, Marcel Gerla, Michiel Goris, Imelda Gorman, Han Hegeman, Dominique Jones, Henk Koezen, Veronique Morat, Jeroen Nijhuis, Maurice van der Putten, Paul Rijk, Cap Sprokel, Roel Teule, Tycho Veenhuizen, Peter Westerink, and Johannes Ziegler. Much later, during my (part-time) PhD at the University of Essex, I received constructive criticism, encouragement and guidance from my dual-faculty supervisors Roderick Main (psychology) and Andrew Wood (finance), as well as my examiners and several external advisors, including Ralph Acampora and Robin Robertson. More recently, this book has greatly benefited from conversations, discussions, and other exchanges with several pre-eminent cognitive scientists (some of whom already acted as the other external advisors to my PhD). Among them are the ‘cognitive’ speakers and moderators of our May 2022 symposium in Panmure House, of which I’d like to name a few: Vivienne Brown, Christel Fricke, Gerd Gigerenzer, Sam Johnson, Julian Kiverstein, Geoff Mulgan, Sören Overgaard, Shannon Vallor, and (especially) Karl Friston and Scott Kelso. I’m particularly grateful to the MMH’s earliest line-up of brilliant advisors and collaborators: Ron Chrisley, Andy Clark, Duncan Pritchard, Dave Ward, and Aaron Schurger. I would also like to thank Harald Atmanspacher, Uziel Awret, Richard Baker, Charles Card, David Laveman, Thomas Metzinger, Robert Prentner, Michael Silberstein, Bill Seager, and the other fellow members of the Society of Mind Matter Research (SMMR). Comments in private conversations and correspondence by David Chalmers, Antonio Damasio, Stanislas Dehaene, Vittorio Gallese, Christof Koch, Orestis Palermos, Jaak Panksepp, Anil Seth, and Giulio Tononi are gratefully acknowledged. I hope this book will refresh and intensify those. In similar vein, a large number of academics in economics and finance have tried to educate me over the years. I especially appreciate the efforts by the outstanding faculty of the Master of Financial Engineering (MFE) program at UC Berkeley, in particular Mark Rubinstein, Rich Lyons, Terry Odean, Francis Longstaff, Philippe Jorion, Hayne Leland, Jonathan Berk and Nils Hakansson. I have fond memories of and thoroughly enjoyed collaborating with my fellow MFE students: Robert Bentson, Albert Chan, Jim Gilliland, Karim-Patrick Khiar, Chulki Kim, Ed Lee, Jun Leng, Ben Meng,

 P.S.: You were right, Fritjof, and I was wrong. Please consider this book my mea culpa.  And for those who wonder, yes, daughter of Gilles Quispel.

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Alex Pabon, Michael Surowiecki, and the rest of ‘Rubinstein’s guinea pigs’ in that eclectic (and now somewhat notorious) inaugural class. I thank the MFE’s former director, Linda Kreitzman, for allowing me in (despite having been a technical analyst). I also received support, during various phases, from a few expert advisors, in particular Richard Meese (my MFE-internship boss and former head of Barclays Global Investors’ FX division), and Hank Pruden (my mentor in technical analysis). Other academics and policy makers—for example as participants in our symposium or as proof-readers—generously shared their macro, micro, finance, and policy views: Tobias Adrian, Robert Axtell, Jo Danbolt, Sheila Dow, Andy Haldane, Andrew Hauser, Eddie Jones, John Kay, Robbie Mochrie, Adair Turner, Mervyn King, and Bill White. Over the years I met many investment professionals, both on the buy and sell side. For practitioners it is arguably easier to accept the concept of the market’s mind. I’ll mention a few of them who have kindly offered both supportive and critical comments on my views, for example expressed during our 2022 symposium: Evangelos Assimakos, David Bowers, David Bowie, Guy Cameron, Juan Ramón Caridad, James Clunie, Emanuel Derman, Louis-Vincent Gave, Alastair Gill, Duncan McInnes, Andrew Milligan, Gareth Murphy, Jim Grant, Dylan Grice, Elwin de Groot, Hal Haig, Ian Harnett, Larry Hatheway, Anatole Kaletsky, Will Kinlaw, David Long, James Montier, John Normand, and Keith Skeoch. I am especially grateful to three living investment legends: Howard Marks, Kiril Sokoloff, and George Soros. But most of all, I want to thank Russell Napier who has been my staunchest advocate and my brother-in-arms for new enlightenment in economics. He is best known for his cult (investment) classic Anatomy of the Bear (2005) and his course A Practical History of Financial Markets (which I attended many years ago). My day-to-day interactions with former colleagues and other investment professionals—be it by co-managing portfolios, creating models, or generally discussing investment themes—have been very instructive and have enhanced my understanding of the market. Other colleagues with backgrounds in cognitive science raised my understanding of our mentality. All helped to make a Dutchman feel at home, wherever that was. I’d particularly like to thank some of those international colleagues and fellow expats over the years: Rogier van Aart, Evan Agapitou, David Brown, Roberto Carulli, Jaco Cebula, Elaine Crichton, Bill Dinning, Colin Dryburgh, Bettina Edmondston, Andrew Fleming, Scott Fleming, Wink Franklin, Stuart Fraser, Gareth Gettinby, Simon Holman, Tom Hurley, Sander van Ittersum, Anchalika Kijkanakorn, Debbie King, Margaret Livingston, Kirstie MacGillivray, Vincent McEntegart, Innes McKeand, John McNeill, Pauline McPhersson, Phil Milburn, Cédric Phounpadrith, Paul Reading, David Roberts, Stuart Rowan, Gregory Turnbull-Schwartz and Mohammed Zeineddine. In the Netherlands, it was a pleasure to work with some exceptional individuals at Van Lanschot Bank (a.k.a. “FvL”): Rob van der Aa, Arno Barens, Jean-Paul van Bavel, Joost Buchner, Glaucia Canabrava, Desiree Claassen-van Dooren, Clara van der Elst, René van Geffen, Frank Kamsteeg, Rob Labadie, Reginald Melchers, Patrick Rutten, Raoul Sprangers, Michel van der Stee, Dirk Verbiezen, Ruurd Verdam, Cees de Vries, and Eric Wening.

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During my investment career I kept mostly quiet about my secret versatile modelling tool, Amibroker (with its plug-ins for, among others, Bloomberg, Datastream, and R), because it offered me a competitive edge. For example, it helped me to build an early risk-factor investing model, long before it became popular. I thank its developer Tomasz Janeczko (the best programmer I know) and the Amibroker community for their advice and support. Others who provided valuable feedback and support include Mazviita Chirimuuta, Flavia Cymbalista, Stuart Leckie, Jay Pocklington, Cris Sheridan, Merryn SomersetWebb, Monica Tamariz, Gillian Tett, and Peter Westerink (my “best man”). Completing this book and getting it published wouldn’t have been possible without Jaime Marshall and my editor Suzanne Ebel. I’m indebted to Russell Napier, Scott Kelso, and Julian Kiverstein for writing, respectively, the foreword, intermezzo, and afterword. I thank the professional team of De Gruyter Publishers, especially Jaya Dalal, Stefan Giesen, Steve Hardman, and Anne Stroka (Integra) for their belief in this project and for getting the book out there. It benefitted from the following additional commentators who took the time and effort to read (parts of) the manuscript: Uzi Awret, James Clunie, Bill Dinning, Madeleine Kemna, Alastair Lees, Robert Prentner, and Danilo Spinola. Without a home institution, while promoting a very heterodox theory, I must confess that my switch from investment management to academia has been difficult. I am thus grateful for the backing I did receive. My research as a pracademic, trying to bridge industry and academia, has been made possible by the generous support of Walter Scott Partners (headed by Jane Henderson and Roy Leckie) and especially the Didasko Educational Company (founded by Russell Napier, chaired by Tony Foster, and led by David Clarke). I thank Adam Dewar and Meg Tulloch for creating and maintaining the marketmind.org website, as well as for making the symposium such a success. Similarly I thank Duncan Pritchard for arranging, and Dave Ward for renewing my visiting scholarship at the University of Edinburgh. Ron Chrisley did the same for my affiliation with the Centre for Cognitive Science (COGS) at the University of Sussex, as did Heather MacGregor and Robbie Mochrie for my visiting professorship at Heriot-Watt University. I also like to thank the Edinburgh Futures Institute, represented by Owen Kelly and Douglas Graham who have been ambassadors for the MMH within the University of Edinburgh community, eventually resulting in me being awarded EFI’s research grant. On a growing number of occasions I have been kindly invited to introduce the MMH and share our insights, including at events organised by the CFA Society UK, the Mercatus Center (George Mason University), and McGill University. Finally I thank Mr Market, the ultimate teacher. He regularly inflicted pain on me but it now, and in turn, pains me to see his fragile state due to long-term abuse. The usual caveats apply. If you think you should have been mentioned here, my apologies. The fact that my memory failed does not necessarily mean that you are forgotten (just send me a gentle reminder, if only for the next edition).

Foreword Price holds up a mirror to reality. Price reflects, in the form of data, what we think of reality and thus it reflects, at least, our consciousness. As a practitioner in financial markets, for over thirty years, I have peered at such reflections on a daily basis. Many economists have told me in the past (though fewer nowadays) that what I see gazing back at me is only the so-called ‘rational economic man’—a pared down version of consciousness at best. It was the predictability of the ‘rational’ actions of this ‘man’ that allowed a seeming greater certainty in economic understanding that was key to pushing the discipline along the spectrum from a ‘mere’ social science towards the ‘hard’ science which is still seen as a goal by many economists. While some academics still cannot see past the rational economic man most now recognise some behavioural anomalies in our thinking, which are also reflected in the mirror of price. The ‘rational economic man’ is no longer a robot but more a somewhat unstable automaton with systemic psychological biases, measurable by psychologists, and measured through price anomalies. Daniel Kahneman, a psychologist who won The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, sees in price the reflection of the two different systems of our thinking—system 1: our unconscious, more instinctive thinking and system 2: our deliberate and logical thought process. The argument runs that the instability of such automata can be attributed solely to that system 1 and system 2 divide which can be understood and, perhaps, managed. There is thus a systemic, measured and thus predictable instability infecting the rational economic man. A belief in the automaton, albeit one we now see is subject to instability, is essential for those seeking to model, manage and perhaps ultimately control economic activity. This book focuses upon why there is more in heaven and earth than is dreamt off in this philosophy. Price, it argues, is a reflection of an extended mind, including consciousness, which transcends system 1 and system 2 thinking. There is nothing mechanical about consciousness. If price reflects consciousness, as The Market Mind Hypothesis posits, then the use of the unstable automaton at the centre of economic thinking must be reconsidered. We need to think again about what price truly reflects if we are to truly understand this wellspring of economic activity. Price is the product of the crowd. Price is determined in a marketplace and since markets began, whether for goods, services or assets those in the marketplace have been engaged in a communal activity. Those involved in any communal activity, whether it be watching a sports match or trading financial futures, sense, even perhaps understand, that there is something else going on in addition to system 1 and system 2 thinking. The price determined by mass participation has often been described, by those who live with markets, as reflecting ‘the mind of the market.’ Few practitioners have sought to formalise what the ‘mind of the market’ might be but George Soros has probably made the greatest strides with his Theory of Reflexivity. Writing in The Financial Times in October 2009 Soros outlined his theory:

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I can state the core idea in two relatively simple propositions. One is that in situations that have thinking participants, the participants’ view of the world is always partial and distorted. That is the principle of fallibility. The other is that these distorted views can influence the situation to which they relate because false views lead to inappropriate actions. That is the principle of reflexivity.

The Theory of Reflexivity, derived from the work of philosopher Karl Popper, was first expounded in Soros’s 1987 publication The Alchemy of Finance: Reading the Mind of the Market. For many practitioners The Theory of Reflexivity contains key truths about the interaction between prices and reality and the impact each has upon the other. That price can impact reality and vice versa strongly suggests that there is some form of consciousness forming beyond the minds of market practitioners. The observations of such a synthesis between mind, price and reality is at odds with an understanding that is confined to explaining price determination as a product of only our collective system 1 and system 2 thinking. The Market Mind Hypothesis seeks to incorporate the work of Soros into a broader understanding of, as its author explains ‘the full richness of human mentality involved in economic activity.’ In reflecting this ‘full richness’ price fulfils a role that extends consciousness beyond the individual mind in a form that encapsulates reflexivity. It also provides room to explore the key subjectivity of ‘radical uncertainty’ so important to economists of the pre-war generation but since side-lined by those seeking to impose certainty to find more ‘science’ in price. The MMH thus questions the assumptions at the very core of economics and finance based upon the growing evidence of an extended mind which has very different properties from the ones assumed by those seeking to model, often with a view to managing, economic activity. More prosaically any investor will realise that a better understanding of how things work also increases opportunities for anyone seeking to profit in a marketplace misunderstood by those who see only a slightly unstable automaton reflected in price. Where does the mind stop? For some, consciousness will never exist beyond the mind and current economic thinking sees a clear stopping place for consciousness at the edge of the cranium. Other disciplines are not so certain that there is such a simple answer to this mind/body problem and Socrates accepted more than two thousand years ago, that the mind could exist beyond the brain. In 1998 with the publication of Being There: Putting Brain, Body and World Together Again Andy Clark, now Professor of Cognitive Philosophy at The University of Sussex, rekindled the debate on this socalled ‘mind/body’ problem. While the debate rages amongst philosophers and neuroscientists the world of economics and finance has chosen instead to follow the path of pursuing the evidence of behavioural anomalies from the starting position of efficiency. That mindset prevents us from exploring the exciting opportunities that the records of price, the core of any economic activity, might provide to help us understand where the mind stops and the economy begins. Economics and finance produce the data that could, perhaps, prove that the mind extends beyond the body yet the disci-

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pline choses, in pursuit of greater certainty in outcomes, to assume that price and other key economic data is a product only of the unstable automaton. In market prices there is a rich seam of information with the potential to solve one of our most important controversies in understanding, that economics chooses to ignore. Instead economics, led primarily by psychologists, continues with the slow chipping away at the belief that price represents efficiency. Like Monty Python’s infamous Black Knight in Monty Python and The Holy Grail the slow dismemberment of this rational economic man, through the discovery of ever more behavioural anomalies, is considered ‘just a flesh wound’ rather than a threat to his very survival. But what happens if the continued progress by psychologists in changing economics leads finally to the conclusion that this particular form of ‘man’ is indeed mortal and not an automaton? If, or when, that happens where do we look for a better explanation as to how markets actually work? The Market Mind Hypothesis explores the reality of all that price reflects. The fictional Black Knight did not triumph though he was slow to perish: one limb at a time. One day, after the mortality of the unstable automation is established, we will need to turn to a different truth. Drawing upon major breakthroughs in other disciplines this book asserts that there is a better description of what happens in the marketplace: “The market extends investors’ minds, distributes their knowledge so it can be shared, and manifests collective consciousness via intersubjectivity”. It is not just market practitioners that have observed a ‘mind of the market’ at work in the prices they see before them. As Patrick Schotanus shows in this book there is an older tradition in economics, pursued by some of the greatest names in economic thinking, that always considered that an extended mind was likely operating. The words of Adam Smith, Ludwig Von Mises, Frank Knight and John Maynard Keynes, which pepper this book, show a belief in a very different form of market mind from the one that most modern economists have constructed. The regular failure of economic models, caused very probably by the imposed construct of efficiency, is increasingly evident but has not led yet to a return to the considerations of Smith, Mises, Knight and Keynes that some form of collective consciousness is created when disagreements as to value produce a transaction and a price. One wonders how these giants of economic thought would be reacting to the huge strides in the understanding of consciousness currently underway. Would they all be spending as much time with the MRI machine and rapid eye movement testing technology as they once spent with their slide rules? Keynes warned us long ago that the pursuit of certainty, for its own sake, can itself lead us into some dark intellectual cul-de-sacs. In the search for truth “it is better to be roughly right than precisely wrong” as Keynes may, or may not, once have said. In the pursuit of what is “roughly right” in economics it is time to embrace breakthroughs in other disciplines that provide greater insight into how we determine price and thus allocate resources. This book will play its role in re-establishing a strand to economic thought that needs revitalising in light of the breakthroughs in understanding in those other, related, disciplines.

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That the ‘rationality’ of the crowd is different from the ‘rationality’ of the individual is not contested by historians, psychologists or almost anyone who has ever been part of a crowd. That crowds amplify and distort beliefs and behaviour is accepted. As an advisor to financial institutions I have always found it interesting that the texts investors usually rely upon to gain insight into crowd behaviour were all published many years ago: Extraordinary Popular Delusions and The Madness of Crowds (Charles MacKay, 1841), The Crowd: A Study of The Popular Mind (Gustave Le Bon, 1895) and Crowds and Power (Elias Canetti, 1960). The huge advances in neuroscience and psychology over the past sixty years have seemingly not impacted the materials upon which investors draw in trying to understand the ‘market mind’ they witness in operation every day. Patrick Schotanus’ book draws upon the rapid advances in other spheres of knowledge to provide a more comprehensive understanding of the ‘extended mind’ that is the mind of the crowd and not the individual. Finally there is somewhere else to turn for investors seeking to understand and potentially profit from a greater understanding of how the crowd, through the ‘extended mind’, shapes pricing. If we do not properly understand the forces of price determination, we do not understand very much in economics. There are important real-world implications from such a failure of understanding. While the belief in the mechanistic view largely persists, with at its core a belief in the semi-predictable responses of the unstable automaton, it creates the temptation for policy makers to attempt to manage outcomes. It is faith in the mechanism, determined by a simplistic belief in human behaviour, that leads to the temptation to manage but, as this book argues, to manage based upon a dangerous assumption. Adam Smith, writing in 1759, warned us of the suboptimal outcomes when the ‘conceit’ of such certainty is imposed upon any society: The man of system, on the contrary, is apt to be very wise in his own conceit; and is often so enamoured with the supposed beauty of his own ideal plan of government, that he cannot suffer the smallest deviation from any part of it . . . He seems to imagine that he can arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chessboard. He does not consider that . . . in the great chess-board of human society, every single piece has a principle of motion of its own, altogether different from that which the legislature might choose to impress upon it. (Smith, 1759, pp. 275–276)

If the mind does indeed extend and is evidenced in price then it is best for society that we have a healthy market mind. The actions of the ‘man of system’ to impose or influence a price disturbs the market mind, limits the distribution of knowledge, and this book argues, distorts cognition. The mechanistic approach to economics does not exist in a vacuum. It leads its votaries to interventions that do not lead to the outcomes they desire but often to a form of mental illness for the market mind that perverts the core outcome which price ultimately leads to—a better allocation of resources. These days “the man of system” rests not just in policy circles but is also ensconced in the pricing of securities through his or her role in the creation of the algorithm. The “conceit” of the “man of

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system” whether through equity index funds or more sophisticated forms of algorithmic management of capital has at its core a belief in a mechanistic view of economics that assumes ‘a principle of motion’ that is dangerously different from that which an extended mind suggests. This approach to ape existing behaviour and capital allocation does not distribute knowledge through the system but creates, as this book argues, black holes of information which are damaging to the more efficient allocation of resources. Through the greater intervention of programmers to allocate capital Smith’s dangerous ‘conceit’ grows in importance every day, while, in other disciplines, the understanding of the danger of such ‘conceit’ is ever more evident. Only when the nature of the extended mind is recognised will the danger of the ‘man of system’ (whether head of a central bank or a financial engineer) be recognised. It is to be hoped that this book will begin the process of questioning whether the assumptions of mechanistic behaviour are creating a dangerous enfeeblement of the market mind. If it is then the risks from this imposed enfeeblement have impacts for everyone in society and not just financial practitioners. The “conceit” of the certainty of human action leads, as Frank Knight warned in 1925, to the rise of either the “monster” or the “imbecile”: Surely the man who would undertake to treat human society merely as material for scientific manipulation, to control it by finding the laws of its response to stimuli and devising stimuli to provoke the responses he might desire, would have to be classed as a monster or an imbecile. He might have abundant intelligence, of the scientific sort, but would be lacking in “sense”. (Knight, 1925a, p. 389)

Intelligence of the scientific sort will seek out scientific questions for it to answer. This form of intelligence has been busy seeking such questions in the data rich field of economics for more than two generations. Questions having been posed, answers have been found, but are they, as Knight warned, lacking in “sense”? Finding the right answers to the wrong questions can be a harmless parlour game but not if pursued by central bankers and politicians who control key levers of economic power. Scientific minds, sure that the unstable automaton will do their bidding, are hard at work devising appropriate stimuli which will almost certainly produce outcomes lacking in “sense” because they are based on a false certainty of response. We are lucky to live in an era where our understanding of the human mind, how it works, where it resides and how it interacts with other minds is advancing rapidly. The Market Mind Hypothesis integrates that progress into economic thinking and concludes that what price reflects is something very different from the unstable automaton sheltering within the core of economic thinking. The consequences from this progress are profound for investors, economists, policymakers and ultimately for citizens. If we embrace the understanding of the extended mind, we may not need to live with ‘monsters’.

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For those who read and embrace the conclusions of this book there is another way forward where “sense” prevails over a form of “abundant intelligence” that has constantly, through its conceit, imposed the right answers to the wrong questions upon society. The Market Mind Hypothesis now begins the process of asking the right questions and pursuing better answers to such questions rather than insisting upon a certainty that prevents us from being ‘roughly right’. The journey to being ‘roughly right’ begins somewhere. It began more than twenty years ago in disciplines that economists chose to ignore, including the work of Daniel Kahneman and others. Those reading this book will understand how this journey now progresses hopefully to an era that perhaps Knight would recognise as a world of more “sense”. Russell Napier Keeper of the Library of Mistakes, co-founder of Electronic Research Interchange, and author of Anatomy of the Bear and The Solid Ground

Contents Acknowledgements Foreword

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Prologue

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XI

Introduction: Opening a Can of Worms from Pandora’s Box Background and Motivation: Mr Market and Me

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Chapter 1 Setting the Stage: Who Am I? 1 1.1 Evolution of Minds and Markets; From Nature’s Jungle to the Economic One 1 1.2 Merging Minds and Markets: Group Minds, Collective Intentionality, and Intersubjectivity 10 1.3 Market Mind over Central Plan 15 Chapter 2 On Ontology: Am I Evil? 23 2.1 Economics’ Hard Problem 23 2.2 History of Mind Matters 30 2.3 The Mechanical Approach to Markets 32 2.4 Market Mind Hypothesis; An Initial Proposition 2.4.1 The Market’s Mind 55 2.4.2 The Market’s Body 69 2.4.3 The Market’s Math and Modelling 72 2.5 Chapter Roundup 75 Chapter 3 On Theory: Am I Right? 78 3.1 Introduction 78 3.2 Coordination Dynamics 80 3.3 Extended Mind Theory 83 3.4 Predictive Processing Theory 87 3.5 Integrated Information Theory 94 3.6 Global Workspace Theory 100 3.7 Conclusion 102

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Chapter 4 On Epistemology: Am I Lucky? 104 4.1 Epistemic Doubts 104 4.1.1 Introduction 104 4.1.2 Doubt about Cognitive Ability 111 4.1.3 Doubt about Model Realisation 120 4.1.4 Conclusion Epistemic Doubts 121 4.2 Is it Safe? 122 4.3 An Invisible Gorilla as (Another) Elephant in the Room Chapter 5 On Methodology: Am I Healthy? 5.1 Introduction 127 5.2 Dependencies 127 5.3 Biases 131 5.4 Supermen 135

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Chapter 6 On Complexity: Am I Emerging? 140 6.1 Understanding Complexity 140 6.2 The Case of Mind as Complex Adaptive System 6.3 Symbols 148

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Intermezzo: Parallels Between Mind and Market. What is Mind? What is Market? 154 Chapter 7 On Discovery: Am I Free? 157 7.1 Introduction 157 7.2 Price as Numerical Influence 161 7.3 Price Discovery 169 7.3.1 Society’s Chain of Discovery 170 7.3.2 Distortions, Interferences, and Consequences 7.3.3 Price Discovery, Innovation and Productivity 7.4 The MMH as Price Theory 180 Chapter 8 On Portfolios: Am I Balanced? 183 8.1 Introduction 183 8.2 Emotions as Portfolios of Psychurities 8.3 Valuation of Emotion Portfolios 188 8.3.1 Quantitative Valuation 188

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8.3.2 8.4

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Qualitative Evaluation 189 Summary and Conclusion 192

Chapter 9 On Empiricals: Am I Verifiable? 195 9.1 Introduction 195 9.2 Spontaneous Volatility; Fooled by Reflexive Randomness 9.2.1 Introduction 195 9.2.2 Noise Trading 197 9.2.3 Readiness Potential 198 9.2.4 Parallels Between NT and RP 199 9.2.5 Findings from the Pilot Project 200 9.2.6 Discussion of Results 202 9.3 The Market Speaks its Mind 204 9.3.1 Introducing AVIR 204 9.3.2 Background and Motivation 207 9.3.3 AVIR 212 9.3.4 Methodology 215 9.3.5 Software Tools 218 9.3.6 Proposed Format Experiment 220 9.3.7 Extended Versions of Experiment 223 9.3.8 Summary, Conclusion, and Future Vision 224

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Chapter 10 On the Hard Problem: Am I Conscious? 227 10.1 Addressing the Critics 227 10.2 Meeting Conditions of Collective Consciousness 233 10.3 In Sum: Investing = Dealing Together (With Our Hard Problem) Chapter 11 On the Worst Case: Am I Breaking Down? 245 11.1 Introduction 245 11.2 Of Wetlands and Debtlands 246 11.3 Watersnoodramp: The Endgame 250 11.4 Painful Lessons 252 Chapter 12 On Closure: Farewell and Good Luck 12.1 Parting Words 256

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Afterword: The Market Mind Hypothesis and 4E Cognitive Science: A PostCognitivist Approach to Cognitive Economics 265

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Abbreviations and Glossary Appendix 1 Bridging Concepts and Terms Appendix 2 Research Manifesto References

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List of Figures List of Tables About the Author Index

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411 413 415

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Prologue It is in this middle field that economics lies, unaffected whether by the ultimate philosophy of the electron or the soul, and concerned rather with the interaction, with the middle world of life of these two end worlds of physics and mind . . . the ultimate nature of those two different things will probably remain, a thousand years hence, as far off as ever. Frederick Soddy, Cartesian Economics, 1921

Edinburgh, May 2022. An eclectic group of academics, investors, and policymakers gather in Panmure House to participate in a unique two-day symposium, titled “The invisible hand extended by the market mind: addressing today’s economic challenges in the spirit of Adam Smith”. A magnificently restored monument, Panmure House is the last and only remaining residence of Adam Smith, where he invited other luminaries of the Scottish Enlightenment for dinner discussions. Here he also completed the final editions of his famous works which made Smith not only the father of economics, but also the first cognitive economist: he combined early cognitive science (in The Theory of Moral Sentiments, a.k.a. TMS, 1759), with early economics (in The Wealth of Nations, a.k.a. WN, 1776). Many seem to have (conveniently) forgotten this important connection, but the recent revival in interest, particularly in TMS (e.g., Sen’s Introduction to the 250th anniversary edition; Collier, 2019), is fortunately leading to a proper appreciation of Smith’s idea(l)s. This connection especially underlines the full richness of human mentality involved in economic activity. For example, Smith’s argument for markets is centred on exchange more broadly. So not merely of goods and services (prominent in WN), but also of ideas, regard, and respect (prominent in TMS). In fact, it was in TMS (not WN) that Smith first introduced the term “invisible hand” to describe the coordinating effect of exchanges. However, cognitively speaking TMS goes much deeper into what such exchanges mean as experiences. For example, when talking about their pleasant variations, Smith gives his version of an early inkling of qualia— the qualities of experiences that characterise the phenomenality of consciousness— when he states: Whatever gratifies the taste is sweet, whatever pleases the eye is beautiful, whatever soothes the ear is harmonious. The very essence of each of those qualities consists in its being fitted to please the sense to which it is addressed. (Smith, 1759, p. 190; emphasis added)

To give a brief flavour of the symposium, I opened it by sharing a kind well-wish from George Soros which he had sent me by email earlier that morning: Dear Patrick, I am pleased to receive your update on the Market Mind Hypothesis. I hope your Panmure House symposium will be successful. I couldn’t attend because I am giving a speech in Davos on May 24th which I’m sure you will read about. As you can see I remain actively engaged in the affairs of the world but as I told you before, my days as an active philosopher are over. That’s just as well because all great philosophers are dead. I hope you will carry on with your own contributions to reflexivity.

https://doi.org/10.1515/9783111215051-205

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The academic speakers at the symposium included distinguished economists Sir John Kay and Anatole Kaletsky, neuroscientists Karl Friston and Scott Kelso, behavioural experts Gerd Gigerenzer and Nick Chater, and philosophers Julian Kiverstein and Duncan Pritchard. The investment speakers included Emanuel Derman, Dylan Grice, Howard Marks, and Kiril Sokoloff. They were joined by policymaking experts, including Sir Geoff Mulgan as well as representatives of the Bank of England and the IMF. Numerous investment professionals of reputable investment firms and other academics from international universities completed the audience. Honouring and building on Smith’s legacy, the symposium launched our research programme, spearheaded by the Market Mind Hypothesis (MMH). In 2023 we will celebrate the tercentenary of Smith’s birth, in our case by firmly establishing this programme, hopefully housed in its own research centre. This book introduces the MMH, a novel economic theory that I have been developing over many years, now increasingly with interdisciplinary collaborators. It complements but mostly challenges mainstream economics. Various economic crises were systemic—to the point of being existential—and almost prevented you from reading it. Instead, they now offer it inspiration, in a radical empirical sort of way: mass anxiety about the unknown mixed with mass mania about easy answers. It all got fuelled by unlimited amounts of money created from thin air. Leading to interest rates and oil prices that first went negative and then dramatically turned. And it spawned addictive apps to trade virtual assets. All very surreal. How to make sense of it? This book is deliberately targeted at an interdisciplinary and multigeneration audience. In the Introduction I’ll explain in more detail why the book is important in light of our economic predicament and how it came about, with the symposium as a seminal event. For now, it is important to that wider public because each of our lives is deeply influenced by (collective) decisions, policies, regulations, and strategies that are based on and justified by mechanical economics, the dominant economic theory I will criticise shortly. So it is crucial to understand how it has contributed to our predicament and what can be done about it. The latter not only makes this a hopeful book but also a book of action, especially education wise. In short, economics is too important to be left only to economists. In terms of style, I very much wrote this book as a devil’s advocate, often explaining topics in black-and-white terms to get my points across. I hope its critical tone does not put off mainstream economic readers. It is nuanced by my own experience of working with savvy economic and investment professionals. I know that the discipline is capable of introspection, self-criticism, and renewal and I hope this book will help those attempting to improve economics in formulating their own critiques and solutions along similar lines. With that caveat, a quick recap. The Global Financial Crisis (GFC)1 initially centred on the 2008 collapse of the US investment bank Lehman Brothers, but subsequently

 For excellent overviews, see Tett (2009) and Lewis (2010).

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morphed into the similarly systemic euro(zone) crisis which started in late 2009. The global economy subsequently struggled to recover and never really returned to health. Then—with a morbid symbolism—the corona virus hit in 2020, exposing that weakness which resulted in the Corona Virus Crisis (CVC). Although less prominent than Lehman it was preceded and accompanied by the so-called Repocalypse: stress in repo and other interest rates that also reached systemic risk levels.2 The UK recently suffered a specific crisis in liability-driven-investment (LDI) whereby the central bank (BOE) had to step in to avert pension funds’ insolvency and further contagion. Finally, in the Spring of 2023 US regulators shut down the Silicon Valley Bank (SVB) and First Republic bank, the largest bank failures in the US since 2008, to prevent contagion. And in Switzerland UBS had to take over Credit Suisse. To top it all, we have been in a “cost of living crisis” due to record inflation. This book will argue that these are not independent incidents, but symptoms of a deeper and ongoing crisis in economics itself, with which we view and treat the economic system. For example, the nature of contagion—for us as conscious agents—has changed due to digitisation as the culmination of mechanisation, especially via digital apps and digital currencies (see Subchapters 6.3, 7.2, and Chapter 11). Those of us who were ‘in the market’ at those times, especially during the GFC, most vividly remember what it felt like. Regardless of whether you were a bull or a bear, we all became rabbits caught in the headlights of those events. During that time, several observers labelled the economic system as “weird”, as in surreal. I call these existential crises “reality checks” (Schotanus, 2013 and Schotanus, 2020): Q: Why is all this so surreal? A: Because that is what a reality check is about. Q: What does it entail? A: Reality checks make you realise; in an ‘in-your-face’ manner. Your beliefs, based on stories, are checked against your actual experience, the sensations you feel. This is experiential valuation.

We are still trying to get to grips with the aftermath of these crises, leading to a lively debate over both their causes and their cures. Many academics, practitioners, and policymakers participate in this debate because they realise that there are lessons to be learned. However, what is required is no ordinary learning. Reality checks are often extremely painful, like a rude awakening. And they have consequences. In the words of Larry Summers: “something is wrong with the economics profession if events like those of 2008 do not change its thinking” (2018). One critic even declared “The End of Theory” (Bookstaber, 2017). Andy Haldane asked after the GFC (specifically regarding

 See e.g. Baer (2020). Both were preceded by the (almost forgotten) 1998 systemic crisis caused by the collapse of the hedge fund Long Term Capital Management (LTCM). Arguably it all started with the 1987 crash, significantly exacerbated by portfolio insurance, a mechanistic investment strategy.

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policymaking): “If a once-in-a-lifetime crisis is not able to deliver that change, it is not clear what will?” (Haldane, 2012). What about a second-in-a-lifetime crisis? Or a third? No, it doesn’t seem that way. Borrowing its (in)famous “as if” concept: as if nothing has happened, mainstream economics hasn’t learned. However, not only were these crises existential in that we came close to economic collapse, a.k.a. financial Armageddon. They were also ontological, concerning the very nature of financial markets and the broader economic system, showing that some of economics’ “as if” assumptions are serious category errors. Crucially, this is about “whether human mental activity is machine simulable or not” (Spear, 1989, p. 891), which mainstream economics thinks it is, as part of its wider mechanical worldview. This is exemplified by Robert Lucas, one of its architects, who thinks of economics as something that can be put on a computer and run (see my full quote in Chapter 2). Combined with our ongoing plight as its consequence, this forces a thorough rethink of economics and the economic system; the MMH contributes to this endeavour. Regarding ontology, it particularly emphasises that the current paradigm is mistaken about its mechanical view of what the economy, the market, and their agents are.3 Specifically, mechanical economics views (and treats) the market as an automaton. But the market crashing was far from anything mechanical. Especially for those in the market who had skin-in-the-game it didn’t feel at all as some kind of mechanical failure, like a car breaking down. Instead Mr Market4 forcefully reminded us that we are him and he is us—a collective, animated being with intersubjectivity. Essentially, sensing his mood we became painfully aware that he was about to go brain-dead and enter a coma. In his 2009 investment letter, legendary investor Seth Klarman called it a “near-death experience”. What stood out was the shared sensation of anxiety, a mental paralysis, which irreducibly accompanied the market’s physical seizure. It was this overwhelming experience that impresses what it is like to collectively be in such a market state as humans. Just like taking snapshots of neural activity won’t capture perceptual experience, static analysis of the ‘mechanisms’ of economic crises don’t convey (existential) market mood.5 In short, agents imply agency. But mechanical economics ignores that our agency is born from consciousness. As economist Frank Knight reminds us: “The first datum for the study of knowledge and behavior is the fact of consciousness itself” (Knight, 1921, p. 200). This hints at the importance of cognitive science—an interdisciplinary field that studies minds—to help revise the paradigm. Specifically, combining cognitive science

 Like others of his time Smith was, of course, influenced by the Newtonian worldview and regularly used mechanical terms. Still, as we’ll see he was much more nuanced about human reality.  ‘Mr Market’ is a moniker and metaphor, used by investors, for the average financial market (traditionally the stock market). Its use should be interpreted as gender neutral. The more general term ‘the market’ refers to the (complex adaptive) system of financial markets, the composite of global bond, equity, currency and other financial markets. Other terminology is explained in Appendix 1.  For instance, I will discuss an alternative research method in Subchapter 9.3.

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and economics the MMH submits the Market Mind Principle which states that markets and minds are very much alike. As I’ll explain, the dynamics within our individual minds are acted out collectively in markets, with multiple feedback loops. Crucially, informed by cognitive science the MMH formalises what investors have always casually assumed, namely that the market has a mind. The message of him almost going braindead (twice!) is thus—first and foremost—about saving Mr Market and sustainably restoring his health as well as, by extension, that of the economy: the financial system [is] the brain of the economy . . . It acts as a coordinating mechanism to allocate capital, the lifeblood of economic activity, to its most productive uses by businesses and households. If capital goes to the wrong uses or does not flow at all, the economy will operate inefficiently. (Mishkin, 2006)

From the outset, let me make my position here crystal clear in terms of the distinction between entity (or creature) consciousness and state consciousness (e.g. Dretske, 1997, p. 98). I consider the market as collective entity to be conscious under normal circumstances, when its agents are consciously discovering prices. It physically freezing up for a prolonged period—thereby suspending price discovery—is comparable to an individual going brain-dead. In that ‘vegetative state’,6 while some level of consciousness could remain, we no longer would consider the market to be conscious entity wise. At best, it would show apathetic and uncoordinated consciousness. I’ll return to this later. Unfortunately, many still believe this debate is simply about reaffirming more strongly the post-GFC conclusion that markets are the problem, that they can’t work without central planning, and that we should get ready for a new kind of socialism— spearheaded by financial repression—that replaces capitalism. This belief is problematic on so many levels. First, it is mostly based on biased and superficial analyses of worrying symptoms observed more widely, like debt addiction, environmental degradation, (geo)political tensions, inequality, non-inclusion, productivity slowdown, and zombie companies. The fact that they all continued to deteriorate while (the supposedly beneficial) state influence expanded over the decades should raise red flags that something common and more fundamental is wrong. We have gradually moved from a relationship society to a transaction society, with the growing mechanisation of markets playing a major role in this shift. Society’s economic challenges are characterised by ‘more of the same’ in that regard, and the sad irony in mechanical—and thus repetitive— thinking itself as dogma lies at its roots. Regardless of whether Einstein actually said it or not, repeating the same thing but expecting different results is insane. This unfor-

 This is different from a coma, in that a subject in vegetative state is unresponsive despite being awake (physically speaking). When they originally coined this term, Jennett and Plum (1972) acknowledged that it “may be criticised on the grounds that observation of behaviour is insufficient evidence on which to base a judgment of mental activity” (p. 737; emphasis added). This will become relevant for the distinction between behavioural science and cognitive science that will be discussed later.

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tunately now applies to the economics profession where the majority somehow expect to find different answers to our predicament by repeating the same thinking, often in a more extreme form (like modern monetary theory or MMT). Instead we get positive feedbacks and cumulative effects that only exacerbate the situation. All mechanical economics can think of is to throw yet more automation, more debt, and more price distortion at it. Second, it naively seeks inspiration from the past ‘successes’ of centrally planned economic activities by countries like China, Japan, Korea, and even Finland. What is forgotten is that these successes were largely achieved on (and regularly over) the backs of global open markets. Moreover, totalitarian regimes have economically survived chiefly because they are on the markets’ coattails, often funded by uncritical investors. Now their growing presence and bad influence are a threat, not just to markets but to free minds generally. We should have known better, and the words of George Santayana come to mind: “Those who cannot remember the past are condemned to repeat it”. Still, ESG investing—selecting securities based on Environmental, Social, and Governance criteria—can potentially play a constructive disciplinary role, as I’ll explain in the first Economic Note. More generally, the MMH argues the case against central planning and totalitarianism, and for individual freedom, initiative and responsibility. What makes it novel is that it is based on scientific cognitive insights, complementing traditional political libertarian arguments. In the words of Giuseppe Vitiello: “‘It doesn’t need a conductor’ is the consciousness. It integrates all of it [into] the combo”. (See full quote in Subchapter 3.3.) Third, the new socialism encounters the reality of our debt situation which epitomises ‘more of the same’. In principle credit can help to create a desired future by financing some self-repaying requirement for that future today. This is its beneficial purpose. However, it also leads to a temptation to do more ‘on credit’ without strict self-repayment preconditions. Unfortunately, recent generations could not resist these Faustian bargains and this has resulted in dangerous indebtedness. For example, the Institute of International Finance estimates that the global debt-to-GDP ratio exceeded a record 350% by the end of 2022. And the Bank for International Settlements (BIS, the central bank of central banks) warns that the US$ 80trillion of off-balance sheet dollar debt via foreign exchange swaps and forwards exceeds the stock of traditional shortterm dollar debt which may lead to a dollar funding squeeze. To paraphrase Margaret Thatcher, we’ve already run out of other people’s money. Consequently, we have now resorted to borrow excessively from future generations. That’s politically convenient as they have no vote in it and being told, via Keynes, that all this doesn’t matter because “in the long run we are all dead” kind of sucks when you are not even born yet. Such dismissal of the interests of future generations is, in the words of polymath Frank Ramsey, “ethically indefensible and arises merely from the weakness of the imagination”. It also gives rise to growing intergenerational tension, which falsely gets attributed to side-show debates. Youth’s anger at Wall Street and the Covid lockdowns is nothing

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compared to their anger at lockups of future earnings to repay past debts. To top it all there is the deteriorating demographic situation, due to aging populations. Moreover, such a belief seriously underestimates the type of rethink required and suggests we would, as per Santayana, not learn any lesson. Rather, as I mentioned, we need to revise our economic understanding at a deeper level. This most crucial lesson is not easy to learn. The reason is twofold. First, due to mainstream’s mechanical worldview our profession has become obsessed with modelling. Confusing the map with the territory has come at the expense of understanding. Understanding, particularly in an ontological sense, is difficult and involves more than just mechanically following instructions. The famous outburst by Richard Feynman comes to mind: I don’t know what’s the matter with people: they don’t learn by understanding; they learn by some other way—by rote or something. Their knowledge is so fragile.

Second, and related, it requires unlearning existing false lessons which continue to be taught, for example to (aspiring) investment academics and professionals. This book attempts to help in that revision endeavour. A key issue that highlights the relevance of cognitive science is that of mental causation.7 For example, in economics we generally assume that inflation expectations can cause inflation itself (e.g. Friedman, 1968; Phelps, 1967; but see Rudd, 2021). However, the overarching example is the wider relationship between the market’s mind and the real physical economy. It showed a dangerous tail-wagging-the-dog dynamic during the systemic crises, which made many experts conclude that this mind~matter interaction is a key lesson. Specifically, Nobel laureates George Akerlof and Robert Shiller—inspired by the implied dualism of animal spirits8—recognised that: We will never really understand important economic events unless we confront the fact that their causes are largely mental in nature. (Akerlof and Shiller, 2009, p. 1; emphasis added)

Numerous researchers have addressed mental causation, including economists. Reflecting on consciousness versus physical conduct, Knight, for example, admitted that “it is surely evident that we cannot logically regard the conscious state as causing or explain-

 This is also known as downward causation (e.g. Voosholz and Gabriel, 2021). See also section A in Appendix 1. In a different context, Malcolm X described it more intuitively: “Once you change your [mental] philosophy, you change your thought pattern. Once you change your thought pattern, you change your attitude. Once you change your attitude, it changes your behavior pattern and then you go on into some [physical] action.”  In their book Animal Spirits, they refer to the ancient and Medieval Latin form spiritus animalis, where the word animal means ‘of the mind’ or ‘animating’. Also, Roger Koppl (1991) makes the connection between Keynes’ animal spirits and Descartes, although he does not explicitly discuss dualism.

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ing the conduct in any significant sense. And yet we do, habitually, and for all practical purposes universally, look at the matter in just this ‘unscientific’ way” (Knight, 1925a, p. 378; emphasis added). Others include Friedrich (von) Hayek and Ludwig (von) Mises,9 the latter—importantly—in the context of the famous mind~body problem: But notwithstanding the advance in physiological knowledge, we do not know more about the mind-body problem than the old philosophers who first began to ponder it . . . Thoughts and ideas are not phantoms. They are real things. Although intangible and immaterial, they are factors in bringing about changes in the realm of tangible and material things. (Mises, 1957, p. 65; emphasis added)

The mind~body problem, to which I’ll return in the Introduction, concerns the (e.g. causal) relationship between mind and matter. Even famous physicists acknowledged it: “Physics relies on a mirror symmetry between mind and nature . . . which is most tightly related to the psychophysical problem” (Pauli, 1957; in Atmanspacher and Primas, 2006, p. 20). Shortly before he died Ilya Prigogine, answering a question about future scientific challenges, stated that “if I was a young researcher now, I would study the mind-body problem. This is the great challenge of the 21st century”. Let’s reflect on this for a moment and explain how the MMH views this, starting with the distinction between the subjective internal world and the objective external world which can be bridged via intersubjective sharing of understanding. Beliefs, hypotheses, ideas, and thoughts are examples of mental constructs. Initially, they originate in the interior of an individual mind which makes them subjective. The external world consists mostly of items, including physical objects, that are actual, factual, or real, meaning that they exist regardless of any subjective mental consideration.10 In other words, you don’t need to think of Big Ben in London for it to exist and be real. On the other hand, we know of objects (like the Higgs boson particle) that were first revealed mentally, say in theories, before we could prove their existence empirically. As we will discuss, by way of creativity, discovery, and imagination you can gain an insight into these worlds. It is novel and (we assume) has never before been mentally constructed. Such insights or ideas are metaphysically causal in that they could offer future possibilities, suggesting changed states of the world. That means they have real (economic) utility. For example, think of a formula that you discover purely from a thought experiment, like Einstein used to do. You can then communicate and share it with other minds, making it intersubjective, including the shared feeling of excitement for a potential breakthrough. Once confirmed or proven—which usually follows the interim sharing with others—your formula is acknowledged to be real and objective in the

 See also Koslowski (2001, p. 165, 166). Mises and Hayek are two of the founders of the Austrian school in economics. The “old philosophers” referenced in the quote included Princess Elisabeth of Bohemia and Pierre Gassendi who pointed out this problem to Descartes.  This is the consensus (scientific) view, so I’m ignoring philosophical fringe views, like solipsism.

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sense that it becomes an item no longer dependent of your subjective mental construction. It can subsequently lead, for example, to new knowledge and mentally cause “changes in the realm of tangible and material things” (see Mises’s earlier quote). Eventually such ideas can move into the economy, as innovations, and have an impact. Viewed then as—what I’ll call the weak form of—mental causation, economics is about “controlling nature and bending natural forces and materials to the will of man” (Knight, 1925a, p. 373). Karl Popper, in his paper on the mind~body problem, explained mental causation more generally as follows: There is no reason (except a mistaken physical determinism) why mental states and physical states should not interact . . . If we act through being influenced by the grasp of an abstract relationship, we initiate physical causal chains which have no sufficient physical causal antecedents. We are then ‘first movers’, or creators of a physical ‘causal chain’. (1953, para. 6.3–6.4; emphasis added)11

Here we get a bit into the nitty-gritty of cognitive science. It is acknowledged, for example, that will power contributes to reshaping the brain—thanks to its neuroplasticity— and that psychological stress can impact physical health (studied via psychoneuroimmunology). However, mental or downward causality remains a complex and contentious topic (e.g. Batthyány, 2009; Crane, 1991; Heil and Mele, 1993; Kim, 2005; Velmans, 2002). Specifically, the terms “causality” and “causation” are considered as slightly misleading. Some completely dismiss it by pointing to the principle of physical causal closure. Others prefer to think of it as efficacy, impact, or influence, thereby affecting conditions.12 In short, casually assuming or stating mental causation often underestimates the complications involved, like those concerning free will, which require cognitive explanations. In other words, if we take Akerlof and Shiller’s conclusion seriously—and I think we should—then we need the help of cognitive science to formalise this. Besides the crises offering empirical inspirations, there are numerous more theoretically based inspirations for the MMH. Produced by brilliant minds across multiple disciplines they include books, experiments, lectures, papers, and other materials which are referenced throughout this book. Here I’ll give a few examples that stand out. I already mentioned Adam Smith and his TMS~WN tandem. The work of Willis Harman features prominently, particularly his book Global Mind Change. So does George Soros’s bestseller which he subtitled “Reading the Mind of the Market”. Frank

 Carl Jung said something similar: “Psyche cannot be totally different from matter, for how otherwise could it move matter? And matter cannot be alien to psyche, for how else could matter produce psyche? Psyche and matter exist in one and the same world, and each partakes of the other” (Jung, 1951, p. 261). This is now known as the Jung-Pauli interpretation of dual-aspect monism.  It is related to Chinese philosophy’s ying, short for ganying (感應; not to be confused with yin) which translates as ‘resonance’. Mental causation is also echoed by those outside cognitive science. Mises, for example, argues that it is about “purpose and of conscious aiming at ends”. Among others, this relates it to free will. For more information on free will and volition see Schurger (2017). For an introduction, see “A Famous Argument Against Free Will Has Been Debunked” in The Atlantic, 10 September 2019.

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Prologue

Knight’s (largely forgotten) 1925 papers are outstanding in highlighting the relevance of consciousness and pinpointing it as economics’ blind spot, especially considering he wrote them four years after his famous work on the distinction between risk and uncertainty. As I’ll discuss, beyond our uncertainty due to the mind~body problem, uncertainty is a fundamental feature of the world that we should welcome.13 Knight also discusses metaphysics, as do Georg Simmel (in Philosophy of Money, 1907), Frederick Soddy (with his Cartesian Economics, 1921), and Deirdre McCloskey (The Rhetoric of Economics, 1983). They are among the few mavericks who dared to explore it for economics. Simmel also offers an early cue for how economics of the mind (i.e. mindas-market) extends to markets: This subjective process of [investment] and gain in the individual mind is in no way secondary to, or imitated from, exchange between individuals; on the contrary, the interchange between [investment] and [return] within the individual is the basic presupposition and, as it were, the essential substance of exchange between two people. (Simmel, 1907, p. 81; emphasis added)

The Sensory Order (1952), Hayek’s remarkable book on the human mind, is another important source for the MMH as it includes his interpretation of the mind~body problem. Throughout his career Hayek primarily focussed on some of the collective aspects associated with this problem: how minds relate to society and distribute its knowledge through markets. Combining his cognitive and economic knowledge Hayek would later specifically acknowledge the similarities between mind and market, particularly in terms of complexity. For example, in terms of mind-as-market Hayek compared neuronal dynamics to “a stock of capital being nourished by inputs and giving a continuous stream of outputs”. Neuroscientist Gerald Edelman favourably judged Hayek’s Sensory Order. Edelman’s own Theory of Neuronal Group Selection is about neurons competing and otherwise acting out as if in a market setting. Sympathy with this view of mind-asmarket is also (implicitly) expressed in comments by researchers like George Ainslie, Jeremy Bentham, Andy Clark, Jerry Fodor, Paul Glimcher, Joseph LeDoux, Scott Kelso, and Edmund Rolls. For example, Clark states that a key task of the mind is “guessing the next states of its own neural economy”. This will be discussed in more detail and in various ways, including the mind~body as a portfolio (called the M~B Portfolio; see Appendix 1-A). Talking about Clark, in 1998 he and David Chalmers published a seminal paper called “The Extended Mind”. It added the fourth E to what is now known as 4E cognition, the interdisciplinary field that considers the mind to be embodied, embedded, enactive, and extended (see Appendix 1-A). It offers the MMH scientific backing in general and useful insights in particular. For example, when Clark and Chalmers talk about a “coupled system” (1998, p. 34), the MMH translates this as the investor and his

 I can only speculate here but I suspect that Knight may have realised that consciousness, i.e. mind~matter reflexivity, is central to true uncertainty. For background more generally on Knight’s “recantation”, please see Fiorito (2016).

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(Bloomberg, FactSet, or Refinitiv) terminal which “deliver the reality of financial markets—the referential whole to which ‘being in the market’ refers, the ground on which [participants] step as they make their moves, the world which they literally share through their shared technologies and systems” (Knorr Cetina, 2003, p. 11. For full quote, see Subchapter 2.4.2). The MMH pushes beyond these reflections and makes the strikingly common dynamics between minds and markets explicit. Although this book, first and foremost, focusses on firming up investors’ parsimonious idea of “the market’s mind”, the underlying Market Mind Principle (and related premise) is more universal and makes it intuitive for both economic and cognitive experts. It also means that various economic concepts can be practically applied to minds, while cognitive concepts can be applied to markets. Some investment readers may be thinking that these theories are too abstract. And, yes, I warn upfront that this book will not propose easily actionable ‘trading ideas’. Nevertheless, it does offer suggestions on how to complement existing investment research to better understand market dynamics in the long term. For that purpose, the MMH takes us deep into the rabbit hole of cognitive economics, a wonderland full of complex psychophysical issues offering an alternative—but more realistic—worldview. In particular we will explore the twilight zone of mind~matter interaction, at the edges of their supposed separation, which spawns that complexity. As the crises have shown, this sometimes leads to crashes as the imbalances between our material (real) and mental (imagined) domains become unsustainable. There is some urgency in that regard. To borrow the words in a tweet (3 May 2021) from neuroscientist David Eagleman, “our brains can’t un-experience” these crises. In other words, they have lingering effects.14 And unless we start revising economics’ paradigm they will be repeated in much more serious forms. So, in honour of The Matrix’s Morpheus, here is my red pill (you’ll understand later).

 For instance, dread risk (Haldane, 2015). A somewhat related phenomenon is hysteresis where, for example, the ‘stickiness’ of unemployment leaves lingering negative effects.

Introduction: Opening a Can of Worms from Pandora’s Box The task is not so much to see what no one has yet seen, but to think what nobody has yet thought, about that which everybody sees. Arthur Schopenhauer

So, let me lay out my stall by opening a can of worms from Pandora’s Box. To get straight to the point, the key issues were already grasped by Knight, almost a century ago: Now the issue, as is plain, relates to the treatment of “consciousness” in human beings . . . The “existence” of consciousness would be left on one side as a metaphysical question, in the case of human beings as in that of the rest of nature. The pertinent fact for economics (and for applied psychology in general, of course) would be that it is useless and a source of confusion in study, destructive of the scientific point of view which is the only fruitful approach to the data. In opposition to this view I propose . . . that we cannot treat human beings as . . . mechanisms, and that we do not want to do so even if it were possible. We necessarily approach the phenomena of conduct from a different direction, and bring to them a different dominant interest, as compared with the phenomena of nature outside the human realm . . . (Knight, 1925b, p. 248). There is nothing to it but to come back to common sense, and the practical necessities of our situation. That is, we come back to dualism . . . (Knight, 1925b, p. 265).

Although at the time Knight was particularly criticising (pre-Skinner) behaviourism, his arguments remain relevant, especially regarding consciousness as the determining factor and (aspect) dualism as a practical metaphysical worldview. What follows in this Introduction will be further explained and explored in the remainder of this book. Appendix 1 is extensive and requires two specific comments. First, it consists of three sub-appendices (A, B, and C) which cover and introduce, respectively, cognitive science, economics, and cognitive economics. As the book attempts not only to (initially) appeal to the cognitive and economic communities (including practitioners), but also subsequently to bring these communities together (for instance via collaborations), these sub-appendices are crucial. Specifically, they explain concepts and terminology in the various fields, as well as the particular interpretations made by the Market Mind Hypothesis (MMH). So, not including the subappendices would have made understanding the book harder for many readers, whereas integrating them in the main text would distract from the major points and make it less fluent. An unfortunate consequence is that there is some overlap between Appendix 1 and the main text. In addition, because of its heterodoxy, I expect this book to attract much criticism, and I’ll return to that in the final chapter. The MMH is a standard-bearer for and specific ‘post-cognitivist’ interpretation of cognitive economics. Cognitive economics is an emerging heterodox theory that challenges mainstream economics (Schotanus, 2022). Consequently, there is not an agreed definition yet (just like there was no such definition for behavioural economics when it started to emerge). Instead, this is a good description of MMH’s interpretation: cognitive economics partners cognitive science with economics, each offering complementary explanations to https://doi.org/10.1515/9783111215051-206

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the other. Cognitive science teaches economics about mentality (present in markets, e.g. herd mentality), whereas economics teaches cognitive science about market forces (present in minds, e.g. your unconscious “System 1” competing with your deliberate “System 2”). Combining these helps to understand the similarities between markets and minds. It culminates in the two-way Market Mind premise: market-as-mind (which roughly can be related to the macrofoundations of economics), and mind-as-market (which roughly can be related to the microfoundations of economics). Its shared underlying Market Mind Principle is intelligent (and sometimes conscious) self-organisation via ‘market’ dynamics, centred on exchange, which augment and support discovery (mainly of values) and invention (mainly of tools). To emphasise: such self-organisation occurs in both markets and minds. Let me explain some terminology. Exchange is a flexible and widely applicable term that covers interaction, interchange, trade, transaction, and transfer. Importantly, it implies dynamism and duration. I prefer this term in most instances for various reasons which go beyond only economics: Exchange is the purest and most developed kind of interaction, which shapes human life when it seeks to acquire substance and content. It is often overlooked how much what appears at first a one-sided activity is actually based upon reciprocity . . . Every interaction has to be regarded as an exchange. (Simmel, 1907, p. 79)

First, an exchange results in a ratio reflecting the respective amounts of the two items being exchanged. In modern markets this is called a price which reflects the amount of fiat currency paid in exchange for a good, service, or security. In financial markets in particular, the buyer and seller agree on price but they disagree on value. Second, while reciprocity applies in principle, an exchange can start one-way when the giver (initially) only receives ‘gratitude’ in return for a gift or when any reciprocal transfer is postponed ‘on credit’. In the extreme think, for example, of how we live on ‘borrowed time’ and eventually exchange Mother Nature’s gift of life with returning to dust after death. Third, exchange is at the origin of any human creation and connection, including relationships. The earliest connections among our ancestors—before any trade could take place, let alone any relationship could be built—was by way of the exchange of looks between two strangers to assess whether to approach or avoid. If a man and a woman liked what they saw, it was followed by exchanging fluids which created their baby. In turn, this baby’s first relationship started via (biochemical) exchanges with its mother. We can further enrich this view with related economic concepts and terms when we realise, for example, that while exchanges create (deep) values we always risk losing these in other exchanges. Furthermore, we may not be able to protect those we deeply value from such losses. Exchanges subsequently increased across other domains, especially via growing trade taking place in markets, contributing to human evolution. It initially consisted simply of barter, but this too has a deeper origin that hints at a universal principle in nature, namely: “the direct exchange of services and resources for mutual advantage

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is intrinsic to the symbiotic relationships between plants, insects and animals, so that it should not be surprising that barter in some form or other is as old as man himself” (Davies, 1994, p. 9). This prominence of exchange, including its preconditions, is still valid in modern times. Specifically, while self-interest is considered a necessary motivation of an economic exchange it is not sufficient. To initiate, complete, and make exchange sustainable other mentalities are required, like confidence, fairness, honesty, and trust. To wit, trust is self-reinforcing (e.g. Falk and Kosfeld, 2006). Indeed, the naïve belief within its community that ‘trust is no longer relevant in crypto space’ got painfully shattered in recent cases. Finally, an exchange does not necessarily need to involve anything physical, like paying a coin for an ice-cream. Apart from looks people can exchange ideas, which is very important for a healthy economy, especially in terms of creativity and innovation. Something novel, in that regard, is often created via integration of exchanged (existing) elements (with, again, a baby as the ultimate example). In sum, it is in and through exchange that any ‘magic’ happens, especially when reciprocity morphs into reflexivity. This brings us to market dynamics. Examples of market dynamics include notions like competition~cooperation,1 consumption~production, deflation~inflation, risk~reward, saving~spending, supply~demand, but also the associated exploration~exploitation, freedom~repression, growth~decline, input~output, invention~discovery, intervention~laissez-faire, possession~deprivation, rational~irrational, transparency~concealment, and even master~servant.2 Each of these pairs combines two, seemingly polar, economic concepts or principles, with their own (cyclical) ratio. Normally, no single aspect of a complementary pair is primary or dominant. Instead their influence fluctuates and its underlying dynamic is caused by the tension between the two aspects, due to their contrary nature. From now on it should be noted that I call these complementary market forces. Overall, and more generally, these contraries are ubiquitous in our world. They are also complementary and dynamical (see Kelso and Engstrøm, 2006, who also offer organism~environment, genotype~phenotype, practice~theory, and many other examples). It extends to the cognitive sphere, for instance concerning metaphysical stances on the essence, existence, and reality of our world. They may even include reflections on negations. In existential terms, think for instance of Shakespeare’s to be~not to be,  I am deliberately using the squiggle or tilde symbol (~) of Coordination Dynamics, a cognitive theory that informs the MMH. The squiggle stands for the complementary relationship between the members of each pair, often involving a level of integration (e.g. Kelso and Engstrøm. 2006). In addition, for the MMH its superscript level signifies the ‘over-and-above’ synergy of (which differs from) the pair. In general, and particularly for the economic terms, these are “complementary notions, each of which presupposes the other” (Simmel, 1907, p. 73), with the squiggle symbolising the “relativity” between the members of each pair (e.g. Simmel, p. 99). Overall, “Thinking narrowly in terms of contraries and the either/or is easy when life is simple. But in complex coordinated systems it seems that sharp dichotomies and contrarieties must be replaced with far more subtle and sophisticated complementarities” (Kelso, 2022, p. 10).  In light of the complicated agency problems in the economic system.

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or Heidegger’s Sein~Nichtsein, which both concern life~death. More generally, the squiggle (tilde) between non-existence~existence symbolises emergence. Other important multidisciplinary pairs include 0~1, inner~outer, process~thing, quality~quantity, Purusha~Prakriti, Shiva~Shakti, and yin~yang. One such metaphysical stance is Hayek’s practical dualism (see Appendix 1-A2). It is specifically about mind~matter, or psycho~physical explanations, which I’ll adapt as portfolioism (to be introduced shortly). While this will not be discussed here, the history of dualism goes back further than its popularisation by Descartes. More generally, we tend to divide our world into pairs of (perceived) contraries, something Kelso and Engstrøm call “dichotomizing” (2006, p. 1). This turns us into closet (practical) dualists, which I’ll discuss in a moment. But first, how does cognitive science differ from behavioural science which informs behavioural economics? At the risk of an early oversimplification, cognitive science is more internally oriented (i.e. on cognition as impression), whereas behavioural science— reflecting its Skinner-legacy—is more externally oriented (i.e. on behaviour as expression). For example, your impression of anger (e.g. internally you’re boiling) is very different from expressing it (e.g. externally you’re yelling), as are its potential consequences.3 This distinction between the ‘internal’ and ‘external’ can also be found in Smith (1759, pp. 387–388) and Schumpeter (1943, p. 63). Importantly, cognitive science takes consciousness seriously and adds phenomena like “affects” and “feelings” (e.g. Heidegger, 1927, p. 178) to the traditional emotions (System 1 or S1) and rationality (System 2 or S2) of dualsystem thinking in behavioural science. So, whereas behavioural economics may focus on observation, cognitive economics considers introspection (e.g. “What it is like”) to be of interest. In metaphysical terms, whereas cognition (like perception) is considered mental, behaviour (like action) is considered physical, and cognitive science is largely about recognising this mind~matter distinction as complementary and, subsequently, understanding and overcoming the related intricacies. The MMH is primarily concerned with market-as-mind, the first leg of the Market Mind premise. It specifically states that: The market—embodying interacting humans and the technologies that connect them—not only distributes investors’ knowledge but also intersubjectively extends their conscious minds,4 thereby manifesting collective consciousness. Prices and their patterns are its informational signatures, and market mood is its immersive phenomenal experience (via sentience) in real time.

Translated in popular terms, Mr Market is not (as Benjamin Graham suggested) a voting or weighing machine but, instead, is an animated collective entity with a mind—

 Acknowledging the relevance of this interiority for economics disputes, for example, the assumption of equilibrium approaches that instability is only due to exogenous factors.  Mind is embodied, i.e. the mind~body. It also includes consciousness, so should be interpreted as conscious mind, unless specified differently.

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warts and all—which investors5 have always casually referred to as “the market’s mind”. The MMH sets out to formalise this, backed by modern cognitive science via 4E cognition.6 In turn, with copious market data just waiting to be explored from that perspective it can offer cognitive science tantalising (empirical) insights, not only into this collective mind but also into mind-as-market. More theoretically, Appendix 1-A explains extensively that consciousness is to the brain what value is to assets, i.e. value is in the “I” of the beholder. This leads to MMH’s interpretation of conscious experiences as ‘returns’ in the mind’s market (or portfolio). And just like returns in markets (and by extension portfolios) cannot be reduced to any specific ‘fundamentals’, conscious returns cannot be reduced to specific ‘physicals’. Both shortcomings are due to the entwined exchanges taking place within (and between) markets, respectively within (and, intersubjectively, between) minds. Moreover, while mental returns as measured by an observer are ‘just’ changes in (biochemical, neuronal, etc.) ‘prices’, as experienced by “I” they reflexively feedback on “I” ’s physical substrate as part of the dual realisation of information. It is of particular interest if this information itself is dualist in terms of contrasting the market with the economy. This will be discussed in detail later (including in Appendix 1) but let me briefly clarify what I mean with it by sharing an example from the GFC. To help me, I’m using two scenes from the movie The Big Short (which I urge you to see, if you have not done so already). In the first scene, Mark Baum (a.k.a. Steve Eisman) and his team at hedgefund FrontPoint Partners meet with Deutsche Bank’s Jared Vennett (a.k.a. Greg Lippmann) for the first time. In his presentation, Jared explains to them the growing risks in mortgage-backed securities, packaged in so-called collateralized debt obligations (CDOs). The conscious realisation of this market information by Mark and his team unfolds in a dual and entangled way. There is the physical processing in their brains that results in the (rational) assessment of profit opportunities. In parallel, however, this is accompanied by an ‘Oh-no’ insight: the phenomenal realisation, instigating a sense of fear, that this will result in a serious crisis in the economy. In Mark’s own words: “financial Armageddon”. The second scene further highlights this contrast in realisation in connection to the dualist macro level of the market and the economy. Charlie Geller (a.k.a. Charlie Ledley) and Jamie Shipley (a.k.a. Jamie Mai) are excited and perform a little dance in one of Las Vegas’s casinos after they closed a deal to short CDOs. Their advisor, the former Deutsche Bank trader Ben Rickert (a.k.a. Ben Hockett) chastises them and impresses on them the reality of the situation for the economic system as a whole: “if we  Used in this book as the general term for all market participants and thus refers also, for example, to brokers, dealers, market makers, and traders.  While echoing elements of organicism in general, I only specifically use anthropomorphic and biocentric language to appeal to the reader’s intuitive understanding of complex topics, especially to contrast it against a mechanical view. In other words, I do not claim Mr Market is human.

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[in the financial economy] are right, people [in the real economy] lose homes, people lose jobs, people lose retirement savings, people lose pensions . . . every 1% unemployment goes up, 40,000 people die. Did you know that?” Ben’s wake-up call has its effect, making them fully realise, with Jamie confessing to its phenomenal impact: “Wow, I just got really scared”. We now know what happened. Eventually dual realisation occurs collectively in the market’s mind as it spreads via prices. In the case of the GFC, this led to the physical freezing up of money and, phenomenally, the experience of existential mood. Clearly, the MMH offers an alternative worldview that is in sharp contrast to the flawed mechanical worldview of mainstream economics.7 The latter views the economy as a machine, the market as an automaton, and humans as (blackbox) robots, of interest only for their behaviour. Consequently, I (and others) call it mechanical economics.8 Mechanical economics conveniently ignores consciousness, which explains why real consumers, investors and other agents literally do not ‘recognise’ themselves in homo economicus—because he is a philosophical zombie, a particular kind of undead—and why mechanical economics itself does not make sense to them. Instead, based on the fact that we are conscious organisms, the MMH offers a more realistic (although not necessarily simpler) psychophysical worldview with a mind~body (a.k.a. mind~matter) perspective of the economic system. Having said that, the MMH does not throw away the economics baby with the bathwater. Specifically, it keeps and adapts the main principles of market dynamics via complementary forces, as well as some core concepts of portfolio management. The fact that minds and markets have a lot in common invites several questions. How does the dynamic complexity in one reflect that in the other? Can we learn more about markets by studying minds, and vice versa? Does our collective behaviour in the financial economy reveal (or, using a psychology term, project) something about our internal economy? How does the individual mind~body relate to the composites it is part of in the economic system? How does the economics of stuff compare to the economics of experience, i.e. stuff happening?9 Crucially, could laws and regularities be derived from or be related to the Market Mind Principle? Could they, in turn, allow cross-fertilisation via modelling and empirical data which will improve our under Mainstream economics basically consists of a mix of neoclassical (strictly speaking new classical) and Keynesian economics. For what follows they are strange bedfellows: the new classical view of the economy is basically of a machine that runs by itself and should be left alone, whereas the Keynesian angle sees a need for engineers to ‘fine-tune’ it. In this book economics includes finance, the theory of financial markets and investing (e.g. the Efficient Market Hypothesis), as a nested discipline. See Subchapter 2.3 and Appendix 1-B for more details.  Models (including algorithms) derived from this worldview subsequently result in mechanical policies, strategies, and products. Related, besides performativity, is “design economics” whereby economists “not only analyze markets” but “design them” (Roth, 2002, p. 1341). See also Chapter 2.  I borrow but reinterpret the terms “economics of stuff” and “economics of stuff happening” from King (2016).

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standing of both? Then there is the flip side. While ordering our lives, minds and markets seem vulnerable. Specifically, “markets weed out inefficient practices, but only when no one has sufficient power to manipulate them” (Chang, 2011, p. 156; emphasis added). As we’ll see, that has been a growing problem, especially via central planning, with many governments routinely controlling up to 50 per cent or more of their nation’s GDP—turning criticism of ‘free markets’ into a strawman. These questions raise important mind~matter issues which otherwise often remain hidden. Mind~matter issues involve the distinction as well as the exchange between the mental and the material. Mind~bodies, for example, are peculiar in that regard. They are subject to complex psychophysical laws,10 e.g. you jumped up because of felt fear. Machines, on the other hand, are subject to relatively simple mechanical laws, e.g. the clock chimed because its long arm hit twelve. In fact, our metaphysical stance determines our perception of mind~matter exchanges which, in turn, colours our views of human exchange. We experience such dualism in our daily life all the time. For example, the initial pain of a mental exchange, like an insult, is transformed into a (different) pain from a subsequent physical exchange, like a punch. Moving to the economic domain, such exchanges occur especially in trade (in all its forms). Think, for example, of the metaphysical transformation when a physical11 asset (say, an office building) becomes tradable via securities (REITS) which primarily have psychological traits, like promises and trust. Money, credit, and currency are all deeply exposed to mind~matter issues, like the claim that a fiat currency is (only) backed by “the full faith” of its issuing government. This turns our fiat system into a form of idealism, making it, what I like to call, metaphysically suspect. Simmel points to its metaphysical history: “The trend towards this theory of money, which may be characterized as transcendental by contrast with the materialistic theory, began with the views of Adam Smith. While materialism asserts that mind is matter, the transcendental philosophy teaches that matter itself is mind” (Simmel, 1907, p. 173). And when central banks talk about “transmission mechanisms” between their policies and the economic system they pretend it is machine-like, whereas in human reality it largely involves mind~matter dynamics. Talking of trust, the Dutch central bank (DNB) recently reported that trust of the Dutch public in its operations has dropped from roughly 90% in 2008 to 66%. It could have been worse: trust in the Dutch government (which issues bonds on behalf of the public) has dropped to 22%. Cattaneo (1861) was among the first to emphasise the (perceived) dualism of economic reality via his distinction between the “physics of wealth” and the “psychology of wealth”. In our economic narratives and encounters most of us make distinctions

 A well-known example is Weber’s law.  I use the terms material (materialism) and physical (physicalism) interchangeably, although there are differences, with the latter basically being a subset of the former. See also Appendix 1.

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between the material and mental all the time (even though we are not aware of simultaneously taking a metaphysical stance): the physical economy versus the psychological markets, the industrial/manufacturing economy versus the experience/knowledge economy, physical versus intellectual property, fiat money versus gold, tangible assets versus intangibles, and so on. Part of this is explained by the fact that our needs or wants—based for example on Maslow’s (pyramid) hierarchy—vary from physiological (e.g. satisfied by food and shelter) to phenomenological (e.g. satisfied by self-actualisation and enlightenment). Even if academics think of themselves as physicalists, they have a problem (and Knight was more explicit in this than Hayek). The moment you start to distinguish in your discussions and writings between the material and the mental, you become a practical dualist. In other words, whoever does not like this label needs to either watch their language carefully or just keep quiet. In any case, this makes the rest of us ‘agents’ closet dualists which—via the related beliefs, perceptions, etc.—has a few consequences. First, this dualist majority ‘votes in’ the mind~body problem—extending it, at least as perception, into the collective economic setting—regardless of the metaphysical stances of other minorities or observers.12 Second, it offers another reason why the physicalism underlying mechanical economics doesn’t make sense (pun intended) to the average agent (see other comments by Knight which are quoted later). Third, it indicates that there could be two distinct aspects to utility maximisation, one material and one mental. It may suggest, for example, that the cliché that money doesn’t make you happy has a deeper meaning. I’ll touch on this distinction when I introduce epistemic utility as an example of mental utility. While forecasting may remain a mug’s game, our understanding of the economic dynamics involved in past events will improve if viewed from a cognitive angle. This is where the MMH differs, for example, from those heterodox theories that focus on narratives: it zeros in on the metaphysical and other cognitive assumptions/implications of those narratives—taking into consideration important aspects like their essence/meaning in terms of type, e.g. as allegory, metaphor, myth and so on. Further, the MMH submits that metaphysical considerations should be included when using economic variables. Comparing GDPs between countries generally does not make sense without consideration of their metaphysical make-up for instance. Take the case of ranking the economic power between a mental ‘knowledge economy’ A and a physical ‘commodity economy’ B. While country A’s GDP (based on a dominant IT sector) may be ten times bigger than country B’s GDP, it becomes economically impotent when the computers running the intangible services of A’s IT sector (centred on users’ social and virtual ‘experiences’) are deprived from the necessary (and scarce) commodity produced by the miners of country B (who can easily sell it instead to country C).

 For more details on metaphysics and markets, see Appendix C2.

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Mind~matter issues are relevant for both individuals and collectivities, including society as a whole. A bubble is in essence a mismatch between mind and matter. Individually a person can ‘live in a bubble’. Person A can surround themselves with a surplus of material wealth but nevertheless mentally lack happiness. Vice versa, person B may look at life through rose-tinted glasses but at some point see their bubble popped by the pin of hard material facts. Similarly, bubbly consumers and/or producers in the real economy may experience the disciplining constraints on their spending by bearish financial markets. More often, however, investors’ irrational exuberance is crushed by collapsing fundamentals in the real economy. Main Street and Wall Street can spoil each other’s parties. With money as mediator it manifests itself most violently when cheap credit (as mental perception) becomes expensive after unlimited supply turns (as physical action) into a drought. Financial instability can thus basically be reduced to a metaphysics mismatch whereby any resulting crisis is the rebalancing in the economic mind~matter realm. Let me summarise the above in Table 1. Table 1: Individual and collective mind~matter realms.

Aspect

Space

Individual (micro-cosmos)✶

Collectivity (macro-cosmos)✶

Portfolioism (i.e. economics of:)

Material

Body

Economy (a.k.a. real economy)

Stuff

Mental

Mind

Market (a.k.a. financial economy)

Experience (Stuff happening)

✶

Adapted from Hayek (1988)

To properly grasp the complex exchange between the above domains—combining what I call the economics of stuff with the economics of stuff happening—we need a metaphysical framework. Unfortunately economics has been allergic to this for a long time, despite clear and convincing criticism that hasn’t lost any of its power: The worst flaw in the hostility to the “metaphysics” that [economics’] modernism sees everywhere is that the hostility is itself metaphysical. If metaphysics is to be cast into the flames, then the methodological declarations of the modernist family from Descartes through Hume and Comte to Russell and Hempel and Popper will be the first to go. For this and other good reasons philosophers agree that strict logical positivism is dead, raising the question whether economists are wise to carry on with their necrophilia. (McCloskey, 1983, p. 486)

Because it is applicable for our purposes, I cite Michael Kirchoff, a prominent advocate of extended consciousness, who makes the general case: “By scrutinizing the metaphysics of what it means for certain Xs to compose a certain Y now, it is possible . . . to turn what might look like a metaphysical dispute into a productive recipe for empirical research and to set certain constraints for how such research must be carried out” (Kirchoff, 2015, p. 51).

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In the first instance, the MMH adopts Hayek’s practical dualism as its metaphysical stance.13 It states that while there is likely to be a “unitary order” (Hayek, 1952, p. 191) to which both the mental and the physical belong, until it is found we will have to adopt a dualist view for practical purposes (thus using mental terms, like “mind”, in our communication). There are two reasons for this recognition. From a popular perspective, most of us would agree with what Knight calls “mutuality” (1925a, p. 390) and Simmel calls “our physicalpsychological inclination” (1907, p. 99), namely that we experience reality in this dual sense, making it intuitive and part of so-called folk psychology. Second, and from a scientific perspective, the challenge to discover this unitary order remains to this day and is, Hayek submits (with a Gödelian echo), mainly due to the structural limitations of the mind understanding itself (Hayek, 1952, p. 186). This is why I like to call myself an ignorant (practical) dualist. At the same time, and contrary to the usual criticism aimed at traditional dualism, practical dualism can increase awareness of mind~matter exchange—by explicating these domains, e.g. in comments, reports, statements, etc.—thereby helping to challenge and overcome the general metaphysical assumption of ‘separation’, implied by a mechanical worldview based on objectivism, positivism, and reductionism. Simmel’s explanation about the wholeness~reductionist dynamic nicely underlines the motivation (ultimately for portfolioism): The development of [a] metaphysical world view moves between the unity and the multiplicity of the absolute reality in which all particular perceptions are based . . . Monism leads on to dualism or to pluralism, but they again create a desire for unity; and so the development of philosophy, and of individual thinking, moves from multiplicity to unity and from unity to multiplicity. The history of thought shows that it is vain to consider any one of these viewpoints as definitive. (Simmel, 1907, pp. 108–109)

Next, the MMH also adapts practical dualism into its bespoke economics-inspired version of portfolioism, recognising the importance of the practical challenge of how to improve mind~matter interaction (for the benefit of society) besides the traditional challenge of theoretically explaining it. Ontologically, the MMH emphasises the dynamic (e.g. animated) nature of our world and—in the spirit of James’ radical empiricism—focuses on the connecting activities and processes (rather than, say, only on elements or things). As the sayings go, ‘opposites attract’, and ‘change is the only constant’. This is nicely captured by the squiggle symbol. So, for portfolioism the squiggle symbolises both the complementarity and the dynamic (via exchange) between the various ‘opposites’. The synergy that ensues is the proverbial ‘proof of the pudding’. Its ‘tasting’ is particularly applicable to the phenomenology of experiences, like the

 Technically, it is a form of dual-aspect monism (for details, please see Appendix 1-A2). Speculatively, perhaps the unitary order is some form of ‘market’. I don’t know and this speculation is not further discussed in the book.

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delight of your A-ha experience that results from the collaboration~competition between your intuition and logic. Central to this dynamic are exchanges aimed at discovery, especially of value. Portfolioism can then be interpreted as understanding the exchanges between (what we perceive as) mind and matter, which share some uniform order that still needs to be fully discovered but seems based on those exchanges. It starts with transcending subject~object and then extends: the unity that results from the [exchange] between the subject and object into which the mind divides itself . . . Only through the continuous dissolution of any rigid separateness into [exchange] do we approach the functional unity of all elements of the universe, in which the significance of each element affects everything else. (Simmel, 1907, p. 116)

On this broader note, portfolioism considers everything in nature—from particles, to DNA, to galaxies—to form a dynamic portfolio. Specifically, portfolioism views our reality as consisting of material resources and mental capital. Both are deemed and denominated as assets: land (something very physical) is an asset, but so is an idea (something very mental). A person can hold both and even create synergy between the two (develop the land with the idea). With that view, negative assets are liabilities. Still, (knowledge of) a liability in A’s portfolio can be an asset in B’s portfolio: A’s guild is exploited by B as leverage. Alone (single asset) or combined (multi-asset) they form portfolios at various levels. Assets can be exchanged and valued in markets, which is how portfolios make up and interact with markets. When a single portfolio forms the market, it is called a market portfolio. In this context, a market is simply the general term for an environment where complementary market forces operate. By interpreting it in metaphysical—in our case, dualist—terms, portfolioism helps to better describe and understand the complex exchanges in the economic system. It offers an intuitive way to think about how we—reflexively between the mental perception and physical action (often with a third introspective overlay, e.g. to the point of meditation)—deal with our world. By the way, talking about metaphysics, God is not a DJ (sorry Faithless) but an entrepreneurial ‘angel’ investor. He created the universe’s ‘market portfolio’ (obviously!), which only He knows and holds, but He has outsourced managing (tiny) sub-portfolios to us. Regarding cognitive economics, I make no excuse for cherry-picking between cognitive and economic science. More bluntly, I see my role as that of a scientific mercenary: in each debate I am loyal to whatever discipline offers me the best arguments. At the same time, there are limitations and constraints. And, obviously, I give my own (biased) spin to it. Importantly, although over time investors have casually referred to the market’s mind, it should be clear that formalising it as a scientific proposition has major implications for both disciplines. Particularly regarding the respective problems they attempt to address. These problems converge into a common problem they share: the aforementioned mind~body problem which I will revisit.

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Fortunately, others have already hinted at such connections between these sciences, on both sides of the aisle. On the right we have our right honourable economic experts like Ainslie, Derman, Hayek, Brian Loasby, Soros, and Didier Sornette making references to minds: “the global behavior of the market . . . is reminiscent of . . . the emergence of consciousness” (Sornette, 2003, p. 241). On the left we have our right honourable cognitive experts like Clark, Daniel Dennett, Lisa Feldman Barrett, Friston, Kelso, and Seth frequently talking about minds using market terminology. Clark states that “spending metabolic money to build complex brains pays dividends in the search for adaptive success” (Clark, 2013a, p. 181, emphasis added). Seth states that “the functional aspects of consciousness concern the role(s) that mental states play in the cognitive economy of an organism” (Seth and Bayne, 2020, p. 440, emphasis added). Feldman Barrett (in her 2018 TEDx Cambridge talk, emphasis added) discusses affective realism (i.e. “you believe what you feel”) and states that “One of your brain’s most important jobs is running a budget for your internal resources, like oxygen and glucose, water, salt, hormones”. As if this wasn’t enough to make the MMH case, she then links it to the mind~body problem: Somehow the sensations of body-budgeting, which are physical, become transformed into affective feelings, which are mental . . . And this transformation of physical to mental is one of the great mysteries of consciousness . . . This may be at the heart of some of society’s most challenging problems, like . . . every financial meltdown of the past century. (Feldman Barrett, 2018, emphasis added)

And finally, Friston talks about value, currency, reward, and gain: Expected value can be expressed in terms of information—and expected information gain has value. In other words, information and value have the same currency and can be combined . . . This is done by expressing (extrinsic) reward in terms of (epistemic) information gain. (Friston et al. 2015, p. 220)14

But to build a theory about the market mind as a shared principle their comments need to be formalised and further specified. The MMH thus interprets the economic system in cognitive terms, while interpreting the mind~body in economic terms, mixing and matching in between to bring the two closer together. Consequently, the MMH talks about the mind~body economy because each person embodies an internal economy. At the same time, it talks about the economic mind~body because the economic system, or external economy, is a collective mind~body. Viewing ‘money as attention’ in the external economy is simply the collective mirror to viewing ‘attention as money’ in the internal economy. The MMH blends economics’ ‘distributed knowledge’ with cognitive science’s ‘distributed cognition’. It recognises the ‘illusion of ex Other examples include LeDoux who states that “when you’re emotional, your brain has been monopolised [by System 1]”. And Jerry Fodor, referring to its alleged epiphenomenal nature, states that “consciousness ... seems to be among the chronically unemployed”. For more such expressions, please see Appendix 1-A.

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planatory depth’ in cognitive science as a particular manifestation of the ‘illusion (or pretence) of knowledge’ in economics. It links the market’s ‘discounting’ of events to the mind’s ‘predictive processing’ of those events. It compares the limitations on ‘free lunches’ in markets with limiting ‘free energy’ in minds. And connecting Hayek to (Marvin) Minsky,15 the MMH sees no place for a ‘central executive’ in the market mind, just like there is no ‘homunculus’16 in the human mind. Again, we are particularly concerned with the mind’s interiority: an agent’s internal mental states culminating in conscious experience, which quality impresses what it is like to undergo that experience, and how this extends into the world, especially when it is shared with other minds, that is, collectively in the market mind. This is an important point: the MMH nudges both cognitive and economic science to consciousness, the final frontier of human mentality, and suggests they explore it together. Specifically, they can collaborate to address their common problem which Hayek, combining his cognitive and economic knowledge, recognised early on: “it is the existence of the phenomenal world which is different from the physical world which constitutes the main problem” (Hayek, 1952, p. 28). This book will interpret economics’ version of this so-called “hard”17 problem and argue that it is its elephant in the room.18 Nested, and separately defined, is the market’s mind~body problem. Again, part of MMH’s novel interpretation is the practical (rather than just explanatory) challenge of the mind~body problem, namely, how to improve mind~matter exchange. The original mind~body problem has bedevilled philosophers, scientists, and other researchers for centuries. As a reminder, the mind~body problem asks to explain the relationship between matter (the brain) and mind (conscious experience). Another way of stating this is how (and why) our physiology gives rise to our psychology, especially its qualities known as phenomenality (via sentience). An example of this is your brain physically processing a 25% drawdown (quantity) in your portfolio accompanied by the hurt (quality) of that painful loss. How is that quality of experiencing a drawdown related to the neurons that physically carry the signal? Hugging physicalism, the EMH considers this as epiphenomenal and not relevant. However, if that is the case, then the hurt of pain from a losing trade is causally inert, which contrasts with the practical reality that it often leads to physical action to close that trade (irrespective of the question whether that is the ‘rational’ thing to do).

 See Hayek’s “The Use of Knowledge in Society” (1945), respectively Minsky’s Society of the Mind (1988).  Still, Pixar’s animated movie Inside Out (2015), involving multiple little people inside a child’s head, is an entertaining and instructive introduction to the human mind for younger viewers.  Originally defined by Chalmers (1995). It is also known as the problem of consciousness. From now on I won’t use quotation marks.  Consciousness’s ‘easy’ version (i.e. access consciousness) was conveniently ‘translated’ by mechanical economics from “awareness” to its proxy “complete knowledge”.

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The failure by theoretical accounts to meaningfully explain such real-life experiences echoes Kantian, Korzybskian, and similar connotations. Adding to the complexity is that a lot of mind~body exchange remains unconscious. Solving the mind~body problem—assuming it is possible—is about bridging the mind~matter gap whereby the challenge is twofold: 1. In terms of theory: how to explain mind~matter exchange. This is appropriately called the “explanatory gap” and is the traditional focus of cognitive science. 2. In terms of practice: how to improve mind~matter exchange. This has more to do with coordination, including organisational issues. Our perception of the world depends on the number and quality of our senses. Because both are finite, perception is one area where the limitations of our minds show up, i.e. the mind’s market is incomplete. We misperceive when our ‘inner’ mental world (our perception) does not match the ‘outer’ material world (our reality). Individual efforts include practising yoga, tai chi, and other contemplative practices, but also taking psychedelics (e.g. Harman et al., 1964; Carhart-Harris et al., 2018). Moreover, this is where action to change the world, such as the free energy principle (e.g. Constant et al., 2020), plays out. On a larger scale, such practical attempts are especially relevant for instances where multiple mind~bodies deal together with states of the world, often tackling problems that cannot be solved by an individual or where collective (species) adaptation via evolution would take too long. This makes it the focus of cognitive economics. Recognising this is one of the distinguishing features of the MMH. Over time, in industry and science, we created methods and tools (like the telescope and the microscope) to transform objects and materials to better ‘handle’ them mentally. We specifically try to improve mind~matter exchange, like sensorimotor coordination. A modern development is digitalisation/virtualisation to dematerialise physical objects (say, into digital assets). The markets have been crucial in (especially funding) these collective efforts. Specifically, the coupled system of investors with their terminals—in functional terms—are so coordinated that they operate the synergistic function of a collective bridge between the physical and the psychological, with prices as informational building blocks. In fact, while scientific tools were developed from the desire to discover, the latter is arguably economically driven—by mental rewards in the mind and monetary rewards in the market. My point is that the mind~body problem reaches into the general (perceived) dichotomy between the mental world and the physical world more deeply. It is underlying economics’ “broad and difficult but unescapable problems connected with the nature of value and its relation to reality, and the methods by which both are tested and known” (Knight, 1925a, p. 373). It concerns numerous practical issues in the economic system, varying from turning an insight into an innovative product to paying for physical labour with a digital IOU. These practical aspects, how to (better) bridge mind~matter or even integrate them, is implicit whenever we make a distinction between the physical and the mental. Because we do this a lot—most of the time without

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realising—it almost constantly looms in the background. On the cognitive side, this is, for example, the MMH’s contribution to the discussion concerning the so-called “meta-problem of consciousness” (Chalmers, 2018). Moreover, it highlights the relevance of portfolioism, MMH’s metaphysical framework. Importantly, the MMH also helps with the key challenge of consciousness research generally: When an experience realizes an information state, the same information state is realized in the experience’s physical substrate . . . We might even suggest that this double realization is the key to the fundamental connection between physical processes and conscious experience . . . It may be that principles concerning the double realization of information could be fleshed out into a system of basic laws connecting the physical and phenomenal domains. We might put this by suggesting as a basic principle that information (in the actual world) has two aspects, a physical and a phenomenal aspect. (Chalmers, 1996, pp. 284–286; emphasis added)

In reply to Chalmers, the MMH argues that the pricing system, if healthy, is such a “system” at the collective level.19 It connects the physical economy with the phenomenal market, whereby complementary market forces act as its “basic laws”, price discovery as its “basic principle” and prices as the dually realised “information”.20 By way of the pricing system we collectively bridge mind and matter in our efforts to benefit from/hedge against states of the world. Again, prices are readily available for new empirical research from fresh, e.g. psychophysical, perspectives. For instance, prices can be seen as the outcomes of our collective “predictive processing” (see Subchapter 3.3), whereby their constellation and dynamics can be considered the market mind’s version of a “controlled hallucination” (Seth, 2021). While such hallucinations often work as our models of impenetrable reality, they are not full-proof and can pop when, in the extreme, a bubble bursts. Importantly, why does the MMH focus so much on market mood? Because it is crucial for both practitioners and academics. When I asked investment legend Howard Marks, during my interview for the symposium, what cognitive scientists could investigate that would benefit investors most, he didn’t hesitate: “mood, and so-called animal spirits, and so-called irrational exuberance . . . clearly there’s so much grist for this mill”. Specifically, in reference to his famous concept of the pendulum Marks emphasised that it was not meant in a mechanical sense but rather that “understanding it as a mood swing is much more useful for our [investment] purposes”. Regarding theory, market mood is a key aspect of the Achilles heel of mechanical economics. Simultaneously, for cognitive science it highlights a complexity because it regularly affects or even dominates subjective experiences. For example, it challenges the exclusion axiom of Integrated Information Theory (see Subchapter 3.4). Finally, we should always re-

 Please also read the quote from Nobel laureate Vernon Smith in Appendix 1.  This, in turn, allocates our resources (just like an individual mind allocates attention) and allows us to make selections.

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member the deep connection between discovery and mood (as its meaningful phenomenal experience): “Existentially a state-of-mind implies a disclosive submission to the world, out of which we can encounter something that matters to us. Indeed from the ontological point of view we must as a general principle leave the primary discovery of the world to ‘bare mood’” (Heidegger, 1927, p. 177, emphasis added). For background, the MMH’s effort is part of the current incarnation of a cognitive revolution which is about a revision of our understanding of the human mind more generally. We have taken a ‘biological/organic turn’ and are moving away from the machineperspective of the mind as a brain-bound (hardware) computer with central execution (software) to that of a complex integrated system of 4E cognitive faculties. As a reminder, the four E’s stand for, respectively, embodied, embedded, enactive, and extended.21 Importantly, for example, “if the extended mind thesis is true . . . [it] is not simply . . . about the epistemology of the mind . . . but about what cognition and the mind are—about the ontology of the mind” (Wilson, 2010, p. 171). The MMH thereby submits the other leg of its premise, mind-as-market: mind, at multiple levels, seems to operate like a spontaneous market (portfolio). The extent of self-organising reflects its level of consciousness, with noise, for example, being of the neuronal kind (e.g. Dehaene, 2015). In Subchapter 9.2 I will discuss the latter’s connection with price noise, linking it back to market-as-mind. Spearheading this revolution is the growing consensus that “the study of consciousness is becoming a science” (Tononi and Koch, 2015, p. 2). This will have a profound impact on research in both the financial and real economies.22 It includes a renewed appreciation of the qualities involved in conscious realisation of information. As philosopher Jaegwon Kim points out, we have seen “a phenomenal growth and proliferation of research programs and publications on consciousness”. Nevertheless, there is resistance: Although consciousness research is thriving, much of . . . science seems still in the grip of what may be called methodological epiphenomenalism . . . It is an ironic fact that the felt qualities of conscious experience, perhaps the only things that ultimately matter to us, are often relegated . . . to the status of “secondary qualities,” in the shadowy zone between the real and the unreal, or even jettisoned outright as artifacts of confused minds. (Kim, 2005, pp. 10–12)

More broadly, this revolution raises serious questions about, and in some cases warnings against, mechanisation in society. This includes, for example, some key issues related to ESG investing which I’ll discuss in my first Economic Note (The spirit of ESG). It originally appeared as an article for Jackson Hole Economics. More than two years later, the challenge it posed (stated in the note) was acknowledged in an article published by the Financial Times (Nangle, 2023).

 To be discussed (also in Appendix 1), but see Clark and Chalmers (1998), Menary (2010b), Newen, De Bruin and Gallagher (2018), and Rowlands (2010).  On the impact on real economies, see e.g. Williams and Poehlman (2017). Theirs is the target article in a special issue published in the Journal of Consumer Research.

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Economic Note The spirit of ESG: awareness of externalities23 There comes a time when one must take a position that is neither safe, nor politic, nor popular, but he must take it because conscience tells him it is right. Martin Luther King Jr. A Proper Sense of Priorities (February 1968) In practice, the “spirit” of an action counts, as well as the action itself, and is often vastly more important. The relation of mutuality must be recognized. The man who expects to influence others must work more through their feelings and his own than through explicit physical stimulus and response. Frank Knight Economic Psychology and the Value Problem (1925) In 2020 China, Russia, and Cuba won seats at the United Nations Human Rights Council (UNHRC). Considering the atrocious track records of their governments in that department this was heavily criticised, particularly by organisations like Human Rights Watch. Why should investors care, especially those involved in ESG investing? Because human rights squarely belong to the ‘Social’ principle in ESG and are central to any broader human interest, including plain survival. To explain, ESG investing involves selecting securities that comply with Environmental, Social, and Governance criteria (for simplicity we include the related fields of responsible, sustainable and impact investing). The FT reports that Assets-Under-Management (AUM) of ESG funds amounted to almost US$ 3trillion at the end of 2021. In a best-case scenario PwC anticipates ESG funds could outnumber conventional funds by 2025. NN Investment Partners (now part of Goldman Sachs) estimates that the green bond market will grow to €2tn by 2023. Many hope that ESG investing, by nudging companies and governments, can help improve the world’s ESG conditions overall. I do too, but with caveats, most prominently that ESG will fail if mechanical economics prevails. ESG is mainly about negative externalities: costs or damages caused by an economic agent that are not (financially) paid by that agent. Instead, the costs are suffered, in various forms, by other agents and society generally. There is a growing awareness that this needs correction, as in ‘the polluter pays’. From its mechanical perspective, mainstream economics likes to judge externalities (as the term suggests) as ‘external’ and separate from the core activities and purposes of agents. Within the broader theme of this book, we should instead see ESG as part of the 4E framework of the market mind: embodied, embedded, enacted, and extended in the environmental, social and governance aspects of the global society and the wider world. Not all externalities can be priced which prevents creating securities to help make markets more complete in covering these risks. Instead, economists like Kenneth Arrow and Amartya Sen have shown, more generally, that incomplete markets need individual and social values (e.g. ethical and moral norms), to fill their gaps. Knight is similarly clear: “the essential point is that economics is a branch of aesthetics and ethics to a larger extent than of mechanics” (1925a, p. 399). Ethics in commerce and markets has a long history, going back as far as Aristotle. I will return to this thought. Regarding caveats, first, we cannot escape the dualist (mind~matter) complications in our economic system which can lead to the perceived separation I mentioned. As this book will regularly point out, to better understand these we have to be explicit about the physical and/or mental properties (assumptions) of the economic problems we confront, as well as those of any solutions. While many of the problems in the Environmental domain are largely physical in nature (CO2 emissions, microplas-

 Most aspects and controversies of investment management, like active vs. passive, fees, valuation, and so on apply to ESG as well. However, I will not discuss these here as the message of this note is different.

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tics), there are numerous mental, especially moral issues (see Gardiner, 200624). And while many of the problems in the Social domain are mental in nature, relating to fairness and/or justice, there are also physical issues, such as working and safety conditions. This duality consequently leads to conflicts (for instance, physically we need gas as a less polluting energy source, but mentally it doesn’t feel right to buy it from corrupt or totalitarian regimes). It also means that certain ESG problems are better tackled from within the physical economy, instead of the mental market. Second, the timing and duration of aspirations and needs plays a role beyond economics’ traditional ‘time preferences’. The physical need for food or energy may be more urgent for one, whereas the need for safety or mental health may be more urgent for another. (See also my earlier comments regarding Maslow’s hierarchy of needs.) Moreover, it is difficult to foresee whether today’s polluter could be tomorrow’s clean-energy pioneer. Third, we need to distinguish between the (individual) personal and the (collective) social dimension in markets. This reaches back to the teachings of Adam Smith. People often think of Smith’s invisible hand, organising markets, purely in terms of competition and selfishness, as captured in Smith’s legendary words in WN: “It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own self-interest”. What is generally overlooked is its contextualisation, made by Smith a few sentences earlier: “In civilised society man is at all times in need of the cooperation and assistance of great multitudes”. What’s more, in TMS Smith states that “to restrain our selfish, and to indulge our benevolent affections, constitutes the perfection of human nature; and can alone produce among mankind that harmony of sentiments and passions in which consists their whole grace and propriety” (p. 31). In general, Smith offers a nuanced view of economic man, considering both his personal and social aspects, which can help us with ESG issues. In that regard, ESG investing offers interesting cognitive challenges to the ideal of homo economicus. In principle, it aligns non-monetary values of assets to personal or social values. This gets at the heart of some of the critique of mechanical economics: “there can be no such thing as a ‘value-free’ social science. Social scientists who consider the question of values ‘nonscientific’ and think they are avoiding it are attempting the impossible . . . [Economics tries] to determine what is valuable at a given time by studying the relative exchange values of goods and services. Economics is therefore the most clearly value-dependent and normative among the social sciences” (Capra, 1982, p. 190). Exploring the social aspects further, the challenges also concern the intersubjective. On the very first page of TMS, for example, Smith emphasises sympathy which we nowadays understand as empathy. ESG appeals to one’s conscience which concerns Smith’s “man within the breast” (TMS; please ignore Smith’s gender label). The “man within the breast” is the representative within you of Smith’s “impartial spectator”. In turn, the impartial spectator is an idealised, morally supreme being, a global citizen representing the best of humanity. Smith’s moral arguments are not religious, nor relativistic, but rather based on aesthetic naturalism (Fudge, 2009): humans are naturally inclined to (similarly) judge certain actions as despicable and others as agreeable. Underlying this more broadly Smith, like others, reasonably assumed that society can only survive if humanity is ‘good’, in the sense that humans care about other humans, including future generations. Still, this is not a given and we have to work at it. Suggesting a real-life thought experiment, Smith asks each of us how the impartial spectator would judge a situation based on the inner judgement (“inner sentiment”) of your man within the breast: how does that situation make you truly feel? In other words, Smith asks, if only for that situation and for a moment, to bring out the best of humanity in you.25

 Gardiner discusses the (mental) morals of climate change. Credit to John Normand for this reference.  A highly recommended alternative view is from Normand (2022). He advices a complementary but more specific application via consequentialism.

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We can apply this to our ESG case, without becoming sententious. For instance, how do you (again, thinking of yourself as representing ‘the best of humanity’) feel about your ESG-fund holding oil stocks?26 Or, suffering a case of green washing, seeing your green bond turning brown upon closer inspection? Conscience and self-reflection are human traits, yet difficult to quantify. Involving ethics and morality, ESG investing also epitomises normative economics, reminding us of David Hume’s warning that the rules of morality are not the conclusions of our reason. Is it commendable that your ESG-fund invests in a bank of which the stock price has risen because its fraudulent actions only received a slap on the wrist by revolving-door regulators (in turn, due to the larger externality of regulatory capture)? Mainstream critics have argued that ESG distorts the function of economics and markets. Some argue that companies only survive by, and should thus exclusively focus on, serving their customers well. Others argue, in the tradition of Friedman, that they should serve shareholders only. In both cases, the argument is that self-interest trumps all. But what if, perhaps unbeknown to those customers or shareholders, their children will suffer the companies’ externalities? Or, vice versa, what if those companies suffer the externalities (say, pollution) caused by their customers or shareholders? Besides my earlier Smithian nuances, the arguments by criticasters reflect, again, the outdated reductionist aspect of the mechanical worldview that we can keep causes and effects neatly separated. Other perceived conflicts within ESG are similarly the result of flawed economic thinking. It should be replaced by the 4E-cognitive view of economic reality, as will become clear throughout this book. For example, on enactment in terms of Predictive Processing (see Subchapter 3.3), there should be a clear understanding of ESG criteria as inputs (i.e. as risk factors) for ex-ante perception (via our analytical models and screens) and ESG criteria as outputs for desired/resulting action (corporate behaviour). Simply put, ESG deals with externalities that individuals cannot solve. In many cases markets—seen as collaborative effort, not just self-serving—can help. There are a growing number of examples where markets can start pricing solutions to externalities (like CO2 emission rights). In other cases morals need to fill the gap and ESG allows an individual “man within the breast” to collaborate with others to attempt representing the impartial spectator. This makes ESG complementary. It is less about quantity, and more about quality; less about the letter, and more about its spirit. Then again, I’m old-fashioned and my ESG views are, shall I say, traditional. Back in 1998 I premiered my thoughts in a supportive article on sustainable investing (a first by a mainstream bank in the Netherlands). It was published in a Dutch newspaper and later included in the book People, planet, profit (Van Poll, 1999). In 2006 I also wrote a report—one of the first from the Scottish buy-side—on the investment implications of climate change. My thinking, however, was shaped even earlier, in part by trailblazers like Calvert Investments in the US and the Dutch ASN and Triodos banks. More importantly, as an answer-seeking MBA-student at the University of Groningen I had the pleasure and privilege to intern in 1990 with the late Willis Harman. Harman was professor emeritus at Stanford University, a cognitive science pioneer, and one of the original futurists. Based on research that started in the 1970s, his 1988 book, Global Mind Change, presciently identified four major challenges the world would increasingly face: 1. Environmental sustainability (climate change, biodiversity-reduction, deforestation, pollution). 2. Inequality (the equity and justice challenge as he called it). 3. Marginalisation (non-inclusion, populism versus globalisation; think also Raghuram Rajan’s The Third Pillar, 2019). 4. Worldview challenge (inaccurate economic thinking).

 For a historic overview of the oil industry’s dubious attempts to influence the climate change debate, see the BBC documentary “Big Oil v the World”.

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The first three are now generally recognised as the main challenges within ESG, which Harman considered all part of Governance in that it is the responsibility of corporations (as the dominant institutions in capitalist societies) to address. Crucially, however, the fourth challenge plays a key role because it frames and thus determines how we economically deal with the other challenges. As Harman points out, the fact that the current economic paradigm led to, “the successes in achieving the goals of the existing order” shows “how profound the required changes may be”. Particularly now that unintended consequences of this paradigm threaten to overwhelm its successes. The sad truth is that as long as we maintain a mechanical economic worldview we cannot properly deal with the other challenges. As will be discussed, mechanical economics has a fatal blind spot namely consciousness (and its related issues, like mental causation). It ignores the consciousness that animates ESG’s conscience, feelings, and ethics in general, and that raises the awareness of emerging ESG-risks in particular. Consequently, mechanical approaches don’t get ESG’s spirit. As applied to ESG, a mechanical worldview quantifies risk factors as rules to screen and filter securities. Bluntly, exposures to the environment, employees, and/or regulations are considered relatively risky. Simply avoiding such exposures is preferred over trying to improve those factors. Guess what happens to the resulting quantitative rankings of stocks? Companies that produce nothing physical (e.g. outsource pollution), have scarce human resources (e.g. outsourced employment), enjoy moats (e.g. exercise monopoly power) and avoid transparency (e.g. exploit offshore havens) rank best. Unsurprisingly, they include many tech stocks. These rankings also end up in ESG indices because they are often based on such mechanised approaches. Yet, that is hardly in the spirit of ESG. By the way, that doesn’t mean such screening-favourites do not get criticised (what happened recently to Apple Inc regarding its compliance with demands by the Chinese authorities demonstrates this). The main ESG drive comes, in that regard, from responsible investment institutions seeing through such screens and actively challenging companies on their strategies and policies. Still, while commendable I suggest we should widen our scrutiny and include investment firms themselves to prevent hypocrisy. Hedge fund legend Sir Chris Hohn took a first swipe. Going forward, here is my challenge to (especially investment) firms who claim to be ESG champions: how consistently are you practising what you preach? For example, do you also do business in countries run by dictators who murder their political opponents (including journalists)? Or trade with lawless counterparties that trample on international laws? Or operate in countries that claim to protect the environment but do the opposite, like tolerating deforestation or sending their fleets into fragile waters? If so, can you please square this circle (without resorting to the trivial ‘every little helps’)? Returning to the issue of human rights a large global bank, for example, states on its website that: “We are striving to be at the forefront of ESG . . . [supporting] socially, responsible themes”. How consistent is that with its chairman stating to its CCP hosts, in 2022, that his and other global bankers are all “very pro-China” at a CCP sponsored financial forum in (oh irony) Hong Kong? Unfortunately, there are countless examples of these. Coming back to the start of this note, the link between ESG and economics is historic. As far as morality and human rights are concerned, the link was firmly established with Smith’s TMS partnering with WN. Second, there is the horrific historic record of central (repressive) planning by totalitarian regimes which inevitably fails their people and their economies. For ESG to succeed, we need to buy into its spirit which—using cognitive (not politically charged libertarian) arguments—is about free minds being able to address externalities (in other words, to allocate their costs). ESG needs recognition of its moral underpinnings as well as (e.g. Smithian) approaches that incorporate human values. It doesn’t need wokeism for this (which seems to have hijacked some of ESG). It certainly doesn’t need algorithmic short-cuts. And if efforts would purely result in a growing number of compliance staff, it would clearly miss ESG’s spirit.

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Finally, why focus in this note on the S in ESG? Because I believe that, largely due to mechanical economics, we particularly risk getting the S wrong, which is about (collective) humanity. And if we get the S wrong, resulting in growing human rights abuses, inequality, and so on, we will never get the broad commitment and support within society required to meet the wider ESG ambitions. So, unless we revise mechanical economics more broadly, including reversing its treatments, ESG investing will become meaningless. Then Benjamin Graham’s long-term weighing will indeed, one way or another, be done by a careless, bureaucratic machine. This year (2023) marks the 75th anniversary of the Universal Declaration of Human Rights. Dedicated to all the human rights activists especially those working in countries with repressive regimes.

What makes the market so attractive for collective cognition research? First, the relationship between individual investors and Mr Market is unique because investing largely takes place anonymously. It means that, as an individual investor, you do not directly trade with me (another individual), nor any other person. You trade with Mr Market. That’s the only one you know. Well, kind of. You know him to be a composite investor, a collectivity. In fact, by investing you become part of the market, often joining a particular crowd, like bulls or bears. These crowds have meetings and also communicate via their terminals and computers, sharing their archetypal narratives of boom and bust. Importantly, as we know, people act differently in crowds. Crowd emotions are contagious and their moods affective. For researchers it means that as we observe the market’s mentality via price moves—including non-linear ones from derivatives—we cannot reduce it to individual mentality.27 Consequently, we do not know how individual decisions become aggregated in prices. For example, we lack insight into asymmetries in information and their impact. This makes the market unique: whereas we can identify the role and impact of individual behaviour in teams, riots and other manifestations of group behaviour, this is impossible in the market. Simple individualistic assumptions about heuristics, intentionality, rationality, as well as their aggregations, are seriously lacking. Second, and referring to the practical challenge of bridging mind with matter, financial markets perform an economic meta-adaptation in that they solve a crucial problem for society: to allocate capital, in a reasonably efficient way, to investments which eventually find their way into physical assets, including the physical activity they produce, in the real economy. In the words of Bill Miller, legendary (ex-)fund manager of the Legg Mason Value Trust: One of the things capital markets do is consider possible worlds. The level and direction of prices reflect the markets’ assessment of the probabilities of possible worlds becoming actual. There are advocates for many of these views. Investors consider the risks and rewards and allocate their money accordingly. (Zenios and Ziemba, 2007, p. 879)

 Even if we have access to some trading records these do not provide a complete picture of the market overall.

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The intention of the market is thus the efficient allocation of society’s material and mental resources. As Hayek and others have extensively argued, this is not something any individual can know, let alone achieve. The market “knows more than any individual investor can know” (Bernstein, 1992, p. 136). Still, that doesn’t make Mr Market perfect. This collective dimension returns us to our main item of concern. I have translated the original mind~body problem into economics’ hard problem, which is overarching, as well as the market’s mind~body problem, which is more specific. Economics’ hard problem asks in broad macro terms how the mental (financial) economy relates to the material (real) economy. In particular, how does market mentality—including extended consciousness—influence the economic fundamentals? The GFC epitomised how the former can impact the latter. It also reminded us of the limitations of most economists’ models which, incomprehensibly, excluded the financial economy (especially the banking system). The market’s mind~body problem, on the other hand, asks in investment terms why the quantities involved in the market’s physical processes and cognitive content give rise to its qualities. In other words, how does the exchange between the physical properties (e.g. real assets) and the cognitive properties (e.g. expectations) lead to market states as we experience them in the shared phenomenal sense (e.g. mood)? And what role, if any, does that phenomenal overlay play? Suppose, for example, that a market exists of two assets (A and B) which each happen to be quoted at the same price on two separate occasions across time. So, on both T1 and T2 Asset A is priced at 100 and Asset B is priced at 200. While those readings may be the same, investors know that it is highly unlikely the market also feels the same on those occasions. As we’ll see, this means that the full market state—as dually realised information—between those occasions differs, despite being quantitatively the same. It is another economic example of an explanatory gap. For economic science this hard problem means that investors’ mentality involves more than rationality or heuristics: it is the existence of consciousness with its (shared) phenomenal aspect that makes it hard. Similarly, for cognitive science it means, first, that the mind~body problem extends into practical issues in our economic system. And second, that consciousness involves more than individual subjectivity: it is the intersubjective aspect that makes it truly hard. Crucially, a treasure trove of empirical data, in particular prices, is available to explore the market from this particular perspective and to gain insights for both economics and cognitive science. This helps solving, for example, the “enormous problem for a theory of consciousness . . . : the lack of data” (Chalmers, 1996, p. 215). It can also help with “attempts at quantifying” 4E cognition (Clark, 2011, p. 214). In agreement with the earlier comment by Akerlof and Shiller, the MMH challenges mechanical economics’ implicit assumption that market mentality is epiphenomenal. In the critical words of Knight, mechanical economics is to be faulted:

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for its vagueness as to the place of consciousness in the interpretation of behavior. The hedonistic man, the selfish man, and the “rational” man are closely related conceptions, all designed for the same function. All reduce, if consistently applied, to the thesis . . . that the scientific man is one who does what he wants to do and whose wants are consistently related to the situation in which the man is placed. Followed out, this really means, as we have shown, simply the mechanistic view of man as an automaton, one whose conduct is in accordance with law in the scientific sense—that is, completely describable in terms of uniform relations to his situation. He may be conscious, but only in an “epi-phenomenal” sense, and consciousness is to be left out of the scientific description of behavior. (1925a, p. 385, fn. 5)

In the case of mood, for example, epiphenomenal means that investors’ feelings of despair and exuberance are causally inert. This assumption can be described by invoking Huxley’s mechanistic mind analogy of the steam-whistle of a locomotive engine: the market’s mentality is then assumed to be like that steam-whistle. Just as the steam-whistle is produced by the engine’s operations upon which it has no causal influence so too, the assumption goes, is the market’s mentality produced by the physical workings of (e.g. microstructural) mechanisms but has no causal influence upon their operation. However, this assumption is flawed. Continuing with the same analogy, the ‘market locomotive’ is a special case because the sound of the whistle is a supplementary property that feeds back on the machinists, those who fuel the engine’s fire that creates the steam. In short, as we now know, the market’s ‘whistle’ should be taken more seriously, not just as a warning ahead of a crash but as its possible cause. Table 2 summarises the previous discussion. Table 2: Worldview mechanical economics versus worldview MMH. Theory:

Metaphysical stance:

Market is:

Market treated:

Market’s hard problem:

Mechanical economics

Mechanistic monism (physicalism)

Machine

Mechanically

Denied/ignored

MMH

Portfolioism (practical dualism)

Mind~body

Psychophysically

Acknowledged/ addressed

As aforementioned, at first sight the MMH seems to simply refer to a concept that we are already familiar with, namely the market’s mind. For example, no investor wonders what George Soros means when he subtitles his first book (1987) as “Reading the Mind of the Market”. And as far as his personification goes, it was Benjamin Graham, mentor of Warren Buffet, who introduced us to Mr Market. Importantly, he is supposedly schizophrenic with a mental state swinging back and forth between euphoria (mania) and despair (depression). On closer inspection, however, this proposition is profound and the implications of formalising it is not sufficiently appreciated. We received an early clue of the implica-

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tions of cognitive science for economics from behavioural economics, with its message of heuristics and biases. This book highlights consciousness as the logical end point in this development, the final frontier of human mentality that economics needs to explore. Making mind~matter issues explicit is important in that regard and ignoring them can be dangerous. A notorious example at the macro level was the absence of the financial economy in the DSGE28 models pre-2008. Then there is the policy of central banks to target the stock market29 to generate their ‘magical’ wealth effect. It is dangerous because central bankers subscribe to the machine doctrine and do not give the impression that they understand the complexity (and potential dangers) involved in such mind~matter manipulation. Current conditions, including in the United Kingdom, offer another example in that regard. It started just a few years ago with central banks’ complaint that the real economy should be stimulated via loosening fiscal policy because their own monetary policy couldn’t do it alone. Combined these policies have since resulted in record inflation and we now see the flipside in that monetary policy often needs to fight ongoing fiscal largesse. All this has severely damaged their credibility. In general such ignorance is prevalent because mechanical economics does not face up to the market’s mind~body problem. Admittedly, acknowledging this problem opens that can of worms inside cognitive economics’ Pandora’s box. Among others, we have to discuss ontology, epistemology, and even metaphysics. Mechanical economics does not want to deal with these, what they consider, slippery topics. Moreover, its mechanical worldview results in mechanical policies, practices and products while promoting an almost exclusive use of analytical methods. This limiting factor is the reason why mechanical economics fails to comprehend the crucial phenomenon of market mood, which escapes axiomatic capture. The cognitive approach, on the other hand, acknowledges the full and integrated spectrum of mental phenomena that accompany physical market processes. But we also need different methods and tools, like new software, to achieve such understanding on the back of the markets’ big data. Excel simply won’t do! Complementary methods inspired by the insights from mind explorers, from scientists to mystics. They include the great and good of the past, like the Buddha, the Greeks, Heidegger, Hume, James, Kant, Laozi, Nietzsche, Spinoza, Wittgenstein, and Zhuangzi, but also a few operating on the periphery, like the Kyoto School, Jung, Krishnamurti, and Watts.30 Importantly, they include contemporary living legends like David Chalmers, Andy Clark, Antonio Damasio, Stanislas Dehaene, Daniel Dennett, Karl Friston, Gerd Gigerenzer, Daniel Kahneman, Scott Kelso, Christof Koch, Anil Seth, and Giulio Tononi. In the process we can turn Pandora’s Box into a toolbox to help think outside of

 Dynamic Stochastic General Equilibrium.  See the October 2012 minutes of the Federal Reserve’s FOMC-meeting.  They weren’t perfect, and each had their flaws.

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it. On that note, the bridging of disciplines and views this book promotes—centred on markets and minds—also means we need to look beyond Western philosophies to inform cognitive economics and include, for example, Buddhism, Hinduism and other Asian philosophies (e.g. Dasgupta,1993, offers an overview of historic Indian economic thought). Along the way I make various arguments. For example, I will acknowledge the important role of evolution by distinguishing between organisms and machines as well as their respective intelligences. Regarding AI, machine learning and big data we need to realise that most (e.g. digital) approaches remain mechanical, thus complying with the current view. In a podcast-interview for Artificiality (February 17, 2023) David Krakauer, president of the Santa Fe Institute—famous for its pioneering research in complexity— puts this succinctly: “Big data hates heresy. Big data is orthodoxy [and] the algorithm simply respects orthodoxy . . . In any area where you genuinely believe in change, machine learning gets into trouble”. Moreover, and paradoxically, we suffer from a lack of clean, raw data. Instead we are left with overabundant-but-contaminated-data, with price distortion being the worst form of such contamination. It complicates our challenge to bridge the material and mental into a truthful reality, something Mr Market31 can facilitate at the global collective level if allowed (and, arguably, used to be better at). Besides methodological change, recent market reality forcefully nudges economics to make the right ontological commitment, regardless of the costs of any epistemological revisions. That is, usually such commitments are expensive but in our case they are dwarfed by the costs of not making a commitment. Or worse by committing (as we have done) to something different in our nature altogether we make Hayek’s “outright error” and risk ending up in Keynes’ “colossal muddle”. As a result, we’ve become overconfident in our machines while sadly losing trust in and between ourselves. It is one thing to acknowledge that we tend to be overconfident and make mistakes. It’s quite another to then conclude not to trust oneself and instead trust an algorithm. Oh, the irony of the behavioural economics doctrine. Finally, if you think—especially after finishing this book—that the MMH is too radical, think again. It is all relative. The Santa Fe Institute judged the MMH as “not radical enough”, following my submission of it as my “Radical Idea” to their 2023 conference on collective intelligence.32

 Although the focus will be on financial markets, I would like the reader to view Mr Market’s case in the context of the overall success of market dynamics over time, including most recently via open (peer-to-peer) marketplaces.  Although I received an invite to attend the conference as a non-speaker, based on my “strong application and interesting research goals”, I respectfully declined via an informal rebuttal of the SFI’s decision.

Background and Motivation: Mr Market and Me There are these two young fish swimming along and they happen to meet an older fish swimming the other way, who nods at them and says: “Morning, boys. How’s the water?” And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes “What the hell is water?” ... It is about the real value of a real education, which has almost nothing to do with knowledge, and everything to do with simple awareness; awareness of what is so real and essential, so hidden in plain sight all around us, that we have to keep reminding ourselves, over and over: “This is water, this is water”. David Foster Wallace Commencement Speech, Kenyon College (2005)

This book is primarily written for younger (and future) generations despite the urgency and relevance for today’s academics, investors, and policymakers. Apart from the unlearning involved it will simply take time to accept, let alone implement, its messages in practical and policy terms. In any case, I like to think that all the readers of this book are interested in both markets and minds and are broadly familiar with the respective theories related to them. In the spirit of David Foster Wallace, I hope this book will contribute to an improved education. An education that includes actual investing—by being in the market with skin-in-the-game—and instils an awareness of what is real and essential in markets, which I illustrate with quotes from investors and traders. To contrast with its assumed rational state, one of them refers, for example, to the market’s “violence” and its “power”. When I mentioned this on a couple of occasions to aspiring economics and finance students, they always ask me what I mean. So here is my short answer: “Your professors will suggest that investing is mostly like a ride on a tour bus on a quiet Sunday afternoon, where you can just hop on and off. Instead it is frequently like Kingda Ka where you can’t get out. And it’s not quiet either: it’s part of the market’s Knotfest”. (You may not get it, but the kids do.) The longer answer is found in this book. It is time to move on from the initial message of behavioural economics to its logical conclusion: the mind~matter perspective impressed from conscious experience. The next step is to study, regulate and participate in markets through this prism. As a result, the MMH contributes more deeply to the debate on the fundamental causes, consequences and cures of the recent crises, aimed at finding solutions that benefit society. Investment management via financial markets is relevant for us all in that regard. Wealth may increasingly be concentrated among the wealthy1 (one of the consequences of mechanical economics) but ordinary citizens depend on and are also

 In their Global Wealth Report (2019) Credit Suisse estimates that the richest 11 per cent of the world population holds more than 80 per cent of its wealth. https://doi.org/10.1515/9783111215051-207

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exposed to financial markets, particularly via their pensions and other savings. Investor education plays a crucial role which has been undervalued throughout history. This became again painfully clear in a number of sad cases of ignorance during the recent retail trading boom, including one which resulted in suicide. One online broker promoted fractional shares as “invest in the stocks you want regardless of share price”. Seriously? And when Elon Musk, CEO of Tesla, tweeted that Tesla’s stock price was “too high” he didn’t necessarily mean it was too expensive. Instead it was ‘too big a number’ and his tweet turned out to be the prelude to a subsequent stock-split which halved the price to facilitate continued participation by its many groupieinvestors. Problem solved. This hype in retail investing is thus not the type of structural participation, backed by thorough education, which is called for. Unless we take measures, this will likely end, again, in tears for another disillusioned generation. And everybody will naively blame Mr Market again. On that note, besides Didasko the importance of investor education has been recognised by the OECD via its International Network on Financial Education (INFE). It also is the spirit of requirements by the UK Pensions Regulator. It urges trustees and others responsible for pension management (which, in a post-Defined-Benefits world, increasingly involves private individuals) “to develop and maintain a set of beliefs about how investment markets function . . . supported by research and experience”. However, mechanical economics as the traditional source for such “beliefs” is being seriously challenged, both in theory and practice. To wit, is the mechanical strategy of liability-driven investment (LDI) really such a good idea for pension funds? Further, it’s not just that surreal negative (real) yields and oil prices, or outsized gains in stocks of bankrupt companies, beggar standard beliefs. They actually have real impacts. In a healthy economic mind~body, real interest rates are positive so that savings (used, for example, for investments) are yielding positive returns and thus create wealth. Those who save are rewarded. However, this has been turned upside-down in frequently absurd ways. To counter this, the MMH aims to contribute to new thinking and help shape an emerging new paradigm, including the proper set of beliefs. Along with sharing its mood generally, a market is experienced by those in it via their specific portfolio exposures. There is a qualitative sensation to changing values of that portfolio. Any attempt to artificially escape or remove this reflexively impacts the market state because it reduces our overall awareness. We are at risk of ending up as the inexperienced fish who wonder what water is. Ultimately this book is about experiential investment education: by exploring what it is like to be in markets. Obviously, this cannot replace the ‘real thing’. Still, its complementary view helps with reminding ourselves over and over of the market’s ‘water’. To paraphrase Wallace (2005): “This is liquidity, this is liquidity”. Allow me one more connected metaphor, namely Heidegger’s 4E-cognition message of “water being ‘in’ the glass” (1927, p. 79), to illustrate the crowding out of awareness by mechanisation: we increasingly only seem to notice “water” when “the glass” breaks or something else interferes with “drinking”.

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On a personal note, for as long as I can remember I have been fascinated by brains, minds, consciousness, and all the questions they raise, particularly those regarding the collective dimension of mentality. This began when I was young and regularly attended concerts, football games, and other emotive public gatherings. I would often ask myself what it was about such events that challenged my sense of self in general and ownership of my sensations in particular. I am grateful to those who helped me to improve my understanding. Subsequently I found out that markets are the most fertile space to explore these matters. My initial introduction to markets started early, when my father explained options and convertible bonds to me (which he held in the company he worked for). At the end of my internship with Willis Harman I expressed my wish to continue working with him. He told me that instead I should take what I had learned to explore the real world of business and make my contributions there. Eventually, I ended up in investment management and experienced market mentality first hand. While working in Singapore I shared the worries about regional contagion (in economics also known as “reputational externalities”) when Barings Bank went bust. After returning to the Netherlands I shared the exuberance of the blowing of the internet bubble, as well as the subsequent despair when it burst (my employer was a subunderwriter for the hyped World Online IPO which turned into a debacle). Finally, residing in Edinburgh I shared the surreal from both Lehman’s collapse and the corona virus effects. If there is one thing that I learned, it is that in the battle between ‘history rhymes’ (because of human nature) and ‘it’s different this time’ (because of technology), one should generally bet on the first. Not only because modern times form just a tiny (and thus biased) subsample of our total history, but also because “the essence of technology is nothing technological” (Heidegger, 1977, p. 19). Being around Mr Market in general, but encountering him during these events in particular, has shaped my thinking about him. It’s not just that I have sympathy with those who have variously described Mr Market as capricious. In light of the insights from 4E cognition about extended minds I also believe we need to take perceptions of the market as an animated “greater being” and “living system” with a “mind of its own” much more seriously, namely as a composite entity with a collective 4E mind, comprised of exchanging 4E minds. This book is thus my version of animating Mr Market, including his mind. Among others, it reflects “the wisdom of crowds. What’s key is that crowds are wise under some conditions and mad when any of those conditions are violated” (Mauboussain and Callahan, 2015). I realise that for many my emphasis on collective human consciousness smells of anthropocentrism or, at the very least, goes against the grain of the growing belief in machines and their ‘intelligence’. As aforementioned, the consensus in that regard is to view and treat Mr Market as some kind of automaton. And, combining it with behavioural economics consensus, that it is best for those intimately dealing with that machine to repress their emotions by outsourcing decisions to other machines. But

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apart from the implications on spontaneous discovery, does this not risk that we become, in the words of Charlie Chaplin in The Great Dictator, “machine men, with machine minds and machine hearts”?2 Moreover, such distancing doesn’t actually help in gaining a better understanding of Mr Market. What if the consensus is wrong? In fact, what do we really know about Mr Market? Well, we know that he brings us together to trade and offers his invisible hand— extended by his mind—to facilitate this. Those who originally described Mr Market knew him through their dealings in equities/stocks. But he is also a member of a wider family, the Markets, whose other members are just like him but handle other securities, like bonds, commodities, or derivatives. The recent crises showed painfully that we don’t know enough about them either. Specifically, it became apparent how closely knit the Markets are, while getting decoupled from their economic environment. Mr Market stands for all the Markets in this book. The remainder of the book consists of twelve chapters. With a question as title, each chapter centres on a particular problem Mr Market struggles with, associated with mind~matter issues relevant in an economic setting. Along the way they touch on most of the current topics in finance and investing, including monetary policies, regulations, and investment strategies. In addition, throughout this book I have scribbled a few Cognitive Notes, Economic Notes, and the occasional Political Note. They are opinionated snippets with anecdotal material exemplifying the topics discussed but viewed from those particular perspectives. As this book was written over several years, in some instances it is clear that my comments preceded recent events. Still, the arguments hold. In terms of Mr Market’s consciousness I slowly build up to its specific proposition in Chapter 10. Those who can’t wait may start there. There is also a section with abbreviations and a glossary. Still, my choice of topics had to be limited. I tried to select and answer key questions but apologise upfront that it leaves many resultant new questions unanswered. The MMH is still under development, in that regard. Also, its messages are not always easy for everybody in the intended multidisciplinary audience, and I make no excuse that I will regularly repeat them in varying formats—mostly with a variation in emphasis or from a different angle—to get them across to all. So, should you feel that there is repetition please remember that there are other non-experts who can use a little reminder. Finally, this book contains an above average number of quotes. I have two motivations for this. First, I follow Michel de Montaigne’s dictum: “I quote others in order to better express myself”. In other words, why reinvent the wheel when, instead, you can give credit to those who already beautifully worded the argument you’re trying to

 United Artists, 1940. The Great Dictator. [Speech transcript]. Available from https://www.charliecha plin.com/ en/films/7-The-Great-Dictator.

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make. Second, and related, the MMH is an interdisciplinary endeavour, and I am not an expert in all these fields, so I like you to also hear it from a proper source. I thereby am the first to admit that I regularly cherry-pick to clearly make my points. In some cases I use square brackets to insert [my additions/edits]. And unless specified as being added, emphases are in the original. In the few cases where I couldn’t find a specific source, please assume it is allegedly attributed. Short quotes and other literal references are in double quotation marks, whereas nonliteral references, including figures of speech, are in single quotation marks.

Chapter 1 Setting the Stage: Who Am I? I believe that he is suffering. Do I also believe that he isn’t an automaton? Ludwig Wittgenstein

1.1 Evolution of Minds and Markets; From Nature’s Jungle to the Economic One A family visits their local market. As they stroll along the stalls, checking the produce, Mum buys some bread, fruit and vegetables. Dad carries the bags and keeps an eye on the kids, who do what kids usually do. Surrounding the market are restaurants, shops, and a post office. At a popular fish stall they grab something to eat for lunch. There they also meet some friends who similarly are collecting their groceries for the week. In fact, lots of people—varying in age, background, culture, gender, religion, and wealth—are visiting the market, exchanging money and products, as well as gossip and news. Both with acquaintances and total strangers, creating a sense of community. This scene occurs everywhere on our planet, be it in Africa, America, Asia, or Europe. And it has happened for thousands of years. It’s the archetypal social gathering in society. Markets reflect what ultimately binds and shapes us. These commonalities also suggest a wider community. Aren’t we all, and above all, global economic citizens? Consequently, should we not all be concerned what happens in and to markets? Going back in time, as hunter-gatherers our ancestors collected physical resources, including tools, for their survival. They complemented this with mental models— legends, myths, stories, tales, and other narratives—to help make sense of the world around them. For example, sometimes they combined tools with stories via rituals. Physical resources and mental models formed their dualist portfolios, highlighting the historic origin of portfolioism. We have evolved since then. The year is 1920. A young student by the name of Friedrich (von) Hayek writes his very first paper. It is not about economics, for which he would become famous later in life and win the Riksbank’s Nobel Prize for Economics. No, it’s about consciousness. The title of his paper, originally written in German, translates as Contributions to the Theory of the Development of Consciousness. Eventually this evolved into Hayek’s cognitive masterpiece The Sensory Order along with his development of the idea of distributed knowledge in markets: “The whole acts as one market, not because any of its members survey the whole field, but because their limited individual fields of vision sufficiently overlap so that through many intermediaries the relevant information [concentrated in prices] is communicated to all” (Hayek, 1945a, p. 86). Roughly forty years later, another young student by the name of George Soros, having studied under his mentor Karl Popper at the London School of Economics https://doi.org/10.1515/9783111215051-001

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(LSE), writes his first post-graduation paper. Similarly, it is not about investing, for which he would become famous later in life. No, it is again about consciousness, captured in its revealing title The Burden of Consciousness. Eventually this would result in Soros’s philosophy of reflexivity. Finally, another forty years or so later market researchers like Mark Douglas and Didier Sornette hit the point home with their observations about consciousness in markets. However, they did not recognise the importance of this insight and never developed it any further. Probably without being aware, all of them followed in the footsteps of the economist Frank Knight. While better known for his work on (the distinction between) risk and uncertainty, shortly afterwards (in 1925) he wrote two stunning, but largely forgotten, papers which we can categorise as cognitive. Specifically, in an implicit critique of mechanical economics’ worldview, which he labelled as “mechanistic monism”, Knight observed: The power of mechanistic logic over common-sense is great! But it does not extend to making the plain man deny that he and his fellows are conscious beings moved by conscious interests. Once more, one who denies the significance of consciousness is simply putting the abstract criteria of a logical system ahead of the fundamental [i.e. psychophysical] principles which form the only foundation for that system itself. (1925, p. 253; emphasis added)

A few decades later, Robert Heilbroner is among those to concur: “the objects observed by the social scientist all possess an attribute that is lacking in the objects of the natural universe. This is the attribute of consciousness . . .” (Heilbroner, 1973, p. 133). These little anecdotes lead to an important MMH-message: the reality of consciousness in markets. This subchapter specifically focuses on the evolution of minds and markets which forms the historic backdrop for the MMH, with shared consciousness as its pinnacle. Its reach is far and covers many areas. In a way it all starts with bacteria, the first organisms to compete and cooperate in order to produce energy from light in exchange for oxygen. Fast forward two billion years. As the most intelligent beings on this planet we now inhale that oxygen to compete, cooperate, and exchange. But we also focus on breathing during our meditation to quiet the internal market in exchange for pure consciousness (and, for some, peak performance). Moreover, we host many of such bacteria, mainly via our stomach’s microbiome which, combined with local ‘gut’ neurons, makes it our ‘second brain’ (formally: enteric nervous system). Like other cognitive economies in our environment (which I’ll mention later), these embodied ‘intelligences’ self-organise by way of exchanges, within and without, which equates them to markets. Boundaries get thereby crossed and become negotiable, making internality and externality fluid. Combined with nanotechnologies (within) and extended technologies (without), we are not just “human-technology

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symbionts” (Clark, 2003). Rather, we form species-technologies portfolios,1 or fund-offunds, each managing a mix of bespoke assets and liabilities to participate in these markets. This is how we predict, adapt to, and act on the world. What consequently emerges is a new psychophysical worldview for humanity based on the symbiosis between (bio)cognition and economics. In reference to the metaphysical framework I provide (see Appendix 1, especially section A), our mind~body evolved to efficiently make exchanges between the inside mental world and the outside material world, whereby perception, sensation, and action follow and often morph into each other, especially via introspection,2 making us ‘sapient’. Because we do this with others, this can be expanded to minds and markets more generally. For example, initially your emotion (enthusiasm) competes with your logic (caution) on whether you should share your insight with other minds. Once you do, it becomes a shared perception. Nature’s values and what they promote can be recognised in minds and markets as shown in Table 1.1. Table 1.1: Nature, minds, and markets. Nature values:

Which promotes:

As recognised in minds/markets:

Variety Competition Cooperation

Diversity Improvement Synergy

Diversification Efficiency Sharing

Freedom is a key pre-condition for healthy minds and markets, e.g. it allows for variety, competition and cooperation.3 In fact, it is a more universal principle. So, what does freedom mean in this context? The MMH states that: Freedom is the Volition to Exchange This applies to much in human life. For example, after evolving from the original bacteria, our freedom to breath is the volition to exchange carbon-dioxide for oxygen (compared to being choked). Freedom of movement is the volition to exchange your current physical location for another location (compared to being in lockdown). Free-

 Instead of technologies, in certain cultures other species, i.e. animals, form the tools that allow the mind’s extension. The classic example is where human and horse become an intelligent unit, e.g. during a hunt. Specifically, the experience of the hunt is, phenomenally, dependent on the horse as extension.  I ignore specifics and variations, like enteroception and proprioception.  On cooperation from an evolutionary viewpoint, see Nowak (2006). For an historic overview of economic cooperation, see Seabright (2004).

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dom of speech is the volition to exchange your views (instead of being censored). Freedom of choice is the volition to exchange adversity into opportunity and become, in the words of Epictetus, an “invincible human being”, “one who can be disconcerted by nothing that lies outside the sphere of choice”. Finally, freedom of trade is the volition to exchange goods, services and securities. Specifically for our purposes, freedom facilitates the spontaneous coordination between mind (will) and matter (act) which underlies (intelligent) self-organisation. Survival by the human species, in its search for desirable but scarce resources, benefited from individual competition encoded in the post-Darwinian ‘selfish’ gene as well as from collective cooperation. The broader link between biology and economics is strong.4 It goes back to the founders of classical economics, like Hume, Mill, Ricardo, and Smith. It led to the idea of the ‘economy of nature’ in biology, a term also used by Darwin.5 Fellow biologist Milne-Edwards specifically applied the economic concept of division of labour to biology. This was recognised by another biologist, Stephen Jay Gould, when he argued that natural selection “is, in essence, Adam Smith’s economics transferred to nature . . . Reproductive success becomes analogous to profit” (Gould, 2002, p. 122).6 Economist John Maynard Keynes argued that “the economic problem, the struggle of subsistence, always has been the most pressing problem . . . not only of the human race, but of the whole of the biological kingdom from the beginnings of life in its most primitive forms” (1930, p. 361; emphasis added). And from a complexity viewpoint, Stuart Kauffman (2000) compares the biosphere with the econosphere. He describes their similarities in terms of adaptability, emergence, and self-organisation, emphasising the role of complementary pairs and ratios (e.g. for “phase transitions”; Kauffman, 2000, p. 232). Humans thereby exemplify a bio~economic tandem in survival: procreate via the exchange of internal resources (i.e. genes), combined with protrade via the exchange of external resources (i.e. commodities). Additional research shows that the Market Mind Principle—intelligent (sometimes conscious) self-organisation via market dynamics, centred on exchange and aimed at discovery, especially of value—occurs more widely. It starts with microorganisms which compete and cooperate to achieve larger goals. Protists show various metabolic ‘merger and acquisition’ tactics whereby they integrate the newly acquired functionality. We also know that cells are basically miniature biochemical factories,  There are numerous interpretations of economics and evolution that inspired this book. For example, for markets as ecosystem, see Lo (2004, 2018). For the implications of evolution as computation, see Beinhocker (2011). For a Darwinian view on economics, see Hodgson (2002). For an example of economic evolution focussed on the human mind, see Loasby (2005). It also includes biological concepts, like bioenergetic cost~benefit analysis and optimal foraging theory.  Again, I personally believe ‘portfolio of nature’ is a better concept but, admittedly, people were not familiar with portfolio management in those days.  Others who recognised such links include Garrett Hardin (1959, e.g. p. 327), another biologist. Among the economists, see Alchian (1950) and McCloskey (1983, p. 487). Among sociologists, Durkheim offered an early analysis of the division of labour (1893).

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and that multi-cell organisms exchange and transform resources for both selfish and collective purposes. Dealing with (Keynes’) “economic problem”, for humans that involves: The many cells that make . . . body parts and exist both as individual organisms with their own conatus and as cooperative members of the regimented society we call the human body, held together by the organism’s own conatus. (Damasio, 2004, p. 132)

Ben-Jacob (1998) specifically discusses how competition and cooperation leads to “bacterial wisdom” and a “super mind” of the genomic web. At a higher level of aggregation, and building on Damasio’s comment, Levin and colleagues (Gawne, McKenna and Levin, 2020) show that bodily parts compete and cooperate for metabolic and informational resources. Should their other research on so-called xenobots acting as basic minds offer similar insights,7 it would further underline the universality of the Market Mind Principle. These are cues with which I take a few introductory steps towards MMH’s ultimate two-legged premise of market-as-mind and mind-as-market. The first step is to see ‘nature-as-market’. The sustainability of ecosystems is about supply and demand between competing and cooperating organisms. Nature’s markets involve exchanges between complementary pairs, here expressed as ratios like amount of water/number of animals, number of wildebeests/number of lions, number of birds/number of viruses, but also number of X chromosomes/number of Y chromosomes, and size of genome/number of genes. We can interpret those ratios as nature’s prices: “do not seek absolute values in the relative world of nature” (Yogananda, 1946, p. 50). The next step is to see ‘nature-as-mind’. Familiar examples are ant colonies, beehives, bird swarms, herds, and other forms of animal group minds. But even plants and trees can process and share information in sophisticated ways and along large networks. They not only adapt but can behave intelligently and form memories (e.g. Raja, et al., 2020). Via species and other higher levels of aggregation it culminates in the Gaia Hypothesis which suggests Earth is a self-organising complex entity. Still, for connecting markets and minds the timeline or type of respective contributions by various disciplines, like biology and economics, is less important than their shared reliance on the underlying complexity. Each view this in their own terminology. Economics always struggled to bridge or otherwise reconcile its so-called microfoundations (individual mentality) with its macrofoundations (market mentality; see Appendix 1). It resulted, for example, in the famous Lucas Critique, named after economist Robert Lucas who came up with it. It basically states that macromodels can fail if the assumed microfoundations are wrong. The Market Mind Principle attempts to bring these together in a novel way if we allow ourselves some leeway in interpretations. First, mind-as-market means that the macrofoundations, exemplified in com-

 Which I speculatively suggested to Levin in personal correspondence.

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plementary market forces operating in a 4E cognitive setting (see 2.4.1.1), can already be observed (albeit in psychophysical formats) in the microfoundations, namely at the individual level of a person’s mind~body economy. Second, the flipside of the Lucas Critique is that microfoundations should include the recognition that ‘macro’ market mentality emerges over and above, as well as different from individual mentality and can impact, even overwhelm it. To emphasise, as is true for the two-legged Market Mind premise itself, these are reflexive, i.e. the macro and micro mutually influence each other. Back to the human mind and Adam Smith, an appropriate and very important observation (especially regarding ‘emergence’) comes from evolutionary psychologists Tooby and Cosmides: natural selection’s invisible hand created the structure of the human mind, and the interaction of these minds is what generates the invisible hand of economics. (1994, p. 328, emphasis added)

Both of these invisible hands are especially active via competition and cooperation which feature prominently in each field, whereby their balance is important.8 In economics, Knight emphasised “the essential fact” that “human beings have both conflicting and common interests” (Knight, 1943, p. 74). And Smith’s nuanced interpretation, which I discussed in the Economic Note in the Introduction, was later echoed by Alexis de Tocqueville when he spoke of “self-interest properly understood”. We can add synergy (a modern complexity argument) to it—particularly relevant in a market setting—through Coordination Dynamics, an influential cognitive theory for the MMH that will be introduced in Subchapter 3.2: synergy refers to the combined effects that arise from interdependence among parts and processes in a given context that are not possible or achievable from those entities acting alone. A strong case can and has been made for functional synergies as the drivers for the evolution of cooperation in complex systems. (Kelso, 2022, p. 2)

In more popular terms, in all games there is dependency: you can’t compete if you don’t have a competitor, who is your “friendly enemy, the necessary adversary” in the words of Zen-master Alan Watts (delivered in a talk at the Esalen Institute in 1965). Think, for example, of martial arts (e.g. aikido) which teaches to ‘collaborate’ with your opponent’s action and force to enhance your own. Regarding cooperation in markets, trade (frequently facilitated by providing credit) is an early form. It often preceded political alliances as it required and supported trust between strangers when managing scarce resources. Cooperation is not only embedded in norms and morals of groups, or broader cultures of nations, but

 Both biology and economics focus on competition and collaboration. They are universal dynamics to coordinate behaviour, with their interaction leading to complexity. Strictly speaking though, this book agrees with Gould and Keynes and thus considers them, first and foremost, to be economic forces.

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also institutionalised in functions. Mises, by translating Ricardo’s law of comparative advantage into his law of association, argued that the division of labour turns initial economic competition into ultimate economic cooperation. One way to think about the importance of balancing competition with cooperation9 is to consider their extremes in black-and-white terms. They have the same outcome but are achieved by different means. If there is only competition, as per Darwinian capitalism, the eventual outcome is a market monopoly through survival of the strongest. In contrast, if there is only cooperation, as per Marxist socialism, the eventual outcome is a state monopoly via central planning. Central banking is thereby a prominent feature, as stated in the fifth measure in the Communist Manifesto of 1848 by Marx and Engels: “Centralisation of credit in the hands of the State, by means of a national bank with State capital and an exclusive monopoly”. Both extremes are unsustainable and, as I’ll explain, they conflict with how mind~bodies work. The failure of mechanical economics shows that simply mixing some of the extremes, like new classical and Keynesian economics, does not result in the desired balance but, instead, produces incestuous market-state monopolies that become so powerful that they outgrow their home economy and expand abroad. Echoing Tooby and Cosmides, it is in the cognitive domain that biology and economics connect. The self-organising nature of neurobiological systems10 has market dynamic characteristics. Like currencies, neurotransmitters signal values in their exchanges (‘exchange rates’) within the human body by binding to specific receptors on target cells and causing various effects, including activation or inhibition of the target cell, modulation of the signal strength, and adjustment of the sensitivity of the target cell to other stimuli. More broadly, competition and cooperation start in the human brain, be it between cortical regions, or between System 1 and System 2, or between intuition and logic.11 They and other market-like forces coordinate individual and collective human behaviour.12 Motivated by our curiosity, they drive the cultural, physiological, and psychological adaptations that lead to human progress. Even our sciences and their theories show market behaviour. Think of the many quantum physics theories that collaborate and compete, like the Copenhagen, Many-worlds, and Pilot-wave interpretations. We particularly value theories for their knowledge or epistemic utility (see Chapter 4). In economic terms, progress more generally centres on value (e.g. wealth) creation which involves more than the hedonic principle of pleasure over pain. In terms

 Also see, for example, Gintis et al. (2005) and Galinsky and Schweizer (2016).  See Seth (2013) and Clark et al. (2016).  See Desimone and Duncan (1995), Kelso (1995), Niebur and Koch (1996).  Competition and cooperation coordinate (economic) progress and both operate at the individual and group level. One way to see this is to realise that the oppositional structure embedded in competition does not exclude cooperation if this improves competitive strength, i.e. former hostiles unite against a common enemy. Vice versa, a former partner cheats and/or joins a competitor alliance.

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of cultural adaptation, and to make sense of it beyond numbers, such economics is accompanied by ‘meaningful’ stories, for example concerning crops, harvests and seasons. When these are passed on from generation to generation these tales become myths. The metaphysical aspect of this, particularly the role of imagination, is nicely worded by Harari, which simultaneously points out its vulnerability: We cooperate effectively with strangers because we believe in things like gods, nations, money and human rights. Yet none of these things exists outside the stories that people invent and tell one another. There are no gods in the universe, no nations, no money and no human rights— except in the common imagination of human beings. (Harari, 2014, p. 16)

Has the interaction between nature (our environment) and nurture (our development)13 intensified with any shift in balance? As part of our mind-over-matter drive there has been a strong tendency in human nature to control Mother Nature in order to increase wealth, albeit not always to the benefit of well-being. It is derived, for example, from the importance of environmental differences which enabled some civilisations to domesticate more efficiently.14 It is also apparent from current themes, like the impact of the economy on the environment or its impact on our climate. More relevant for the MMH, research by Ferguson (2008), Rubinstein (2006) and others plot specifically the history of money, finance and financial markets in supporting humanity’s explorations of our planet and its resources. Bernstein (1998), for example, describes an emerging financial world to manage the risks of real-world events in terms of their (potential) economic impact, culminating in the benefits of diversification and risk sharing. For our purposes we can conclude that the psychophysical problem in economics originates in scarcity, desire and the accompanying uncertainty. I would further argue that, although nature determines the overall constraints, our survival in modern times primarily takes place in the economic ‘jungle’.15 It increasingly seems to revolve around economic themes with the perceived level of well-being closely correlated with the level of wealth. This was perhaps best captured by Bill Clinton’s famous 1992 campaign slogan: “It’s The Economy, Stupid”. Such economically driven survival—echoing Keynes—is ultimately played out in financial markets whose dominance has been growing. According to Bob Woodward in his 1994 book, The Agenda, at one point Clinton asked the rhetorical question: “You mean to tell me that the success of the economic program and my re-election hinges on the Federal Reserve and a bunch of [expletive deleted] bond traders?” Later his campaign leader James Carville famously

 Ignoring noise (random disturbance) for a moment, although it could be considered as included in both.  See Diamond (1997).  The two remain connected at several levels, not the least of which is in our emotional brain which struggles to distinguish between biological and economic threats. This argument echoes, for example, some of the arguments made by the Ecological Dominance-Social Competition model in biology.

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concluded that in case of reincarnation it is best “to come back as the bond market. You can intimidate everybody.”16 This trend was, at least until recently, fuelled by three forces: globalisation, marketisation, and financialisation. Globalisation is the integration process of a growing number of developing countries and their citizens into the global economy with financial markets providing the means to exchange capital. This trend not only takes place internationally but can also be observed within these countries via marketisation whereby public services like health care, education, and even the arts are being provided by private (competing) institutions and/or are managed according to market principles. Finally, financialisation refers to the creation—by way of securitisation and other financial engineering—of tradable securities linked to previously illiquid assets such as property (e.g. mortgage-backed securities), events (e.g. weather options), pollution (e.g. emission rights), art (e.g. Bowie-bonds) and cloud services (e.g. Performance Units). These instruments, including derivatives, allow buyers and sellers to hedge or gain exposure to such assets and thus subscribe to their risk~return profiles.17 To conclude, minds and markets co-evolved. This contributed to modern Western civilization in the form of elected government, individual freedom, enlightenment and religious tolerance, which allowed humanity to, often spontaneously, deal with many of nature’s challenges while better protecting itself from its own shortcomings. Key to that organisation is the financial system, which is now at the heart of modern society, whether we like it or not. In fact, it can make or break it. However, as Chapter 2 will discuss economics is not an impartial spectator to use Smith’s term. While the GFC brought us very close to financial Armageddon,18 the CVC has resulted in a different kind of economic dystopia. Quite simply, as R.E.M sang, we came close to “the end of the world as we know it” (Berry et al., 1987). More than anything, these events and their lingering consequences have highlighted gaps in our understanding of financial markets. The urge to search for new economic thinking and acting remains

 Now, of course, it is only a shadow of its former self, having lost all disciplinary power and, like other financial markets, having become addicted to cheap money from central banks.  See Shiller (2009).  Should the financial system collapse, all financial transactions would cease, as would eventually the exchanges of goods. ATMs would not provide cash anymore, shelves in supermarkets would become empty, and electricity supply would be shut down. An orderly society would revert to chaos, a new reality, a new world. In a 2010 BBC interview (http://news.bbc.co.uk/today/hi/today/newsid_8914000/8914062. stm), former Chancellor of the Exchequer Alistair Darling describes how close the UK came to this in 2008. He and others subsequently confirmed this again in the documentary The Bank that Almost Broke Britain. Former US Treasury Secretary Hank Paulson also admitted as much as far as the world overall is concerned. See also Chapter 11.

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to this day.19 One area of that gap concerns the collective dimension of mentality, which I’ll discuss next.

1.2 Merging Minds and Markets: Group Minds, Collective Intentionality, and Intersubjectivity So, the evolution of minds gradually involved markets. Those for interest rates have existed for thousands of years, whereas those for stocks are more recent. Founded in 1602 by the Dutch East India Company, the Amsterdam Stock Exchange (now known as Euronext Amsterdam) is the world’s oldest stock exchange. Figure 1.1 shows a graph combining Dutch interest rates and Dutch stock prices over the past five hundred years. Dutch Stock Market Index and Interest Rate (from 1602-2022) 1,00,000.0

14

12 10,000.0 10

8

1,000.0

6 100.0

4

2 10.0

1.0

1602 1612 1622 1632 1642 1652 1662 1672 1682 1692 1702 1712 1722 1732 1742 1752 1762 1772 1782 1792 1802 1812 1822 1832 1842 1852 1862 1872 1882 1892 1902 1912 1922 1932 1942 1952 1962 1972 1982 1992 2002 2012 2022

0

Index Amsterdam Stock Exchange

-2

Interest Rate

Figure 1.1: History of Dutch interest rates and stock prices. Source: De financiële canon van Nederland, Martien van Winden (2010; Founder Dutch Investment Fund Hoofbosch).

 Among those pursuing a revision of the paradigm is the Institute for New Economic Thinking (INET), founded by George Soros. This movement also includes various, often more Keynesian minded economists, like Mariana Mazzucato, Thomas Piketty, and Kate Raworth. Still, none acknowledge, let alone address, economics’ hard problem.

1.2 Merging Minds and Markets

11

Earlier I mentioned that investors and other market observers regularly, albeit casually or implicitly, refer to ‘the market’s mind’. Here I’ll offer and reiterate some examples: – It is mentioned by old-school ‘Roaring Twenties’ traders like Humphrey Neill (1931, p. 222), a contemporary of Jesse Livermore. – Other investors have echoed it since, including Howard Marks (2011, p. 76). – In the academic literature there are numerous papers: Bruguier, Quartz, & Bossaerts (2010), De Martino, et al. (2013), Thirkell-White (2007) which mention it in one form or another. Beinhocker (2007, p. 38) reminds us that “Arrow and Debreu showed that prices act like a nervous system, transmitting signals about supply and demand throughout the economy”.20 – Michael Shermer (2008) titled his bestselling book The Mind of the Market, arguing that the market is “like a collective organism” with a “mind of its own”. – I already mentioned George Soros (1987) who subtitled his first book, The Alchemy of Finance, as “Reading the Mind of the Market”. What was not understood until recently, neither by Soros nor other commentators, is that reflexivity implicitly raises the issue of the market’s mind~body problem. We explained this crucial message, in the process reverse-engineering reflexivity, in a paper (Schotanus et al, 2020). In cognitive science terms, reflexivity can be compared to continuous reciprocal causation, whereby “some system S is both continuously affecting and simultaneously being affected by activity in some other system O” (Clark, 2008, p. 24). – Mark Douglas connects the market mind to mindfulness. He writes about “trading in the zone”, whereby: “your mind and the market are in sync. As a result, you sense what the market is about to do as if there is no separation between yourself and the collective consciousness of everyone else participating in the market. The zone is a mental space where you are doing more than just reading the collective mind, you are also in complete harmony with it” (Douglas, 2000, p. 90; emphasis added). As an aside, this echoes “flow” experiences of other peak performers, like artists and athletes. In 2020 Lewis Hamilton won the Spanish Grand Prix and described his experience: “It was like a clear zone. The clarity I had when I was driving, I am sure I’ve had it before but I don’t really know how really to get into that zone . . . I wouldn’t describe it the same way as Ayrton [Senna] would. It’s not an out-of-body experience. Just in my highest form, I would say”. – In similar vein, numerous articles (e.g. Knorr Cetina and Bruegger, 2000) and books (e.g. Koppel, 1998; Schwager, 1995; Zaloom, 2006) contain interviews with traders, describing their experiences with the market. These materials invite the

 As I will argue in Subchapter 2.4.1.2, it is more correct to view (e.g. Arrow-Debreu) securities as forming the market’s physical neural system and “transmitting signals” in the symbolic form of prices.

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reader “to see the market and trading as they really are—alive, dynamic, robust, and not to be dissected and analysed like a corpse lying on a slab . . . Trading has less to do with the science of computation and more to do with . . . consciousness” (Koppel, 1998, p. xiii; emphasis added). Sornette then hammers it home: “The global behavior of the market . . . is reminiscent of . . . the emergence of consciousness” (2003, p. 241). On the other hand, Charles Smith went out in search of the market mind, but “after a fairly lengthy journey we seem to have finally found this mind in what can only be called the ‘mindless’ quality of the market as a whole” (Smith, 1981, p. 142).

In addition, expositions on public psychology (e.g. Akerlof and Shiller, 2009), the wisdom of crowds (e.g. Surowiecki, 2004) as well as their madness (e.g. Mackay, 1841) highlight the collective mental forces that are intrinsic to the way market dynamics work. Apart from these references the premise of the market’s mind is implied in any discussion on whether the market is rational or not (e.g. Rubinstein, 2001), let alone whether Mr Market suffers from bipolar disorder (e.g. Cheung, 2010). However, none of them formalise the market mind, including its conscious quality, by way of 4E cognitive science. This is what sets the MMH and this book apart. Conceptually, the market mind links to ideas like McDougall’s (1927) “group or hive mind”; Russell’s (1982) “global brain”; Muthukrishna and Henrich’s (2016) “collective brain” and Mulgan’s (2018) “crowd intelligence”. A popular example of those ideas suggests the internet, connecting human minds via applications, forms a global brain, with Wikipedia and social media reflecting collective knowledge. The general idea of a collective mind is thousands of years old. From ancient times it was told in both Eastern and Western traditions, sometimes in the form of myths and fairy tales. More formally, various theoreticians, including Locke, Hobbes, and Smith have argued (implicitly or explicitly) that cognition in general has a group or collective dimension. Among others, Bergson, Deleuze, Durkheim, Husserl, Latour, Merleau-Ponty, Nietzsche, Plato, and Teilhard de Chardin, while representing different viewpoints, discussed the collective aspect of mind and consciousness, like identity and intersubjectivity. Le Bon and Jung pointed to its unconscious origin.21 Groups in markets include ‘bulls, ‘bears’, ‘contrarians’, and ‘hedgies’, but also ‘asset owners’, ‘buysiders’, ‘sell-siders’, ‘quants’, and ‘traders’. Investors generally identify with these groups which all mix and submerge in various markets. They, in turn, form groups themselves, like ‘bond markets’, ‘commodity markets’, and ‘equity markets’, all supported by the ‘foreign exchange (or FX) markets’ and overlayed by the ‘derivatives markets’. As we’ll see, group identity and mentality has also implications for the notion of ‘self’, especially when market moods overwhelm.

 Other early sources include Martin (1920) and Freud (1921).

1.2 Merging Minds and Markets

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Contemporary cognitive scientists have followed up with similar and updated arguments. Among them are Bettencourt, (2009); Donald, (2001); Gilbert, (1989); Hut and Shepard, (1996); Manzotti, (2017); Mathiesen, (2005); Kirchhoff and Kiverstein, (2019); Overgaard and Salice, (2019); Pacherie, (2017); Pettit, (2018); Randrup, (1999); Robinson, (2013); Rupert, (2011); Schwitzgebel, (2015); Stuart, (2011); Valencia and Froese, (2020); and Vold, (2015).22 The argument that applies most to the MMH runs roughly as follows: The position we defend here is that the mind has no fixed boundary. The locus of conscious experience can smoothly shift from on occasions being inside of the head of the individual to on other occasions forming out of a nexus of interactions between brain, body, and environment. (Kirchhoff and Kiverstein, 2019, p. 1)

When that environment includes other people, the resultant conscious experience depends on the states of more than one embodied agent, and in this sense becomes shared, that is intersubjective: “The intersubjective is something that exists within the communication network [e.g. the market] linking the subjective consciousness of many individuals” (Harari, 2014, p. 117; emphasis added). Knight believed that this reigned supreme: “communication between consciousnesses is the fundamental fact of knowledge and the nearest we can ever get to an “ultimate” in human experience” (1925a, p. 381). Specifically, neural synchronisation is involved, whereby: the boundaries of the conscious mind could also be subject to constant renegotiation during an individual’s interaction with his/her environment and with others, pointing to a mechanism that neurally binds us together and opens us up to an extended conscious mind in social interaction (Kelso and Engstrøm, 2006). An upshot of this proposal is that it can potentially validate our most intimate experiences: when we become aware that ‘we’ are sharing a moment with someone else, it is no longer necessarily the case that we are fundamentally separated by our distinct heads—we could really be two distinct individuals sharing in one and the same unfolding experience. (Valencia and Froese, 2020, p. 5)

This has also serious implications for cognitive science itself in its study of consciousness. For example, Nagel’s familiar statement that every subjective phenomenon is essentially connected with a single point of view is incomplete as far as highlighting the hard problem is concerned. In particular: that viewpoint gets distorted in our sociality. There are many intersubjective phenomena, and it is this downward causation by entangled mentalities that makes the hard problem truly hard. The influence of other minds in our lives contaminates individual subjectivity to such extent that the phenomenal aspect can no longer be considered isolated and pure. In cases like empathy there is no exclusive ownership of the experience. Smith already hinted at this in TMS. And Margaret Mead famously stated that helping someone else through difficulty is where civilization starts. The qualitative characteristic of such experiences is

 For a critical overview, see Rupert (2011). Sources on specific reflections, like those on shared agency and intentionality, include Tuomela (2007) and Bratman (2014).

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exactly due to the phenomenal presence of “the other”, including the acute awareness that the experience would not exist without “the other”. In those instances your socalled privileged access offers an impression that can only be described as fleeting due to ongoing exchanges between conscious minds which (dually) realise new information in a shared way, e.g. intracranially. It becomes empirically interesting when there are footprints of these shared realisations. Specifically, in markets any price you experience (e.g. at the exact moment you buy a share) is also set by the experience of others (i.e. Mr Market, representing the seller who enjoys a profit/suffers a loss). Moreover, that price is attached to a security that acts as the object of collective intentionality and is quoted on an external exchange with technologies and other scaffolding which form part of the extended physical realisers of consciousness in markets. In short, consciousness involves too many shared experiences which have no clear boundaries between subjects. This applies especially if subjects are dealing together with practical consequences of the hard problem, like those in the economic system. The results of psychological states that extend beyond individuals and into collectivities—e.g. with shared agency—can be surprising and even counter intuitive. For example, “a group can in principle believe that p even though everyone in it personally believes the opposite” (Gilbert, 1987, p. 201). So-called collective or social neuroscience adds to the debate, by raising and answering these types of questions: What if embedding the brain in a group changed how it works? And even more boldly, what if the laws of wisdom were to be found in how brains interact in a group, rather than in how neurons interact in a single brain? (Sliwa, 2021, p. 397)

It acknowledges and investigates the social dimension of cognition, especially how information processed among and between collaborating brains can lead to intelligence (e.g. Dumas et al., 2014; Edelman and Tononi, 2000; Hirsch, 2015; Tognoli et al., 2020). Various social mind theses have resulted from this e.g. Dunbar, Gamble and Gowlett (2010). Other researchers with related themes include Adamatzky (2005), Harman (1988), Lovelock (1979), McLuhan (1964), Rheingold (2002) and Surowiecki (2004). In our case, while its wisdom may be questionable at times, the market and its pricing system show how information is processed among and between multiple brains at a global scale. Social neuroscientists should take note, especially because of the availability of relevant data to investigate. Let’s look at a popular example in economic thinking that can be interpreted in group mind terms: Keynes’ famous beauty contest which is his metaphor for popularity of securities in the market. Success in Keynes’ beauty contest, namely to correctly anticipate degrees of average opinion, ultimately relies on the Theory of Mind (ToM) and intersubjectivity, particularly joint awareness and attention (paid to prices). ToM is a cognitive science concept that describes our ability to theorise about the mind, including those of others, and to attribute states to it (see also the [early] description by Knight, 1921, p. 208). We use it all the time in our interactions. In the case of mar-

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kets, investors know that the other investors also have minds, all paying attention to the market simultaneously. Consequently, their assessment of the mental state of other investors—for example grouped as ‘bulls’ or ‘bears’—is derived from the overall state of the market. So, when Keynes talks about “fourth, fifth, and higher degrees” this should be interpreted as the higher order of ToM. Unfortunately, especially in speculation, we are prone to overconfidence and overstepping our own capability. In other words, we push ToM to the limit in that we believe we can actually mind-read, culminating in anticipating the overall market behaviour, say by extrapolation. For example, the case of a “greater fool” bubble, which is exclusively based on trusting the next buyer to pay more, relies disproportionately on ToM faith. Finally, Hayek was once asked about the parallels between the human mind and the economic system. Here is part of his response: In both cases we have complex phenomena in which there is a need for a method of utilizing widely dispersed knowledge. The essential point is that each member (neuron, or buyer, or seller) is induced to do what in the total circumstances benefits the system. Each member can be used to serve needs of which he doesn’t know anything at all. (Hayek, 1982, pp. 325; emphasis added)23

Like Hayek (and many others) the MMH views the human and market minds as complex adaptive systems. Also, the MMH views Hayek’s dispersed knowledge in terms of distributed cognition (via the Extended Mind Theory; see Subchapter 3.2). However, there are also differences with Hayek’s view which was coloured by his subscription to methodological individualism and internalism. On that note, and as will be explained in Subchapter 2.4, to overcome internalism the MMH views securities as the information emitters of the market rather than robotic agent (as per traditional agency models). In cognitive terms, securities are the ‘neurons’ in the market mind that signal information, in this case price signals.

1.3 Market Mind over Central Plan You have probably already concluded (correctly) that the MMH is more sympathetic to classical liberalism/libertarianism than socialism. But there is a peculiar cognitive reason for this. It follows from the evolved similarity between economic and cognitive dynamics in general, and the evolutionary success of the human mind in particular. The key insight that cognitive science offers in support of a voluntary market economy and against a centrally planned economy is the absence of a homunculus.24 In other words, in our own minds there is no central executive who manages our

 See also Hayek’s ‘The Sensory Order After 25 Years’, his written contribution to “The Second Penn State Conference on Cognition and the Symbolic Processes” in 1977 held at Pennsylvania State University.  For more details, see Appendix 1-C.

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mentality, including our knowledge. Instead, a healthy mind operates almost like the ideal ‘free market’ that libertarians hold so dear. Spontaneous bottom-up competition and collaboration between conscious and unconscious forces, including specialised functions (e.g. for the division of mental ‘labour’), result in coordinated behaviour of the overall mind~body, albeit with a fair amount of random (e.g. neuronal) noise added to it. Each force contributing to the ‘portfolio’ according to circumstances, and the mind~matter match between expected and real values. Distortions, but also too much competition or too much cooperation, lead to unproductive arms races or monopolies in the mind’s economy. Those distortions are often caused by mind-control mechanisms in the form of drugs, cults, dogma, propaganda, and so on. At the microlevel they interfere with the mind’s natural drive to limit free energy, just like healthy markets limit free lunches, as I’ll discuss later. Although we don’t want to install an economic homunculus, we do need an institutional framework that ensures, especially, that the rule of law is upheld (for fair play). Discovery, supported by freedom and transparency, should be the guiding principle of our economic laws and regulations. It is echoed nicely by a principle from open-source software development, known as Linus’s law: “given enough eyeballs, all bugs are shallow”. I like to paraphrase it for our purposes as “given enough free idd-minds, all distortions are shallow”. To explain, let’s compare this, first, to the finance concept of a complete market. In simple terms, although we can’t cover for all states of the world, the wider the available set of securities to hedge/profit from such states the more complete the market is. Similarly, the broader the population of free independent-anddifferently-distributed minds (or idd-minds), the healthier the market mind. Consider the recent growth in retail investing via apps and trading forums on Reddit and other social media, with a preference for so-called “meme stocks”, like GameStop (Clunie and Schotanus, 2022). Promotors claim that it democratises investing. However, what is missing (again) is education and transparency. Education could turn retail investing from Gamestop’s follow-the-herd behaviour into independent thinking, thus leading to better-informed decisions. On fair play and transparency, the Financial Times (29 January 2021) reports that “beyond personal gain, many pleaded for the idea that the world of finance needs altering, with Redditors urging others to write to their political representatives to ask for more transparency from institutional investors”. Participation from DIY retail investors in markets, reducing the growing concentration of assetsunder-management among a few large institutions, could then become a welcome revolution and a sustainable force for good. In any case, from a portfolio perspective (and backed by nature) we know that diversification is important to achieve efficient allocation. This raises the question whether recent developments—many supported if not advocated by the EMH— have been beneficial in that regard. It brings us back to my earlier comments in the

1.3 Market Mind over Central Plan

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Prologue25 which are related to the discussion on active versus passive (or index) investing. In terms of assets-under-management, the latter has grown fastest and is expected to become bigger than the former. Even John (Jack) Bogle, the late founder of Vanguard and inventor of the first index fund, warned about the dangers of this continuing: “If everybody indexed, the only word you could use is chaos, catastrophe. The markets would fail”.26 Actually, the critical issue here is the difference between discretionary and mechanical investing. It much more clearly highlights the ratio between mindful, respectively mindless investors. In investment parlance, the growth in mechanical (particularly passive) investing and the accompanying concentration in assets have reduced heterogeneity.27 In our case this concerns diversity in mentality full stop, not just decision making. I take my cue from Hayek in two parts. First, he captures what is known as the local knowledge problem in a question: How can the combination of fragments of knowledge existing in different minds bring about results which, if they to be brought about deliberately, would require a knowledge on the part of the directing mind which no single person can possess? (Hayek, 1936, p. 54; emphasis added)

This problem, of course, is solved spontaneously via the market, not by the “directing mind” of some central executive: It is indeed the source of the superiority of the market . . . that in the resulting allocation of resources more of the knowledge of particular facts will be utilised which exists only dispersed among uncounted [conscious] persons. (1974, p. 27; emphasis added)

Let’s interpret Hayek’s comments about distributed local knowledge among “different” and “dispersed” minds in terms of a population of idd-minds. If the number of idd-minds within the market reduces, due to concentration or mechanisation, its efficiency arguably diminishes. Specifically, many mechanical investment strategies only use internal market data (e.g. market capitalisation; bid-ask; momentum) for their trading without reference to the fundamentals that determine asset values. As I will discuss in Subchapter 2.3, this leads to an unhealthy narrow-mindedness in Mr Market, with knowledge and awareness not sufficiently distributed. Algorithmic (e.g. high frequency) trading is another manifestation of mechanical investing. Some worry about the detrimental effect of a lack of “human volume” in trading. During a panel discussion at LSE it was argued that when “human volume wasn’t in the markets spreads got wider, prices got worked [so] end-customers [got] worse prices than they would normally” (Tett, 2020). But it would also explain several other recent phenomena, most worryingly the growing disconnection between global real economies and global financial markets. Appendix 1 contains more details.

 See also Appendix 1 for a more detailed discussion.  Stated at Berkshire Hathaway’s 2017 shareholder meeting.  See, e.g., He and Shi (2011).

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In terms of distributed cognition (including Hayek’s distributed knowledge), idd-minds are conscious of some local information relevant to their real and/or financial market. Laws should be designed to allow such minds to discover, covering issues like ownership of and responsibility for those discoveries. A related issue is access to pure and untainted data. Unfortunately, this is one area which is not only insufficiently thought out in principle but also where we have gone astray (see Chapter 7). To counter, we should ambitiously aim for “open mds”: open market, data, and source. Government can play a crucial role here. Securing open access to markets with untainted data and exercising a different type of power would be a big improvement on centrally planning the economy. I make some recommendations in Chapter 12. On that note, an example showing the exact opposite of what I mean was reported by the Chinese Securities Journal in April 2020, at the height of the first wave of the CVC when the global economy was in lockdown. More than half of the 249 Chinese A-share companies that provided preliminary first quarter figures indicated that their profits supposedly were above those of the previous year. If this wasn’t enough bluff, 49 firms declared that profits had doubled. Political Note Global Political Mind (and killing the goose with the golden eggs) You see Sirs, You have leaders—religious leaders and political leaders I don’t know why—why you have leaders at all . Once you realise you are responsible entirely for yourself That you are in a jungle . . . where you have to make your own way out, Where there is nobody to lead you . . . . If once you realise it, in your heart, not just intellectually Then you are a man, a human being, free to walk straight . . . But we don’t want all that Krishnamurti Hopefully the MMH—inspired by enlightened thinkers known across the world—can, in turn, inspire diverse communities with a shared passionate belief in markets to bring them together and overcome their political differences. This particularly applies to the East and West where I would point, for example, to the similarities between, respectively, the philosophy of Zhuangzi and the philosophy of Adam Smith despite their different backgrounds. Notably, both promoted individual freedom and shared a healthy scepticism towards central planning. The Zhuangzi texts imply, among others, that one needs freedom to remove fixations and to nourish life, thereby indirectly criticising central planning. Let’s just say that this makes him less popular, e.g. compared to Confucius, in certain circles. In politics, before the 1900s the subject of economics was called “political economy” for a reason. Politics continues to play a role. Is there a global political mind? And if so, is it a healthy mind with a shared narrative based on free conversations, like Bohmian dialogues? After all, these aim to spawn complementarity (e.g. reconciliation) between seemingly contrary parties.

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Let’s start with the basics. As the dynamics between competition and cooperation play out in the (usually male) politician’s inner world, they are influenced by his perception of those same dynamics playing out in the outer world that he, in turn, interacts with.28 Specifically, there are continued shifts in alliances, with former hostiles joining forces because cooperation improves competitive strength against a common enemy. Mental events in the outer world, like receiving an insult, can trigger an instinctive overreaction and result in a defensive tilt in the individual mind, possibly followed by physical violence. It is undeniable, in that regard, that recent geopolitical or geoeconomic tensions—including shifting alliances—suggest a move to a multipolar world where the West, especially the US, is losing influence and global power is becoming more widely distributed. However, this does not necessarily mean a healthy rebalancing. Because, in contrast, developments in domestic politics suggest a move in the opposite direction toward polarisation. Most worryingly is nationalism, with power concentrated in a smaller group or even in the hands of a single (and often self-proclaimed) strong man. China’s president Xi, Hungary’s prime minister Orban, Israel’s prime minister Netanyahu, India’s prime minister Modi, Russia’s president Putin, Turkey’s president Erdogan, former US president Trump, and many rulers in Africa and the Middle East are examples of this. The psychological profiles of these strongmen make for uncomfortable reading and are strikingly similar. In the context of inequality, for example, all of them are obscenely (and often suspiciously) wealthy.29 In cynical terms, we are basically ruled by a bunch of very rich but also very bad leaders. Profilers would conclude that they are sociopaths30 who (often subliminally) manipulate minds aimed at raising fear and lowering trust. They are not interested in Bohmian dialogue. More broadly, the top three global powers appeal, respectively, to America’s exceptionalism complex, China’s imperialism complex, and Russia’s victim complex to justify their behaviour for the home crowds. Instead of dealing frankly and openly with the problems and traumas of their nations, these ‘leaders’ repress them by playing tough. Also, they are not the type of people to submit their country’s ‘sovereignty’, i.e. their own power, to some collective impartial spectator. It’s simply not in their nature. Further concentration of their domestic powers is the threat to the current world order, which cooperative origin goes back to the Treaty of Westphalia of 1648. It resulted in a long period of globalisation, inspired by free trade. Long ago it was promoted somewhat naively by Richard Cobden, campaigner for the repeal of the Corn Laws in the nineteenth century: “I see in the free-trade principle that which shall act on the moral world as the principle of gravitation in the universe—drawing men together, thrusting aside the antagonism of race and creed and language, and uniting us in the bonds of eternal peace”.

 See also Subchapter 3.3.  In many cases links to organised crime, basically the central planners of the black markets, have been made. As part of the solution we should be less sensitive to outdated taboos to legalise these markets, thereby largely removing this cancer to the economic system by way of legal market forces. Even the Netherlands (my birthplace), not known for strong men, nor for many taboos, is now suffering from this threat to an open and free society after the murder of journalist Peter R. de Vries, allegedly by organised crime.  We can argue about whether some were born as psychopaths.

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World Trade (as % of World GDP) 70.0

60.0

50.0

40.0

30.0

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0.0

1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

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Figure 1.2: World trade as a percentage of global GDP. Source: World Bank According to data from the Worldbank, world trade as a percentage of global GDP reached 60% before the GFC (see Figure 1.2). This is now under threat, with growing protectionism, trade sanctions, and similar measures. Viewing modern geopolitics by applying practical dualism, we can look, for example, at the psychophysical make-up of their countries’ GDP, rather than just its size. You could then expect, say, a strong man presiding over a relatively small physical economy to use its products (e.g. commodities) to try to damage the larger psychological (e.g. finance and/or IT based) economy of his enemy, and vice versa. As we have seen, weaponizing banking, currencies and insurance is answered by weaponizing gas, precious metals, and oil. And judging ‘economic power’ purely by the size of GDP is flawed. Instead, it depends on the circumstances which are determined by mind~matter exchanges, including manipulation. Apart from valuing their population mainly as voting machines to secure their next election, politicians in general are opportunistic in their application of metaphysical views. Before elections they primarily employ idealism,31 by focussing on intangible emotions, promises, and visions. Once elected, there is a clear shift to physicalism to gain physical control. If opposed, they complain about physical constraints which prevent them from ‘getting the job done’. This is why politicians do not like constraints, such as a gold standard to money, because it would require having to tighten budgets or raise taxes which make them unpopular. In worse cases they resort to physical violence to keep promises. Propaganda via social media is the strategy used to employ both material and mental tools to manipulate information in order to affect consciousness. For example, elements of the goal of the physical act of a political murder are similar to those of the mental act of releasing fake news: not just to serve up the ‘meal’ of information (i.e. grab attention) but also to ‘spice up its taste’ (i.e. valuation; see Appendix 1-A) to the point of making an impression.

 Not to be confused with ideals in the traditional sense, idealism is the view that reality is mental in nature, if only because all that we can experience about objects outside our minds remains inside them, i.e. as knowledge.

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As an aside, there are various conspiracy theories surrounding planned globalisation in general and the establishment of a global government in particular. I’m sceptical in every regard. It seems to me that such thinking or intentions ignore the fact that the success of both politics and markets starts locally. But only ‘free’ markets have shown to be able to spontaneously widen this, especially via distributed knowledge. Cracks occur when politics forgets its roots, shows global ambitions (e.g. requiring ‘allegiance’ from industry in return for monopolies), and interferes with the competition and cooperation that should be left alone. Earlier I argued against any central executive. That includes globalists, i.e. the so-called ‘Davos men’, but also the global military-industrial complex. Fair markets are our common ground. They are the determining factor for coordinating our global society which produces prosperity and can help solving shared problems. Their exchanges cross borders, cultures, ideologies, and other ‘separations’. Unfortunately, the growth in central planning over the past decades—exemplified by growing influence from totalitarian regimes, central bank distortions, and government lockdowns—has been accompanied by increased interference in and manipulation of markets. The crises are a consequence of this. In turn, they impact other areas whereby economic and non-economic catastrophes become intertwined, leading to a global polycrisis, as some have called it, with similar features as the reality checks. This brings me to the positive message of this note, especially regarding the shared (e.g. ESG) challenges the global society faces. In his fabulous introduction to the 250th anniversary edition of TMS, Amartya Sen reminds us that Smith’s concept of the impartial spectator gets around the limitations of the traditional social contract. Those limitations are due to the social contract being confined to members of sovereign states. Specifically, the impartial spectator and markets offer a way towards global reasoning: “In many examples in each of his books, Smith did, in fact, make good use of the reach of global reasoning . . . Similar issues remain extremely alive today . . . [For example,] what can be said about the environmental challenges we currently face has to be based on global reasoning about the sharing of obligations and burdens, rather than on a strictly contractarian line of analysis confined within the limits of a sovereign state” (TMS, 1759, p. xix). In that regard, the recent economic experience is a blip on the screen of our total economic history. Continuing in the Smithian tradition, the evolutionary argument that the market economy has shown to be superior to the planned economy is strong, backed by historic record. Any criticism and scepticism should be limited to capitalism, respectively socialism, as they are the politically motivated interpretations (and implementations) of those two economic systems. To compare the capitalism of Mill and Friedman with the socialism of Hegel and Marx is academic. In both cases power concentrates. We end up with corporatocracy, respectively statism as political systems. Both are anti-markets. In principle, markets could allow people to trade freely among themselves in a dispersed, diversified and decentralised manner. Even if some members of the ‘Markets family’ are occasionally (e.g. schizophrenic) patients or growing teenagers (e.g. with tantrums), they would generally be benefactors. In fact, it is the political and other manipulations—serving special, rather than public interests—that weakened market economies and any damage, reputational or otherwise, should be attributed to these causes. (See my earlier straw man comment in the Prologue). My point, via the MMH, is twofold. First, here too we can follow Smith’s advice, in this case by each inviting our “man within the breast” to recognise leaders for who they are. How would a morally balanced, global free citizen view them? A citizen who, once educated in cognitive economics, would recognise a political ‘freebie’ for what it is. I suspect the resulting view of the impartial spectator is frequently of leadership that is dangerously opportunistic, while lacking humility and openmindedness, especially regarding the global problems that exceed parochial knowledge and requires global solutions. Second, and related, the economic laws which follow from the cognitive laws will overcome the political hubris from any “man of system” in general and strongmen in particular:

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Chapter 1 Setting the Stage: Who Am I?

The man of system . . . is apt to be very wise in his own conceit; and is often so enamoured with the supposed beauty of his own ideal plan of government, that he cannot suffer the smallest deviation from any part of it. He goes on to establish it completely and in all its parts . . . He seems to imagine that he can arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chess-board . . . [risking that] the game will go on miserably, and the society must be at all times in the highest degree of disorder (Smith, 1759, p. 275–276). Nobody can afford their behaviour because by destroying markets they kill the goose that lays our collective golden eggs. Indeed, popular estimates for the largest economies of the future based on extrapolations of data over the past decades will turn out to be wrong for reasons explained in this book. Stated differently, the dominance of economic realities over political wishes will be enforced again, however painfully, by (conscious) human nature just as it has been throughout history. So, while you can be bullish on their countries and peoples, you should be bearish on men-ofsystem and their mechanical/repressive policies, regardless of regime. The recent protests in both the East and the West are a sign for this. Going forward I will borrow Smith’s term “man of system” and use man-of-system (multiple: menof-system; both meant gender neutrally) for all those who (wish to) engineer the economic system and/or organise society top-down. It particularly emphasises their mechanical/systematic approach to achieve this.

Setting the scene, I started this chapter with a family visit to a local market, concluding that markets reflect what ultimately binds us. Perhaps you reply, ‘but my local market does not exist anymore’. This is exactly part of what should concern us. At a larger scale, markets could help to rebuild a sense of global community if we let them . . . but we don’t. Markets, in their natural psychophysical form, are deteriorating or disappearing all together. Perhaps this is the cause, not the consequence of societies (mentally) breaking down, exactly because they are extensions of our minds. This book is about markets as the solution, not part of the problem, and how to better understand them—and by extension us. In the remainder I will mainly focus on the financial markets as a prominent case-in-point.

Chapter 2 On Ontology: Am I Evil? It ain’ what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so. Mark Twain

2.1 Economics’ Hard Problem Now, the issue, as is plain, relates to the treatment of “consciousness” in human beings . . . [i.e. the] insistence that we ignore . . . the existence of consciousness . . . In opposition to this view I propose . . . that we cannot treat human beings as . . . mechanisms, and that we do not want to do so even if it were possible. Frank Knight

A hypothesis usually addresses a particular problem that is central to its research efforts. In the MMH’s case I call it the market’s mind~body problem. Here is an earlier thought: What is the relationship between the phenomenal (sentient/qualitative) and the physical (functional/quantitative) properties of the market? Specifically, how and why are the physical processes in the market accompanied by an experience which completes its state? (Schotanus, 2014)

This problem is nested in the wider hard problem of economics which I will discuss first. At the macro level most of us perceive the economic system as dualist: it combines the physical real economy of markets in goods and services—“the economy”— with the psychological financial economy of markets in securities—“the market”.1 The hard problem of economics concerns the complex relationship between these two domains, including the question concerning the leading~lagging sequence. That is what earlier reflections by Simmel, Soddy, Knight, Akerlof and Shiller, as well as others (including, as we will see, Jerome Powell), ultimately boil down to. Importantly, for cognitive science it highlights that the original mind~body problem should no longer be viewed as just a theoretical challenge but due to its extension leads to practical issues. These, in turn, have implications for how to address the original problem. First, I will elaborate on this in the next Cognitive Economic Note ([Im]material Money).  This is meant, of course, in relative terms: both have material and mental aspects. Still—putting it in sharp contrast to make my point—the economy is about the physicality of (the means of) production combining with the physicality of labour to produce and subsequently consume physical commodities. The market, on the other hand, is about the psychology of their exchange, whereby “the price or money-form of commodities is, like their form of value generally, a form quite distinct from their palpable bodily form; it is, therefore, a purely ideal or mental form” (Marx, Das Kapital, 1867; Chapter 3, Section 1). https://doi.org/10.1515/9783111215051-002

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Cognitive Economic Note (Im)material Money Money is the root of all evil. Apostle Paul The lack of money is the root of all evil. Mark Twain In this note I would like to explain how, within markets and via exchanges, matter and mind are specifically related to money.2 Let’s start with barter, the direct exchange of goods (and sometimes services) without the use of money. Imagine two of our ancestors whose respective tribes regularly barter finding two identical eggs laid by the same hen, one after the other. As the same simple food, they are physical products that are equally valued. So, while there is no real need, should they decide to exchange eggs it will be a simple exchange: one egg for an identical other egg. Each existed there and then. It turns out, however, that different hens (of the same species) lay different sized eggs. So, another ancestor finds a supersized egg and brings this for exchange to the next barter event. While the same simple food, the supersized egg is valued more than normal eggs due to its larger proportion. In fact, it is exchanged for three normal eggs making its price, expressed in units of the latter, 3. Again, all four items involved in the exchange existed as physical entities. Things get more complicated, valuation wise, when the nature of the goods are different. Historic examples of valuation differences include beaver furs that native American Indians exchanged for blankets and cooking utensils. Those valuations were based on the utility of these goods as experienced by the respective agents using them. And whatever the prices, they were reflecting units of physical items that existed and were present there and then. Ignoring a few interim phases, the final step in exchange history involves modern markets where money facilitates exchanges. Now things get a bit weird. As previously mentioned, a key area where metaphysics reigns is our fiat currency and credit system. It is faith-based: faith in fiat (see also Appendix 1-B). That is, it is the belief that fiat-based IOUs, issued by governments, remain “credit worthy” which, in turn, implies an imagined future. It means that money3 is metaphysically suspicious, primarily because it is a mental construct not backed by anything physical: “Money is the representative of abstract value” (Simmel, 1907, p. 118). Even in the case of physical coins and notes, for example, their value is believed to be higher than the costs of their materials. (As an aside, such physical cash does not earn interest and can be viewed as a free loan to/hidden tax from the government. This is called seigniorage.) But faith in fiat currency is circular: faith in the currency relies on faith in the men-of-system which, in turn, relies on faith in (future) citizens to have faith in both and ‘do their duty’. In the case of debt, or credit in general, its value depends on future minds’ willingness to use their bodies to physically earn, create wealth and pay taxes with the fiat currency. In other words, the metaphysical issue with fiat money is not simply about it not being backed by something physical, like gold. Rather it is about such purely physical backing having been replaced by dualist mind~bodies, many of whom do not exist yet but are nevertheless expected to honour the IOUs. This is the ‘real’ backing men-of-system bank upon with lots at stake. One key dilemma is to balance the need to allow freedom for economic agents against the need to enforce fiat on those agents. Mon-

 Readers who wonder why philosophers like Heidegger are relevant for markets should read carefully.  Technical aspects, like the distinction between endogenous and exogenous money, will not be discussed here.

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ey’s mind~matter complexity particularly leads to complications when safely issuing new debt (and/or printing new money) relies on additional constructs that are metaphysically suspicious, like beliefs about inflation. Specifically, there is more to Milton Friedman’s statement that “inflation is always and everywhere a monetary phenomenon” (Friedman, 1963). Finally, the 2023 failure of SVB signifies, for our case, how digitisation—as the summit of mechanisation4—not only changes money but, by extension, also the metaphysical nature of a bank run. While the mental panic driving it remains the same, the physical execution is different. Whereas it was digital in the case of SVB, the run on California’s Ivy Bank, which failed in July 2008, was largely physical: physical queues of human bodies to physically get their hands on their money. A female depositor expressed this to a CBS reporter at the time (emphasis added): “I think it’s mass hysteria. I think this is similar to what happened in the Great Depression. And I think that everyone wants their money, and they want to touch it and hold it and see it”. Again, money is metaphysically suspicious, and if we get the overall matter~mind balance wrong (e.g. when immaterial promises require too much faith), things can become dangerous (as historic collapses of monetary systems have shown). More generally, faith supporting the ‘intangible space’ in the economic system (e.g. IT-sector, SPACs, etc.) is more volatile (and vulnerable) than faith put in fixed stuff. Why? Because the implied (and often required) mental causation for things to ‘materialise’ in the former case means that you move, in Knightian terms, much further from risk to true uncertainty (and thus closer to the mind~body problem, i.e. our ignorance of mind~matter interaction in the economic system).

On the topic of economic cycles and financial cycles, the Bank for International Settlements (BIS) published a survey titled “Asset prices and macroeconomic outcomes”. It highlights the difficulty in understanding the “macrofinancial linkages” between the (physical) economy and the (mental) market. That includes their respective growth, inflation, and health. Figure 2.1 shows a graph depicting two estimates of, respectively, the economic (or business) cycle and the financial (or credit) cycle over time.

Figure 2.1: Economic and Financial Cycles. Source: Bank for International Settlements (BIS), https://www.bis.org/publ/arpdf/ar2014e4.pdf.

 See also the Economic Note on cryptos in Subchapter 3.5.

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We can think of their correlation in similar matter~mind terms as being respectively those between the bodies and minds of economic agents. Specifically, the business cycle captures physical activity in real economic variables like factories, offices, pipelines, tankers and so on. The credit cycle, on the other hand, primarily reflects psychological activity in monetary variables and prices. There clearly is no one-to-one correspondence between them and, worryingly, since around the 1980s they have become more disconnected.5 Ultimately the BIS implicitly wonders what type of causality is involved between asset prices and macroeconomic fundamentals. Subsequent questions about whether correlations suggest causation and, if so, in what direction can similarly be raised. Among others, it underlines once again the relevance of Soros’s reflexivity. Like the individual, there needs to be a healthy, coordinated balance between physical and mental activity in the economic system. This is hardly ever analysed, let alone explained, in explicit mind~matter terms. Related issues include inequality which bottomed in many countries in the 1970s. At first sight, its increase over the last five decades or so, especially between the very top and bottom segments of the population, seems to follow two events. In physical space, the 1971 removal of whatever remained of the Gold Standard. In mental space, the 1987 removal of downside risk (a.k.a. the Greenspan put). I submit that, more likely, it is due to their underlying common driver: the emergence of mechanical economics as the dominant paradigm that advocates and justifies such removals. Mechanisation, which facilitates control, strengthens the status quo and benefits the rich and powerful. Things have not improved; Oxfam reports in its 2023 Global Inequality Report, that since 2020 the richest one per cent have captured almost two-thirds of all new wealth (i.e. twice as much money as the rest of world’s population). What that means in terms of economics’ hard problem will become clear during this chapter, which will make two key points. Both are theoretically supported by cognitive theories and empirically inspired by the lessons from the GFC and the other crises: 1. Mechanical economics erroneously considers the economic system to be a network of machines and automata populated by robots. Specifically regarding the market, this amounts to a category mistake because it first and foremost consists of humans whose embodied conscious minds are relied upon for exchanges aimed at economic discovery, particularly of values.6 Mechanical economics’ ontological commitment is thus incorrect and becomes very expensive when put into practice via policies, strategies, and products which, consequently, mistreat Mr Market and turn him into a (“doomsday”) machine.7

 And, by extension, any dislocation between the market and the economy.  Unless, of course, you do think we are robots. But we should avoid falling into reasoning traps. Something like: “Humans have consciousness. Humans are machines. Therefore, machines have consciousness.” In any case, we should make a clear distinction between living biological machines and inanimate ones. As argued in Appendix 1-A, consciousness is a trick Mother Nature played on us for our own good and simply assuming we can replicate it is an insult to evolution.  A reference to the subtitle of Michael Lewis’s bestseller The Big Short.

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In contrast, the MMH views the market as our shared extension, a collective mind~body. Further embodied in the wider economic system, this is the proper perspective. This argument will be expanded upon in the remaining chapters.

Next, I will go into more detail on the market’s mind~body problem. Mechanical economics, per the EMH, describes a market state as being in equilibrium, correctly valued, and informationally efficient. That description in turn is based on how certain functions are performed in various domains for which mechanical economics has detailed explanations. These include order routing, custody, clearing and settlement. Other functions are in the form of the combined analyses by investors with a fundamental, quantitative and/or technical view. Even behavioural interpretations, like loss aversion and utility maximisation, are functional. Such functions perform the physical (including brain) processes in the market. However, the resulting description cannot convey properly—that is, meaningfully let alone exhaustively—the full market state. In other words, why does a proper understanding of a market state—popularly called “bullish” or “bearish”, for example—require the inclusion of the experience to which the physical processes of that state give rise? Somehow leaving this experience out, by focusing exclusively on explaining the processes, does not sufficiently convey the market state. Intellectually analysing and describing these processes, as is traditionally done in mechanical economics, does not extend to knowing market states, say when Mr Market ‘goes mental’ and you have ‘skin in the game.’ Allow me to highlight this by returning to my simplified example in the Introduction of a market that consists of two assets, A and B. On two occasions through history A is quoted at 100, and B at 200. According to the EMH, this supposedly already reflects all relevant information and indicates those two market states are the same. Still, for the sake of argument, let’s further assume that secondary characteristics, like valuations, are also equal for each asset on those occasions. Nevertheless, for participants these states can feel completely different at those respective moments, implying the 100 and 200 ‘knowledge’ readings are ‘imperfect’. What does it mean when such quantitatively equal states feel dissimilar? It must mean that there is an additional qualitative aspect to information being realised. Moreover, it could lead to different subsequent decisions. Yes, there is also narrative economics occurring: when investors see correlations breaking down, divergences widen (say, between value~growth), and patterns emerge, Mr Market is telling a story. However, at the same time but at a deeper level, by feeling such dynamics via their portfolios, investors experience the mood of that story. To paraphrase Howard Marks, they feel the pendulum swing when it changes. The idiosyncratic mood completing a market state is due to the active dynamic of price discovery: it is the verb, not the noun. That dynamic is, after all, what investors experience as ‘reversals’, ‘squeezes’ or ‘trends’. By being in the market with their portfolios, reversing annoys, being squeezed hurts, and trending up elevates. It may be irrational or random, but it is still real in a phenomenal sense and cannot be “un-experienced”.

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So, while some are subjective, I focus on those experiences investors share as market moods. Notably, price discovery—which I will discuss extensively in Chapter 7 —and mood mutually influence. They are the market’s own reflexivity or Mr Market’s self-reflexivity, manifested in mood~momentum feedback, for example. Yet, as previously mentioned, the EMH considers this as epiphenomenal and irrelevant. However, it raises certain questions that mechanical economics consequently leaves unanswered, for example regarding the mood~momentum feedback: why do states of optimism lead to different choices than states of pessimism? Why when the market is crashing everyone rushes to sell, and when it is growing, everyone rushes to buy? (Bechara and Damasio, 2005, p. 362; emphasis added)

This points to the crucial role of price dynamics. More generally, price discovery is the active process that instantiates the market’s active externalism. To be precise, the ‘internalist’ essence of the market mind which, at the same time, is our collective externalism lies in the exchanges between the exchangers, not their individual internalism.8 The exchanges between conscious minds is the invisible hand that creates the constellation of prices (quantitatively ‘out there’), accompanied by their phenomenal realisation as shared mood (qualitatively ‘in here’). This negates the epiphenomenal argument. To elaborate, the most obvious translation of Popper’s “abstract relationship” in markets is the ratio between what the buyer pays and what the seller hands over in return. This number-as-price is what Hayek (a friend of Popper) called an “abstract signal” and “a kind of symbol” (see also Subchapters 6.3 and 7.2). In our modern monetary system, price is the amount of currency that the seller receives from the buyer.9 Prices emerge as discoveries within the collective search for the elusive intrinsic (‘real’) value of the purchased goods, services, or securities. This process includes the physical manifestation of exchanges, namely as actions through the execution of trades.10 Due to the uncertainty involved in discovery (because, by definition, it deals with the unknown) prices are symbols of perceived value (as in the eye, or “I” of the beholder). Still, they can be powerful initiators of Popper’s causal chain. This underlines that an economic exchange involves the crossing of the broader boundaries between the psychical and the physical, with prices as the informational building blocks, the dualist symbols, of the psychophysical bridge. More importantly, as acknowledged by Akerlof and Shiller, economic laws are subjected to psychophysical laws due to mental causation. Popper leads us back to his student Soros, who implicitly translated mental causation in investment terms via his philosophy of reflexivity: “markets [via prices] can affect the so-called fundamentals which they are supposed to reflect” (Soros, 1994). In

 For a general discussion on methodological approaches, like internalism, see Chemero and Silberstein (2008).  Often expressed in number of units of an imponderable paper or crypto currency. I have more to say about money in Appendix 1.  “Pulling the trigger” involves other mental properties, like trust and free will.

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his book (1987) Soros explained how, in his view, beliefs alter facts. Soros’s version of reflexivity thus specifically deals with the relationship between prices and fundamentals. This is not limited to how the former impact the latter, but importantly also includes how, by way of fundamentals but also momentum, prices influence (other) prices. Unfortunately, Soros missed the crucial link to the broader underlying mind~matter issues, despite the revealing title of his first post-graduation paper. What is more, he never developed reflexivity further because, by his own admission, he simply wasn’t able to lift it from its original “level of abstraction” and he “got so lost” in his “philosophical explorations” that he “gave up”.11 The best way to describe why mainstream never liked reflexivity, as well as where the missing link of consciousness fits (in italics), is to borrow a quote from cyberneticist Heinz von Foerster: In the general case of circular closure, A implies B, B implies C, and ─O! Horror!─C implies A! Or in the reflexive case: A implies B, and─O! Shock!─B implies A! And now Devil’s cloven-hoofed foot in its purest form . . . of self-reference: A implies A.─Outrage! (2003; emphasis added)

With thinking participants who are conscious you cannot avoid von Foerster’s last step. It reaches beyond the standard “reflexive case”—also keeping in mind Soros’s acknowledgement of “self-reflexivity” (Soros, 2013, p. 313)—and thus cannot escape “Outrage”. However, there is nothing mysterious about this. Translated for the economic setting it simply means that Mr Market is us, that prices impact prices and not just fundamentals, and that we experience all this by way of market mood. For an investor, market moods are irreducible. They provide experiential knowledge, and you start to recognise and distinguish moods because of how they feel when you are in them. The question above, defining the market’s mind~body problem, can then be restated as: What is market mood and why does it exist? This leads us back to our explanatory gap. It puts the physical~phenomenal in much clearer focus which is key. Because this is what investors encounter all day by being exposed to prices: experiencing the physical elements of the market that then lead to the narratives, and so on. Somehow, mechanical economics’ ‘periodic table’ just doesn’t describe properly what its elements combine into as far as investing reality is concerned. The market’s mind~body problem is thus broad based but particularly concerns this peculiar aspect of market mentality: how to account for the mood of the market that, in a qualitative intersubjective way, infuses market states. That both investors—

 Soros (1987, p. 46, and 2009). For a detailed cognitively inspired interpretation of reflexivity, including references, see Schotanus et al. (2020).

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like Howard Marks earlier—and policymakers take market mood seriously is reflected in the following respective quotes: Markets are not supposed to have moods. Yet they do.12 (George Soros, 2009) The linkages among monetary policy, asset prices, and the mood of global financial markets are not fully understood. (Jerome Powell, 2018)

‘Not fully’ was specified earlier by one of Powell’s predecessors: We can model the euphoria and the fear stage . . . [but] . . . Their parameters are quite different. We have never successfully modelled the transition from euphoria to fear. (Greenspan, 2009)

Frankly, even this ‘modesty’ is hubris, an example of the illusion of explanatory depth (see Rozenblit and Keil, 2002). In this case, the pretence of knowing what mood is and the overconfidence in modelling it. Instead, the essence of the market in general and its mood in particular is its interiority, namely how it feels to be in it: I don’t know how to explain it. It’s so wild. If a guy sees it who’s not in it, all he could say is, ‘They should be locked up!’ It’s so violent when it takes off. It’s violent, the power of the market . . . when it starts moving . . . You’ve got to be in it all the time to know where the market is. (Hassoun, 2005, pp. 107–108; emphasis added)

Comprehending collective mood and its shifts involves sentience. Not only do analytical methods of investigation fail to grasp it (as I’ll explain later, mood is not the same as sentiment, in that regard). Also, Mr Market’s mood is felt together by investors, over and above and different from their individual moods. In short, the market’s mood conveys what it collectively feels like to be (in) the market and is exemplary for market consciousness. This raises various questions, including about its causality, like how it can constrain the behaviour of market participants. After a brief historic overview on mind matters, I will discuss the EMH and the mechanical worldview that currently dominates economic sciences and practices in Subchapter 2.3. I will introduce the MMH more formally in Subchapter 2.4 and discuss both the market’s mind and body. Subchapter 2.5 will provide a roundup.

2.2 History of Mind Matters Before challenging mechanical economics from an ontological angle, I place it in the broader context of how the underpinnings of modern science have shifted. It should be read with the comments of the previous chapter (especially 1.1) in mind. I also briefly discuss how this influenced cognitive science.

 Another market practitioner, Bill Blain, chipped in: “At the moment, the mood feels miserable’. Blain’s Morning Porridge, broadcast on 22 November 2018.

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Modern science originated roughly in the seventeenth century as part of the Enlightenment, with the dualistic philosophies of Descartes and Mersenne playing an early role. The main metaphysical assumptions of objectivism, positivism, and reductionism became the intrinsic premise of modern science. According to Harman they form an interrelated theoretical network from which, among others, expectations can be derived. However: When there is an “anomaly”, or a failure of observations to conform to scientific expectations, it means that somewhere in that network there is a falsity. But there is no standard logic for discerning just where in the theoretical network the falsity lies. Thus in the face of an anomaly we must consider revising all elements of the network . . . In short, when experience contradicts science, the science must change, but there is no infallible logic for determining exactly what to change in one’s theory. Karl Popper’s insistence that theories are never proved, but only falsified or not, seemed at one point an important insight; however, in today’s science to talk about “verification” or “falsification” of theory sounds naïve and simplistic. (Harman, 1994, p. 7)

Acknowledging this complication we must accept that the adaptation of any theory, particularly those in the social sciences, goes beyond the idea that we use some allencompassing experiment to either verify or falsify scientific hypotheses. Rather there are basically two ways in which theories transform. The first way is via relatively small anomalies which simply pile up, initially slowly but often turning into a cascade. In that case, anomalies act like viruses in that they multiply and start to infect the whole structure, whereby a theory’s death, to paraphrase Max Planck, “advances one virus at the time”. The second way, closer in spirit to Kuhn (1962), is more radical and is caused by those anomalies which are so surreal that they defy ‘reality’: they become reality checks of ontological commitments. In that case, and in turn, the implication is that: our epistemological convictions about how we acquire knowledge, and about the nature of explanation, justification, and confirmation, are subject to revision and correction. (Harman, 1994, p. 7)

This has already occurred to some extent in cognitive science. Roger Sperry, who received the Nobel prize in medicine for his research on split-brains, was one of the first to point out that a cognitive revolution started in the 1970s involving a turnabout in the conception and treatment of the mind which “has vastly transformed previous scientific descriptions of ourselves and the world” (Sperry, 1994, p. 99). The resulting shift away from the blank slate approach of behaviourism has recently accelerated with findings in cognitive fields like: – Neuroscience: consciousness (Seth, 2021); coordination (Kelso, 2022); emotions (Damasio, 1994); integrated information (Tononi, 2015); mirror neurons (Gallese, 2001); predictive processing/active inference (Friston, 2010); volition (Schurger, Sitt and Deheane, 2012), – Philosophy: epistemic luck/risk/value/utility (Pritchard, 2016); extended mind (Clark and Chalmers, 1998); intentionality (Dennett, 1987); mind~body problem (Chalmers, 1995),

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and affective/evolutionary psychology: feelings (Panksepp, 1998); instincts (Tooby and Cosmides, 2005).

Affective psychology, for example, is relevant as it deeply involves the phenomenality of consciousness. In an overview of behavioural finance, behavioural economist David Hirshleifer concludes that affective psychology elucidates “the central role of feelings in decision making”, but that it “has only partially been incorporated into behavioral finance”: More theoretical and empirical study is needed of how feelings affect financial decisions, and the implications of this for prices and real outcomes. This topic includes [e.g. Smith’s] moral attitudes that infuse decisions about borrowing/saving, bearing risk, and exploiting other market participants. (Hirshleifer, 2015, p. 43; emphasis added)

A common thread between some of these findings is an interaction between innateness and development of psychological functions suggesting the complementarity of nature and nurture. Crucially, the influence of the unconscious is acknowledged (e.g. Gigerenzer, 2007; Mlodinow, 2012; Kandel 2012). In addition, artificial intelligence (e.g. Bostrom, 2014), collective intelligence (e.g. Bergson, 1907; Mulgan, 2018; Sloman and Fernback, 2017), and distributed cognition (e.g. Bettencourt, 2009; Menary, 2010; Huebner, 2014) have broadened our perspective of minds. In fairness, the strict interpretation of the aforementioned assumptions started to be challenged much earlier by the findings in quantum physics (e.g. Heisenberg’s uncertainty principle), followed by the implications from complexity theory. The effect quantum physics had on Einstein, for example, underlines the experiential impact of this kind of reality check: “It was as if the ground had been pulled from under one, with no firm foundation to be seen anywhere, upon which one could have built”. Why does this sound so familiar to investors, particularly those relying on mechanical economics for explanations for the market’s behaviour during the reality checks?

2.3 The Mechanical Approach to Markets We have indeed at the moment little cause for pride: as a profession we have made a mess of things. Friedrich Hayek The Pretence of Knowledge, 1974

Mainstream economics is a partnering of the strange bedfellows of new classical and Keynesian economics.13 In this section I will explain further why I call it mechanical

 It is also known as the New Neoclassical Synthesis. Mathematically speaking, for example, new Keynesian models are similar to the (e.g. real-business-cycle) models of new classical economics. Also, I am not going to distinguish between macro- and microeconomics or other details, unless they are relevant to my arguments. Still, for brief descriptions see Appendix 1-B.

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economics and what it is about. In short, viewing the economy as a machine, as per new classical economics, is attractive for Keynesian economists if it means treating the economy as such. It namely suggests it is something one can control centrally. Control benefits from predictability and this is facilitated, in turn, by way of mechanistic rules in models targeting pre-determined outcomes via central planning. However, because the outcomes of its flawed view end up in policies and practices that have a real impact, mechanical economics is not an innocent bystander (in contrast to many other sciences). Nor are its practitioners: “From my 50 years in this business, I would contend that well-meaning Western economists have done more damage to the wealth, freedom and wellbeing of the world than any Marxist thinker preaching an overthrow of the capitalist system . . .” (Gave, 2023, p. 1). As far as historic associations go, especially regarding its emphasis on (command and control via) modelling and central planning, Plato’s ‘cybernetics’ is among the earliest. In Plato’s Alcibiades Socrates has a dialogue with Alcibiades, an aspiring statesman, about government. He refers to κυβερνήτης (kybernḗtēs; cybernetics) as the knowledge or skill attributable to a “steersman” or “governor” who is able to engineer the state. Already this presumed ability encounters our critical context provided by 4E cognition. Much empirical research over the past decades has highlighted numerous anomalies challenging mechanical economics. A famous early example illustrating the complexity (rather than mechanisms) between global real economies and global financial markets is the study by Meese and Rogoff (1983) who showed that a random walk model outperformed the main REH-models in forecasting exchange rates, thereby questioning the assumed causality between fundamentals and prices. Following the turmoil surrounding the collapse of Lehman, criticism of mechanical economics in general, and the REH/EMH in particular, has grown. Part of the problem is that it is motivated by physics envy which it tries to replicate (see Mirowski, 1988; Soros, 2009; Lo and Mueller, 2010).14 However, as Emanuel Derman, the ‘Quant of Quants’ and a former physicist, so pointedly explains: “In physics you’re playing against God, and He doesn’t change his laws very often. In finance, you’re playing against God’s creatures, agents who value assets based on their ephemeral opinions”. Or vice-versa, in the words of Feynman: “Imagine how much harder physics would be if electrons had feelings”. Many initiatives are under way to revise the current paradigm, including ours. To repeat my criticism: mechanical economics’ main flaw lies deep at the ontological level because it erroneously views and treats the economy as a machine, the market as an automaton, and humans as robots. This mechanical perspective was notoriously captured by Robert Lucas, one of the founders of the REH. With echoes of, for example, Thorstein Veblen Lucas sees economics simply as:

 Preda points out that already from the 1850s “engineers transfer the vocabulary of physics to the valuation of railway securities. They require observation and analysis in this process. Sheer luck or emotions are seen as irrelevant” (2005, p. 152).

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something that can be put on a computer and run. This is what I mean by the ‘mechanics’ of economic development—the construction of a mechanistic, artificial world, populated by the interacting robots that economics typically studies, that is capable of exhibiting behavior the gross features of which resemble those of the actual world. (1988, p. 5; emphasis added)

Among its characteristics are extreme assumptions of rationality and self-interest which—conveniently—imply a repetitive pre-determined behaviour, towards equilibrium, that can be quantified and modelled. In turn, the resulting models (including algorithms) facilitate, justify, and promote mechanical practices. Together they strengthen and sustain an overall mechanical approach to an economic system which, in contrast, is far from mechanical. Consequently, this is unnatural and has dire fallouts. Critique of mechanisation has a rich history, varying from the early general reflections by Hegel, via more economics-coloured views by Heilbroner and Schumpeter, to the modern updates by Mirowski (1988), Kay (2011), Bookstaber (2017) as well as Frydman and Goldberg (2011) who specifically criticise “mechanical markets”. In particular it questions the obsession with equilibrium. Decades ago the economist Joan Robinson suspected a psychological complex: There is also a psychological element in the survival of equilibrium theory. There is an irresistible attraction about the concept of equilibrium—the almost silent hum of a perfectly running machine; the apparent stillness of the exact balance of counteracting pressures; the automatic smooth recovery from a chance disturbance. Is there perhaps something Freudian15 about it? (1962, pp. 77–78; emphasis added)

This mechanical worldview explains its reliance on mathematics and its focus on quantification. Initially this only started out as a tool in early commerce, exemplified by Fibonacci’s Liber Abaci (1202). It subsequently slowly morphed into a doctrine, especially after Louis Bachelier (1900) made the category error by applying the mathematics of physics to model financial assets as if they were moving particles. (In Subchapter 2.4.3 I will explain why it became indoctrinated.) Nowadays it is reflected in three dominating (and overlapping) manifestations in the economic system: 1. Quantitative analysis: designing and applying financial models (e.g. Capital Asset Pricing Model [CAPM], Black-Scholes options model, Gaussian copula) as well as economic models (e.g. DSGE, ISML). 2. Financial/monetary engineering: designing and implementing financial products (e.g. derivatives, smart-beta ETFs, Over-The-Counter [OTC] structured products), respectively monetary policies (e.g. inflation-targeting, Quantitative Easing/Tightening [QE/QT], repo operations, Yield Curve Control [YCC]). 3. Systematic investing: mechanising the investment process (e.g. index tracking [passive investing], algorithmic/high-frequency trading [HFT], liability-driven investing [LDI], risk management [VaR]).

 Actually, it is rather more Jungian but I will not go into detail here.

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But like the Sirens, the aesthetic appeal of mathematics can be deceiving, giving the appearance of an exact science. However, its map is not the territory and using it as a compass can be outright dangerous. Take the REH assumption that subjective probabilities eventually turn into objective probabilities. In the words of John Muth: “expectations . . . (or more generally, the ‘subjective’ probability of outcomes) tend to be distributed, for the same information set, about the prediction of the theory (or the ‘objective’ probability distribution of outcomes)” (Muth, 1961, p. 316). This despite the doubt raised by, no less than, Paul Samuelson about “where the [objective] probability distributions are supposed to come from” (1965, p. 48).16 Robinson’s criticism has been shared by others. One was Hyman Minsky who— turning earlier insights from Irving Fischer’s Debt-Deflation Theory into his own Financial Instability Hypothesis—made the distinction between the use of self-funded hedge finance and debt-fuelled speculative finance to argue against any mechanical stability in the economic system. The MMH particularly builds on his work by emphasising the mind~matter nature underlying the relationship between the real and financial economies. More generally, Bertalanffy pointed to the inability of equilibrium approaches to deal with anomalies and novelty in complex systems. Specifically, they are: Inadequate for phenomena of change, differentiation, evolution, negentropy, production of improbable states, creativity, building-up of tensions, self-realization, emergence, etc. (Bertalanffy, 1969, p. 23)

Crucially, this worldview has wider practical consequences in terms of actual treatment of the economy as a machine, the market as an automaton, and their agents as robots. It not only ignores the crucial fact, as already emphasised by Knight (1925b), that humans have conscious minds. Also implicit is the tacit assumption that such mechanical entities, in case they do not run smoothly (i.e. fail to reach equilibrium), can be calibrated, fixed, nudged, optimised, and tweaked, by pulling a lever here, pushing a button there, and turning the switch in the middle. Alternatively, they can simply be replaced, removed, or otherwise switched off. Not surprisingly, it is consequently coloured by automation bias which translates into: – the aforementioned over-reliance on mathematical models (including AI-algorithms), – overconfidence in engineering (including design), – and an obsession with control (including intervention, litigation, and/or manipulation). Regarding that last point—and to prevent any potential confusion—even if the Chicago-School section of mechanical economics argues to leave the automaton alone, it does not mean that (particularly powerful) market participants or policy makers will.

 In Chapter 4 I will use this dubious belief in an epistemological framework and rename objective probabilities as true probabilities.

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On the contrary, the underlying belief of dealing with ‘merely’ a machine encourages tweaks and other interferences to ‘revalue’ in the eye of the dominating beholder, often (falsely) under the banner of normative economics: ‘We should do something!’.17 Chicago’s predetermined knowledge, in that case, gets replaced by the (pretended) omniscience of the man-of-system (or monopolist, or quant-programmer), piggybacked by uncritical investors. I will highlight three areas where mechanical economics turns into such dubious practice.

1. Policymaking Mechanical thinking is popular among—often more Keynesian minded—policymakers who think that, with help of their models, they can steer the economy with mechanical rules, regulations, and policies. In particular, oiling the wheels of the larger machines, central banks operate the monetary (printing) machine, regularly relying on goal-seek models with predetermined outcomes (such as meeting inflation targets) and offering free puts to prevent market corrections. Janet Yellen, the former Chair of the US central bank, the Federal Reserve (Fed), believes monetary policy is like driving a machine: “Right now our foot is still pressing on the gas pedal . . . Our foot remains on the pedal so that we can make sure economic expansion remains strong enough to withstand an unexpected shock” (Yellen, 2017). However, as Steve Forbes, referring to Mises, pointed out earlier (Forbes, 2015), thinking that “money printing” to pump up the economy is like “pumping gas into an engine” is wrong. For one thing, thinking that you are dealing with a machine raises a pretence of knowledge (‘Look, I have a manual’) and physical fixability (‘Look, I have tools’). Regarding markets, Fed Chair Jerome Powell shares such thinking: “What happened was markets stopped working . . . What our tools were put to work to do was to restore the markets to function. And I think, you know, some of that has really happened” (June 2020 press conference). Central bank policies are more generally based on erroneously assumed transmission ‘mechanisms’. Then there were the lockdowns during the CVC where it was assumed that governments could simply push an on/off button to first shut down their economies and then turn them back on— just like machines—without any consequences. Considering that, as the MMH purports, its substrate is psychophysical instead, there are consequences, most worryingly, for mental health. Additionally, these all seem to ignore Goodhart’s Law which states that measures to control the economy centrally will turn unreliable once targets such as “2% inflation”, “Zero-Covid”, or “10Y yield pegged at 0%” are set. In Chapter 5 I discuss the extreme nature of central-bank-cum-government intervention—to the point of financial repression—and the resulting distortion of prices plus likely misallocation. This

 Like raising rates during the Great Depression and bailouts during the GFC. Yes, I’m channelling the late Anna Schwartz here with her criticism of the Fed: “firms that made wrong decisions should fail”.

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has also been covered extensively by others. What is novel and should be emphasised as regards price discovery is the cognitive, in this case phenomenal, argument about the significance of values in terms of discovered meaning and sensemaking. A mechanical approach (say, via predetermined rules) to the pricing system means that Heidegger’s general critique of such “assigned” value systems applies: The context of assignments or references, which, as significance, is constitutive for worldliness [our existence in the world], can be taken formally in the sense of a system of Relations. But one must note that in such formalizations the phenomena get levelled off so much that their real phenomenal content may be lost, especially in the case of such ‘simple’ relationships as those which lurk in significance. The phenomenal content of these ‘Relations’ and ‘Relata’ . . . is such that they resist any sort of mathematical functionalization”. (Heidegger, 1927, pp. 121–122)

Let me end here, first, by just repeating that the Market Mind Principle, with the dismissal of homunculi, rejects omniscience by authority. Finally, a related area is the institutional and regulatory framework in support of mechanisation. Here we see: – Regulatory capture, or the growing influence from technocratic or special-interest institutions, including lobbying firms, populated by revolving-door accountants, consultants and other ‘experts’. A report in the Sunday Times stated that Washington DC had 175 registered lobbyists in 1971. This has now exploded to over 12,000, spending billions annually. This trend has been replicated throughout the Western world. – Increased bureaucracy reflected in growing amounts of regulations and rules (a.k.a. red tape), moving away from principles. The growing size of bank regulations (from Basel I to Basel III) is a good example, but so are the demotivating bulkrequirements for entrepreneurs to start a company and the growing armies of compliance staff. – This is accompanied by a shift from spirit to letter of the laws (although this does not prevent regulatory arbitration or accounting tricks). For an overview, including the history of capitalism’s ‘legal coding’, see Pistor (2019).

2. Business Corporations embracing mechanical economics—via obsessive control of some ‘moat’, often through horizontal shareholding—results in the elimination of competition. This is manifested in (both private and state) monopolies and other forms of market dominance. Among the bad consequences is that pricing power is handed to the dominant few. This corporatocracy has been on the rise, spreading into many industries, with banks and technology giants as poster boys. Let’s call it “the Big 3 in Everything”. Below are a few examples (see also Tepper and Hearn, 2019) of how this market concentration manifests itself:

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The core sinners in our information economy are the tech giants, whose exclusive access to data results in a societal information asymmetry. Harvard’s Shoshana Zuboff (2019) calls it “surveillance capitalism”. Yanis Varoufakis, Greece’s former finance minister, calls it “techno-feudalism” (Varoufakis, 2021). Stucke and Ezrachi (2022) investigated how their growing power stifles, or rather “toxifies” innovation. Their recent encroachment into financial markets via partnerships with various exchanges (e.g. Chicago Mercantile Exchange, London Stock Exchange, and Nasdaq) is worrying. In May 2020 the Financial Times reported that: “The largest 1 per cent of investment groups manage 61 per cent of total industry assets. This is 243 times that of the bottom 50 per cent, compared with 208 times at the end of last year and 105 in 2010”. What are some of the consequences? A recent study shows that concentration leads to “higher volatility and greater noise in stock prices, as well as greater fragility at times of crisis” (Ben-David et al., 2020). Furthermore, the study found that “large institutional investors exhibit . . . correlated behavior, which reduces diversification”. Similar concentration in other industries leads to problems varying from food and medicine insecurity, via electricity outages, to dismal broadband service. Four commodity conglomerates—Archer-Daniels-Midland Company, Bunge, Cargill and Louis Dreyfus (known as ABCD)—dominate the global grain trade, by way of their 70–90 per cent market share. We can now add vulnerable supply chains by outsourcing to single suppliers into that category, exemplified by Germany’s reliance on Gazprom, the Russian gas supplier. At the time of writing, Bloomberg18 reports that in the US: – Two corporations control 90% of the beer Americans drink. – Five banks control about half of the nation’s banking assets. After the collapses of SVB and First Republic recently, their market share will likely increase at the expense of smaller regional banks. – Many states have health insurance markets where the top two insurers have an 80% to 90% market share. In Alabama one company, Blue Cross Blue Shield, has an 84% market share and in Hawaii it has a 65% market share. – When it comes to high-speed Internet access, almost all markets are local monopolies; over 75% of households have no choice because there is only one provider. – Four players control the entire U.S. beef market and have carved up the country. – After a few mergers in the past years, three companies will control 70% of the world’s pesticide market and 80% of the U.S. corn-seed market. Investment legend Jeremy Grantham points to another sign in these industries that something is wrong: “Profit margins are probably the most mean-reverting series in finance, and if profit margins do not mean-revert, then something has

 https://www.bloomberg.com/opinion/articles/2018-11-25/the-myth-of-capitalism-exposed (Accessed 4 December 2018).

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gone badly wrong with capitalism. If high profits do not attract competition, there is something wrong with the system, and it is not functioning properly”. Later in the book I link this to the recent bout of inflation. Then there is the average age of the top 100 companies. In many countries it has significantly increased over the past decades. This reflects a lack of renewal, suggesting that those fashionable ‘disruptors’ either fail to have an impact or more likely sell out early by joining the comfort of an oligopoly. In turn, buying such potential competitors with cheap money from central banks obviously helps the incumbents to keep the status quo. Related is the decrease in the number of new company formations and listings, diminishing the healthy cleansing by creative destruction.19 The Sunday Times reports that start-up rates have been falling in 16 out of 18 western economies over the last few years. This is also reflected in the growth in zombie, or undead, companies. Deutsche Bank estimates that their share of total US companies reached almost 20% in 2020. Zombie companies are dead except for the fact that cheap money keeps them alive (e.g. via bailouts). Being undead, they merely pay the interest on their debt. Unfortunately, because this allows them to compete unfairly they weaken healthy companies, thus further infecting the economic system like true zombies. In a 2018 report the BIS concludes: “Lower rates boost aggregate demand and raise employment and investment in the short run. But the higher prevalence of zombies they leave behind misallocate resources and weigh on productivity growth . . .”. (Banerjee and Hofmann, 2018)

The industry behemoths employ various tactics, particularly tweaking the machine of bureaucracy. Corporatocracy, and more specifically crony capitalism, spawns policy making by friendly entities, using revolving doors between the public and private sectors; its members are not elected democratically or selected competitively. Their decisions are thus not subjected to public scrutiny, respectively market (e.g. bargaining) forces. They are blind to conflict of interests because they see their interests as ‘aligned’. It translates into mechanisation in the real economy mostly by means of favoured (e.g. lobbied) ‘solutions’, including automatic (e.g. ‘IF-THEN’) budgets, guarantees, subsidies, and other measures. For instance, after a number of interim phases TBTF (Too-Big-To-Fail) banks have now morphed into TBTC (Too-Big-To-Care) because a central bank decides that IF a bank is about to fail, THEN we bail it out even though they may be TBTS (Too-Big-To-Save). The drawback is that these policies often become addictive or predictable and thus can lead to economic misbehaviour, especially moral hazard. While this is sometimes recognised and corrected, more often such

 A term introduced by the economist Joseph Schumpeter. See also, e.g. Moules (2015).

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schemes are inherited and deemed hard to remove, leading to ‘automatic renewal’. Again, the underlying current of growing concentration of wealth, e.g. via stock ownership, is a related worry.

3. Investment The mechanical view is held by many investors (e.g. Dalio, 2015), with Wall Street supplying the engineering tools. Mechanisation, backed by the assumption of quantification, manifests itself in two forms which I touched on briefly before. First, it appears as complex (derivative) products that are ‘structured’ and modelled on computers. One example is the notorious CDO (Collateralized Debt Obligation)—made famous in the movie The Big Short20—which promised investors payments from pools of loans packaged in various risk tranches. It turned out many CDOs contained toxic loans such as subprime mortgages or even other CDOs (CDO-squared) and they eventually contributed to the GFC. John Thain, former CEO of the NYSE and later Merrill Lynch, explained the dangers of such machine-dependency: To model correctly one tranche of one CDO took about three hours on one of the fastest computers in the United States. There is no chance that pretty much anybody understood what they were doing with these securities . . . I think that the degree of complexity that was created in the securities, and the lack of anybody’s ability to really understand . . . was simply an error and a bad thing. (Thain, 2009)

Second, it is increasingly applied in investment management by means of mechanical (or computerised, also known as systematic) investment strategies that are ‘monitored’ by—mostly pro-cyclical—value-at-risk (VaR) policies. For example, the stigmergic-like algorithms behind so-called momentum strategies work along the lines of ‘IF the index goes up (down), THEN we buy (sell)’. The origins of mechanical investing go back to earlier crude strategies, like portfolio insurance that contributed to the 1987 crash. Nowadays this includes passive investing, high-frequency trading (HFT), liability-driven investing (LDI), robo advisors, and automated trend-following (e.g. CTA) approaches, as well as more advanced variations like volatility-control and risk-parity. Theoretically these strategies are supported by modern finance, a nested discipline within mechanical economics. Unfortunately, as the former derivatives trader Satyajit Das (2010) pointed out, modern finance is incomprehensible to ordinary men and women, which is often exploited by the industry. Still, the amount of assets managed by such strategies has grown significantly, a trend that is expected to continue. Advocates argue this is justified by low-cost access/liquidity, relative performance, and other ‘benefits’. However, as a form of momentum trading passive investing causes its

 Based on Michael Lewis’s book of the same name (2010).

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own version of market concentration in popular indices. For example, the S&P500 has experienced a record decline in the number of companies required to replicate its performance (which is the essence of index-tracking). This is because these strategies, in their artificial mechanical way, direct capital flows into the least number but often biggest index components to achieve such replication, all in a bid to minimize expenses. This can create a positive feedback loop. As indicated in Chapter 1, a larger problem is that many mechanical strategies only use internal market data for their trading without reference to the fundamentals that determine asset values. Consequently, as they become dominant Mr Market turns inside himself, as it were, caring less about the economy or the world for that matter. Stated differently, and keeping Hayek’s arguments of distributed local knowledge in mind, any trickle down of fundamental news via (assumed) information efficiency is diminished—if it occurs at all. Crowded inward-looking strategies as well as the related concentrations in wealth, market making and so on privatise information and further decrease the number of conscious market participants, and thus idd-minds. This leads to an unhealthy narrow-mindedness in Mr Market, with knowledge and awareness not sufficiently distributed. In other words, these strategies do not do discovery and do not contribute to bridging the economic mind~body. The main reason some of them have been ‘successful’ is because they piggyback on the wider and growing mechanical treatments that operate on the economic mind~body. This makes them largely speculative and, in some cases, parasitic or predatory. The GameStop and other meme-stock sagas are illustrative for this, and I will summarise an earlier article I wrote for Jackson Hole Economics here, which highlighted key conclusions and other insights from an MMH perspective. I have also been guided by Charlie Munger’s dictum that showing the incentives can tell you the outcome. Overall, it yields a broader message—aimed specifically at policymakers—on how to fix our broken markets: 1. Investing: Automation makes investing seem effortless but if investors are not incentivised to learn about investing, its outcome will not be understood and will frequently disappoint. Einstein advised to make things “as simple as possible, but not simpler”. The latter was a warning, one not heeded by mechanical economics. Mechanical economics has made investing easier and simpler—through exchange traded funds (ETFs), smartphone-based trading apps and cheap margin debt. But on the back of an absurdly complicated theory, mechanical approaches have also made markets more complex, for example via derivatives, black-box algorithms and distorting monetary policies. There is now growing tension between these two developments. For instance, while information asymmetry has always been an issue in markets—institutional ‘smart money’ invariably has the edge—the inequality in financial literacy and investor education is a big problem. In the case of GameStop, even if the instigators were experienced and well-informed traders, the majority of followers—the proverbial ‘greater fools’—were not.

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2. Business models: Retail investors are not the customers of Robinhood and other commission-less trading venues. Instead, HFT market makers—like Citadel and Virtu— are their customers and form the heart of their business model, called Payment-ForOrder-Flow (PFOF). Basically, the flow of orders from retail investors is the product that Robinhood sells to HFT firms. The latter subsequently make money from such flows in a mechanised, riskless manner that many consider predatory. Although HFT firms deny front-running,21 executing personal orders by free trading comes at a hidden cost, similar to sharing personal data from free services on the internet. In short, Robinhood’s claim to “democratise investing” sounds as hollow as Google’s infamous “don’t be evil” motto. 3. Control: Worrying linkages between family offices, hedge funds, market makers, and trading platforms have been revealed by this saga. Something similar occurred between SVB and its venture capital backers. Crucial plumbing and liquidity of financial markets is increasingly dominated by a cabal of unlisted private entities, owned by the top 0.1% of the top 1%. Opaque rules by clearing houses further obscure a transparent view of collateral, leverage, margin, and other risk factors. Actions are taken behind closed doors. In MMH’s terms, Mr Market’s body receives unsupervised treatment—including organ shut-downs (arbitrary trading outages) and shock therapy (self-serving bailouts), often obscured from public vision. 4. Conflicts: In their criticism of both short-sellers and the Reddit retail crowd many commentators have pointed to the numerous instances of conflicts-of-interest. One that stands out was the ‘investigation’ planned by US Treasury Secretary Janet Yellen. It is awkward for two reasons. First, as former Fed Chair she is co-responsible for creating the easy-money conditions that over-greased the market-trading machines. Second, as widely reported, after retiring Yellen got paid almost US$ 1million by Citadel for guest speeches. Call me cynical, but by the time you read this I suspect that this investigation has either been cancelled or has not produced any impactful results. 5. Manipulation: In the real economy this includes earnings manipulation which seems especially prevalent ahead of recessions. Accounting professor Messod Beneish of Indiana University developed an indicator—the so-called M-Score—that attempts to flag this risk by tracking various balance sheet and profit-and-loss variables. It recently reached its highest readings since the late 1970s. In terms of track record, it previously raised red flags at Enron and Wirecard prior to their accounting scandals and eventual bankruptcies. Crucially, manipulation also occurs in the financial system. According to securities laws market manipulation means that an artificial price is created or maintained in a security. Regulators will investigate, for example, whether manipulation took place in meme stocks. However, such laws do not apply to central bankers. Yet

 But see https://www.finra.org/sites/default/files/fda_documents/2014041859401%20Citadel%20Securi ties%20LLC%20CRD%20116797%20AWC%20sl.pdf (Accessed 12 May 2022).

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appearances matter. Central banks set bad examples by buying distressed assets, bailing out fellow bankers, setting negative interest rates, ‘tolerating’ Libor rigging (Verity, 2023), and creating positive wealth effects for the already rich, all of which fuels anger and frustration among the 99%. As an aside, should financial education improve, thereby raising awareness of these matters, that anger will only grow.22 6. WIT because YOLO: Whatever-It-Takes because You-Only-Live-Once. These are two mantras of the retail trading crowd (perhaps inspired by Mario Draghi’s earlier commitment to saving the euro). The meme-stocks sagas are a symptom of the larger issue. It appears that a significant number of these traders have been motivated by factors beyond profit maximisation, including revenge. That may be irrational, but it is also a warning. There is a genuine risk that attention-induced trading (e.g. Barber et al., 2022) turns systemic, particularly now that social media chats can swiftly refocus on more dangerous short-squeeze candidates. Before long, radicalized traders may find ways to sow financial stress or even create a more devastating (digital) bank run—the finance equivalent of France’s gilet jaune (yellow vest) protesters. This is hardly a fruitful direction of travel. These three areas of policymaking, business, and investing do not stand alone. The MMH asks a simple question: “what happens when you treat the market, which is a collective extension of conscious minds attempting to discover prices, as an automaton?” One can then recognise various symptoms of serious maladies affecting our economic mind~body. Take the MMH’s emphasis that investing involves practical dualism: a mental ‘thinking’ side (like investment decisions) and a physical ‘action’ side (like trade execution). Those incentivised to facilitate the latter, like HFT-firms, do not care much about the former. Stated differently, they care about quantity (frequency of trading; the more the better), not the quality. Making investing physically more ‘efficient’ doesn’t necessarily improve investors’ mental understanding and resilience. Arguably, it makes them more vulnerable. It is another example of the imbalance between the mental and physical. The ongoing market mechanisation that turns Mr Market into a ‘gamed’ machine is compounding the unintended consequences. There are many straight-forward measures that policy makers can take to address the issues raised above. One improvement would be to separate financial firms and their regulators by using much stricter rules to prevent lobbying, revolving-door appointments, post-retirement speeches/endorsements, and other conflicts-of-interest. Another would be a Tobin HFT-tax. In Chapter 12 I offer more suggestions. Longer term, the MMH posits that economics needs to revise its flawed paradigm that justifies and motivates selfreinforcing mechanisation. By extension, genuine investor education—taught through

 Now you are a cynic when you think that this is why such education is not a priority.

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a new curriculum—is needed to improve investment outcomes. And that requires more than hearings and investigations conducted by conflicted parties.23 I want to return to the importance of balancing internal and external data and the risk of diminishing market efficiency, in this case, informational efficiency.24 The financial economy’s informational efficiency operates in two directions in that regard, outward and inward. It operates outwardly in terms of funding efficiency: efficiently allocating capital for the real economy, which requires that it focuses on economic or external information. The financial economy operates inwardly in terms of arbitrage: no free lunch, which requires that it focuses on market or internal information. The financial economy’s informational efficiency, the extent of correctly assessing states of the world, thus follows from its ability to consider both external and internal information. Where things go wrong is when the financial economy becomes dominated by market participants who only focus and trade on internal information. Again, this is increasingly the case due to the growth in passive investing, other systematic strategies (like CTA trend following), as well as derivative strategies (like so-called optiongamma trading) that impact the underlying securities. An additional aspect arises from the level of consciousness involved in generating this information. Based on earlier work by Hayles (2017; see also Appendix 1-A) on the technological unconscious in trading, Beverungen and Lange (2018) investigated “the ways in which the ‘costs of consciousness’ are accounted for and negotiated in highfrequency trading”. They conclude that “traders actively develop modes of awareness accounting for the costs of consciousness, and that the necessary ‘stupidity’ of highfrequency trading algorithms as well as competition pose limits to the full automation of financial markets”. Still, and more broadly, removing consciousness by outsourcing to mindless machines does not help adaptation by the overall economic system to states of the world. Meaningful efficiency is not about HFT in that regard. More generally, short-term efficient (e.g. promoted by the EMH) does not necessarily mean long-term optimal. Neither does market health necessarily improve by raising the number of transactions or lowering bid-ask spreads. Increased conscious participation by a growing number of idd-minds is. Why? Because, apart from distributing knowledge, it diversifies awareness and sensemaking. Knowledge may apply to so-called small world situations with risk (but remember reflexivity25). However, we can only hope to be aware and try to make sense of a large world with uncertainty. Such heterogeneous mentality is the source of true liquidity and when it dries up, it reflects a cognitive black-out and disengagement. There is also a related data issue. Prices further back in time were set by conscious humans via discretionary trading, that is, “human volume”. More recently, prices are increasingly set by machines through systematic trading. Apart

 For more background, see for example Kauflin et al., 2020.  I discuss this, as well as funding efficiency, in more detail in Appendix 1.  For example, see quote from Soros on knowledge below, shortly after the Cognitive Note.

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from the statistical implications of such sample difference, we should question the broader meaning in terms of the reflected mentality-as-information in prices. I highlight these areas because that is when the problems start: when mechanical economics’ view turns into machinations namely actual treatments, or rather mistreatments. At that moment initially innocent thought experiments, laboratory trials, and computer simulations with flawed assumptions turn into real-world ones with damaging tangible impacts that have become very expensive for society. This is all very questionable: Surely the man who would undertake to treat human society merely as material for scientific manipulation, to control it by finding the laws of its response to stimuli and devising stimuli to provoke the responses he might desire, would have to be classed as a monster or an imbecile. He might have abundant intelligence, of the scientific sort, but would be lacking in “sense”. (Knight, 1925a, p. 389)

Specifically, manipulation equates to actual mistreatment of the mind~bodies at all levels in the economic system because it distorts26 perception (of reality) and risks suboptimal behaviour. This will be explained further but in short, think of your own mind~body. It is healthy if it is free (e.g. for your mind to think and for your body to move) and unhealthy if not.27 What we will diagnose is a form of iatrogenesis, where a treatment that should cure instead causes and/or exacerbates a disorder in the mind~body. Consider these medical examples. Fentanyl is a medicine, an opioid painkiller that can lead to addiction. Hypnotherapy is used to treat conditions or change habits but can result in self-deception manifesting as false memories. As indicated, in the case of Mr Market the mistreatment is in the form of manipulation, particularly price manipulation. This is primarily due to financial repression, financial engineering, and other interferences that supposedly serve ‘the greater good’ but actually limit discovery. Those who commit economic iatrogenesis act against economics’ Hippocratic Oath: primum non nocere (first do no harm) to the economic mind~body.28 This argument follows from accepting the Extended Mind Theory, because it requires that we update our notion of ‘abuse’, to include manipulation and other (intentional)

 Apart from prices, distortion can occur in many ways, one of which is via software. For example, Axtell points out, when criticising the Walrasian Arrow-Debreu model, that: “Unfortunately, the embodiment of this ideal type in CGE software, especially when utilised for policy purposes, institutionalises a series of propositions that more behaviourally realistic and decentralised models reveal to be false.” (2005, F209; emphasis added). Others are innocently dressed up as ‘nudges’. In the next section I will give more examples.  It may help to think of the constraints during the CVC in that regard. Losing freedom due to a lockdown is generally not beneficial for your mental health. Also, getting infected by Covid itself keeps you homebound or, worse, bedridden.  Larry Summers called his interpretation “iatrogenic volatility” (Larry Summers: ‘I’m concerned that what is being done is substantially excessive’ Financial Times (ft.com). [Accessed 12 April 2021].

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harm done towards the extended domain of our minds that forms part of the market mind (for the general argument, see Carter and Palermos, 2016).29 Moreover, putting mechanical economics into practice—by implementing mechanical methods, policies, regulations, strategies, and techniques—creates an environment that is favourable to mechanisation, further reinforcing it. Mechanisation begets mechanisation (see Figure 2.2 and also Appendix 1):

Invites

Market viewed as Machine => Machinations => Market acts as Machine

Self-fulfilling

Figure 2.2: Mechanisation begets mechanisation.

Yellen, Powell, and other men-of-system are blissfully unaware of this. They are like that old cartoon joke of an unempathetic doctor. When asked by their bedridden and ill patient (in our case Mr Market): “What’s wrong, doc?”, they look around and reply that the roof leaks, the windows are broken, and the place needs some painting. Mechanical economics’ blind spot—as Knight identified in 1925—is the fact that humans have conscious minds with which we experience the world. Specifically, connected via trading and supported by way of technologies our minds collectively extend into real and financial markets (see next subchapter). That includes our consciousness: it does not somehow get cut-off, or remain unaffected, let alone vanish. Instead, it is critical for discovery, distributed market awareness and sensemaking. In the case of the CVC it exemplified itself for many as lingering anxiety and outright fear. So, ignoring consciousness because it is inconvenient does not make it go away. Neither is thinking you have dealt with it because the assumption of complete knowledge somehow includes awareness. Crucially, there is nothing mechanical about consciousness, in particular the experience of discovery to which I will return later. To further clarify the mind~matter complexity, let’s focus on debt for a moment. The total levels of debt (against GDP) suggest that our IOU-promises have grown to

 There is one possible caveat/counter argument: if one accepts the (e.g. Kantian) idea that moral status derives from having “intrinsic value”, not necessarily from consequences, then it does not logically follow from my ‘extension’ argument that one should ascribe some moral status to the market mind. This, in turn, questions enacting any well-being laws. Still, I don’t think we can escape eventually having to invite Smith’s impartial spectator, for example, but this discussion is beyond the scope of this book.

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levels we simply cannot keep.30 An alternative, and arguably better measure is interest payments to GDP. It would signal a warning, for example, when it exceeds long-term average real GDP growth. In any case, this situation is the consequence of the rinse-repeat conviction that there is a need to turbocharge the economic machine by pulling future demand into the present. With credit as its tool, fuelled by ramshackle incentive structures and facilitated by central banks, it frequently leads to misallocation to financial and other unproductive assets, which can reach bubble territory. Credit plays its Mephistophelian role in two parts here. First, in terms of the alchemy of money itself, fiat money is created out of thin air supported by nothing but a promise. So, the first part is about money itself as IOU. Second, credit turns one feature of economic causality on its head: it facilitates consumption before production by transforming a future real asset into a current financial liability. In the real economy production (which generates income31) precedes consumption: you cannot consume something before it is brought into existence (and paid for/demanded by income). Income is thus an asset derived from physical assets and physical action performed by a body (labour) or a machine (manufacturing). Debt, on the other hand, is a financial liability derived from mental assets and mental action performed as trusting and believing, in other words having ‘faith’. Next, it is not just the ratio of income and debt that counts. It is also how and where income, respectively debt, is spent in the real and financial economy that determines the sustainability of the economic system. Let me give a simplified and extreme example of gaming the economic system to make my point. Suppose a majority of companies in an closed economy issue debt. Instead of investing the proceeds in productive assets, they spend it on self-serving tax-free share buybacks for their executives. The latter, in turn, fire employees to further raise current profits. It turns out that those profits are largely tax-exempt due to ‘tax-efficient’ holding structures, so they don’t benefit society. This would add to imbalances in the economic mind~body. In short, income is earned only once and cannot pay twice, for both real consumption (by those employees as customers) and financial repayment. Even before the recent turmoil the economic mind~body already showed zombie32 like symptoms. Basically, interfering in price discovery and other self-organising processes prevents creative destruction, the natural cycle of economic life and death. Instead, the economic system becomes populated by zombies, with cheap credit sustaining these undead, whereby banks simply no longer price in default risk (Sekine, Kobayashi and Saita, 2003). More generally, the signalling by prices becomes distorted, consequently misguiding agents’ behaviour by misinforming their decisions.

 For an historic overview, see Reinhart and Rogoff (2011).  I ignore here the fact that income is generally paid in fiat money. I also distinguish it from income generated from financial assets.  In this case I believe it is correct to interpret this both in terms of economic zombie (i.e. “undead except for the debt”) and cognitive zombie (i.e. “lights are on but there is nobody home”).

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As a result, rather than reinvigorated, the economic mind~body becomes infected by attitudes and behaviours that weaken it. That is what zombies do, after all. Economic Note Gaming Bailouts Gaming the policy and regulatory framework, due largely to its inherent weaknesses including loopholes and vague criteria, is a consequence of mechanical control. It mostly exemplifies the removal of any spirit of the law. Again, machines do not get spirit. One typical example occurred during the CVC. In March 2020 the Bank of England (BOE) launched its Covid Corporate Financing Facility (CCFF) to support UK businesses. Any losses by the BOE are covered by the UK Treasury, so the taxpayer to this day is on the hook. To qualify, companies had to have investment grade status and were not allowed to pay dividends, buy back shares or issue bonuses. Furthermore they also had to “make a material contribution to economic activity in the UK” and be “UK incorporated, including those with foreign-incorporated parents and with a genuine business in the UK”. As reported widely in the media, it turned out that the largest beneficiaries were controlled by billionaire families and overseas multinationals. Specifically, chemicals giant BASF received the highest amount (£1bn) despite only employing 834 people in the UK. There are numerous other instances of such abuse across the world. Bloomberg, for example, reports that cheap-money policies of the US Fed designed, in cooperation with the US Treasury, to sustain employment were misappropriated: “Companies as diverse as Sysco, Toyota Motor Corp., international marketing firm Omnicom Group Inc. and movie-theatre chain Cinemark Holdings Inc. borrowed billions of dollars—and then fired workers”. The borrowed funds mostly went to buy back shares and pay dividends, benefiting company management and other wealthy owners of those shares.

If we look closer at central banks, for example, we can identify a couple of issues. Regarding the wealth effect that they promote as beneficial for the real economy, there is the issue of incentives. Taking Charlie Munger’s cue, Matt Stoller (2020) points out that important senior members of the Fed are multi-millionaires. After digging into their financial disclosure forms, he finds that they are all invested in types of indexes which strike him as violations of Section 10, part 5 of the Federal Reserve Act. He pointedly concludes that these officials have public positions in which their policy decisions affect their personal portfolios in similar ways. This has since become an ethics scandal and has resulted in the resignations of several Fed officials, including the then vice-chair Richard Clarida and Atlanta Fed chief Raphael Bostic. Clearly this doesn’t help their credibility. The broader issue is the dubious motivation that tweaking the machine of the financial economy—through the printing machine and other monetary tools, like quantitative easing (QE)—will materialise as improvement in the machine of the real economy (like growth). This is a typical case of mainstream’s metaphysical assumptions which are Von Weizsäcker’s poorest ones33 at play. In short, what we observe with the recent crises is how category mistakes eventually lead to reality checks. Besides ignoring consciousness, let’s be specific about why the REH, and by extension the EMH, offer an unrealistic view of human mentality (see also Appendix 1-B). I

 See the section “Preparations” in Appendix 1.

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previously mentioned that the assumptions of exclusive rationality and self-interest are primarily based on convenience, namely that modelling becomes easier. Second, it assumes market participants have complete or perfect knowledge and have learned from their mistakes. And finally, they are assumed to be aware of all this. This view has already been formally criticised by many (including Spear, 1989). In practice, new investors who have not yet learned from their mistakes join the market every day, while experienced investors leave. Also, much of the knowledge that drives investor behaviour is tacit. Then there are the biases and other unconscious drivers of which we are not aware. This behaviour is further compounded by the technological unconscious. As Herbert Simon argued in 1971, this makes investors bounded rational at best, aiming for satisficing outcomes (see also Evolutionary Rationality in Appendix 1-B4). Moreover, the REH assumes market participants do not change their forecasting strategies. Physically, this is as if brains are fixed. However, neuroscience has shown instead, that brains show impressive flexibility, including plasticity. In addition, modern cognitive insights suggest not only that our minds update their estimates but also the hypotheses underlying those estimates, which changes their forecasting strategies.34 This will be discussed in more detail in the next chapter. This does not absolve behavioural economics (see Appendix 1-B4). It is, surprisingly perhaps, also guilty of subscribing to a mechanical worldview: As different as their explanations are, behavioural economists . . . have followed their conventional colleagues in the belief that models must generate sharp predictions to qualify as scientific . . . Consequently, behavioural finance modellers also formalize individual decision-making and market outcomes with mechanical rules that they specify in advance. Whether based on the conventional standard of rationality or behavioural considerations, the contemporary approach to macroeconomics and financial modelling is thus much like engineering the movements of “interacting robots”. (Frydman and Goldberg, 2011, p. 47; emphasis added).

Earlier I raised the issue of the irony of behavioural economics. It teaches us that we are gullible. In particular, that we are easily convinced by our own opinions and become overconfident. It basically advocates to mistrust our own minds. As part of the wider movement in mechanical economics, we have seen this morph into a shift in allegiance: we now trust models and machines to the point of overconfidence. Like social media, their algorithms turn us into a kind of zombie. As I’ll discuss in more detail in chapters 6 and 7, the element of surprise in discovery —especially its phenomenality—is very important. In terms of Shannon’s entropy, for example, when you are surprised and learn something new, thereby gaining knowledge, the information involved is maximized. In contrast, the mechanical perspective, and its

 What this means for our interactions and rationality was discussed by Gintis from a game theory perspective: “Common Knowledge of Rationality is a powerful and often highly implausible assumption concerning the community of mental representations across Bayesian agents’’ (2009, p. 119). Psychological game theory advances this further into emotions and so on.

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related belief in engineering outcomes, leaves no space for surprises and the novelty that they spawn. External surprises are ‘anticipated’ (as in pre-determined) while internal surprises, i.e. insights to creatively deal with those external ones, are not required.35 It means that the resulting mechanistic approach to markets is not only ‘mentally constraining’ but dangerous because of the actions based on these flawed models that feed back into the economic mind~body. Crucially, because it crowds out creativity and sensemaking by zombifying conscious minds it has major implications for the full process of discovery, starting from technological breakthroughs in the real economy to the discovery of their prices in markets. In fact, discovery becomes a misnomer in a mechanistic economy, regardless of whether the latter is assumed or engineered as such. Instead, based on the stylised fact that we are not machines but they our tools with which we extend our minds, the mind~body perspective, as advocated by the MMH, offers a more realistic, ontologically sound, and overall healthier framework. Mechanical economics is vulnerable to criticism because it has not joined the current phase of the cognitive revolution. Specifically, its premise embeds the reductionist assumption that the efficiency of the market can be fully explained by the rationality of its participants. Price discovery, enacted by trading, can—according to this view—be separated and isolated from supplementary developments in markets, in particular mood shifts. In other words, it assumes that equilibrium is an independent mechanistic process driven by the steady state of rationality. This insistence by mechanical economics on separation, identified earlier as part of a mechanical worldview,36 is quite broad (e.g. the real from the financial economy, the market from its participants, theory from practice, and rationality from emotions). It is at the core of its own identity crisis, as well as that of the market. Let me try to explain this in a different way. Reality checks confound expectations. Statistically, this can be reflected, for example, in Mr Market’s skewness, ‘doubleheadedness’, and other return patterns that defy the normal distribution. Following Tarnas (in Harman and Clark, 1994), let me clarify this further in cognitive terms via the double bind concept of anthropologist Gregory Bateson in the next Cognitive Note (Double Bind). Cognitive Note Double Bind Together with his team, Bateson developed double bind to explain schizophrenia and PTSD as a result of conflicting messages (in relationships) between two or more people. In this case I apply Bateson’s criteria to the relationship between a typical investor and Mr Market, conditioned by mainstream economics. Remember, the investor is trading with Mr Market, not directly with another individual; For example, they blame him for any ‘suffering’, like getting filled at a bad price. Also consider the opening quote from Barton Biggs in Appendix 1-C5.

 As an aside: those generated by AI, say via deep-learning, are black-boxed and are not understood.  See also Appendix 1-A2.

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Dependency: the investor’s relationship to the market is one of economic survival, thereby making it critical to assess the nature of the market accurately, so the investor needs to be engaged, or at least be aware of the market’s state. Remember the quote from Subchapter 2.1: “You’ve got to be in it all the time to know . . .”. That means they need to be able to receive its ‘signals’. Communication: As a nested discipline within mechanical economics, finance prescribes how the market communicates, and its models suggest how information should be interpreted. However, the investor’s mind receives mixed messages: contradictory or incompatible information about the situation as experienced in the market. In particular, its interiority by way of inner qualitative sensations is incoherent with the prescribed scientific impressions (say, by model readings) of the market as its exteriority. Specifically, the mixed messages consist of the following: – Modern finance (i.e. the EMH): The market is efficient, so the price communicates nothing but rational behaviour. Irrational exuberance (Shiller, 2000) does not exist (because collectively we are all rational). – Behavioural finance: The market is anomalous, so the price communicates systematic non-rational heuristics and biases. Irrational exuberance exists (we all suffer from emotions, particularly collectively via herding). The messages are inconsistent, except for one thing: they both suggest that there is no role for S1 inputs, e.g. emotions are irrelevant, respectively bad. Epistemology/methodology: modern finance insists that the investor’s mind cannot achieve direct understanding of the market beyond public knowledge. Specifically, the investor cannot experience the price in a meaningful way, if only because price sensations (qualia, especially via mood)37 are considered epiphenomenal. Related to this is the issue that the only acceptable type of research method is analytical, which only supports S2. Existentially: the investor collectivity cannot desert, nor can it contradict (read: beat) the market. But it can perish with the market. It becomes clear that, in Bateson’s terms, the victim and the perpetrator are one and the same. Mr Market is a schizophrenic because we collectively create the separation of identities in one mind: – Like the post-Copernican dilemma of being a peripheral and insignificant inhabitant of a vast cosmos, mechanical economics suggests that the average investor is a disposable robot in a global economic machine. – Like the post-Cartesian dilemma of being a conscious personal subject confronting an impersonal universe, mechanical economics suggests that the average investor is a rational individual confronting the efficient market, itself a mindless but superior composite investor. – These dilemmas are compounded by the post-Kantian dilemma of there being no possible means by which the investor can know the market in its complete essence (see also Roll’s second critique, 1977). – Finally, to deal with the post-Kahneman dilemma between S1 and S2, behavioural finance suggests that a key (evolved) medium with which we build a relationship and understand another being, or even a collective group of beings, namely S1 (emotions), should be switched off. Instead we should optimise S2 by mechanically outsourcing decisions to machines.

 For an interpretation of risk perceptions as qualia, see Olsen (2014).

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Arguably separateness, which for our purposes is basically about ignoring consciousness, continues to linger. To some extent it is appropriate for the natural sciences,38 but it simply is not applicable to understanding markets. Consequently, as others have shown, the REH’s formal mathematics is about knowledge that cannot be both complete and consistent. The mind~body perspective explicitly points to the deeper underlying problem of our incomplete knowledge, namely regarding the relationship between mind and matter. This has implications for economic utility (maximisation) because epistemic utility raises its head (something discussed by active inference; see Subchapter 3.4). As an aside, mechanical economics ignores the phenomenal aspect from experiencing utility by an agent, for example that of being immersed in consuming or using a product. This relates to Heidegger’s hammer analogy (Heidegger, 1927, p. 98; see also Dotov, Nie, and Chemero, 2010). I also connected this previously to reflexivity. It seems that Soros was particularly inspired by Popper’s quoted reflections on the original mind~body problem when he stated his trading view of its extension: “What I could not properly resolve was the nature of the relationship between the [mental] mode of thinking and the actual [physical] state of affairs. That problem continued to preoccupy me” (Soros, 2010b, p. 8). Subsequently he has always insisted on the inseparability of economic facts and thinking agents, echoing his mentor’s interpretation of mental causation: Thinking participants cannot act on the basis of knowledge. Knowledge presupposes facts which occur independently of the statements which refer to them; but being a participant implies that one’s decisions influence the outcome. Therefore, the situation participants have to deal with does not consist of facts independently given but facts which will be shaped by the decision of the participants. (Soros, 1994; emphasis added)

Here we are only interested in relevant facts: those that eventually become known to the participants as the verdicts on their decisions from which they learn. They arrive later and are experienced by agents as dually realised information in System 3 or S3 (see Appendix1-A4), all of which is outside their fast (S1) and slow (S2) thinking. This phenomenal ‘tasting of the pudding’, I submit, completes the loop of reflexivity because it connects the initial decision (made by either S1 or S2) via the accompanying feeling of ‘what it is like’ (in this case, to make an S1 compared to an S2 decision) climaxing in its (again, experienced) outcome. This is crucial for learning. Moreover, just to emphasise the importance of consciousness, the flipside of this is unawareness which has a growing literature (Schipper, 2021). An agent who is unaware of an event cannot even assess its occurrence/non-occurrence, let alone its meaning. I have more to say about this in Chapter 6.

 Something along the lines of: “The laws of nature to which objects are subjected do not depend on human thought or behaviour”.

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Reflexivity itself has a rich history in the social sciences. A related modern concept is performativity, a principle described within economic sociology. Performativity, according to Michel Callon, one of its founders, is the process whereby economics and its models are actualised: they not only describe the markets but shape them at the same time. This echoes enactivism, suggests mental causation, and relates back to distributed cognition (which also has a rich history in sociology; e.g. Berger and Luckmann, 1966). The MMH emphasises that the collective invasiveness of mentality, spawn by exchanges, is the most crucial aspect in economic dynamics. Although financial markets are exemplary of this in empirical terms, this collective dimension applies more broadly. Here, Roger Sperry already anticipated social neuroscience decades ago: Mental forces direct and govern the inner impulse traffic . . . the causal potency of an idea, or an ideal, becomes just as real as that of a molecule, a cell, or a nerve impulse. Ideas cause ideas and help evolve new ideas. They interact with each other and with other mental forces in the same brain, in neighboring brains, and, thanks to global communication, in far distant, foreign brains. And they also interact with the external surroundings to produce in toto a burstwise advance in evolution that is far beyond anything to hit the evolutionary scene yet, including the emergence of the living cell. (Sperry, 1965, p. 82, 83; emphasis added)

There are many related variations of such causality. The more complex exchange between bottom-up and top-down causation is termed circular causality (Kelso, 1995) and the macro-micro feedback loop in complexity science. This type of causality is also recognised in ethology, the root of evolutionary psychology, and is called niche construction.39 The above makes clear that the assumption of separation between mechanical economics (as observer) and the market (as the observed) is untenable. Arguably this already starts to blur when finance academics participate in the markets, say by comanaging a hedge fund (e.g. LTCM) or simply by investing via their pensions.40 It becomes problematic if their models start to shape the objects they are supposed to only describe ‘objectively’.41 And it is tragically defeated if the founders themselves no longer believe in the assumptions, purpose, and applicability of their models. In the words of Markowitz (2005, p. 29): “My own conclusion is that it is time to move on”.

 Niche construction is the feedback process whereby a population spontaneously modifies its environment to its own benefit, but then adapts in turn to this modified environment, leading to follow-up modifications. The result is a reflexive co-evolution of the population and its environment. Its relevance here is the problem that our environment is increasingly mechanically modified for us by menof-system into a mechanical one to which we struggle to adapt and/or feel constrained by.  According to Gigerenzer (2007) Markowitz uses the 1/N rule to equally allocate his cash across assets in his personal portfolio.  See MacKenzie (2008) who, from a performativity perspective, analyses the effect of the theory of options and of similar derivatives upon the market for such derivatives. He notes how the BlackScholes model only started to show the implied volatility skew after the crash of 1987.

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This puts a finger on the raw nerve of mechanical economics. Causality is central to modern science but has always been difficult to determine within the financial system. The traditional search is to find physical causes (reflected in economic fundamentals) for the mental reaction (reflected in price moves). Apparently fundamental news is not the sole source for price changes (e.g. Cutler, Poterba, and Summers, 1989). Regarding big macro events, in October 2000 the Federal Reserve Bank of Minneapolis held a conference on the great depressions across the globe during the twentieth century. None of the presented research found causes for them. The editor of the Minneapolis Fed’s Quarterly Review, Art Rolnick, instead concluded that “economists are, indeed, storytellers”42 (in Fettig, 2000). No wonder Shiller is endorsing narrative economics (which largely originates with McCloskey). Economic Note Capped CAPM The Capital Asset Pricing Model (CAPM) is the cornerstone model of Modern Portfolio Theory (MPT), both based on the EMH. It provides a framework to describe (expected) risk and return and draws conclusions, among others, on the efficiency and optimality of portfolios, including the market portfolio. Although it remains central to the practice of mean-variance optimisation of portfolios it exemplifies the erroneous assumptions underlying mechanical economics and the empirical implications which follow from this. Specifically: 1. The CAPM is not logical, let alone realistic in its assumptions. In particular, the assumption of being able to borrow limitlessly is wrong which means that if “investors have limited borrowing capacity, then it no longer follows that the market portfolio is efficient”. (Markowitz, 2005, p. 17) 2. The CAPM is not tractable, nor testable. In particular, “the market” as a portfolio cannot be observed (Roll’s second critique, 1977). 3. The CAPM is therefore not empirical because we cannot make any meaningful statements due to the failure of both 1 and 2.

These difficulties lay bare the epistemological, if not ontological, issues involved in markets. They have been compounded by the ‘radical empiricism’ of the reality checks. As I mentioned, the future is not just unknown. It is unknowable, and it is this that men-of-system can’t seem to accept, not realising that with their algos and models they implicitly (by “as if”) assume a holy grail. Instead of wasting time on coding such ‘holy grail’ models, men-of-system should spend more time on “inquiring” (as in Heidegger, 1927, p. 24) and asking fundamental questions instead of making modelconvenient assumptions. In psychology terms, infinite complexity due to mind~matter exchange is their complex. More generally, mainstream economics puts the cart before the horse: the theory had to be shaped first such that it could justify tractable mathematical modelling by a science that pretended to be ‘exact’ in order to appear authoritative. That was the priority, reality be damned. Add to it Friedman’s instru It has always been thus. As Jung (1916, Para 30) explained, we can keep from a child all knowledge of earlier myths, but we cannot take from him the need for mythology.

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mentalism to motivate developing models as tools for those ‘authorities’ to project they are in control, and it becomes clear why mechanical economics has prevailed. Instead, all should take heed from the earlier warning by Derman. Inspired by Spinoza and others, he explains—with true authority and clarity—the limitations and risks of models in a beautiful little book, Models. Behaving. Badly. (Derman, 2011). In that spirit I like to paraphrase boxer-philosopher Mike Tyson (“Everyone has a plan until they get punched in the mouth”): Everyone in mechanical economics has a mechanical model until it breaks down in the face of psychophysical complexity. Increasingly we are all painfully losing as a result. In the final analysis, this demands of economics a premise which, at the very least, reflects an acknowledgement of these issues. Perhaps surprisingly, such a premise already implicitly exists—at least among actual market participants—namely that of the market’s mind, and it leads to the proposition I will discuss next. It is based on leading edge cognitive theories, to be introduced in Chapter 3, which centre on 4E cognition. These attempt to explain the nature of mind and consciousness in general, and address, directly or indirectly, the hard problem in particular.

2.4 Market Mind Hypothesis; An Initial Proposition We may, then, set aside the conception of a ‘collective consciousness’ as a hypothesis to be held in reserve until the study of group life reveal phenomena that cannot be explained without its aid. For it may confidently be asserted that up to the present time no such evidence of a collective consciousness has been brought forward. William McDougall The Group Mind, 1920

2.4.1 The Market’s Mind Much has happened since McDougall wrote these words. How far have we travelled since then to consider this final frontier of human mentality? In cognitive science, and regarding consciousness in general, some distance I would say. My Introduction quoted neuroscientists Christof Koch and Giulio Tononi on the science of consciousness. In terms of group consciousness, there is growing support from 4E cognition research, frequently pointing to its conditional role. Overgaard and Salice state that “if there is no such thing as group consciousness, then we cannot literally ascribe beliefs to groups” (Overgaard and Salice, 2019, p. 1). In the economic system we have also come a long way. The influence of the financial markets on society, particularly by means of financialisation, has grown. To put a number on this,43 the value of all listed stocks in the world increased from roughly 20%  Ignoring the stock versus flow argument.

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of global GDP in the 1980s to almost 140% in 2020. The value of bonds also exceeds 100% of global GDP. In other words, the value of traded stocks and bonds (so, even if we exclude other securities, like derivatives) exceeds 250% of the world economy. On the one hand financial innovation produced derivatives, securitisation,44 and other risk sharing solutions. Based on money, which itself relies on a faith/trust foundation, this influence resides firmly in the mental domain. These solutions removed common risks and related worries about physical issues, like housing, bodily ageing (i.e. retirement), and the weather. On the other hand, and consequently, this development has revealed collective mentality phenomena with ‘tail-wagging-the-dog’ effects. In short, the mental is seriously overweight against the physical. This and related issues require cognitive explanations, as discussed in the Prologue. Looking at theory, despite their differences, the main academic factions studying economics already seem to agree, at least implicitly, on the foundational assumption underlying the MMH. It can be stated as follows: Financial markets form a complex composite of a large number of interacting human minds whose mentality, connected and extended by way of technology, is reflexively expressed in prices and their patterns.

I specifically use the term mentality because I deliberately want to include all properties and capabilities of the human mind, if only because none of the factions explicitly excludes any.45 In terms of thinking, for example, I do not want to discuss here whether it is rational or not. Neither do I wish to make the distinction between discretionary thinking, expressed in manual buy or sell orders, and mechanistic thinking, expressed in coded orders via computer algorithms. Instead, what is crucial for now is that this agreement ultimately leads to the MMH’s main proposition: The market extends investors’ minds, distributes their knowledge so it can be shared, and manifests collective consciousness via intersubjectivity, with prices as informational signatures and market mood as its phenomenal experience.

Criticism that the MMH is unrealistic because the technologies which connect investors ‘would also have to be conscious’ is based on a misinterpretation of extension. In one of his strongest defences of the Extended Mind Theory, Clark states: Suppose it were essential, for any system to count as properly cognitive, that the system be capable of conscious awareness. We would not want to insist (indeed we would be crazy to insist) that every proper part of that system be capable of such awareness. (Clark, 2010, p. 89).

 Admittedly, options have existed since early Greek times, and in the sixteenth century loans were already bundled into ‘tranches’ and sold to investors.  Even though some properties are ignored/not considered relevant (see the previous section, for examples).

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Let me also be very clear on something else of importance, emphasised in general terms by Coordination Dynamics. There is no dichotomy anywhere, because the investment variation to Clark and Chalmer’s question (i.e. “Where does the investor mind stop, and the rest of the market begin?”)—like its original—cannot be answered as there is no clear cut-off/separation. Consequently, asking specifically whether the market has or whether it is a mind is simply missing the point. The market mind embodies —through the market microstructure, which includes the biological substrates of our bodies—all the investors’ minds while itself being embodied, embedded, enacted, and extended in a wider environment. What sets it apart (but not in a dichotomist/reductive way) is the mentality over and above the individual mentalities due to those conscious minds exchanging, leading to intersubjectivity and the like. In complexity terms, the whole of the market mind is “both more than and different from the sum of its complementary aspects considered in isolation” (Kelso and Engstrøm, 2006, p. 47). This ‘excess’ is the unique characteristic that investors experience (phenomenally) as mood. To conclude, based on 4E cognition the market mind results when conscious human minds— supported by technologies and tools—exchange, in the process discovering prices. Among others, because cognition and consciousness often overlap and cannot be isolated (e.g. Kiverstein, 2016), distributed cognition implies distributed (or collective) consciousness. Still, while comparable in some respect, the market mind is not the same as any individual mind. Cognitive science recognises degrees of consciousness which vary across species. To determine this for other complex adaptive systems is difficult. Still, as part of its working hypothesis the MMH submits that as a very rough approximation, the market’s degree of consciousness, measured at any time T, correlates with three variables (in a yet undetermined relationship): 1. The number of conscious idd-minds making discretionary investment decisions.46 2. The number of securities that are ‘alive’ in that market, i.e. illiquid (or zombie) securities are excluded, identified by way of stale prices, for example. 3. The number of price changes across those securities (compared to T-1). There are various levels of analysis which can help explain this proposition further, whereby each compares it to our own minds. I start with translating 4E cognition for the market’s mind. 2.4.1.1 The 4 Es of the Market’s Mind The market mind is embodied, embedded, enacted, and extended. I will explain each. This eventually leads to the MMH argument that the complementary market forces, viewed in a 4E cognitive setting, can be related to the macrofoundations in economics if

 Admittedly, this will be difficult to establish exactly, certainly compared to the other variables.

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we freely interpret the latter as augmenting and supporting discovery (e.g. via pricing) to coordinate economic activity. Embodied The market mind is embodied because its mental efforts essentially depend on the physical structures—including human bodies, buildings, IT, and screens—in which it is incorporated. The felt quality of disappearing liquidity, for example, cannot be divorced from key bodily parts—the financial system’s plumbing, including (physical) collateral chains—freezing up. More generally, as Charles Schwab’s chief investment strategist states, “the plumbing system that connects QE (or QT) to asset prices is indirect and complex . . . while the psychological system connecting them tends to be more direct” (Sonders, 2021). I will discuss the market’s body in more detail in Subchapter 2.4.2. Embedded The market mind is embedded because its mental efforts are situated in the wider economic environment which offer opportunities and threats. Its environment includes cultural traditions, legal frameworks and rules of engagement (e.g. Granovetter, 1985; Clark, 1996). Culture, for example, is a key intersubjective force that, like a mortar, binds physical and psychological elements of the market. Referring specifically to my earlier ‘market completeness’ comments according to Arrow and Sen, it fills gaps with morals and values thereby upholding the market’s psychophysical architecture across individuals. As part of its total package, culture enables the market to help solve societal problems that individual genetics, S1 (emotions), nor S2 (rationality) can achieve. In cognitive terms, its environment provides the market “affordances” (Gibson, 1979), which can be constraining, liberating, protecting, and so on. For example, it forbids to trade on inside information. In contrast market interference and manipulation mean, as I stated before, that criticism of “free markets” is a straw man. Enacted The market mind is enacted because it is a complex adaptive system that not only relies on its environment (due to its embeddedness) but also acts upon it. Its states lead to changes in the world that both shape and are channelled by existing transmission structures, for instance the design of market (micro)structure, processes, and products that facilitate capital flows. History is full of stories how Mr Market changed our wider economic setting, with the industrial revolution as its pivot. One example of such a story in the physical domain is funding for infrastructure, like rail roads and residential housing. An example in the psychological domain is risk sharing (hedging) using derivatives. Such market-induced changes in the world feed back into market activity, in an example of the kind of reciprocal, circular causality emphasised by enactive approaches to the mind, like sensorimotor enactivism. See Gallagher, Mastrogiorgio and Petracca (2019) for further explanation.

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Extended The market’s mind is the composite of our own extended minds, supported by advanced and extensive technologies. Specifically, the investor and her terminal form economics’ version of, what Clark and Chalmers call, a “coupled system” (1998, p. 8; see also Prologue). In turn, these coupled systems collectively form, what Knorr Cetina and Bruegger call “epistemic things” (2000, p. 3). This results in supercharged and supersized “active externalism” (Clark and Chalmers, 1998, p. 8): it extends to the wider real economy in support of collectively adapting to states of the world. That the market mind is extended is basically the modern update of earlier reflections on group minds and more particular the arguments provided by Smith, Hayek, and other pioneers of distributed cognition. The MMH adds that it is unrealistic to place the boundary of such extension at the frontier of consciousness, let alone the skull. In other words, cognitive extension includes consciousness by way of intersubjectivity, primarily manifested as market mood. For the MMH, this last property, extension, is the most important of the 4Es. It not only includes incorporation and integration of the tools investors use into a collective coupled system, but also the cognition they help to produce. The cognition collectively produced in extension will namely be incorporated and integrated back into the individual minds which constitute the market mind. In the process this can particularly lead to the phenomenal realisation of information as the emergence of market mood that changes the overall phenomenological status of the market as coupled system. In broad strokes, let’s remind ourselves what this is about. In terms of intentionality, individual investors have their specific (for instance, ESG-coloured) goals. They express their behaviours, directed towards those goals, through their trading strategies. For example, investors can focus on a specific sector like energy. Their portfolios will also integrate any local information, like that received from the companies in that sector. So, as a group they form specialised cognitive, ideally heterogeneous, clusters within the economic system. And what they share is the common goal of increasing wealth, i.e. generating profits in their respective portfolios. What they, as a collective, do not explicitly pursue, but what nevertheless emerges —via the invisible hand—from their exchange, is the most effective (albeit not perfect) allocation of resources for the broader society. Together with their technologies they achieve a socio-technical symbiosis. They form an integrated mixture of carbon and silicone extended intelligence, resulting in a wide distribution of informational resources. Consequently, having ‘eyes and ears’ in all corners of the world, the market embeds any news in prices. In turn, this allows us to collectively adapt to changing features of that world. Regarding the unconstrained nature of this portfolio of organic and worldly resources, Clark calls it the “Hypothesis of Cognitive Impartiality” for general cases: Our problem-solving performances take shape according to some cost function or functions that, in the typical course of events, accord no special status or privilege to specific types of operations (motoric, perceptual, introspective) or modes of encoding (in the head or in the world). (Clark, 2011, p. 197)

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It explains why “the storage, processing, and transformation of information is spread so indiscriminately among brain, body and world” via “a motley crew of neural, bodily and external resources” (Clark, 2011, p. 197). This last point of diversification is important and something I discussed in terms of both idd-minds and live-securities. Namely, by implication, a lack of diversification can impair “our problem-solving performances”. With that caveat, “once we start to question our received visions of the normal division of labor among brain, body, and world, it becomes clear that there is no barrier to the realization of cognition and control supporting organisations by very complex admixtures of neural, bodily, and environmental elements” (Clark, 2011, p. 213). Can we be more specific about aspects of collective and shared mentality? Among others, the MMH needs to confront the challenges of individualism and internalism (see Appendix 1). For example, in the cognitive literature (e.g. Léon et al., 2017; Thonhauser, 2018) a number of requirements have been proposed in order to judge emotions as shared. The Dutch cultural psychologist Batja Mesquita argued further that: “Many cultures don’t think about their emotions as something that lives inside of an individual, but more as something between people. In those cultures, emotions are what people do together, with each other. So when I’m angry, that is something that lives between you and me” (Pogosyan, 2018). Chapter 8 asserts in more detail, by applying portfolioism, that market emotions meet those requirements. For now, market emotions occur at various levels of aggregation, from groups to asset classes to the overall market. To explain, let’s look along several aspects at the shared emotion of fear for ‘bulls’ in the hypothesised situation of a correction in the market which hurt their ‘long’ position: 1. Intentionality: bulls share the directedness of fear, in the sense that their minds are occupied by it, in light of their focus on the danger that their wealth will diminish. 2. Affectivity: bulls not only share fear in a functional cognitive way, that is in an evaluative sense, but also in an affective, experiential way. They feel similarly stressed, with possible symptoms including sweat, irritation, and headache. 3. Plurality: bulls are aware that other bulls partake in the experience of fear, reflected in the faces and voices of their colleagues, in communication with clients and in the media. 4. Integration: bulls sense their togetherness (especially vs the ‘bears’) as characteristic for their experience of their (market) suffering. Finally, we can judge markets’ collective agencies properly when we: understand agency as a spectrum that varies along dimensions of individuality, interactional asymmetry, and normativity rather than as an all-or-nothing concept in which necessary and sufficient conditions either are—or are not—instantiated. On such an understanding agency is not necessarily lost when, for example, interactional asymmetry is temporarily reversed or absent. Furthermore, it can help explain how agency may be being instantiated even if it may not be clearly visible at both the level of the collective and the component at the same time . . . [be-

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cause] both the agency of the components, and the agency of the collective are spectra, and both will differ individually not only along the dimensions of individuality, interactional asymmetry, and normativity but also through time. (Stapleton and Froese, 2015. p. 233–234)

A more detailed explanation of extension will follow in Subchapter 3.2. Next, I will address the issue of internality from a particular angle. 2.4.1.2 Securities as the Neurons of the Market Mind There are a number of aspects to the MMH that make it stand out compared to other heterodox theories. Here I will discuss its view on securities as the ‘neurons’ of the market mind. A security is the financial instrument (historically in the form of a certificate) for which the price is paid in the exchange between buyer and seller. This distinguishes the MMH from most traditional agency and (neural) network models which use artificial robots (agents) for ‘neurons’. Like neurons: – Securities pass on information. Their main signal is quantified in their price, which also contains noise, just like neuronal noise (Schotanus and Schurger, 2020). But they also represent other information, for example about the issuer, and whether it is a stock or a bond. – Securities cluster in complex (e.g. hierarchical) layers and networks, by way of assets, industries, sectors, and so on. They form the market mind’s ‘cortical regions’, ‘neuronal assemblies’, and ‘affective systems’, which have feedback-loops among themselves as well as with the environment. Price discovery, involving the simulcasting of information from such networks, leads to the market’s awareness. – Whereas brains are the central organ to house neurons, supplemented by the stomach and the heart, the exchanges (CBOT, IEX, NYSE, SGX, TSE) are the equivalent for securities. These points are very important to understand. The MMH considers securities as the market’s ‘neurons’ because there has to be a shared physical structure located outside individual agents to be able to extend and connect their minds. Why? Because otherwise we keep encountering the issues of individualism, internalism, and personal intentionality. In contrast to the MMH, approaches like agency modelling generally simulate individual investors via bots as the ‘nodes’ in the network from which the price signals originate. Their problem is that there is no way for those nodes to connect because there are no external objects of shared intentionality. It is only because of the physical (i.e. paper, now mostly digital) existence of securities that the market can act as a valuation system, with prices as their main conduits of information. Consequently, prices are not associated with individual buyers or sellers. Prices give information about the security they are quoted for which are ‘out there’. Moreover, it is the security where collective intentionality and joint attention is focussed. In short,

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securities facilitate the “active externalism” (Clark and Chalmers, 1998), or active cognition, required for the extension of individual investor minds into the market mind. Stated differently, securities are the physical47 conduits in active externalism in the case of the market’s mind. They meet the requirement whereby subjects’ “cognitive processes and mental states can be partly constituted by entities [securities] that are external to the subject[s], in virtue of [their] interacting with these entities via perception [price] and action [trade]” (Chalmers, 2019, p. 5). The infrastructure, technologies, and other physical structures that facilitate their existence and functioning (e.g. via issuance, trading, custody or collateral) complete the ‘scaffolding’ of that extended mind. For all clarity, securities remain largely symbolic (e.g. AAPL, BRK.A, and PG): “We shouldn’t suppose that the properties of vehicles must be projected into what they represent for subject/agents, or vice versa” (Hurley, 1998, p. 1). Securities’ prices convey the consumption and production of information, the dual realisation of which (see Appendix 1-A) results in distributed cognition (quantitatively) and intersubjectivity (qualitatively). In addition, securities not only store legal claims (like ownership) but embody the narratives as well. So, when you buy AAPL you buy (into) the Jobs~Wozniak story, when you buy BRK you buy (into) the Buffett~Munger legend, and when you buy TSLA you buy (into) the whole Elon Musk show.48 At the same time, securities are obviously not literally neurons and the differences between the human and market mind arise generally from the differences in the interfaces, e.g. connections, between their respective subsystems. 2.4.1.3 Other similarities and Main Assumptions What are some of the other similarities between our mind and the market mind? – Like our mind, the market mind allocates resources. I’ve discussed this already. – Like our mind, the market mind is Bayesian to the extent that it is continuously testing hypotheses as part of its predictive processing (see Subchapter 3.3). Whereas our mind is busy limiting free energy, the market’s mind is limiting free lunch (e.g. via arbitrage). Still, uncertainty means for both that attributions of probability are not fixed and that probability distributions, while sometimes stable, can change abruptly and discontinuously. In extreme cases, these changes turn into reality checks and show up, for example, in schizophrenic ‘double-headed’ return distributions. Still, evolutionary rationality criticises Bayesian applications and highlights their limitations (see Appendix 1-B4).

 Albeit often digital.  Basically, for investors ‘Apple’ is its stock. Many of the narratives are told as hero tales, like monomyths (Campbell, 1949). On a separate note, there is also a flourishing business in collectable historic securities, like (expired) bond and share certificates, particularly those ‘with a (rich) story’.

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Like our mind, the market mind is active during certain hours. At other times it is in various states of sleep, depending on which (and where) securities are actively traded. Like our mind, the market mind acts partly unconsciously. ‘Forgotten’ experiences of past events (albeit recorded in historic prices) and the activity surrounding private information are examples of unconscious market processes that provide context of which Mr Market is not aware. Private information (including inside information) is thus one example where awareness at the micro (individual) level does not translate into macro awareness.49 The growing technological unconscious (see Appendix 1-A) adds another dimension to this. Like our mind, the market mind’s health can vary. Monetary policies are examples of physical drugs and psychological treatments that can affect its health. There is a direct link here. From neuroscience we know that anticipated financial gains (say due to QE by the Fed to generate the wealth effect) stimulate the same part of the brain as drugs like cocaine do. Finally, hypes are examples of narrowmindedness which reflect an unhealthy attitude. Like our mind, the market mind impacts the world through action. The latter was discussed in the section on enactment in 2.4.1.1. Whereas goal-directed movement (e.g. Mises’ “purposeful action”) is the transmission with which our mind impacts the world, capital movements (e.g. via investments, flows, and trades) are the transmission with which the market mind impacts the real economy. Like our mind, the market mind can lose consciousness. The GFC (via Lehman’s collapse), the CVC (via repo-stress), and the LDI-crisis (via gilt-stress) almost caused a coma. Whereas neurons stop communicating in the human mind, securities stop communicating in the market mind, starving the economic system of price signals.

Let me further specify the main assumptions and observations underlying the MMH, starting with the components that form the market mind from the bottom-up: – The investor’s mind is a complex adaptive system at the microscopic level, like any human mind (e.g. Bressler and Kelso, 2001; Edelman and Tononi, 2000; Hayek, 1952; Kelso, 1995; McClelland, Rumelhart and Hinton, 1986; Morowitz and Singer, 1995; Port and Van Gelder, 1995; Thelen and Smith, 1996). Its complexity results from the multilevel exchange of its mind~body components, including with other mind~bodies. – Causality is elusive and mental efficacy a challenge to pinpoint. As highlighted in Predictive Processing Theory (chapter 3), the investor’s mind—in particular its internal causal perception—is able to track the external causal structure despite having no direct access to its causes. Specifically, causes are hidden because the

 Even if an individual investor trades on their private information (which, by the way, may be illegal), this would have a negligible impact on prices (assuming there is no cornering of the market).

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only inputs that brains can work with are derived ‘data’ in the form of the effects of stimulated senses. The investor’s mind exists and is experienced as real at the individual level. Specifically, the interiority of the investor’s mind, i.e. the subjective experience of consciousness, is arguably the most unique among its emerging properties. In the spirit of both James (1890) and Nagel (1974), there is ‘something it is like’ to be an investor. For the individual investor this experience is not an illusion but a phenomenal fact with distinct quality: the feeling to participate and be invested in the markets is trenchant. By taking a long, short or flat investment position an investor makes a commitment to (potential) states of the world and the exposure and subsequent outcome of such a trade matters; it makes a difference. Such outcomes are reflected in but also impressed by prices. That difference is not limited to the individual investor. The investor’s mind is not isolated from its environment—in this case financial markets—which embody millions of investors’ minds, directly or indirectly. Their deliberations (by discussing themes), interactions (by way of trading) and interconnections (using computers) create an extended composite mind, a complex system at the macroscopic level.50 The conduits for those exchanges are securities, with their prices acting as (symbolic) information carriers. This composite mind evaluates physical events and objects in the real economy, and reflexively expresses its mental responses (i.e. valuations) in prices51 and their patterns. Its internal causal assessment, by way of price discovery, therefore has the ability to track the external causal structure without direct access to real-economy causes. This is comparable to the primary function of cognition in the human mind. Consequently, we can state that the market exhibits mental states which, objectively, are expressed via price constellations with securities as contractual scaffolding. Moreover, these states embed the intersubjective essence of the market’s mind: by way of price dynamics—with emphasis on movement—they are experienced collectively by investors. They are the market’s version of shared sensations (e.g. Robinson, 2013; Newen, de Bruin and Gallagher, 2018), with varying degrees of uniformity in those experiences. It adds the plural first person perspective, or rather the second person perspective (e.g. Hut and Shepard, 1996; de Quincey, 2005; Kelso and Engstrøm, 2006), over and above the (singular) first person perspective. Unfortunately this complicates the original hard problem even more.52 At the same time, this can be no excuse to simply ignore it. In fact, this has been

 For an overview of collective cognition from a complexity perspective see Palermos (2016) as well as the references therein.  As well as other derived data like volume, open interest, returns, flows, etc. Still, the 80/20 rule applies here, in the sense that prices probably make up 20% of the data in markets but likely contain 80% of the information.  For details, see Appendix 1.

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the case for too long (considering, again, the early but forgotten criticism of Knight in 1925). Historic time series are the recorded memories of the market’s mind.53 From an evolutionary perspective, the regularity and uniformity of their patterns, across time and locations, suggests a common denominator in market mentality.54 In the words of legendary trader Jesse Livermore: “Nowhere does history indulge in repetitions so often or so uniformly as in Wall Street. When you read contemporary accounts of booms or panics the one thing that strikes you most forcibly is how little either stock speculation or stock speculators today differ from yesterday. The game does not change and neither does human nature” (Lefèvre, 1923, p. 180). Ignoring noise, that common denominator is likely a combination of a shared primordial source and a shared singular culture. In other words, nature and nurture complement each other and their influence is broadly the same for all investors. The MMH speculates that (our sense of) the nature of numbers themselves plays a role (see Chapter 7).

Economic Note Déjà vu Day Traders In the spirit of ‘market history rhymes’, the recent years offered plenty of déjà vu moments for (researchers of) internet-bubble day-traders. Let me give a few examples. First, the internet’s discount brokers have now been superseded by commission-free brokers, like Robinhood, whose apps allow (too?) easy trading. Second, today’s version of the trader bulletin and message boards of the internet bubble are the popular trading forums on social media. The most famous of which is Wallstreetbets on Reddit. It mobilises hordes of retail traders whose combined buying resulted in sudden price spikes in stocks of struggling companies like GameStop and Nokia in January 2021 and Bed Bath & Beyond in September 2022. These so-called short squeezes caused major problems for numerous hedge funds who had been anticipating that their prices would drop and thus found themselves on the wrong side of those trades. It eventually resulted, among others, in the dubious decision by the commission-free brokers to block most trading in some of these names, leading to outrage among its users and queries from regulators. (See also Clunie and Schotanus, 2022). Earlier I shared my criticism on their HFT-connections which reveals where the incentives of these brokers lie. I am sceptical, in the broader scheme of things, that this sudden activism by retail investors will result in a true and beneficial counter movement. It unfortunately has many elements of the market manipulation (including that of its rules and regulations) MMH deplores. On the one hand the ‘smart money’ leaders of the retail mobs need to recruit new traders as useful ‘greater’ fools to pump up prices to achieve short squeezes. On the other hand, the institutional establishment and their lobbied politicians become suddenly and selectively worried about such third-party manipulation, preferring their own. Instead, to truly democratise investing we need to get individual investors actively involved via education to help bring about a renewal of the economic system that is sustainable.

 In fact, we can access those consciously, but traditional static analysis fails to make those ‘come alive’ like relived memories in the human mind. See also Chapter 9.  Ignoring, for the moment, my earlier comments about disappearing consciousness due to mechanisation.

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A final déjà vu is from the similar easy money-making attitude of modern day-traders, albeit this time funded by the central bank-treasury tandems. A Wall Street Journal article quoted an unemployed 22-yearold woman who had put a portion of her stimulus check into her trading account at Robinhood. Her motivation? “It was basically free money, so, you know, I decided to play around with it . . . You might lose some, you might win some. It’s like a gambling game”. It further turned out that she had doubled her money trading stocks so she decided to graduate to options trading because “you can make a pretty good amount of money in one day” (Zuckerman, Frankl-Duval, 2020). Thank you, men-of-system.

2.4.1.4 Conclusion The foregoing leads to the stylised fact that the market is conscious because its human participants are conscious. Their consciousness is not cut-off and does not suddenly disappear once they participate. On the contrary, using technology it extends in its full richness by way of their bottom-up exchanges, i.e. by trading. Trading results in prices through which, from the market’s perspective, information is not simply “communicated” (as per Hayek, 1945, p. 523), but rather “realised both physically and phenomenally” (following Chalmers, 1996, p. 284). It is this specific emerging dynamic of price discovery that leads to market consciousness, including its ‘complexity’ ability to self-organise, which reaches over and above and differs from any individual consciousness. Anyone denying this stylised fact not only denies the phenomenon of market mood and its influence. That person also needs to explain how consciousness somehow ceases, sometime, somewhere, at some boundary in the market.55 According to this proposition, prices are thus set “as if” investors are conscious,56 rather than (per the EMH) “as if” they are rational (e.g., Rubinstein, 2001, p. 15). This adjustment allows the MMH to escape the complications (raised by Lucas and others) when diluting rationality. The anomalies highlighted by behavioural economics become ‘normal’ or, stated differently again, a rational market is a special case, or state, of a conscious market. In terms of discounting for example, I’d like to freely interpret Jean Piaget’s view of a (developing) mind: the more we must adapt to new information, the more conscious of it we have to be. Such awareness also relates to the EMH’s requirement of complete knowledge and the “thinking slow” strategy of behavioural economics. In market terms, as more real-world news arrives, prices adjust more extensively, reflecting deeper conscious involvement by the market’s mind. In other words, ‘the market wakes up to the fact that . . .’.57 That realisation can, in turn,  Arguably, this challenges cases which consider the market to be static minded (i.e. rational only, e.g. Rubinstein, 2001) or, worse, mindless because one explains consciousness away by assuming mindless agents (e.g. Gul and Pesendorfer, 2005).  E.g, “people have to be treated both as if they had feelings and as if the feelings and attitudes of the person who is attempting to influence them also made a difference” (Knight, 1925a, p. 389, 390; emphasis added).  Of course, in case of bad news this can turn into a panic whereby the market enters a flight-freeze mode, driven more by the unconscious rather than by conscious engagement.

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spawn emotional or rational responses. At the same time market information is signalled back to the broader economic system, e.g. regarding funding conditions. This reflexive loop between, roughly, internal (psycho) and external (physical) information results in their dual (psychophysical) realisation and basically establishes the overall awareness of the economic system as a whole. The MMH submits that a ‘state of the market’ ultimately includes a collective mentality. In particular, prices are conduits for market states which are not static but have internal dynamics. Certain characteristics of a market state concern physical processes, like transfers, flows, and production, involving physical parts, like buildings, machines and products. Others concern cognitive processes like decision-making, discounting, and utility maximisation. Although these processes can be analysed, they do not describe the full market state. There is something in addition, namely the lived experience: what it is like to be in that state as (part of) a collectivity. In cognitive terms, we can distinguish between a global and a local state of market consciousness. A global state reflects a market’s overall awareness and responsiveness, often combined with a level of (physical) action via trading, i.e. liquidity. In general, during bubbles the market can be ‘drunk’ or be ‘dreaming’ and not aware of dangers, whereas it can become totally unresponsive at the depth of a crash. In comparison, local states concern particular conscious contents with distinct qualities, like emotions, perceptions, or thoughts. Often these are also ‘local’ partition wise, for example because they only affect part of the market crowd (bears or bulls) or because they are related to specific securities, companies or sectors. A recent specific example is the market in 10-year Japanese government bonds where no trading took place for a record three days (mainly due to the fact that there is only one dominant idd-mind, the BOJ, active). As mentioned in Appendix 1-A, cognitive science makes a distinction between access consciousness and phenomenal consciousness. Market mood falls into the latter category: the sensation investors collectively experience in a qualitative sense, like despair in a market crash or exuberance in a bubble. Although a complete perception of the market’s state escapes them, a certain feeling for it seems to agree with how investors actually experience it. Knorr Cetina and Bruegger call it “intersubjectivity with the market”, and quote an anonymous trader: All this (amounts to a) feeling (for the market) . . . When someone feels the market, then they can anticipate (it) and can act accordingly. When you are away from the market, and you lack this feeling (for it), then it’s incredibly difficult to find it again. (Knorr Cetina and Bruegger, 2000, p. 153).58

 Similar findings have been reported, for example, by Schwager (1995), Koppel (1996), Zaloom (2006), and Coates (2012). This also highlights the difference to thought experiments about collective minds, like the China Brain (Ned Block, 1978), in that market mood, as a phenomenal manifestation, is intersubjectively felt by investors. In other words, it is not just assumed, functionally, to be ‘out there’. It actually is and there is data available to support it.

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This is one level of complementarity which applies to the market. Its quantitative processes are accompanied by a qualitative experience, a feeling shared among participating investors, albeit at varying degrees of strength and uniformity. It means that information, embedded primarily in prices, feels like ‘something’ for participants in markets. Prices make an impression, over and above any rational signalling. Earlier I mentioned the history of the idea of group minds and collective mentality. A group mind involves various socio-cognitive concepts, in particular active externalism, collective intentionality, distributed/extended/integrated cognition, mirror neurons, social ontology, and Theory of Mind. They point to the collective-social dimension of reciprocal exchanges between conscious subjects where human complexity emerges, supported by technological tools that connect and extend their minds. The most interesting aspect of that emerging complexity is the synergy which the exchanges spawn. It’s that famous whole which not only exceeds to sum of the individual parts but also exceeds the individual capacity of those individual parts to create it themselves. Moreover, it leads to self-organisation in the form of patterns which coordinate (e.g. via constraints, feedback) the individual components of those patterns (e.g. via connections, interrelationships). Again, researchers in the economic sciences have hinted at these phenomena (see Section 1.2), whereby it should be clear that the market’s main emerging complex property is price discovery with its self-organisation reflected in price patterns. It culminates in Sornette’s now familiar answer to McDougall, namely that this is reminiscent of the emergence of collective consciousness. There are many aspects to this phenomenon. Bruguier, Quartz and Bossaerts. (2010) argue that Theory of Mind (ToM), interpreted as the ability to infer information from prices (while being involved in setting those themselves) can explain the increased awareness of traders to the presence of insiders. Specifically, ToM can identify malicious from benevolent intent. This leads to an important distinction between two awareness dimensions at the foundation of price discovery. They each produce an elusive aspect to any trade, which often occurs between strangers, emphasising the uncertainty in, and thus symbolism of, prices: – Time: a biased past is linked with an unknowable future via a fleeting present (see also comments on intrinsic time in Appendix 1-C2). – Trust: the disagreement on value is only resolved via the exchange of intrinsically worthless money, leaving an agreement on costs. Separation plays up again. A trade combines cooperation (agreement) on the one hand and competition (disagreement) on the other. Similarly, it is the imbalance between demand (i.e. buyers) and supply (i.e. sellers) that ultimately make prices trend, reverse, or show other patterns which over time reflect the outcomes of capital allocation. Contrary to other group collaborations, this process is spontaneous, under normal circumstances, without the need for any explicit goal seek. Price discovery via trading (which, by definition, requires more than one individual) spawns the ‘intelligent behaviour’ as suggested in reflections by Smith, Hayek, V. Smith, Sornette, and many others.

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In conclusion, the MMH describes and explains the mind~matter dynamics that underlie the economic system. It emphasises the conscious nature of discovery, mood, and other mentalities that perceive and shape economic reality. In contrast to physicalism’s causal closure, and beyond standard reflexivity, market consciousness is self-referential: A implies A, with prices as the consensus of the value in the “I’s” of the beholders. It makes the MMH’s interpretation of cognitive economics a form of second-order behavioural economics with emphasis on meta-level agency.59

2.4.2 The Market’s Body Although the focus of this chapter, and in fact the book, is on the market’s mind, I would like to address the matter of the market’s body briefly here (accepting the risk that it may raise more questions than it answers). This is thus primarily about embodiment, the first E of the 4Es. Specifically, it concerns so-called constitutive conductors (i.e. the physical basis) of investors’ conscious experiences in the market mind. Besides their own bodies these can include—due to extension—external components that form part of, what I call, the market’s body and function as mediators to facilitate investing. Apart from the role of securities as the market mind’s ‘neurons’, I also mentioned earlier the physical properties of a market state. They consist, first, of the institutions that facilitate and regulate trading. ‘Institutions’ is a term that refers both to the customs, laws, and rules themselves and to the agencies that design, implement, and enforce them. This includes, for example, physical and virtual exchanges as well as banks, mutual funds and other financial institutions. When we talk about a market seizing up, for example, it can include instances of so-called fund gating where open-ended mutual funds suspend redemptions when investors scramble for the exit. The embodiment of the market, within the larger environment of the overall economic system (including the real economy), also includes the gamut of electronic equipment, from computers to telephones, which form the networks of information and communication that facilitate trade in today’s markets. It further involves tangible processes like the aforementioned order routing, custody, clearing and settlement which have physical properties. And last, but not least, it consists of the human bodies which physically handle activities involved in trading, including pushing keys on a keyboard, shaking hands, signing contracts and so on. Combining mind and body, it is not a stretch to suggest that the market can be perceived like an animated entity. Jim Grant, publisher of the eponymous Grant’s Interest Rate Observer, stated that “[The market is] a living economic organism with proven powers of monetary adaptation”. Many market participants have expressed this along similar lines (see earlier quotes in 1.2). Knorr Cetina concluded earlier that

 In the spirit of Von Foester (2003).

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markets are “epistemic things”, adding that they are built around flow architecture with computer screens as the centrepieces: the terminals deliver much more than just windows to physically distant counterparties. In fact, they deliver the reality of financial markets—the referential whole to which “being in the market” refers, the ground on which [participants] step as they make their moves, the world which they literally share through their shared technologies and systems . . . [They] visually “collect” and present the market to all participants . . . the screen is a building site on which a whole economic and epistemological world is erected. It is not simply a “medium” for the transmission of prereflexive interactions. (Knorr Cetina, 2003, pp. 11 and 13; emphasis added)

The chat and messaging functionalities of the terminals are important for such interactions. However, those screens display, first and foremost, prices. So screens form part of the physical scaffolding that facilitates prices to act as the psychophysical building blocks of the bridge that connects the real and imagined worlds: via their dual realisation as prices-as-information. I have devised a framework (see below) to consider this from Capra’s work (1996).60 It lists key criteria for any complex “living” system and has been adjusted by me to apply specifically to the economic system. It should be viewed in conjunction with the 4E-framework of the market mind (2.4.1.1).

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Purpose of existence: the principles which lead to the market’s self-organisation.

Survival under conditions of scarcity, while confronting uncertainty. Competition and cooperation lead to coordinated behaviour via: – Allocation of scarce physical resources (quantitative): survival and growth via evaluation and exchange of assets etc. Transfers are facilitated monetarily. Uncertainty is quantified (as risk), i.e. expressed mechanically thereby aligning it with explicit, analytical knowledge, e.g. numerical models.

 In turn, Capra was particularly inspired by Bateson, Maturana, Prigogine, and Varela.  Early economic reflections described man’s economic struggle as one of having scarce physical and mental resources (e.g. commodities, respectively knowledge) while having to ‘deal with’ abundant physical worldly challenges, respectively the omniscience of God(s). In the Jewish/Christian traditions this is viewed as a consequence of original sin. Such ‘dealing’ is infused with uncertainty, whereby true uncertainty remains unanswered by the various modern economic approaches. Still, as I mentioned, there is pretence of knowledge. Keynesian economics suggests appointing a homunculus (i.e. a central planner) to achieve an ‘optimal state’ of the market’s mind~body. New classical economics similarly implies knowledge to achieve its assumed equilibrium, namely via a Laplacian Demon in the form of predetermined models.

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(continued) –

Allocation of scarce mental resources (qualitative): survival and growth via evaluation and exchange of emotions etc. Transfers are facilitated neuronally.62 Uncertainty is qualified, i.e. expressed symbolically thereby aligning it with implicate, experiential knowledge, e.g. numbers, narratives.

Principles of portfolio management can be applied to both (i.e. portfolioism). Prices fill the numerical space of discovery bridging these domains, where meaning in the market’s mind transcends individual consciousness, e.g. price ≠ value. 2)

Pattern of organisation: the configuration of relationships that determines the market’s essential characteristics.

Organisational closure achieved through communications. – Interpersonal or collective communication is embodied in post-social relationships (Knorr Cetina and Bruegger, 2000). Communication takes place via the exchange of external information (e.g. analyst reports, company announcements, government statistics, contracts), or the exchange of internal information via the security exchanges (e.g. quotes, volume, order flow). – Intra- or transpersonal communication is embodied in the relationship with oneself and with the market.63 Communication takes place via exchanges based on explicit knowledge (S2; analysis, quantitative research), tacit knowledge (S1; intuition, qualitative research64) and experiential knowledge (S3; skin-in-the-game participation). Pattern emerges, among others, from (often simple) principles which frame the relationships.65

   

See Kuhnen and Knutson (2005). See Steenbarger (2003). See Cymbalista (2002b). See Hayek (1967) and Kelso (1995).

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(continued) 3)

Structure: the physical embodiment of the market’s pattern of organisation.

Cohesion through a shared platform. – The real economy of networks to produce and exchange physical goods and services which include, for example, transport links and pipelines. Tools to build, expand, and maintain the networks include buildings, trucks and pumps. – The marketplace of flow architecture to produce and exchange securities which include, for example, exchanges and trading floors. Tools to build, expand, and maintain the structure include telecommunication equipment, computers, and their screens. – The collective of human bodies, i.e. buyers and sellers, to compete and cooperate. Tools to build, expand, and maintain this collective include shared bodily and neuronal adaptations, for example mirror neurons and number sense,66 but also adaptations that support empathy and ToM.

4)

Process: the activity involved in the continual embodiment of the market’s pattern of organisation.

Price discovery (mental activity) and trading (physical activity). – The market’s ‘life’ process consists of price discovery and trading, a reflexive process that organises the market and gives rise to its cognition. In complex psychology terms, it is a ritual through which the collective investor community interprets—by way of stories—and interacts with the symbols of the market (i.e. prices), thereby reinforcing its values.

These criteria are interdependent. Moreover the dividing lines between the physical and the mental within each criterion are, as always, blurry. For example, structure is not only physical but: – in terms of boundary, it is also of a social symbolic nature via trust, beliefs, expectations, confidentiality, and so on; – in terms of order, it is also of a psychological symbolic nature via stories (e.g. investment themes), events, personalities (e.g. gurus), etc.

2.4.3 The Market’s Math and Modelling While viewing it from their own preferred angle and describing it in their own terminology, four of the key speakers at our symposium—Emanuel Derman, Karl Friston, Gerd Gigerenzer, and Dylan Grice—have argued that economics is “using the wrong

 See Dehaene (1997) and Butterworth (1999).

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math” and that we need “new models and tools”. Without putting words in their mouths, what do they mean? Just to be clear, this book will not offer any MMH equations, let alone ‘The MMH Model’. Instead this subchapter will give some hints about common ‘math-grounds’ for potentially fertile investigations, as part of a wider research agenda, after further outlining where and why economics’ choice for the ‘market’s math’ and the related ‘market’s models’ went wrong. Let’s first summarise what others have already criticised about economics’ particular use of math and models. These critiques (some of which I have already discussed) generally point out that the math and models used often: 1. Rely on unrealistic assumptions about human mentality, which can lead to incorrect predictions, policy recommendations, and investment strategies. 2. Ignore important factors such as social and political institutions, historical context, and power dynamics, which can lead to a limited understanding of economic phenomena. 3. Are highly complex which can make the discipline inaccessible to scrutiny by non-experts and can obscure the underlying assumptions and logic of economic models. 4. Neglect other forms and methods of inquiry, such as qualitative research and historical analysis, which may provide important insights into economic phenomena. Let’s look at the history of mathematics itself. What is often forgotten is the divergence between the development of mathematics in the physical sciences (where economics sought its inspiration, eventually leading to its “physics envy”) versus that of mathematics in economics. It is important to remember how (and in what sequence) the two influenced each other. Surprisingly perhaps, sources like Bernstein (1998), Weintraub (2002), Rubinstein (2006), and others suggest that mathematics was initially and generally (so, including the mathematics of the physical sciences) influenced and stimulated by the use of mathematics in economic activities (especially by Fibonacci’s Liber Abaci, 1202). These often had strong ethical considerations (on usury for instance). As mathematics developed further across disciplines, approaches started to diverge roughly in the latter half of the twentieth century. A highly abstract, mechanical (e.g. algebraic) approach was embraced by and remains dominant in mainstream economics (see McCloskey, 2005; Arthur, 202367). However, due to complexity, quantum phenomena, and other influences such an approach has, ironically, been largely rejected by the physical sciences.  The original instance of the terms I just mentioned to emphasise the active dynamic of price discovery, “it’s the verb, not the noun”, was contained in Schotanus (2022, p. 103). Independently Arthur raises this to a higher level with his “Economics in nouns and verbs’ (2023) by connecting it to the mathematics (to be) used.

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We should ask why this is the case, and here I return to my earlier comments in Subchapter 2.3 about economics as a doctrine. To further clarify, what I mean when I say that the cart was deliberately put before the horse is that economic reality had to be considered mechanical to allow and facilitate the kind of ‘convenient’ mathematical modelling (e.g. with pre-determined outcomes) desired by its modellers. This, in turn, supported the latter’s claim of ‘scientific expertise’, thereby creating ‘economic authority’ in matters that eventually reached beyond economics. No one expressed this danger of becoming removed from true reality better than the mathematician John von Neumann. He observed that when a discipline engages in such navigation: It travels far from its empirical source, or still more, if it is a second and third generation only indirectly inspired by ideas coming from “reality”, it is beset with very grave dangers. It becomes more and more purely aestheticizing, more and more purely l’art pour l’art. This need not be bad if the field is surrounded by correlated subjects, which still have closer empirical connections, or if the discipline is under the influence of men with an exceptionally well-developed taste. But there is a grave danger that the subject will develop along the line of least resistance . . . In other words, at a great distance from its empirical source, or after much “abstract” inbreeding, a . . . subject is in danger of degeneration (von Neumann, 1947, p. 196).

I mentioned previously that this was later interpreted by McCloskey (1985) for the economics case, now known as the McCloskey Critique. In similar vein, Paul Romer, former World Bank chief economist, famously called this “mathiness” (2015). Derman put it more bluntly as “pseudoscientific hodgepodge of complex mathematics used with obscure justification” (2004, p. 3). It boils down to pretence of precision, added to Hayek’s pretence of knowledge. In investing this leads to an inconsistency of current practice and implicit risk: Most investors accept that markets are, to a greater or lesser degree, inefficient and devote themselves to exploiting the opportunities on offer. But by a nice irony, they have continued to use tools and adopt policies constructed on the assumptions of efficiency. It is a costly mistake. (Woolley, 2010, p. 137)

So-called model-risk occurs when the implicit ‘objectivity’ of mathematics results in models’ homogeneity. This was observed by Beunza and Stark in their three-year study in a derivatives trading room of a major investment bank. They call it “reflexive modelling” which, in turn, can cause contagion. They pointedly judge quantitative models to be “disembedded yet entangled; anonymous yet collective; impersonal yet, nevertheless, emphatically social” (2012, p. 1). The fact that risk models, like Value-atRisk (VaR), have become largely standardised is a particular worry. Here is a description of what this means in terms of mechanised trading during the CVC by Nomura’s investment strategist Charlie McElligott: Effectively, in a “VaR” risk management world where volatility is the exposure toggle—the implications of vol resetting LOWER (following a “macro shock” spike) then has a tendency to contribute to

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sling-shot HIGHER in spot Equities, as vol control & target volatility strategies mechanically need to re-leverage risk exposures, while “expensive vol” / inverted VIX term-structure, per the back-testing model-driven systematic community, signals an “all-clear” to load back into “short vol” behaviour . . . all of which feeds into the risk virtuous cycle (April 29, 2020; emphasis added; source unknown).

Donald MacKenzie (2008) argues the case for performativity whereby financial models shape markets and—to underline their mechanical influence—should be viewed as “an engine, not a camera”. Fortunately, following its own abandonment of the ‘mind-as-computer’ view cognitive science is increasingly challenging and criticising this mechanical approach, offering hope to start repairing all the damage. Together with collaborators I am coauthoring some exploratory papers that delve further into this, where we attempt to make economics’ mathematics and models more realistic by applying those from cognitive science. In some cases this means we can retain a part of the existing mathematics and models by adjusting it. We particularly invite other cognitive scientists to join us in this endeavour, emphasising again the availability of copious real data which could satisfy their empirical research ambitions. Here are four examples of the potential (and non-exclusive/overlapping) mathematical areas where we can make progress, for example in the context of the cognitive theories I’ll discuss in Chapter 3: – Enhanced HKB-modelling to add potential novel (anti-phase) syncopation activity to traditional (in-phase) business cycle synchronisation. – Enhanced Lagrangian, Lyapunov, path integral, and other approaches to model economic decision-making across S1 and S2, while considering it is realised in S3. – Enhanced Bayesian approaches, to include epistemic utility to traditional economic/functional utility maximisation or, alternatively, for portfolio (decisionmaking) optimisations via Markov blankets. – Enhanced approaches to Algorithmic Information Theory (AIT), in a Gödelian context to model market inconsistency/incompleteness which I discussed in my PhD thesis. A more recent interpretation is by Arthur (2023).

2.5 Chapter Roundup Tech expert Tim O’Reilly captures well how most people generally fear the market as automaton: We are already in the thrall of a vast, world-spanning machine that, due to errors in its foundational programming, has developed a disdain for human beings, is working to make them irrelevant, and resists all attempts to bring it back under control. It is not yet intelligent or autonomous, and it still depends on its partnership with humans, but it grows more powerful and more inde-

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pendent every day. We are engaged in a battle for the soul of this machine, and we are losing. I’m talking about . . . “the market”. (O’Reilly, 2017, pp. 231-232; emphasis added)68

The problem, as this book argues, is that the market is not natively a machine with “disdain for humans” but has been turned into one by the cult of mechanical economists and investors,69 cheered on by other men-of-system. The “errors in its foundational programming” are due to their mechanical worldview. Where I agree with O’Reilly is that we are facing “a battle for the soul” of the market (and the MMH is the David in that fight). That last little detour on the market’s body should, in that regard, not distract the reader from other main points so far. First, it is important to recognise that the mechanical worldview acts like a contagious virus where mechanisation begets mechanisation: it advocates and implements mechanistic policies, practices, and products in a biased self-reinforcing loop. In particular, AI requires more and more data which, in turn, requires more automation to collect, distribute, and store it. The latest instalments include AI-driven mechanisms to expand and protect economic and political moats of all shapes and forms, often at all costs. Instead of being disruptive these technologies strengthen the status-quo. More generally, it means that our environment increasingly invites and promotes mechanical behaviour. In other words, we become more mechanical, relying on machines to guide us, while becoming less spontaneous, sensitive, in short less human. This concern does not equate to an anti-technology stance or accepting technological determinism. Nor does it turn the MMH into some kind of Luddite theory; 4E cognition has a generally benign view of AI (including machine-learning), quantum computing, and other technologies in that they could potentially augment and form beneficial extensions of our minds (see, for instance, Chrisley, 2009 and Clark, 2003). A recent example is DeepMind’s AlphaFold which can help to better understand proteins. And most of us developing the MMH are excited about the potential of digitisation to decentralise and disintermediate (e.g. via distributed ledger technology, like blockchains). However, to reiterate, technology is not naturally neutral as Hegel, Heidegger, and others already pointed out.70 A particular risk is that automation bias morphs into an automation paradox characterised by reliance on an elite of expertowners-cum-social-engineers (see also Zuboff, 2019). The consequences are far reaching and could result, for example, in worsening marginalisation and/or losing the ability to value (see Appendix 1-A7). Second, market mentality exists because no investor is capable of individually determining the prices that can guide society’s optimal—including meaningful—alloca-

 Similar but earlier critiques include those by Castells (2000) and Capra (2002).  For those readers who are gamers, think Warhammer’s “Cult Mechanicus”.  For a deep dive, see The Question Concerning Technology and Other Essays by Heidegger (1977). For an additional modern view see, e.g., Lanier (2010).

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tion of resources. Only the shared discovery of prices, coordinated by transparent trading and involving a deeply connected network of humans and their technologies, leads to such allocation and system-wide adaptive behaviour regardless of what menof-system may think. Third, unless one denies the existence of individual consciousness the inescapable conclusion is that the market—where these individual consciousnesses exchange—exhibits collective consciousness. In particular, the extended complex nature of its mind makes locating and timing the cut-off of consciousness in the continuously shared dual realisation of information impossible. In fact, its intersubjectivity significantly contributes to the market’s (self-organising) dynamics. It provides the qualitative valuation of price moves (i.e. returns) in a collective sense. Market mood is reflexive: like information it is both produced and consumed by investors, e.g. we ‘take in’ the mood. In Hayek’s terms (ignoring internalism), the market’s order embodies and enacts the entangled “sensory orders” of individual investor minds. Consequently, the market as a multi-layered collective mind~body provides the proper premise for any emerging new economics paradigm. It acknowledges the existential questions that need to be addressed and helps to diagnose more clearly the dismal state of both the discipline and Mr Market. Going forward, our multi-year research program aims to further strengthen the theoretical bones of the MMH and put empirical meat to them by means of engaging research projects (see Appendix 2). In terms of ambition, we aspire to be: – Theoretically progressive: with the Extended Mind Theory at its core, and surrounded by satellite 4E cognition hypotheses, the MMH will predict novel facts. For example: how can the differences in people’s metaphysical stance explain differences in investment choices, decisions, or preferences. – Empirically progressive: those predictions can be empirically tested but may require new methods and tools to do so. For example, one of our projects will involve the development of novel software (see Chapter 9).

Chapter 3 On Theory: Am I Right? To perceive the world differently, we must be willing to change our belief system, let the past slip away, expand our sense of now, and dissolve the fear in our minds. William James

3.1 Introduction What could fuel new thinking in economics, especially finance? An obvious starting point is behavioural economics as the traditional challenger to mechanical economics (despite adhering to some of the latter’s assumptions). It is primarily informed by psychology, so we could look at related disciplines that could raise that challenge to the next level. We are particularly interested in finding common but frontier ground among these disciplines from which to grow such new thinking. In my view, that ground is already being explored by cognitive science, especially in consciousness research. For that purpose I previously shared insights from Coordination Dynamics which significantly influenced the MMH. I will briefly summarise it in the next section. The other subchapters summarise a selection of additional useful cognitive theories1 that similarly contribute: Extended Mind Theory, Predictive Processing Theory (including Active Inference), Integrated Information Theory, and Global Workspace Theory. Together they offer support for arguing the case of full market mentality beyond simply rationality and biases. While different in their approaches, they can help strengthen the MMH’s theoretical bones. In particular, they agree on the importance of (realised) information, especially for bridging mind and world, and implicitly the physical and the phenomenal. This points, again, to the relevance of prices in the case of markets. While borrowing from the work of its respective pioneers, I will interpret these cognitive theories with my investor hat on. This means, for example, that I will show sympathy with some of the criticisms that challenge the default interpretations. My overall assessment will have to wait for Chapter 10, when I will provide closing arguments before resting my case. Again, this exploration should be mutually beneficial to both the economic and cognitive sciences. For economics, these theories are informative in that they reflect a cogni-

 There are often various interpretations of each theory, so I only discuss their main characteristics. There are also other theories that could potentially be considered, but are not mentioned here, including the critical brain hypothesis, harmonic brain modes, quantum approaches to brain and mind (e.g. Atmanspacher, 2017), recurrent process theory, and philosophical doctrines like panpsychism (e.g. Seager, 2020). For an overview of specifically consciousness theories, see Seth and Bayne (2020), especially their Table 1. https://doi.org/10.1515/9783111215051-003

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tive revolution, away from the ‘mind-as-computer’ metaphor, and scientifically support a shift from a mechanical to a psychophysical perspective. In that regard there also seems to be a growing sympathy, at least among some cognitive scientists, for the necessity of biological materials, i.e. living beings, to produce and sustain consciousness. In other words, the role of evolution should not be underestimated in developing full mentality (something AI should take heed of when it assumes replication). In return, for cognitive science an economic interpretation of these theories clarifies and emphasises the important role of exchanges and other economic concepts, like valuation, in coordinating mentality and action. This chapter will further support my overall argument that the theoretical partnering of these disciplines, empirically manifested in markets, provides a robust framework to improve our understanding of the collective dimension of human existence, which is central to both. Political Note Academic Collaboration versus Political Correctness The Market Mind Principle—centred on discovery, exchange, and valuation—applies to our collective efforts to progress knowledge. It points to a form of relativism. Any scientific market, until it discovers some absolute truth (e.g. a ‘theory of everything’), exchanges one theory for another. Their value (or worth) depends on each other, so they are relative. That is especially the case for theories between disciplines. I support these efforts and am all for interdisciplinary academic collaboration. Our research is a showcase for this. But it is not easy to achieve, particularly when academics from other disciplines join a debate among specialists during a crisis. An infamous example occurred during the GFC when a heated dispute erupted between Paul Krugman (a liberal Nobel economist) and Niall Ferguson (a conservative historian). It showed how (accusations of) dogma and ignorance can turn, what should have been, an academic debate into a nasty (and frankly embarrassing) tomcat fight. More generally, Krugman belongs to those economists who, to borrow from earlier criticism by McCloskey, “appear to believe that economics is too important to be left to the open-minded, and especially must never be left to anyone lacking faith in some approved formula for achieving knowledge” (McCloskey, 1983, p. 485, fn. 3). On the other hand, the CVC showed the flip side. Scientists without any economic background argued for an economic lockdown based on unproven computer models. They then subsequently accused critical economists and computer scientists of meddling in ‘the science’ and argued they had ‘no expertise’. There are worrying trends more generally within academic discourse. What has become known as the cancel, snowflake or woke culture produces category mistakes. It means, in simple terms, that for a hammer everything looks like a nail, even if it is not. Take a scientific disagreement expressed as critique, like “Based on new data, A beats B, in contradiction to your hypothesis”. This is immediately coloured by the offended woke party who spins it as something like “Based on [your biased selection], [your] A [bullies] [my] B, [thereby disrespecting me]”. The end of free speech, and by extension scientific progress, starts when expressing a scientific disagreement is immediately hammered as fascist, racist, sexist, or otherwise politically incorrect. Needless to say, such situations are to be avoided. To stay healthy we need healthy debates. As far as free speech in general is concerned, I highly recommend the 2012 speech by comedian Rowan Atkinson. Among others he argues that “if we want a robust society we need more robust dialogue, and that must include the right to insult or offend”.

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For what it is worth, here are three suggestions for key requirements to secure healthy scientific debate. They consist of (e.g. legal) protection and (e.g. financial) support for, respectively, action, people, and institutions: 1. Protection and support for free expression of opinions about the topic at hand. A metaphysical framework may help to structure the debate. Specifically, explicate ontology, epistemology, and methodology. Also, is the discussion about a physical or a mental construct? Does it exist yet? 2. Protection and support for academic staff to encourage, guide, and organise such debate. For example, in the Netherlands a dedicated hotline, called WetenschapVeilig (ScienceSafe), was established in November 2022 for academics to report any threats or intimidations, as well as receive support. 3. Protection and support for academic institutions to provide the environment and facilities for such debate. At the risk of coming across as a hammer myself, I believe current attitudes in these but also related societal debates (e.g. on age, class, culture, and gender) reflect our metaphysical confusion—not helped by social media—regarding mind~matter, often directly involving the mind~body. For example, I’m hurt/I’m expected to be hurt, I voice my objection/I keep quiet, and I feel violated/I use violence. Finally, as far as economics is concerned, the cocktail of physics envy and mechanisation produces a mono-disciplinary approach and singular culture which leads many to see matters of fact as simply facts of matter. In his defence for an interdisciplinary approach, Simmel argues: “the fact that two people exchange their products is by no means simply an economic fact. Such a fact—that is, one whose content would be exhausted in the image that economics presents of it—does not exist” (Simmel, 1907, p. 53). And yes, that was written more than a century ago.

3.2 Coordination Dynamics Coming together is a beginning, staying together is progress, and working together is success. Henry Ford

Inspired by complexity, Coordination Dynamics is a theory that seeks to understand how individuals and other complex systems coordinate their movements in different contexts. This field is multidisciplinary and involves perspectives from scientific areas such as biology, physics, neuroscience, and psychology. Coordination Dynamics can be applied to a broad range of contexts, varying from the study of social interactions to the control of robots. The MMH adds markets to the mix. Scott Kelso, one of Coordination Dynamics’s founders spoke at our symposium and wrote the intermezzo for this book. Much of my interpretation (with the usual caveats) is based on his work and that of his colleagues. There is one particular quote from Kelso’s seminal book Dynamic Patterns that I would like to start with because it captures beautifully some of the key issues discussed throughout this book: Like mind and matter, the concepts of information [e.g. as intention] and dynamics have long been held distinct and separate. Usually they are taken to refer to fundamentally different but alternative modes of describing complex systems . . . Intentions and dynamics thus appear to be irreconcilable . . . [and] intention as cause of motion lies outside scientific explanation. Mind

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simply cannot get into motion. The two are logically incompatible. But look what is done [via Coordination Dynamics]. Instead of treating dynamics as ordinary physics . . . coordination or pattern dynamics are informational from the very start . . . self-organized Coordination Dynamics are, at their very roots, informational. (Kelso, 1995, p. 144–145)

The dual realisation of that information is essential in economic discovery. At the heart of Coordination Dynamics is the idea that coordination emerges from the exchanges between individual components of a system. These individual components can be anything from neurons in the brain, to limbs of a person, to persons in a group, or to bonds, stocks, and other assets in the market. The exchanges between these components can be studied using mathematical models that capture the dynamics of the system. There are many different types of coordination that can be studied within the framework of Coordination Dynamics. For example, rhythmic coordination refers to the coordination of movements that have a regular temporal structure. In the economic case, this can include the seasonality of certain commodity prices and the comovement of interest rates. Metastable coordination refers to the coordination of movements that are not fixed, but can switch between different states, such as that between a boom and a recession, consensus investing versus contrarian investing, growth-phase versus value-phase, or the reversals in bear and bull markets. One of the key concepts in Coordination Dynamics is that of self-organisation. Self-organisation refers here to the ability of a system to spontaneously organise itself into a coordinated state without the need for central or external control. This can be seen in many different contexts, from the synchronisation of fireflies or bird flocks to the coordinated movements of human crowds (e.g. in stadiums, traffic, and markets). Among the most important models in Coordination Dynamics is the Haken-Kelso-Bunz (HKB) model. The HKB model proposes that coordinated behaviour arises from the interaction of multiple nonlinear oscillators. The model assumes that each oscillator represents a different component of behaviour, such as the timing of a movement or the force required to produce it. When these oscillators interact with one another they can synchronise and produce coordinated behaviour. (For a practical application, see Zhang et al., 2016). The HKB model has been used to explain a wide range of phenomena, including the coordination of muscle movements during walking and running. Studies have shown that when people walk or run together, their movements become more synchronised over time. Kelso is famous for performing experiments involving rhythmical and synchronising finger and hand movements. This suggests that the HKB model is a potential framework for understanding the (spontaneous) coordination, but also herding, of investors in markets. Later I will discuss the example of synchronised handclapping in this context. Another important concept in Coordination Dynamics is the idea of phase transitions. Phase transitions occur when a system undergoes a sudden change in behaviour as a result of a small change in a parameter. In the context of markets, phase transitions can occur when a small change in the behaviour of one individual (e.g. the CEO of a company) leads to a sudden change in the behaviour of a group (e.g. the employees of/ investors in that company). Another example is a change in behaviour of a marginal

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investor. Phase transitions have been observed in a wide range of coordinated behaviours, including human movements in dance and sports. For example, studies have shown that when two people dance together, their movements can transition from a random, uncoordinated state to a more synchronized, coordinated state. This transition is thought to be a result of small changes in the movements of each individual that lead to a change in the dynamics of them as a couple. Related are fluctuations, like those of prices. Coordination Dynamics does not view fluctuations as just noise or random variation. Instead, they can be seen as a fundamental aspect of how coordinated systems function. Fluctuations in the timing of movement patterns can serve as a signal for the coordination of behaviour between individuals, allowing them to adapt to changes in the environment and maintain stable patterns of interaction over time (again, wait for my example of handclapping). Coordination Dynamics thereby emphasises the role of feedback loops which are critical for maintaining such stability and resilience in the face of changing environmental conditions. By continuously adjusting to feedback signals, coordinated systems can maintain a balance between stability and flexibility, allowing them to adapt to new challenges and opportunities. To further clarify stability, it specifically refers to the ability of a system to maintain a coordinated behaviour over time, despite the presence of external disturbances. Studies have shown that the stability of coordinated behaviour is influenced by a wide range of factors, including the complexity of the task, the make-up of the individuals involved, and the strength of the coupling between the individuals and/or their technologies (e.g. “coupling system”, Clark and Chalmers, 1998). Information coupling is of particular interest. It refers to the exchange of information between the components of a system that allows them to coordinate their movements. Information coupling can be thought of as a form of communication between the components of a system and is thus especially relevant for the pricing system. One potentially interesting application of Coordination Dynamics follows from studies of human movement. By extending this to studying the dynamics of eye, hand or body movement by traders, researchers can gain insights into how the brain controls this movement while interacting with each other on a trading floor and exchanging on markets. This research could lead to better software and other investment tools, as well as better risk management approaches. Finally, Coordination Dynamics suggests that consciousness arises from the coordinated activity of different parts of the nervous system. Specifically, it proposes that the subjective experience of consciousness is related to the integration of information across different levels of the mind~body economy, especially neural oscillations and the exchanges between different brain regions in the nervous system. In our terms, consciousness is an emergent property of the mind’s market activity, which is physically executed by the nervous system. The collective aspects of what happens to consciousness if nervous systems exchange with other nervous systems are studied by related fields, like social neuroscience.

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3.3 Extended Mind Theory All media are extensions of some human faculty, psychic or physical. In this electronic age we see ourselves being translated more and more into the form of information, moving toward the technological extension of consciousness. Marshall McLuhan

As I mentioned, the Extended Mind Theory (EMT)2 was formally introduced in a seminal paper by philosophers Andy Clark and David Chalmers (Clark and Chalmers, 1998) which they open with the now legendary question “Where does the mind stop and the rest of the world begin?” It has subsequently been amended, clarified (e.g. Clark, 2011) and enhanced, for example via cognitive integration (e.g. Menary, 2006), intersubjectivity (e.g. Stuart, 2011), phenomenality (e.g. Pacherie, 2017) and sociality (e.g. Gallagher, 2013). Clark explains EMT as follows: The actual local operations that realize certain forms of human cognizing include inextricable tangles of feedback, feedforward, and feed-around loops; loops that promiscuously criss-cross the boundaries of brain, body, and world. The local mechanisms of mind . . . are not all in the head. Cognition leaks out into body and world. (2011, p. xxviii)

So, cognition is spatially distributed over brain, body and world. Cognitive status is thus accorded to parts of the world that are not part of the brain. They implement, instantiate or otherwise support mental states and processes. Apart from the bodybeyond-brain, key among these are tools and other mind~bodies. I will discuss this now in more detail. EMT generally subscribes to embodied cognition, meaning that the brain links via the nervous system with the body proper.3 In a way, then, the body is the first extension and I’ll return to this when discussing intuition. However, EMT specifically states that the human mind extends beyond the body by means of two further types of extensions. First, the mind uses tools to advance its understanding of the world. Artefacts, like pencils and notebooks, and technological devices, like our smartphones and laptops, help our mind to coordinate our lives. EMT argues that they are then extensions of our mentality. To overcome certain criticisms of EMT, cognitive integration emphasises that mentality consists of hybrid processes, where internal processes using the brain are integrated with external processes using tools such as a computer doing calculations. Second, and often together with artefacts and tools, the individual human mind extends via other human minds. Although its origins go back to early interpretations

 Also known as the Extended Cognition Hypothesis or Extended Mind Thesis. Other sources include Dunbar, Gamble and Gowlett (2010), Menary (2010), and Rowlands (2010).  Often referred to as the “embodied mind” (Varela, Thompson and Rosch, 1993). This can involve other organisms as well. Recent research (Sampson et al., 2016) has shown that gut bacteria seem to play a role in Parkinson’s disease and autism.

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of group minds, its modern version is known as distributed cognition or distributed mind.4 This extension is most relevant for the MMH. It particularly focusses on the exchanges between human minds and how these not only complement but often supplement individual mentality. Sociality also points to the interpersonal space into which minds extend, including intersubjective affects. Crucially, it is the diversity in distributed cognition, by combining specialised local knowledge, that benefits extended minds. Both tools and other human minds can lead to cognitive enhancement, whereby tasks are completed that could not be achieved (e.g. as efficiently) without these extensions. A simple example is a retail investor who collaborates with his financial advisor to make an investment decision. Here the external advisor’s cognitive processes become integrated into those of his client. Or an institutional investor who uses, say, the Bloomberg consensus estimates of sell-side economists for his macro model in Excel. A final example is money as the ultimate tool to make trade, in particular the required calculations, efficiently possible due to its three functions as a store of value, a measure of value, and a medium of value exchange. In general, markets and their tools allow multiple minds to extend and integrate to discover prices via trading. This is wide-scale cognitive enhancement that, in principle, benefits society. Hayek saw the market as the “extended order” (Hayek, 1978, p. 67) of the “sensory order” (Hayek, 1952) of the individual mind, and EMT’s distributed cognition links to Hayek’s “distributed knowledge”. It also connects to the EMH’s ‘collective judgement’, Graham’s “voting/weighing machine” being an example of this. EMT supports the MMH because it provides a robust cognitive framework to think about the role of other minds as well as trading tools in shaping investors’ mentality to the point that the market itself becomes an “epistemic thing” (Knorr Cetina and Bruegger, 2000). At the same time, the market mind turns the parity principle (Clark and Chalmers, 1998) on its head, as it were: it performs functions which cannot be performed within individual minds. The final extension concerns consciousness which I have already covered extensively. Metaphorically, a number of researchers have compared the mind to an orchestra. Neuroscientist Walter Freeman and physicist Giuseppe Vitiello, for example, argue that: Better yet, it is like a jazz combo, which doesn’t need a conductor . . . the combo creates a field to which every player listens, adapts and contributes, so that every player is melded into the unity of sound and rhythm. Global and instantaneous . . . “It doesn’t need a conductor” is the consciousness. It integrates all of it. The result is the sound and rhythm of the combo. (Vitiello, 2017, p. 163; emphasis added. In similar vein, see Kelso and Engstrøm, 2006, p. 93)

 Sources include Dror and Hamad (2008), Huebner (2014), Palermos (2016), and Sloman and Fernbach (2017). Other topics of relevance to the collective aspect of EMT include distributed agency, social ontology, and collective intentionality.

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Their comment on consciousness, for example, echoes my earlier interpretation of an internal market without central planning. For our purposes we can translate the above in terms of the collective setting of the market. To make the initial connection, I will add another “rhythm” quote, this time from an anonymous trader in Schwager’s The New Market Wizards: Every market has a rhythm, and our job as traders is to get in sync with that rhythm. I’m not really trading when I’m doing those trades. There’s trading being done, but I’m not doing it . . . There’s buying and selling going on, but it’s just going through me. It’s like my personality and ego are not there. (Schwager, 1992, p. 412)5

So, we interpret Freeman’s “consciousness” as the market’s consciousness (involving price discovery) with market moods as the phenomenal overlay that is felt (e.g. rhythmically) by all players. I have more to say about this, also in practical terms, in Subchapter 9.3. To summarise, EMT states that mentality is not purely intracranial but is also extra- and transcranial. Specifically, the human mind extends beyond the brain (and body) into the world by way of: 1. Tools, artefacts and aids which are used to support the brain in cognitive tasks. 2. Other human minds, whereby exchange with those minds completes cognitive tasks, particularly ones that exceed the capability of the individual mind. The MMH translates EMT in a market setting, with investors physically and phenomenally connected via trading tools, respectively market mood. The screens and terminals on their desks open windows to the world. Computers, software, telephones and other equipment provide the tools to interact with that world, including other minds. Together with investors’ bodies they form the market’s body (see Subchapter 2.4.2) that aids in exchanges, where the pricing system forms EMT’s: external symbol system [that] becomes integrated with pre-existing cognitive capacities in ways that significantly modify the nature of those capacities. We can, of course, distinguish between the parts of those capacities that are internal and those that are not, but this is already to concede that the overall cognitive process itself is extended. (Wilson, 2010, p. 180)

The result is an extended cognitive system where information is realised, coordinating collective behaviour. Hopefully it is also clear that EMT relates to the earlier comments on separation, in this case between mind and world, including other minds. It highlights the difficulty in pinpointing this separation when we can no longer consider our skull or even our skin as the clear physical boundary between (inside) mind and (outside) world. As ancient Buddhist texts express it (freely translated): through the sensory faculties

 Although anonymous, if you read carefully it’s kind of obvious who it is, but it is not my place to reveal this.

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contact is made while, reflexively, through contact feelings arise. A quick way to see this is to consider again collective experiences which are intersubjective and irreducibly shaped by the exchange between multiple subjects. For example, where does the experience of intercourse start and end for you? Similarly, where does the experience of trading start and end for you? Or even, to paraphrase Clark and Chalmers, where does the market mind stop and the rest of the economic world begin? Finally, there is one more dimension into which the mind extends, namely time, which I touched on before. Imagination allows the mind to explore different worlds, particularly future ones that need to be created into existence without any precedent (e.g. Suddendorf, 2013). Whereas expectation extrapolates the past, imagination lets go of it. Importantly, consciousness is central to imagination in that it allows us to ‘see’ such imagined worlds in our mind’s eye. Knight explains imagination as creating in our minds an “image” of a future state of the world: “consciousness, the ‘image,’ is both present and operative wherever adaptations are [physically] dissociated from any immediate stimulus; i.e., are ‘spontaneous’ and forward-looking”. (Knight, 1921, p. 201). Notably, a conscious being “can react to a situation before that situation materializes; it can ‘see things coming.’ This is what the whole complicated mechanism of the nervous system is ‘for’ ” (Knight, 1921, p. 200; my emphasis).6 In other words, unconstrained by the lack of historic ‘big’ data, we can simulatively experience such worlds. As we know, visionary entrepreneurs are masters at this and subsequently write the scenarios to actually design them. This was memorably expressed by both Henry Ford: “If I’d asked my customers what they wanted, they would have said ‘a faster horse’” and Steve Jobs: “A lot of times, people don’t know what they want until you show it to them”. The common thread is spontaneous and creative change. Legendary management theorist Peter Drucker, in his classic Innovation and Entrepreneurship, emphasises this beautifully by repeating one of the key messages of this book in his criticism of mainstream economics. Namely how theory is responsible for shaping practices and by extension how flawed mainstream economics is in terms of understanding ‘free’ minds (or spirits): Every practice rests on theory, even if the practitioners themselves are unaware of it. Entrepreneurship rests on a theory of economy and society. The theory sees change as normal and indeed as healthy. And it sees the major task in society—and especially in the economy—as doing something different rather than doing better what is already being done. This is basically what Say, two hundred years ago, meant when he coined the term entrepreneur. It was intended as a manifesto and as a declaration of dissent: the entrepreneur upsets and disorganizes. As Joseph Schumpeter formulated it, his task is “creative destruction” . . . . But . . . the concept of the entrepreneur and of entrepreneurship, is independent of classical economics and indeed incompatible with it. Classical economics optimizes what already exists, as does mainstream economic

 Hopefully you now see, for example, why I use the investment concept of ‘discounting the future’ for the mind-as-market. Also, as I mentioned, Knight subsequently expanded on these reflections in his 1925 papers which the economics profession largely ignored.

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theory to this day . . . It focuses on getting the most out of existing resources and aims at establishing equilibrium. (Drucker, 1985, p. 26; emphasis added)

Finally, in the past, Clark and others offered arguments for why the extended mind does not extend to consciousness, especially centred on the speed of operation, i.e. conscious experience requires high-bandwidth processing, which (supposedly) only the brain and its neurons offer. These arguments have largely been rebuffed by others. In any case, they generally do not hold in our case. The MMH argues, first (and in contrast to the prevailing consensus), that consciousness can have a collective (i.e. intersubjective) dimension. Specifically, the extension occurs collectively by way of price discovery. It is physically supported by human bodies and the technologies that connect them, whereby prices simultaneously convey cognition (i.e. access consciousness) and felt quality (phenomenal consciousness; a.k.a. mood). Second, as far as speed is concerned, price formation generally reflects the market’s awareness, whereby prices ‘instantly discount’ information. Moreover, this becomes “supersized” (or hybrid, e.g. Searle’s “silicon chips for neurons”) when price changes are due to HFT whereby—via fibre cables, microwave networks, and advanced computers—trades can occur so fast (e.g. in nanoseconds) that it even escapes human awareness.7 Unfortunately, as I discuss elsewhere in the book, this leads to various other, mostly problematic, issues.

3.4 Predictive Processing Theory You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete. Buckminster Fuller

Predictive Processing Theory (PPT)8 considers humans to be self-evidencing creatures. It views the human brain as a prediction organ that employs sensations to query the environment; sensations act as inputs that are matched against predictions. Here are Freeman and Vitiello again, implicitly acknowledging the ‘projection’ of mind-asmarket onto market-as-mind:

 This is related, for example, to “supra-consciousness” (Hayek, 1967), “macroscopic intelligence” (Sornette, 2003), and “cognitive non-conscious” (Hayles, 2014). See also Subchapter 8.2.  Although more commonly known as Predictive Processing (PP), I used the abbreviation PPT for consistency with the abbreviation of the other theories. Main sources include Clark (2013a) and its responses, as well as Friston (2003), but it also has roots in the work of Geoffrey Hinton, David Knill, and Alexandre Pouget. Colombo, Elkin and Hartmann (2021) offer a critical view. Related are the Free Energy Principle (Friston, 2010), Active Inference (Constant et al., 2020), as well as Predictive Coding which is the computational approach of PPT, using simulations to replicate the workings of the brain (see Kwisthout, 2014).

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Brains thus form hypotheses and test them by intentional action into the environment. The brain action-perception cycle is therefore at the basis of the knowledge of its world . . . The environment, as seen from the brain, is thus like its own image. (Freeman and Vitiello, 2016, p. 10)

Dynamic models in the brain hold multi-level hypotheses about states of the world which are continuously tested by comparing the predicted sensory stimulations against actual sensory stimulations. This occurs as we encounter the world and sample new data that can offer evidence for or against the hypotheses. Any differences between predicted and actual sensations are called prediction errors and the aim is to minimize these, so that perception cooperates with action to reduce surprisal.9 This process can be considered as the cognitive version of kaizen, the Japanese concept for continuous improvement (originally applied in manufacturing). But there is more to its relevance for economics. For starters, in 2013 Clark wrote a target article about PPT which received around 50 commentary articles in response, authored by an array of experts in various fields. These were subsequently published in a special edition of Behavioral and Brain Sciences which has since become a key reference. Among the commentaries are several which read as if they are discussing forward-looking markets that anticipate and bridge certain states of the world, with prices as shared expectations: Worlds that exist outside the individual, and in time-windows, which extends beyond the here-andnow of interaction; worlds that, somehow, get internalized . . . [with] shared expectations that are communicated in interactions, mediated by representations, solidified through materiality, and extended into an action space, going way beyond the physical body and into proximal and distal forms of technology . . . Something about how humans can bridge the material and symbolic, and something about how they in and through interaction can share both external and internal time, may be critical. (Roepstorff, 2013, p. 225; emphasis added)

I will discuss the economic relevance in more detail later. First, let me list the main characteristics of predictive processing: 1. It is (perceived as) indirect. We experience the world through the veil of our senses. The brain is only in contact with the world via sensations, like auditory and visual signals. There is no ‘hot line’ between brain and world. On the other hand, this does not mean that there is a clear boundary between mind and world. See Clark’s thoughts on this (2013a, p. 199). 2. It is (bounded) Bayesian. The initial expectations of sensory stimulations are based on priors, if available, which get amended into posteriors as new data, in the form of actual stimulations, arrive. 3. It is hierarchical. High level representations predict lower-level ones. High level information flows down and influences low level information and vice versa. In

 Here I will ignore the role of introspection, next to perception and action. Also, the aim to reduce surprisal should be distinguished from the urge (or need) to discover, leading to the desired internal surprises we call insights (e.g. A-ha/Eureka instances). This will be discussed regularly in the book.

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consciousness, such modelling culminates in what Seth (2021) calls “controlled hallucination”. It is reflexive. Perception and action feedback to each other in their shared goalseek to close the gap between mind and world. Perception and action work in tandem to recalibrate the senses, thereby eliciting a new stream of sensations that are preferred, i.e. less erroneous, to the previously perceived stream. It leads to a large extent to self-fulfilling prophecies (Clark, 2013a, p. 186). It remains dualistic for practical purposes. There is the implicit distinction between the mental (i.e. psychological perception, e.g. beliefs) and the material (i.e. physical action, e.g. trading).

The Bayesian angle means that expectations about sensory inputs are derived from existing knowledge, specifically the current belief regarding such inputs.10 This is prior to any data sampling. Statistically, these famous Bayesian ‘priors’ are the probability distributions that quantify expectations ahead of new sampling. In other words, priors are distributions reflecting reasonable expectations based on knowledge or belief before sampling from actuality. Once this sampling has occurred, ‘posteriors’ are the revised distributions11 that update the expectations, depending on whether the sampled data, arriving through perception, is evidence against the held hypothesis. So, in mind-as-market terms, the brain invests in and employs hierarchical generative models to update, in a Bayesian fashion, its representations of the world. Moreover, it is motivated by minimizing loss in the form of prediction errors. This drives learning, action-selection, and recognition, aimed at successful perception~action pairing: Perceptual learning and inference is necessary to induce prior expectations about how the sensorium unfolds. Action is engaged to resample the world to fulfil these expectations. This places perception and action in intimate relation and accounts for both with the same principle. (Friston et al. 2009, p. 12)

PPT’s perception~action focus thus deals with the exchanges between a cognitive system and its environment. Through perception the world affects the mind and via action the mind affects the world. Reflexively, action is informing perception whilst being caused by it. Still, there are caveats. PPT is largely silent on “true uncertainty”, which is “that higher form of uncertainty not susceptible to measurement and hence to elimination” (Knight, 1921, III.VII.47). In other words, there are large world situations and rare events when insufficient knowledge is available and thus no priors can be formed rationally (see also Gigerenzer’s critique in Appendix 1-B).

 Some readers will wonder where the priors originated from, i.e. if there is a chicken and egg situation here. In general, PPT is silent on the question of innateness (e.g. see Clark, 2013b).  A posterior is called a conditional distribution because it conditions on the newly observed data.

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There are a number of other topics related to PPT, like free will. It is also in the spirit of earlier reflections on perception and action, particularly regarding its unconscious and reflexive ‘modelling’. Allow me two similar quotes from, respectively, Carl Jung and Antonio Damasio, to highlight this: Endless repetition has engraved these experiences into our psychic constitution, not in the form of images filled with content, but at first only as forms without content, representing only the possibility of a certain type of perception and action. When a situation occurs which corresponds to a given [form], that [form] becomes activated. (Jung, 1934, para. 99; emphasis added) Hidden behind those images, never or rarely knowable by us, there are indeed numerous processes that guide the generation and deployment of those images in space and time. Those processes utilize rules and strategies embodied in dispositional representations. They are essential for our thinking but are not the content of our thoughts . . . Dispositional representations exist in potential state, subject to activation, like the town of Brigadoon. (Damasio, 1994 pp. 108, 104; emphasis added)

As far as economics is concerned, PPT’s Bayesian angle immediately raises criticism by mechanical economists, specifically regarding true uncertainty: Unfortunately, the general hypothesis that economic agents are Bayesian decision makers has, in many applications, little empirical content: without some way of inferring what an agent’s subjective view of the future is, this hypothesis is of no help in understanding behaviour . . . John Muth (1961) proposed to resolve this problem by identifying agents’ subjective probabilities with observed frequencies of the events to be forecast, or with “true” probabilities, calling the assumed coincidence of subjective and “true” probabilities rational expectations . . . [T]his hypothesis . . . will not be applicable in situations in which one cannot guess which, if any, observable frequencies are relevant: situations which Knight called “uncertainty”. It will most likely be useful in situations in which the probabilities of interest concern a fairly well-defined recurrent event, situations of “risk” in Knight’s terminology . . . In cases of uncertainty, economic reasoning will be of no value. (Lucas, 1977, p. 15)

We can also link PPT, for example, to Austrian economics. Specifically, PPT’s view on action is consistent with Mises’ view of purposeful action. PPT action is directed at affecting the world in order to minimize prediction errors concerning that world. That echoes Mises’ “purpose”, namely “to substitute a more satisfactory state of affairs for a less satisfactory. [Man’s] mind imagines conditions which suit him better, and his action aims at bringing about this desired state” (Mises, 1949, p. 13). Another clear connection lies in Hayek’s work on the Sensory Order which contains earlier intuitions: “all we know about the world is of the nature of theories and all ‘experience’ can do is to change these theories” (Hayek, 1952, p. 143). In more detail: At first the pattern of movement initiated will not be fully successful. The current sensory reports about what is happening will be checked against expectations, and the difference between the two will act as a further stimulus indicating the required corrections. The result of every step in the course of the actions will, as it were, be evaluated against the expected results, and any difference will serve as an indicator of the corrections required. (Hayek, 1952, p. 95)

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Importantly, PPT considers the socio-cultural context of human perception, action, and attention, including their institutional settings. In a sense, predictive processing aims to achieve a self-fulfilling prophesy in that we structure our world, our environment, in order for our sensory predictions to come true. In short, we like to be correct. PPT thereby raises an implicit question, namely to what extent do we indeed “create our reality” (Harman, 1988)? Simply interpreted as a self-fulfilling prophesy there are plenty of arguments in support (Clark, 2013a). Investing, of course, plays a big part in this in terms of collectively steering the economic system to help in that structuring, including a rich history since the Industrial Revolution. By way of their portfolios investors realise information and turn perception into action through the resultant flow of capital. Together they are continuously dabbling in their own stochastic art of price formation. PPT helps to interpret properly, which is to say in terms of neuronal principles, what drives such discounting of news in prices. Clark’s comment is appropriate here: “predictive processing may, from time to time, and in the right circumstances, extend beyond the confines of the brain through, for example, action-based structuring of information flows” (Clark, 2013, p. 194). Markets thereby form the only domain offering empirical data on collective predictive processing under real conditions of (economic) survival. In short, the multi-level testing of hypotheses and minimizing of prediction errors in the individual mind is occurring at the market level when multiple investors are testing hypotheses and minimizing the difference between price and value: Making an investment decision is like formulating a scientific hypothesis and submitting it to a practical test. The main difference is that the hypothesis that underlies an investment decision is intended to make money and not to establish a universally valid generalization . . . Taking this view, it is possible to see the financial markets as a laboratory to test hypotheses. (Soros, 1987, p. 14)

Accordingly, both predictive processing and investment management have a hypothetical view of the world. At the individual level, by putting money at risk an investor is testing a hypothesis about modelled states of the world, which involves (often painful) Bayesian learning constrained by incomplete knowledge: Uncertainty precedes its risk—you don’t even know exactly what the option is currently worth because you don’t know whether the model you are using is right or wrong. Or, more accurately, you know that the model you are using is both naive and wrong—the only question is how naive and how wrong. (Derman, 2004, p. 259)

The market reflects a consensus expected state of the world, with information discounted via prices. That world is the ultimate model and both real and investment life, to nuance Soros, do not take place in a laboratory, meaning that they do not involve repeatable experiments without impact on the environment. PPT complements EMT nicely as it suggests a model of the human mind “that makes structuring our worlds genuinely continuous with structuring our brains and

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sculpting our actions” (Clark, 2013, p. 14).12 But we can be more specific about how PPT’s view of minds relates to markets and their extended mind. In a nutshell, and in the spirit of Soros’ earlier quote, cognitive and economic survival both depend on successfully testing hypotheses, making the least number of errors while prospering. More formally, market participants extend their internal, or cognitive, modelling to external, or economic, modelling. The Black-Litterman model, for example, is a Bayesian application used in portfolio management to optimise portfolios, especially vis-àvis the market. In both cognitive and economic modelling, modelling includes other participants who also happen to model. Moreover, in both cases we generate hypotheses based on fragile assumptions and imperfect theories that underlie the models. With these we attempt to bridge past and future events. Based on past times, each time we want to be better prepared for the next time. Subsequently, we attempt to bridge the mental economies with the material ones. We match the subjective experiences to the objective realities, using narratives, to make sense of what we sense. Now the crucial part: exactly because we face a world that we share with others, and thus face its states together, there is a collective dimension to all of this modelling. I would argue that this already applies to cognitive modelling, but it certainly applies to economic modelling. Ultimately, the testing of the hypotheses is played out in markets. Freely interpreting Clark in such a setting, we: Take a highly processed cognitive product (such as an idea about the world), clothe it in public symbols, and launch it out into the world so that it can re-enter our own system as a concrete perceptible . . . and one now bearing highly informative statistical relations to other such . . . perceptibles. (Clarke, 2013, p. 15; emphasis added)

To be clear, in markets securities represent (and allow exposure) to ideas about the world. In terms of public symbols, for example, FAANG is the acronym that refers to the first letters of stocks of five prominent American technology companies: Facebook (FB), Amazon (AMZN), Apple (AAPL), Netflix (NFLX); and Alphabet (GOOG). Their prices are Clark’s “public symbols” that transcend language, cultural, and other barriers in virtue of their nature (i.e. being numbers). They bear those “highly informative statistical relations to other such . . . perceptibles”. Price discovery, in PPT terms, is the market’s self-organising, adaptive capability in terms of dealing with the surprises and surprisals resulting from contrasting predictions with real world news. The error-reducing concept in PPT is formalised via the information-theoretic version of free energy, originally developed in thermodynamics. In this instance, free energy stands for the (quantified) difference between predicted sensations (as generated by models) and the actual sensations (as experienced). The free energy principle is related to active inference (see Cognitive Note: Active Inference) and states that complex adaptive systems aim to minimize free energy. In other words, the free energy principle seems to argue that it is better for survival to avoid surprises. This led  Again, this statement would likely be endorsed by Mises, for example.

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to one of the criticisms of the free energy principle, namely the so-called dark room problem. The dark room problem states that the free energy principle implicitly suggests it is best to retreat to a dark room and stay put. There have been several responses by cognitive scientists to this problem (e.g. Friston et al., 2012). If we accept the premise of the MMH, namely that mind and market dynamics are similar, then finance also has an interpretation of the dark room problem that can help solving it. It involves arbitrage. First, free energy in markets is limited because there is no free lunch. The driving force for efficiency in markets is the minimization of its free energy by way of discounting: prices almost instantly reflect surprises. In other words, any free lunch is arbitraged away via profit maximisation by rational investors. In the process, individual forecasting errors by participants, both up and down, get cancelled out. Still, such price adjustments can be volatile which, in itself, can surprise investors. So how can investors avoid any surprise? Basically, they should move all their money into cash, removing all investment exposure. That is economics’ equivalent to moving into a dark room. In the economic jungle, however, this is not a real option if only because inflation will eat away at one’s capital. Similarly, something else will cognitively ‘eat away’ in a dark room. Instead our innate curiosity makes us want to know and to experience. It drives discovery and exploration, but not mechanically: “curiosity is one motive which obviously cannot be [physically] reduced to uniformity of sequence . . . Intellectual problem-solving activities can be discussed only in terms of quality of conscious experience” (Knight, 1925a, p. 387). To be precise, consciousness is the portfolio of (option) returns from those endeavours (see Appendix A-4). If we zoom in on time, for example, I previously mentioned how duration and time play a role in investing. In PPT terms, the market has a temporal horizon and temporal depth. It means, for example, that research should be able to formally link the experience of “intrinsic time” by investors to the level of market stress (e.g. during crises). In summary, PPT considers investors to be bounded Bayesian learners who, in their exchange, attempt to reduce mutual prediction error. In fact, nowhere does predictive processing take place at a more deeply global level than in the financial markets. It is through the economic system, with arbitrage and capital allocation in markets steering actions in the real economy, that we attempt to adapt to the state of the world, while changing it at the same time. In offering a robust framework for cognitive dynamics PPT is well suited to improve our understanding of investors’ minds in general, and for supporting the MMH in particular. Cognitive Note Active Inference Closely related to PPT is the free energy principle, a.k.a. active inference. Knight offers an early grasp of the idea: “We perceive the world before we react to it, and we react not to what we perceive, but always to what we infer” (Knight, 1921, p. 201). In the section Background I mentioned that I would like you to also hear the MMH story from various experts in the specialised topics related to it. Active inference is one of those topics. So, while we

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are preparing a paper on it, for now I would like to share a brief explanation from its originator, Karl Friston. (This is private correspondence; slightly edited for clarity only). Here the explanation focuses on its interpretation of utility, whereby conventional economic utility is “utilitarian”: “In contrast to conventional expected utility formulations, the utility in active inference covers every kind of outcome in all its attributes. Some preferences can be very precise (e.g. “I don’t want to be bankrupt”), some will be more accommodating and less precise (e.g. “I’m happy to work in Geneva or Lausanne but would prefer Interlaken”). On this view, active inference regards utility as supplying constraints in the spirit of multiple constraint satisfaction or—technically—in the spirit of Jaynes’ constrained maximum entropy principle. Read in this way, the balance between epistemic (maximising expected information gain) and utilitarian (maximising expected utility) is just an expression of the imperatives that underwrite decisions and choice behaviour; namely, to resolve uncertainty, under the constraint I do not incur any surprising outcomes that would violate my prior preferences (i.e. prior beliefs about being viable and successful). In consequence, Andy [Clark] is absolutely right that both imperatives are met jointly at the same time at every decision point. In fact, the decomposition of expected free energy into expected information gain and expected utility is just one way of interpreting the underlying imperatives. When rearranging the terms mathematically, one can also express this as minimising expected inaccuracy (i.e. ambiguity) and expected complexity (i.e. risk). Both interpretations afford the same units of measurement for epistemic and utilitarian value (and ambiguity and risk); namely, bits of information. On the other hand, whenever encountering a new situation, there will be a systematic change in the relative contribution of epistemic and utilitarian components; simply because there is more expected information gain earlier on. As one becomes familiar with the situation, the novelty declines and the expected information gain gives way to prior preferences and the accompanying expected utility”.

3.5 Integrated Information Theory Integrated Information Theory (IIT) is a theoretical framework for understanding consciousness as integrated information. In particular, it identifies the essential properties of consciousness (called axioms) from where it subsequently infers the properties of physical systems that can account for it (called postulates). This is the reverse of the approach by other theories which usually start from the brain and then reason how it could give rise to consciousness. IIT’s main developers are Giulio Tononi, Christof Koch, and their collaborators.13 IIT considers such physical systems to consist of components (e.g. neurons) that are in a state, while being able to change that state. The basic premise is then that consciousness emerges from the integrated information within a system, not from the individual components of that system. This implies that consciousness is not just the sum of the activity of individual neurons, but rather arises from the way in which these neurons interact and communicate with one another. According to IIT, con-

 See particularly Tononi (2015), Tononi and Koch (2015), Tononi et al. (2016), while Aaronson (2014) gives a critique.

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sciousness is an intrinsic property of certain physical systems, such as the brain, and emerges as a result of their unique organisation and dynamics. IIT proposes that there are certain fundamental properties that a system must possess in order to support consciousness. These properties include: – Integrated Information: a conscious system must contain a high degree of integrated information, which refers to the amount of information that is generated by the system as a whole and cannot be broken down into its individual components. According to IIT, the degree of integrated information present in a system can be measured using a mathematical formula and is known as the phi coefficient (Φ).14 – Exclusion: a conscious system must also be capable of excluding certain types of information. In other words, it must be able to filter out irrelevant or distracting stimuli in order to focus on the most important information. – Composition: a conscious system must be able to combine different types of information in order to form complex, meaningful representations. – Causal Power: a conscious system must be able to influence its own future states and the states of other systems through its causal interactions. IIT proposes that these properties are necessary, but not sufficient, for the emergence of consciousness. In other words, a system that possesses these properties may not necessarily be conscious, but a system that lacks these properties cannot be conscious. The two key elements of IIT, differentiation and integration, are defined in terms of axioms and postulates. Here I will attempt to translate and apply one of these to an appropriate economic setting with an example of a differentiation axiom, emphasising its contingency (in option terms: to become ‘in-the-money’). Imagine you are a fundamental investment analyst on a company (factory) visit before issuing your updated investment recommendation to clients. Your experience of that visit may include phenomenal distinctions specifying numerous spatial locations. These concern several positive concepts, such as a factory (as opposed to no factory), a machine (as opposed to no machine), a conveyor belt (as opposed to no conveyor belt), a black colour (as opposed to no black), and higher-order combinations of distinctions, such as a black conveyor belt (as opposed to no black conveyor belt). Vice versa, it can also specify many negative concepts, such as no truck (as opposed to a truck) on the company’s parking lot, no computer (as opposed to a computer) in their offices, and so on. Similarly, an experience of pure darkness and silence—for example, after getting stuck in the company’s broken elevator—is the particular way it is. It has the specific quality it has (no machine, no conveyor belt, no black, nor any other object, colour, sound, and so on). This ‘optionality’ makes your visit “what it is like” and differs it from other experiences. IIT similarly extends such reasoning to differentiation’s postulate, respectively integration’s axiom and postulate.

 A more detailed discussion of its mathematical framework in general, and Φ in particular is beyond the scope of this book, so I refer to the literature.

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With its focus on consciousness, information is interpreted differently in IIT than in Shannon’s theory of communication. Shannon information is extrinsic to any subject. It measures the amount of signal versus noise. It is not compositional nor qualitative, and it does not require integration or exclusion. In contrast, IIT distinguishes a qualitative and quantitative aspect to the information content of an experience. The qualitative aspect, the quality of the integrated information, concerns the form of the associated conceptual structure.15 IIT treats conceptual structures as patterns or shapes which underpin specific kinds of phenomenality. Translated in portfolioism terms, these are the pay-off structures of consciousness’ options. The spatial nature of your factory visit as visual experience can be related to the cause-effect structure of grid-like mechanisms in the visual cortex. Ultimately, all qualitative features of every experience correspond to patterns specified by a system of elements in a state, whereas the quantity (of integrated information) is calculated via Φ. Critics of IIT have raised a number of objections to the theory. In one of his interviews with Robert Kuhn for the series “Closer to Truth”, the well-known mathematician Gregory Chaitin discussed IIT and admitted that he “didn’t understand the math”. One of the specific criticisms is that the Φ measure is difficult to calculate in practice, as it requires knowledge of the complete causal structure of the system being studied. Some have pointed out, using the ‘unfolding argument’, that IIT requires recurrent processing. The unfolding argument claims that any recurrent process can be unfolded into one without recurrence which would—problematically for IIT—result in zero Φ. Specifically, using theorems from the theory of computation, and applying the unfolding argument, Doerig et al. (2019) show that causal structure theories like IIT “are either false or outside the realm of science”. Others have argued that IIT does not provide a satisfactory explanation for why consciousness arises from integrated information, or how integrated information could give rise to subjective (i.e. phenomenal) experience. Despite these criticisms, IIT has been influential in shaping the way that consciousness is studied and understood in the field of neuroscience. It has also led to several new experimental techniques for measuring the degree of integrated information present in a system, including methods for measuring the coherence of brain activity and the degree of synchronization between different brain regions. Applied to the economic system, it should be clear that prices and other market data can be considered (integrated) information in the IIT sense. Next, I will list characteristics of the market which, following a selection of IIT’s requirements and predictions (per Tononi, 2015), suggest it is a candidate for a conscious system. 1. IIT predicts that loss and recovery of a system’s consciousness are associated with a breakdown, then restoration of that system’s capacity for information integration. The capacity for information integration in the market is determined by the num-

 This makes it closer to the original meaning of informare in Latin, namely ‘to give form’.

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ber of available, and actively quoted, securities, based on the concept of complete markets (Arrow and Debreu, 1954). In the unrealistic situation where markets are fully complete, information is perfectly integrated because it is possible to create any portfolio of securities that has a payoff for any conceivable state of the world (making the market fully deterministic). These securities can be considered the markets’ neurons which signal information, primarily via their prices. Like neurons, they are clustered, in this case across regional, sectorial, and individual asset class, reflecting specialised (economic) functions. In terms of the analogy to the physical brain, the closest we get to some kind of market cortex is the network of exchanges where securities are listed that can be traded by investors. Ultimately, the level of consciousness of the market correlates with the quote activity of securities which, in turn, relates to the conscious engagement of agents with idd-minds that provide the liquidity to the market. As I explained, in 2008 the market came close to a near fatal ‘stroke’ and ‘the ceasing of consciousness’. When securities expire, de-list or otherwise become permanently non-tradable that part of the market’s cortex is removed, whereas if their prices become stale (e.g. due to temporary circuit breakers) that part of the market’s cortex becomes inactive. Each has different implications for information processing. In general, the activity states that matter most for the market’s mood are the price changes, i.e. volatility, in those sectors that have the biggest market capitalisation, i.e. sectors in which attention of the collective investor is, literally, invested most heavily. These are likely candidates, for example, for the market’s “minimum partitions”.

Nevertheless Tononi and Koch (2015, p. 13) argue that IIT “aggregates”, like groups, are not conscious. By extension, this means that the market cannot be conscious (see also List, 2016). Apart from the points raised above, the MMH tackles this particular IIT view from various angles. First, I repeat my earlier argument: if we consider the mind to be extended, with cognition distributed, we cannot then suddenly cut off consciousness at the skull. Second, I point to the simple fact of scale: there is no known system where information about the world, including agents’ beliefs, expectations, and feelings, is more extensively integrated (and available as data) than the market. Third, we need to seriously consider the possibility that the market is significantly conscious because a partition of its components is not without consequences (as the GFC showed) and the existence of its mood challenges the exclusion axiom. The latter is the synergy of information the system realises. Specifically, it states that the information realised by a system as a whole is not simply the sum of the information generated by its individual parts (i.e. content), but rather the result of the exchanges between those parts (i.e. process). Finally, it is strange that Koch elsewhere has stated that collective consciousness is conceivable in the form of the internet (Reese, 2018). In Chapter 10 I will posit why the market is a much stronger candidate than the internet. According to IIT, consciousness is an intrinsic, fundamental property of a system, and is determined both by the nature of the causal components and by their states. Tononi has suggested viewing the conscious mind as a “democratic society” and the

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unconscious mind as a “totalitarian society”. Hayek, of course, looked at this from the opposite direction. In particular, he placed the challenge of economics, the “economic problem”, in the broader context of society: how to optimally use its knowledge to allocate resources. He concluded that the market and its price system, as a single mind of a different kind, could solve this problem. Crucially for our current topic: We must look at the price system as such a mechanism for communicating information if we want to understand its real function—a function which, of course, it fulfils less perfectly as prices grow more rigid . . . The most significant fact about this system is the economy of knowledge with which it operates, or how little the individual participants need to know in order to be able to take the right action. In abbreviated form, by a kind of symbol [i.e. price], only the most essential information is passed on and passed on only to those concerned . . . The problem is precisely how to extend the span of our utilization of resources beyond the span of the control of any one mind; and therefore, how to dispense with the need of . . . control, and how to provide inducements which will make the individuals do the desirable things without anyone having to tell them what to do. (Hayek, 1945, p. 528; emphasis added)

Translated in the MMH terms, the market mind utilises collective knowledge to allocate resources in a way that the individual minds that provide pieces of that knowledge cannot. It achieves this by means of price discovery, whereby prices symbolically communicate information in concentrated format that guides individuals’ behaviour. A rigid treatment of prices via any mechanistic approach that aims for “control”, such as central planning, jeopardises the benefits. Revisiting Hayek at the end of this section on IIT is appropriate. Considering individual minds and their information processing in isolation does not fit our economic life. Instead, Hayek saw the economy as an extension (the “extended order”) of the “sensory order” of the individual mind. Second, Hayek provided the arguments for why a market can calculate and process data in a way an individual cannot. These arguments thus help the case to argue that a (superior) collective consciousness can exist parallel to (but not necessarily accessible by) individual consciousness. This was also part of the argument made by Sornette, where the “emergence of consciousness” occurs “at a macroscopic scale that individuals at the microscopic scale cannot perceive” (Sornette, 2003, p. 241). Third, the price system was not designed by humans. Rather we learned to use it after, in Hayek’s words, we “had stumbled upon it without understanding it”. It means, in response to IIT’s scepticism, that we may not be able to describe all systems that integrate information up to the level of consciousness. Economic Note Immaterial Bitcoin It from bit symbolises the idea that every item of the physical world has at bottom—at a very deep bottom, in most instances—an immaterial source and explanation; that what we call reality arises in the last analysis from the posing of yes-no questions and the registering of equipment-evoked responses; in short, that all things physical are information-theoretic in origin and this is a participatory universe. John Wheeler (1989)

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Cryptocurrencies, or simply ‘cryptos’ are all the rage these days. Bitcoin’s rise to fame became exponential, as did its price. Then it crashed. Only to rise like the proverbial phoenix, reaching new highs (albeit with lots of volatility) . . . before dropping again. One of its latest dive-iterations was caused by FTX, the boom-to-bust crypto exchange. The reason for discussing cryptos, and the distributed ledgers that support them, is that tokenisation—the process of creating a digital representation of non-digital assets on a blockchain—is part of the wider digitisation with which we try to bridge better the physical and mental worlds. Proponents see these as a libertarian escape from intermediaries, particularly the government. Bankers, including central ones, have expressed scepticism about bitcoin’s value, noting that it is ‘not real’ and that ‘nothing is backing it up’. From the sound of it, their scepticism is perhaps understandable: another crypto, ether, echoes aether, the invisible substance that permeates the universe according to medieval science. Robert Shiller also commented on the rise of cryptos. He repeated his thesis of the similarly ethereal animal spirits and narratives, stating that “markets are driven by stories” without necessarily a link to fundamentals. The latter is risky. Following the collapse of FTX, the FT noted that crypto was hit by a brutal collision with [physical] reality. The question implicitly asked by crypto-critics, namely: “Is there anything of substance?”, is the mirror image of the question traditionally asked by philosophers: “Is there anything beyond substance?” The latter is raised as part of our broader metaphysical debate regarding mental causation. In reference to Subchapter 2.1, Shiller implies with his thesis that Popper is right: mental constructs, like stories (perhaps irrationally) impact markets and, by extension, the economy. In the extreme, can ‘faithbased’ bitcoin indeed damage the real economy? Or is it a clever alternative way, and possibly hedge, to facilitate transfers between real, financial, and virtual domains? Cryptos are going through society’s chain of economic discovery (see Subchapter 7.3). But it is too early to determine whether their rise will spawn higher-level properties—like those related to value— that will have a truly novel top-down influence on the economic system. We are also back to square one: the difficulty of determining their metaphysical nature, including that as perceived by (potential) users. Beyond its digital essence as software (i.e. bits), is bitcoin treated as a currency or a commodity? Again, this is relevant as higher-level properties supervene on this. After all, an asset cannot be both an attractive investment (because its value goes up) and an attractive currency (because its value is stable). As far as the critics are concerned, central bankers should be careful throwing stones from their glass towers. Fiat money is not exactly of any substance itself. Instead it is created out of thin air, borrowed into existence. What cryptos and fiats have in common is exactly their non-material nature and dependence on the mental state of trust. Specifically, although superficially cryptos and fiats seem to differ regarding their trust in decentralization, both supervene on trust in other human beings. In the case of cryptos you need to trust those governing the rules and making decisions about crypto forks, for example. Also, the accelerating switch from embryonic trust in machines to full-blown automation bias has major implications for human exchanges in that regard. Whether or not bitcoin is a ‘greater fool’ bubble is almost trivial in this context. Allow me to explain. Previously I mentioned the ‘greater fool’ assumption, i.e. trusting the next buyer to pay you more than your purchase price, in the context of Theory of Mind (ToM). Importantly, both fiats and cryptos are vulnerable under conditions where ToM is judged to be no longer reliable. The most extreme situation would be when trading has become dehumanised, that is fully mechanised. This would have two main characteristics. First, no human minds are involved in trading. Second, automatons make all the ‘discoveries’. This would make the market meaningless for humans. Nevertheless, in such a world we will have fully succumbed to that particular version of overconfidence called automation bias. So, machines and technology allow us to extend our minds, but we have to get the balance right.

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3.6 Global Workspace Theory The Global Workspace Theory (GWT) is a theory of consciousness centred on working (or workspace) memory in the brain. Working memory is short-term in nature with its components having limited capacity to retain information. Again, it highlights attention as a scarce commodity. Among GWT’s main developers are Bernard Baars, Stanislas Dehaene and Jean-Pierre Changeux.16 Starting with Baars (e.g. 1997a) GWT was originally developed, ironically, as a translation of early AI into brain operations. Specifically, the brain is viewed as a broadcaster of data that is centrally stored (like AI’s “blackboard”) and accessed and distributed via its specialised functions. Further, this brain-wide sharing of information from workspace memory to various functional modules is consciousness according to GWT. The title of Baars’ paper refers to the “theatre” metaphor to explain GWT: In the working theatre, focal consciousness acts as a ‘bright spot’ on the stage, directed there by the selective ‘spotlight’ of attention. The bright spot is further surrounded by a ‘fringe’ of vital but vaguely conscious events . . . The entire stage of the theatre corresponds to ‘working memory’, the immediate memory system in which we talk to ourselves, visualize places and people, and plan actions. (Baars, 1997b, p. 292)

Other cognitive models similarly use the theatre metaphor, but all emphasise that this is not of the Cartesian variation: there is no homunculus or some kind of conductor on the stage. More recently, Dehaene (2014) and Changeux expanded GWT by finding empirical evidence that backs it. In behavioural experiments in laboratories they manipulated subjects’ experiences while identifying the brain regions that support those experiences. GWT states that (access) consciousness facilitates: – accessing, disseminating and exchanging information, plus – global coordination and control. Among the main characteristics of GWT is its emphasis on the role of the unconscious in the mind: “These unconscious elements are as important as the conscious ones, because they give us natural comparison conditions” (Baars, 1997b, p. 293). It amounts to an “unconscious audience” with “multiple specialized capacities”. Baars then rephrases that “the great bulk of these . . . operate all at once, in parallel with each other, as one great society” (emphasis added). It is all based on the fact that “psychologists have become convinced that the real work in navigating through the problem spaces of our lives is done unconsciously for most of us most of the time” (Baars, 1997b, p. 304). This echoes our earlier discussion on mental systems. The exchange between the deliberate S2 and the unconscious S1 includes cooperation. Baars emphasises the un-

 Again, I’ve drawn primarily from their work, particularly Baars (1997a), Baars (1997b), Baars (2005), Dehaene (2014), and Dehaene and Changeux (2011).

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conscious thereby exercises “great influence on whatever becomes visible on stage”, channelling Jung: As a rule, the unconscious compensation does not run counter to [the deliberate], but is rather a balancing or supplementing of the [deliberate] orientation . . . the unconscious supplies all those contents that are constellated by the [deliberate] situation but are inhibited by [deliberate] selection, although a knowledge of them would be indispensable for complete adaptation. (Jung, CW 6, para. 694)

Here too the economic view in the form of a market is appropriate, not just as a better alternative to the metaphorical “theatre” or “society”. The way markets deal with information, like discounting, is even more relevant. Following Baars (1997b), I translate and apply the majority of his “functions of consciousness” in terms of a healthy market mind. 1. Adaptation and learning function For complex adaptive systems, like minds, the more novelty is encountered the more conscious involvement is needed (e.g. the view of Jean Piaget; see Subchapter 2.4.1). In markets, as more news arrives price moves increase (both in size and frequency). This raises volatility and thus the need for investors to pay more attention (e.g. for risk control/compliance). As stated in Subchapter 2.4.1, the overall level of price action is one input to the MMH’s proxy for the level of market consciousness. 2. Definitional and contextualizing function Every market mood is shaped by contextual factors of which market participants are not always conscious. Not only is mood, instantiated by market participants, influenced by previous experiences (that operate subliminally), but also by private information that is not (fully) reflected yet. Market content and context intertwine and are inseparable. 3. Access to a self-system Referring to an imaginary Mr Market in this book is deliberate. What if Mr Market would reveal himself beyond how we currently think of him? We can, of course, not ask him directly about his sense of self. However, the many strikingly similar descriptions in the literature to a greater being imply that investors experience their encounters with the market as if they are engaging with an animated entity that is larger than us. Or rather, is us? A shared sense of collective self complements the other functions of market consciousness. 4. Prioritizing and access control function Baars explains this function by highlighting how “by consciously relating some event to higher-level goals (like good health), we can make it conscious more often and thereby increase the chances of successful adaptation”. In similar fashion, ESG (see note in Introduction) is increasingly popular and being integrated in investment man-

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dates. By consciously relating pollution, climate change, child labour and other ills to these sustainable and ethical goals, the market as a whole “can make it conscious more often and thereby increase the chances of successful adaptation”. Investors with such mandates form groups and, exactly because of their joint intentionality with regard to these normative issues, raise normative questions (see also Sosa, 2009). 5. Decision-making and executive function The market is not an executive but instead, if allowed, spontaneously self-organises in a 4E setting and exercises its control and coordination by means of price discovery. This recruits awareness of investors and makes prices publicly available and transparent. The market is used to make decisions, most importantly regarding allocation. When Baars states that in his mind’s theatre “an actor can call out a question to the audience, which may then respond with specialized knowledge that is not otherwise available”, it sounds like an echo of Hayek’s claim about the role of local and distributed knowledge in markets. 6. Error-detection and editing function The law of one price means that differences between the same securities are arbitraged away. So are most edges in skills, making alpha (or excess return) so elusive. Arbitrage removes errors in the market, even though the process that detects them is globally unconscious because it involves information that is only locally conscious, is insider information and/or concerns the technological unconscious. So, in markets too, “it seems that conscious input is monitored by unconscious systems, which will act to interrupt the flow if errors are detected” (Baars, 1997b, p. 307). In the final analysis, the MMH has sympathy with GWT but finds that its metaphor of an audience in a theatre is too passive. There is much more exchange taking place in the mind, driven by market forces and aimed at (value) discovery. Chapter 9 will discuss our pilot project and specifically relate some of Dehaene’s work to the topic of noise trading.

3.7 Conclusion This ends the chapter on the main cognitive theories which are of relevance to the MMH. Focussed on consciousness, they describe a complex but subtle coordination between the psychical (perception) and the physical (action) when mind~bodies encounter the world. This coordination can be described and interpreted in market terms. It suggests, for example, that supply and demand originate with anticipated experiences, either based on memories or imaginations. The implied psychophysical laws converge on the impressions when information is doubly realised as consciousness. On occasions its quality, i.e. what it feels like, dominates the impression.

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In contrast, justified by their physicalist belief in engineering, men-of-system are dangerously manipulating the economy to fit the theory, to force facts to confirm it. Generating disinformation, it conveys the wrong impression. In the final analysis, this just does not feel right for many people. I will have more to say about this in Chapter 8. The next chapter is about epistemology.

Chapter 4 On Epistemology: Am I Lucky? 4.1 Epistemic Doubts Of all axioms of utility theory, the completeness axiom is perhaps the most questionable. Like others of the axioms, it is inaccurate as a description of real life; but unlike them, we find it hard to accept even from the normative point of view. Does “rationality” demand that an individual make definite preference comparisons between all possible lotteries . . . ? Robert Aumann In short, lottery cases illustrate that our judgements about the epistemic riskiness of a belief is not a function of the probabilities concerned but instead reflects how modally close the relevant possibility of error is. Forming one’s belief that one has lost the lottery merely by reflecting on the odds in question is epistemically risky, even though the odds are massively in one’s favour. In contrast, forming one’s belief that one has lost the lottery merely by reading it in a reliable newspaper is not epistemically risky, even though the odds are not in one’s favour to such an extent. Lottery cases thus emphasise the necessity of safety for knowledge, for it is only safety which eliminates epistemic risk of this sort, by excluding the possibility of error in close possible worlds. Duncan Pritchard

4.1.1 Introduction Epistemology1 is often a baffling topic. While I have tried to keep it as simple as possible, this chapter is probably the most difficult of this book. Epistemology is concerned with the nature of knowledge, including its reach and validity. Epistemology addresses what we know, how we know it, and how we know its value. In turn, this is relevant for decision-making and for issues like risk and uncertainty, including model uncertainty. It also concerns fields like scepticism and nihilism which each have different (and in their case diminishing) views on what we (can) know. This chapter will discuss various epistemic topics, like epistemic rationality, epistemic luck and epistemic risk. Overall it underlines the relevance of epistemic utility. This is different from economic utility which is what mechanical economics is mainly concerned with. Economic utility is the satisfaction or usefulness that individuals derive from consuming goods and services and, by extension, the value they place on them. Epistemic utility, on the other hand, refers to the usefulness of information in general and knowledge in particular, regardless of any economic benefits. Epistemic  For a thorough introduction to epistemology, see the work by Duncan Pritchard, in particular Pritchard (2005), Pritchard (2006), Pritchard (2010), and Pritchard (2016). This subchapter references regularly to his work (but any errors are mine). For a different view on “varieties of risk”, see Ebert, Smith and Durbach (2020). https://doi.org/10.1515/9783111215051-004

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utility is particularly concerned with the value of information and knowledge to gain a deeper understanding of the world in general and, consequently, to deliver better decisions. As such it is still relevant in terms of economic considerations but (applying practical dualism) is more closely associated with the mental essence of discovery. For example, epistemic utility—as the pursuit of knowledge and understanding—can lead to the discovery of new products, services, and technologies which, in turn, can create new markets. It may also help to improve existing markets by providing consumers and producers with better knowledge about goods and services. This can lead to more informed decisions, greater competition, and ultimately, better outcomes, which is particularly important in our times of big data and misinformation. However, we must start with (and limit ourselves to) discussing what epistemology means for economics itself. Any theory is supposed to produce knowledge. Its cognitive success is judged by the quality of that knowledge. The recent reality checks and the mainstream beliefs they beggar force a reassessment of economics in that regard. In the earlier words of Harman, “our epistemological convictions about how we acquire knowledge, and about the nature of explanation, justification, and confirmation, are subject to revision and correction”. This is particularly the case for REH because it pretends to produce knowledge (on behalf of mechanical economics) exactly because its premise is that economic agents have perfect knowledge. It is this claim that I’m particularly targeting. Many in the profession have started to question not only what they actually know about the economic system, but also how this knowledge comes about. In other words, how do economists know that they know the economy, respectively how do investors know that they know Mr Market? On the question of whether we learn from history, especially our mistakes, Jim Grant notes that “progress is cumulative in science and engineering, but cyclical in finance”. On a more serious note, economists like Hayek, Knight, and Shackle reflected deeply on epistemology. As Knight says: “One who aspires to explain or understand human behavior must be, not finally but first of all, an epistemologist” (Knight, 1925a, p. 374).2 Economists are, or should be, motivated by one embarrassing event in particular.3 Referring to the GFC that was raging during her visit to the London School of Economics (LSE) in 2008, the late Queen Elizabeth II famously asked the attending economists: “Why did nobody see it coming?” Of course, what she was actually asking was: “Why did none of you, the experts, see it coming?” Although we all appreciate the need for decorum during such events, to this day I regret she did not raise this

 Nobel laureate Robert Aumann is one of the few modern economists who has dealt with epistemology for many years. His interpretation of Interactive Epistemology is based on game theory and focusses on knowledge and belief when there is more than one agent or ‘player’.  If nothing else, it was embarrassing because it exposed the hubris of the earlier outrageously overconfident claims by (i) the most celebrated economist that the “central problem of depressionprevention has been solved” and (ii) by the most powerful central banker that problems in the subprime housing sector would be “contained”.

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question explicitly because the economists clearly did not get it. This was painfully reflected by their denial in their subsequent reply to Her Majesty, instead blaming “financial wizards” and “bankers” while admitting only to a lack of “imagination”. Which is ironic and trivial at the same time because there is no imagination (e.g. about a novel future) if you use models with predetermined outcomes. Talk is now of a ‘new normal’. But as Jung said: “Normality is a fine ideal for those who have no imagination”. Anyway, even with the benefit of hindsight our economists specifically failed to recognise the need to revisit the belief system responsible for producing and justifying economic knowledge. As we saw, the assumptions underlying REH and, by extension the EMH, have already been challenged from many angles. Early on, Hayek and Knight argued that we have incomplete knowledge, and here they will be joined by Bertrand Russell. Robinson ridiculed equilibrium, and Simon advocated bounded rationality. Behavioural economics has subsequently pointed out the biases and heuristics that distort our decision making. I also agree with more specific challenges, like uncertainty and novelty, as well as those using the fallacy of composition argument (see Kirman, 1992). In this subchapter (4.1) I will complement those challenges with my challenge of REH’s own rationality. We can, per 4E cognition, consider REH and its model as a tool extending our minds and contributing to the production of cognition. Could it then be that REH, while relying on the rationality of agents, is itself not meeting that requirement? And by extension, while relying on the perfect knowledge of agents, is itself— via its application by experts—not producing knowledge?4 Not surprisingly, my challenge centres on the passivity, or even removal, of human mentality due to REH’s underlying mechanical attitude. Philosopher William Seager (2014, p. 147) offers a way to state this more formally in epistemic terms. He makes a distinction between: – Absolute (or strong) epistemological (or epistemic)5 dependence: “X is absolutely epistemologically dependent on Y iff it is impossible to understand X except via an understanding of Y”. and – Weak (or reductive) epistemological dependence: “X is reductively epistemologically dependent on Y = it is possible to understand X via an understanding of Y”. In this book, based on the market-as-mind strand of the Market Mind Principle, I argue that the strong claim applies for understanding the market. Freely interpreted: the market is absolutely epistemologically dependent on the 4E mind because it is impossible to understand the market except through an understanding of the 4E mind.

 If all this sounds very abstract, you are right: all the while we are forced to largely operate in REH’s self-proclaimed ‘as if’ environment where shifting goal posts almost make it impossible to target criticism.  Again, to keep it simple I don’t make a distinction here and will use them interchangeably.

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At the same time, we have incomplete understanding of the market because we do not understand our minds fully (again, echoing Hayek, especially 1952). In what follows it is perhaps helpful to employ Hayek’s practical dualism and differentiate the mental world (e.g. of perception) from the physical world (e.g. of action), as well as the subjective from the objective and internal from external. Such distinctions can apply to coupled systems: “inner and outer aspects of the coupled system can for certain purposes usefully be treated as distinct components” (Sutton, 2010, p. 196). There are subtle variations in that regard. For example, epistemic action occurs internally in the head whereas pragmatic action occurs externally in the world. The former is often used to reduce uncertainty in preparation for the latter, i.e. to improve its chances of success. In the first instance I will make the distinction between epistemic and practical rationality. I will focus on the former to show REH’s two main epistemological weaknesses: 1. A dubious belief-formation process due to the lack of cognitive ability or “real agency” (Kelso and Engstrøm, 2006, p. 105) required to produce knowledge. I highlight two flaws: 1.1 The enforcement of its model, in terms of predetermined beliefs, means that the representative agent, far from being perfectly knowledgeable, is the robotic variation of the so-called “brain-in-a-vat” or BIV. 1.2 Inter alia, I will discuss the implications of the cognitive concept of epistemic luck for the econometrician, who is the creator of the REH-model in use. 2. A belief in mental causation while holding a mechanical worldview, making it dubious. As already discussed, this is like squaring a circle. Epistemic rationality is concerned with both the truth of underlying beliefs as well as the belief-forming process, including epistemic (in this case subjective) probability. The truth of beliefs is their quality in terms of accurately describing the world. Specifically, a true belief accurately describes the world (the earth circles the sun) whereas a false belief does not (the earth is flat). The belief-forming process is about how to attain true beliefs where, at least for our purposes, it assures that a true belief is an instance of knowledge and not, say, luck. In particular, a true belief can only be considered knowledge if it is the product of cognitive ability, an example being “due to my ability to comprehend evidence I know that this belief is true”. Also, epistemic rationality is primarily associated with the psychological domain, i.e. the mental world. For example, if you believe that Warren Buffett outperformed the S&P500 over some period, and your belief is based on undisputed evidence you have, then your belief is epistemically rational. Notice that this is part of your perception and you are not doing anything with this belief except holding it in your mind. Another occasion where epistemic rationality is relevant is in the use of counterfactuals. For example, most economists believe that economic conditions today would have been much worse if central banks had not continued to intervene. This is not (and never will be)

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an epistemic rational belief because there is simply no reliable evidence available to support such a claim.6 Practical or instrumental rationality, on the other hand, is action oriented and is thus primarily associated with the physical domain, i.e. the actual world, including its objective probabilities. It is the rationality in means (including choices) to pursue goals.7 For example, if your goal is to benefit from Warren Buffet’s investment acumen, and you believe that you can only do so by investing with him, then it is practically rational for you to buy Berkshire Hathaway stocks. This time notice that there is no mention of (any ability in gathering) evidence for your belief but that you act on it anyway with that trade, making it rational. In short, epistemic rationality is about gathering true beliefs, whereas practical rationality is about acting on beliefs. Or, to put it differently, epistemic rationality is about achieving truth of beliefs, whereas practical rationality is about achieving the results implied by beliefs. Moreover, true beliefs do not necessarily count as knowledge. My belief that the battery of my car is dead (because I left the lights on) is true. But I will not know this until I actually fail to start the engine.8 This also applies to statements based on beliefs. So, when Soros casually writes that “knowledge is represented by true statements” (Soros, 2013, p. 312) this is not necessarily correct. For example, after I ask you to state what card is on top of a concealed card deck your choice—perhaps with Mötorhead playing in the background—of the ace of spades turns out to be correct. Your statement (backed by a belief) is true but is generally not considered knowledge. True beliefs are necessary but not sufficient for knowledge. This distinction is important. Knowledge is not only what a theory pretends to produce, but also a requirement for sustained successful actions: any success of actions that are not based on knowledge will peter out. Knowledge is evidential in that regard. On the surface it seems that practical rationality is the type of rationality that the REH should only concern itself with. After all, at the individual level the REH emphasises goal-seeking agents who optimise their behaviour by maximising economic utility ‘within the model’. They find aggregation via the representative agent who we will name Paul. Paul will be our acting ‘model’ agent going forward. We will call this micro-wise practical rationality. At the same time, and more importantly, the REH is a modelling technique with practical applications used by econometricians for policy

 Those who argue that model simulations can provide such evidence obviously miss the point. In fact, there is empirical evidence that, at times, the economic conditions have been worse than at the depth of the GFC, despite bigger interventions since then.  In spirit it is consistent with the instrumental methodology, favoured by Milton Friedman who preferred the narrow “predictive power” of a theory above the realism of its assumptions. Although freely interpreted one could consider his positive economics—which suggests testing of assumptions/ beliefs is valueless in evaluating theories—to be an extreme case of practical rationality.  Ignoring other potential causes, etc.

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making or finance professors to judge active investors. It projects what we may call a macro-wise or, better still, model-wise practical rationality, in particular on the modeller using it. In our case he is named Gene, a highly intelligent econometrician. In turn, the separation between micro-wise and model-wise practical rationality forms my justification for the higher-level separation between epistemic and practical rationality, which is our focus here.9 Econometricians not achieving goals, as expressed in the Queen’s critique of “not see it coming”, makes model-wise practical rationality doubtful and opens the door to check for epistemic rationality of the REH itself, i.e. model-wise epistemic rationality. The initial target of that check is offered by Sargent: “In rational expectations models, people’s beliefs [as in forecasts] are among the output . . . They are not inputs” (Evan and Honkapohja, 2005, p. 566; emphasis added). This exposes the REH to attack if the quality of that output, aspiring to be knowledge produced by mechanical economics, is dubious, as evidenced (if nothing else) by recent events. Specifically, following the GFC Jean-Claude Trichet, former president of the ECB, stated that “as a policymaker during the crisis, I found the available models of limited help. In fact, I would go further: in the face of the crisis, we felt abandoned by conventional tools”. Importantly, these micro-wise output-beliefs (“output-beliefs”) need to be distinguished from the model-wise REH-belief (“model-belief”). The latter is the belief in any REH-model itself: “the belief, among economists and non-economists alike, that . . . the REH really does capture the way reasonable people think” (Frydman and Goldberg, 2011, p. 22). A particular element of that model-belief is the belief that any subjective probability distribution is the true distribution. In summary, while both hold the model-belief: – Whether Paul is achieving his goals is a question of micro-wise practical rationality. – Whether Gene (and thus, by extension, his [e.g. ECB] client) is achieving his goals is a question of model-wise practical rationality. By appealing to (while relying on) Gene’s rationality to recognise inconsistencies in the overall epistemological structure of the REH, we will attempt to help him mend his ways. We start by phrasing the REH’s model-wise practical rationality. Its goal is to produce economic knowledge (e.g. forecasts) by modelling (e.g. via DSGE) the economy based on the belief in homo economicus. Similarly, in finance its goal is to produce market knowledge by modelling the market (e.g. via CAPM) based on the belief in the rational investor. The belief in homo economicus, respectively the rational investor has already been questioned extensively, making the micro-wise epistemic rationality

 Under certain circumstances one can see epistemic rationality as a special form of practical rationality. I will not go into this here.

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of those particular beliefs doubtful. But, as we now know, the REH can initially get away with this by pointing to its (ambition for) model-wise practical rationality and using arguments derived from it to defend itself. For example, Lucas’ well-known rebuttal of the criticism by behavioural economics is basically supported by arguments which point to the practical (i.e. model-technical) implications and inconsistencies of assuming systemic irrationality, like “you can’t fool everybody all of the time”. The same applies to the main arguments used to defend the EMH. The EMH even accepts that, micro-wise, individual investors can act irrationally but then maintains that, model-wise, the market overall is mentally rational and physically efficient. But again, the epistemic rationality of the belief in homo economicus, respectively the rational investor—however doubtful—is not what I want to focus on here. Instead, I target the model-belief that sustains the pretence of (producing) knowledge. What is required is to challenge the REH as a true belief overall and not just one to act upon. For that we need to look at the belief-forming process which produces the beliefs from and about the model, and judge these in terms of knowledge.10 The key aspects of this introduction can be captured in Table 4.1, whereby the non-heading cells contain the types of rationality and/or beliefs that apply to the row, by column headings. Table 4.1: Rationality Variations. Rationality Epistemic Entities (i.e. beliefs/probability)

Practical

Paul

Micro-wise Model belief + Output-beliefs

Micro-wise

Gene

Micro/Model-wise Model-belief + Output-beliefs

Micro/Model-wise

Model

Model-wise Input beliefs (i.e. objective probability)

Model-wise True probability belief

In summary, the financial crises and other empirical failures invite closer scrutiny and widen the rationality criteria to include epistemic rationality. Does the REH deserve our trust in its own rationality and knowledge generation in that regard? Does it contribute to maximising epistemic utility? As I mentioned, my strategy to spread doubt is two-pronged: I challenge cognitive ability and I question the mentality

 To emphasise: discussing the truth of these beliefs is different than discussing the truth of the belief in the homo economicus! In other words, there is a difference between beliefs from the model and beliefs about the model.

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assumed by the REH while it paradoxically embraces the mechanical worldview (which assumes insentient machines and epiphenomenalism).

4.1.2 Doubt about Cognitive Ability So, for reasons just explained, proper rationality goes beyond practical rationality. Contextualised with epistemic rationality we can question the REH’s wider validity in terms of producing knowledge. In particular, I want to test this via the requirement that knowledge involves a true belief which is due to cognitive ability. In turn, knowledge is the precondition to make the success of actions (from practical rationality) sustainable. We are able to use this approach because the REH can be seen as aspiring to be a theory of knowledge, namely economic knowledge, both in terms of (e.g. model) input and output. Such ambition brings demands and requirements, particularly if it assumes perfect knowledge among agents: In crediting an agent with knowledge we are thus, amongst other things, crediting her with having a relevant cognitive ability which played some key part in the production of the target true belief. An adequacy condition on any theory of knowledge is thus that it is able to accommodate the ability intuition. This entails that all theories of knowledge should include an ability condition of some sort (i.e., an epistemic condition which can accommodate the ability condition), or should at least include an epistemic condition which, amongst other things, does the work of an ability condition. (Pritchard, 2010, p. 3; emphasis added)

4.1.2.1 Doubting the Representative Agent Remember, both Gene and Paul share the model-belief. That is, Gene believes his REHmodel is true and believes it is the model that Paul also believes to be true (which is forced upon him within the model itself). In addition, they share output-beliefs which contain subjective probability distributions about variables. That is, Gene’s beliefs about particular economic variables are equal to the beliefs of Paul which, in turn and as output, are produced by Gene’s model.11 A standard example of those beliefs is the expectation of an economic variable x which, for example, could be a price: xt = Eðxt ωt−1 Þt−1 + ϵ This equation simply states that the value of x at time t equals the expectation (E) of its value in the previous period, conditional on the available information set (ω) at that earlier time. Importantly, ω includes the model itself that generates the expectation.

 Arguably, this makes them psychological twins in the cognitive world.

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The only way to not meet that expectation is via a random unpredictable error12 (ε) which reflects unexpected external shocks. The first instance of truth concerns the assumed true (i.e. objective) probability of this equation. Although formally we could, we will not dispute the existence itself of such probability. Instead, what is debatable is the belief that Paul’s subjective probability is that true probability.13 Here I rephrase this as the “true probability belief”. To be clear, the question arises of the truth of the true probability belief so we will investigate whether this and other beliefs meet our criteria of knowledge. This is all very abstract. So let me try to make this more intuitive in terms of how this works within Gene’s “as if” REH-model where Paul is the (hypothetical) acting agent. Imagine Paul is asked to give his belief about the investment climate following, say, a change in government policy. In particular, he is asked about the variable ‘temperature’ by way of reading a measuring device called investhermometer. Unbeknownst to Paul, this device is randomly fluctuating within a range around some value. Moreover, Paul doesn’t know that Gene makes sure—by adjusting another device, the investhermostat—that the investment climate within the model reaches this range at the very moment of Paul’s reading. Voila, equilibrium. This then is the extent to which Paul has maximised his economic utility and the expectation is reliably ‘correct’, i.e. by being tweaked within the model. It also means that in this setting we will accept, reluctantly but for the sake of argument, such micro-wise beliefs as true. Importantly, however: they are not knowledge. The more general argument by Pritchard applies here: In the relevant sense such beliefs are reliable, in that they are indeed more likely to be true than false (indeed, they are pretty much guaranteed to be true). But this is not a case of knowledge, and the primary reason for this is that such reliability does not reflect a cognitive ability on the part of the agent [Our Paul]. (Pritchard, 2010, p. 3)14

In other words, in order to count as knowledge true beliefs require an ambitious production method, founded on cognitive ability. It forms one of three challenges to epistemic rationality which suggest modesty regarding beliefs in general. Any of these materialising negates epistemic rationality if a subject (e.g. Gene) holds on to them anyway: 1. Fallibility: the risk of being erroneous, e.g. a belief is simply wrong. This has been covered extensively, most famously by Popper but also in biases and heuristics in behavioural economics, so I will not discuss it here (but see Chapter 5). 2. Intractability: the risk that the cognitive task to maintain the belief is too complicated. This has also been discussed extensively elsewhere, for example in complexity  Usually with a mean equal to zero.  For example, in reference to Sargent’s comment in Appendix 1-B2, since he puts God into the REHmodel, and God surely knows the objective distribution, we can infer that this belief is held by the architects of the REH.  This also counts in case any imagined end-user of the model, like a policymaker, has been included in the model. Her behaviour in the model will be similarly constrained like Paul’s in terms of not interfering with the outcome of the expectation as true.

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theory and pedagogy, as well as economics (e.g. Spear, 1989) so I will not discuss it here. Cognitive inability: the risk of the belief not counting as knowledge. I will discuss this here.

Pritchard defines what is at stake: A true belief, no matter what else of epistemic relevance can be offered in its favour . . . will not count as a case of knowledge if it is not the product of cognitive ability. Call this the ability intuition. (Pritchard, 2010, p. 2)

He further points to the recent literature to formulate two accounts of cognitive ability (COGA): COGAWeak

If S knows that p, then S’s true belief that p is the product of a reliable belief-forming process which is appropriately integrated within S’s cognitive character such that her cognitive success is to a significant degree creditable to her cognitive agency.

COGAStrong S knows that p iff S’s true belief that p is the product of a reliable belief-forming process which is appropriately integrated within S’s cognitive character such that her cognitive success is primarily creditable to her cognitive agency.

An important distinction between these two claims is the difference in the level of integration demanded before acknowledging that an agent has knowledge of the target belief. Specifically, “iff” suggests necessary and sufficient conditions for COGAStrong. I will not go into further detail but refer to Pritchard’s paper which discusses why COGAStrong faces “obstacles which are not faced by its competitor principle”. It leads to the conclusion that, for our purposes: We are thus led to a relatively weak account of cognitive agency, and thus of cognitive ability, albeit one that demands far more of a belief-forming process if it is to be knowledge-conducive than that it be merely reliable. As we will see, such an account of cognitive agency in fact fits rather neatly with the extended cognition thesis. (Pritchard, 2010, p. 11; emphasis added)

Here the extension mainly consists of the practical policies, regulations, and strategies in the economic system that are based on and rely on knowledge (not beliefs) produced by the model. The contours of my strategy to undermine the REH by questioning its production of knowledge—indeed, assumed to be perfect knowledge—are hopefully clear by now. So let me reiterate what I already hinted at. My first argument concerns Paul and must seem trivial to many but at the same time should be surprising. As a reminder, within the model Paul not only represents us but also Gene (thus Gene’s “micro-wise” labels in table 4.1). Whereas the usual criticism of the REH points to the unrealistic ideal of the all-knowing homo economicus I submit that, on the contrary, Paul actually has no cognitive ability. His beliefs are not intrinsic to him but enforced externally by the model as input-beliefs, in similar

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fashion to Descartes’ evil demon. Moreover, he is not able to question the source of his information or its epistemic pedigree. There is thus no way that Paul comes “to know both what the true source of the reliability of his belief-forming process was and that it was reliable” (Pritchard, 2010, p. 6). Despite his reputation as a rational agent with super intelligence, Paul is cognitively impotent and expresses a behaviour that only consists of actions and beliefs of a robot-in-a-vat. While perhaps trivial, advocates of the REH, including Gene, may try to move the goal posts and raise the objection that my first argument is a straw man: the REH does not assign that level of cognitive ability to agents because they do not need it in the model. However, my first argument certainly negates the belief that, in the words of Frydman and Goldberg, the REH “does capture the way reasonable people think”. Specifically, the rebuttal by REH-advocates does not hold for Gene, thereby opening the back door to my second argument. Some readers may already have guessed that Gene is in trouble here. Not only is he not an impartial observer (but rather is meddling with[in] the model, thus the “model-wise” in table 4.1). Also, despite the objection it is undeniable that Paul somehow must represent Gene inside the model (making Paul ≈ Gene). I pile on the pressure by confronting Gene with epistemic luck and risk. 4.1.2.2 Doubting the Modeller We were seeing things that were 25-standard deviation moves, several days in a row. David Viniar, former CFO Goldman Sachs

Initially we must acknowledge Gene’s cognitive ability to come up with the model. But this is not sufficient to meet our COGAWeak requirement. So, next, we are going to apply the cognitive concepts of epistemic luck, respectively risk to ‘test’ a specific market module of Gene’s REH-model, called the Equilibrium-model.15 In this model, Paul is the representative investor. But first, an important little Economic Note (Fama’s Confusion). Economic Note Fama’s Confusion In an interview with John Cassidy of The New Yorker (13 January 2010), Eugene Fama showed how confusing the complexity of the economic system, and thus the challenge of forecasting, can be for mechanical economics due to its blind spot. In this case, it concerns the leading~lagging dynamic between the psychological market and the physical economy. To the first question on how he thought the EMH had fared during the crisis, Fama replied (emphasis added): “I think it did quite well in this episode. Stock prices typically decline prior to and in a state of recession. This was a particularly severe recession. Prices started to decline in advance of when people

 This subchapter is to a large extent adopted and amended from Pritchard (2016). Any errors are mine.

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recognized that it was a recession and then continued to decline. There was nothing unusual about that. That was exactly what you would expect if markets were efficient”. However, to one of the later questions, namely whether the start of the credit crisis predated the recession, Fama replied (emphasis added): “I don’t think so. How could it? People don’t walk away from their homes unless they can’t make the payments. That’s an indication that we are in a recession”. Then follows the nail to the coffin. To the next follow-up question of whether the recession predated August 2007, when the subprime bond market froze up, Fama answers: “Yeah. It had to, to be showing up among people who had mortgages. Nobody who’s doing mortgage research—we have lots of them here—disagrees with that”. For the record, the S&P500 did not peak until much later, namely 11 October 2007 at 1576.09.

The key message of this note emphasises what is one of the main themes of this book, namely that the relationship between the real and financial economy is complicated because ultimately it is psychophysical. This includes issues like mental causation, correlation, and reflexivity. In other words, Fama’s confusion (and the implicit failure by the EMH) confirms why mind~matter exchange, and particularly consciousness (e.g. awareness of mood) is economics’ hard problem. Here I want to focus on certain beliefs about the market and their (potential) impact on the wider economic system. Based on the previous deliberation, especially REH’s claim of perfect knowledge, raises the bar of what we can reasonably demand from such a knowledge-producing theory, if only because practical rationality relies on it. The primary argument that I will lay out is the following: an agent can hold a true belief despite not actually knowing whether his belief is true or not. The fact that his belief is true could simply be due to other factors, generally called epistemic luck. Epistemic luck is any instance when somebody accidentally, coincidentally, or fortuitously has a true belief. In cognitive science, the consensus generally considers knowledge to be incompatible with and to exclude such luck. In other words, epistemic luck denies knowledge from true beliefs. In contrast, if you know, then your cognitive success (i.e., that your belief turns out to be true) is not due to luck. Among the characteristics of epistemic luck of any true belief are lack of control (because other factors determine it) and valence (it turned out well for the believer). My motivation to use epistemic luck in the case of the REH is twofold. First, a predetermined model is like a stale monitor (which I will discuss in detail below). According to Frydman and Goldberg, with “a bit of luck, a fully predetermined model might adequately describe . . . the past relationship between causal variables and aggregated outcomes [e.g. agents’ beliefs] in a selected historical period” (Frydman and Goldberg, 2011, p. 22; emphasis added). They, as well as others, consider this luck because during other times some form of novelty enters the economic system which cannot be accounted for. My second motivation comes from the EMH’s criticism of active investing, basically that beating the S&P500 is pure luck (and not due to skill). For our purposes I rephrase this as follows. A true belief that one has superior knowledge compared to the market, confirmed by actual outperformance (as evidence), is considered luck. On

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the flipside, the REH’s belief in passive investing to become dominant because it is ‘the rational choice by rational investors’—confirmed as true by its popularity (as evidence)—is a case of epistemic risk, because the belief turns out to be false. Specifically, while any dominance of passive investing confirms the truth of the REH’s belief, it is eventually self-defeating because it leads to a lack of price discovery which makes the market inefficient, and thus makes ‘tracking’ the market irrational. I will explore this further shortly. The concept of epistemic luck was popularised in the 1960s by the philosopher Edmund Gettier (leading to the “Gettier problem”), following earlier reflections by Plato and Bertrand Russell, among others. Here is Russell’s famous “stopped clock” example: It is very easy to give examples of true beliefs that are not knowledge. There is the man who looks at a clock which is not going, though he thinks it is, and who happens to look at it at the moment when it is right; this man acquires a true belief as to the time of day, but cannot be said to have knowledge. (Russell, 1923, pp. 170–171)

How can we translate this in an economic setting, and give an interpretation to Frydman and Goldberg’s “luck”? Think of the clock as a snapshot of a moment in history. Then replace the broken clock with a stale monitor, which reflects say, a chart of historic values for a variable that measures some economic activity. Our economist then looks at the monitor “at the moment when it is right”, i.e. the chart value is equal to its current value. As a clever reader, you probably already correctly guessed that our stale monitor can also represent a lucky sample of data. A true belief alone, therefore, falls short of being knowledge. In case such belief is held or produced by a theory, it thus would not count as cognitive success for that theory. Nevertheless, it can instigate behaviour when acted upon, thereby moving from the mental to the material world and having real impacts. With this epistemic framework there are various ways to judge the EMH beliefs. Specifically, I will show how a few of these may have contributed to imbalances in parts of the economic system. As a reminder, my ultimate goal in this subchapter is to provide ammunition for Gene to start doubting his model and, more generally, for the readers to doubt the REH as generating useful knowledge in contributing to our epistemic utility. For starters, the framework extends to Gene’s true distribution belief which he coded in his model. I need to address this at multiple levels. First, although we have granted that a true probability (as in ‘true belief’) may exist, knowing the truth of this is a different matter. In other words, it is questionable Gene knows whether his most accurate estimate, i.e. Paul’s subjective probabilities, is true (even if, in fact, it is). Some will simply say that the reason is obvious: only God knows the true probability is true. What complicates matters is that, in contrast to the objective distributions of insulated and sterile experiments by the physical sciences, economics does not have the luxury of such testing. In addition, others will point out that the whole situation is

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tainted because Gene’s interference in the model removes objectivity (even if such objectivity continues to be claimed by REH-advocates). However, I want to give a reason that Gene is likely to be more sensitive to. REHadvocates have proposed the so-called market selection hypothesis (MSH) to justify the assumption that investors know the true distribution. It is based on natural selection and states that investors with incorrect beliefs will eventually be driven out of the market by those with rational expectations. In other words, only rational investors, in our case represented by Paul, will survive. Nevertheless, several researchers have contested this. Yan concludes that natural selection “is unlikely to effectively eliminate the impact of investors with incorrect beliefs or to provide a satisfactory foundation for the assumption of rational expectations” (Yan, 2008, p. 1944). In fact, incomplete markets may threaten the survival of rational investors (Blume and Easley, 2006). I will return to this shortly. Let’s contextualise this by looking once more at the active versus passive investment debate.16 In short, active investing aims to outperform a particular benchmark, usually an index like the FTSE350 or S&P500. Passive investing has no such ambition and simply tracks that index. The EMH argues that active investing is futile because the probability of outperforming (underperforming) is negligible (significant). By extension of the MSH this means that, according to Gene, rational investors are almost surely passive investors. I will now provide a few examples that neatly tie this together. First, I want to further clarify how epistemic luck and risk apply in an investment setting. Imagine the following situation. Two investors, Anna and Betty, each select independently a portfolio of stocks for the same amount of money. They like investing but, otherwise, we are not concerned here with their motivations. Over the next twelve months Anna does some reading about efficient markets and finds out about the extremely low probability of outperforming the FTSE350. Based on this alone she forms the true belief that, at the end of that period, her portfolio is a ‘loser’. Betty on the other hand reads the performance results from her official broker statement. Solely based on this statement she forms a similar true belief: her portfolio is also a ‘loser’ in that it has underperformed the FTSE350. This leads to a puzzle. Anna doesn’t know she has a loser portfolio, whereas Betty does. The fact that Anna’s belief is true is due to luck, whereas that of Betty is due to knowledge. However, from a statistical point of view, the probability in Betty’s case (with a one-off statement as the base of belief) is nothing like as overwhelmingly in support of the truth of her belief as it is in Anna’s case (with years of statistical data as the base of belief). Why then is knowledge present in the second case and not the first? Is knowledge not a straightforward function of the strength of one’s evidence, probabilistically conceived?

 Although I did not extrapolate this here, it can also be applied, for example, to extreme market events.

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These questions and related issues can be addressed with the so-called modal account of luck and risk. Basically, an event can be modally close even if it is probabilistically unlikely. In our case, the possible world in which an investor outperforms, while probabilistically far-fetched, is in fact modally close. The possible world in which one feels like Warren Buffett is very alike to the possible world in which one is dumping one’s favourite stocks in disappointment. All that needs to change is that a few stocks perform differently. This is related, for example, to research showing how historically relative stock performance has been concentrated in a few stocks, leading to positively skewed distributions. So, given how Anna formed her belief, there is an ‘easy possibility’ that her belief is false at that specific moment. This is called epistemic risk. In contrast, Betty’s true belief does not seem lucky at all. Given how she formed her true belief, she could not have easily formed a false belief, because the likelihood of a heavily regulated and compliant broker misprinting a statement, while probabilistically not negligible, is not modally close. In conclusion, our judgements about knowledge are sensitive to the modal closeness of error as opposed to its probabilistic closeness. Consequently, a true belief can fail to be knowledge even despite the odds being massively in its favour so long as the possibility of error is nonetheless modally close. In such cases we judge the agent’s cognitive success to be too risky to count as knowledge. Why do I bring this distinction between modality and probability to your (and Gene’s) attention? Well, among others it explains why people continue to try to ‘beat the market’ as a modally close event, but do not place bets on modally far-fetched events with similarly low probabilities. Such events require significant changes in the real world to even become remotely possible. This is why ‘beating climate change’ through sufficient investments has proven to be so difficult, for example. Another example would be gold bugs betting on a return to any gold standard. More importantly, however, is that separation between possible and real worlds is relevant for that between the financial and real markets. True beliefs in the financial economy are not necessarily knowledge for the wider economic system. Allow me to explain this further by progressing to the final stage of spreading doubt. By default Gene believes that the market is efficient. It means that per his Equilibrium model rational agents are passive investors. They believe in the extremely low probability of outperforming the market and thus passively track its index. Moreover, Gene’s Equilibrium model incorporates the MSH which suggests that eventually only rational agents will survive because they hold a superior distribution of outcomes, namely the true distribution.17 Or rephrased in a belief mentioned earlier: everybody will eventually behave according to the model. This is basically what we see happening in the real world with the growth in passive investing to the point that it starts to dominate in terms of assets under management (AUM). However, it is influenced by fundamentally (pun intended) flawed thinking of me-

 Strictly speaking, also assuming that markets are complete, etc.

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chanical economics. The problem is, as I explained before, that this is purely based on informational efficiency within the market (i.e. awareness of internal information). To put it more bluntly, passive investors do not care about the real economy. They do not want to know. Nor do they need to, according to Gene. Any relevant information about the real economy is assumed to ‘trickle down’ efficiently into stocks. However, this is only possible if active investors are busy gathering and trading on such information and, by extension and longer term, if funding efficiency through the optimal allocation of capital back into the real economy is assured. But that will cease if all investors become passive.18 In short, what we have here is a serious case of epistemic risk. There is another issue Gene needs to consider which does not rely on rational investors being passive. What if, quite likely, the market is incomplete? As just indicated, research suggests there is a chance that this threatens the survival of (a significant number of) rational investors. Per the REH this threat is then not only already known to rational investors but should also be reflected in the true distribution. However, according to the REH rational investors do not change their strategy (whatever this may be), despite the fact that continuing it may eliminate them. But that is irrational! How can such a situation exist? Looks like we have another puzzle. On the one hand, rational investors’ belief (A) is in the form of the subjective probability distribution which, in turn, is believed (B) to be the true distribution. Both belief A and B are true in this setting here. But while rational investors act on A via historic data this does not reflect any knowledge about the true distribution, including the one going forward. In fact, the only reasonable explanation to justify not changing their strategy (despite the threat of elimination) is that rational investors actually do not know that A equals B, i.e. they do not know the true distribution. This means, in conclusion, that the EMH cannot have it both ways, in that rational investors keep their strategy and know the true distribution. One of them must be sacrificed. Unless we start to realise that the true probability belief is not epistemically rational, and that the informational efficiency belief leads to funding inefficiency, we will continue to see damage from mechanical economics building. We have now arrived at the point where Paul, in true Gnosis fashion, says to Gene: “Since it has been said that you are my twin and true companion, examine yourself so you may understand who you are”. So, should you meet Gene and should it turn out that he dabbles actively, say in factor premia, ask him if that is because he examined himself and realised that the EMH is not rational. To recap, empirical failure, as expressed by the Queen, renders practical rationality suspect. It raises red flags which, in turn, summon investigation on the grounds of epistemic rationality. I focussed on the REH’s claims regarding rationality and perfect knowledge to judge its model-wise epistemic rationality. It turns out that, in simple terms, the REH cannot have its cake and eat it too.

 Some readers will recognise the link to the Grossman-Stiglitz Paradox.

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4.1.3 Doubt about Model Realisation After focussing on epistemic rationality to spread doubt about the extent and validity of beliefs based on mechanical economics I would like to return for a moment to its practical rationality. What justifies acting on its beliefs, for example by way of policies? What seems to be overlooked is that the REH can only be practically rational (ignoring empirical counter evidence for a moment) if you believe in the truth of a mechanical world. I refer to Appendix 1 where I discuss the metaphysical assumptions underlying this worldview. I will translate those for the particular case of the REH in light of the foregoing (with doubts in brackets): – The belief in an objective economic system which the econometrician can hold at a distance and study in isolation (even though he intervenes model-wise). – The belief that the economic system is what is physically and independently measurable (even though there is downward causation of mentality). – The belief that we come to really understand an economic phenomenon through studying the behaviour of its elementary parts (even though the synergistic exchanges between those parts dominate the system’s overall behaviour). I have already addressed the first belief, which is further criticised by performativity. The third belief ignores complexity and makes out that one cannot see the forest for the trees. I implicitly criticise it throughout this book, especially when discussing complementarity, intersubjectivity, market mood, and so on. Here I want to briefly address the second belief, using the central bank policy of forward guidance. Forward guidance is probably one of the strongest instances where the inconsistencies in REH-beliefs and acting upon those via REH-models and their forecasts become clear. I take my cue from the US Federal Reserve’s official definition19 “forward guidance about future policy can influence financial and economic conditions today” (emphasis added). Specifically, Ben Bernanke, former chair of the US Federal Reserve, argued that forward guidance “affects longer-term interest rates primarily by influencing investors’ expectations of future short-term interest rates” (Bernanke, 2013; emphasis added). Let’s get this straight: you have what is basically an extended mental tool (guidance) that supposedly affects prices by influencing mental states in order to shape the physical economy? Clearly that is quite a commitment to mental causation. But why has the Fed never shared its motivation to make such a commitment? Specifically, what is the Fed’s metaphysical stance? For example, it is a safe assumption that the Fed, based on its use of DSGE and other models, subscribes to mechanical economics. Consequently, if the Fed governors are, to paraphrase Knight, mechanical monists

 Available from https://www.dallasfed.org/ research/papers/2019/wp1906.

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how can they explain such mental causation? Not to quibble, but do they use some variation of Jaegwon Kim’s arguments (see Kim, 2005) regarding qualia, for example? After all, their other tool of creating the wealth effect is about making people ‘feel good’. What about the Fed’s assessment (if they have one, that is) of the potential risks of its psychophysical meddling? On that note, the impact of balance sheet normalisation, Quantitative Tightening (or QT), was supposed to be neutral. Instead events, from turmoil in 2019 to the bank failures in 2023, showed it to have a real impact physically (in flows) and mentally (e.g. in confidence). In turn, it painfully exposes the Fed’s lack of awareness, in the broadest sense of the term. Throughout this book I make the argument that any suggestion of mental causation, be it by Akerlof, Bernanke, or any of the others I have referenced, raises the question of the underlying metaphysical assumptions. A theory assuming a mechanical world devoid of cognitive ability to support a proper and spontaneous beliefforming process raises doubts about the validity of its knowledge (production). It certainly denies mental causation. By making this connection the MMH pulls away, in a Wizard of Oz sense, the curtain that hides the REH-wizard running his ‘machine’. It points out the nature of the REH-emperor’s new clothes to reveal his nakedness. Apart from the reality checks there is lack of empirical evidence in support of the REH. In his 2005 interview Thomas Sargent already admitted that previous empirical tests were “rejecting too many good [REH] models” (Evans and Honkapohja, 2005, p. 568). In general, the “as if” nature of mechanical economics leads to the impression “as if” rationality emerges from irrational agents and efficiency from anomalies. Emergence is a very complicated topic, e.g. is it conservative or radical emergence? I am reminded of the famous New Yorker cartoon by Sidney Harris where a colleague, looking at an equation on the blackboard, tells its author to be more explicit in step two which states “Then a miracle occurs . . .”. Finally, I believe mechanical economics is vulnerable to other cognitive challenges like internalism/externalism, but I will not explore these here.

4.1.4 Conclusion Epistemic Doubts The earlier confusion by Fama (see 4.1.2.2) regarding the uncertainty in the economic system due to mind~matter exchange is echoed by Lucas who, we saw earlier, throws his hands up in surrender when “in cases of uncertainty, economic reasoning will be of no value”. It points to one of the key themes of this book. The mind~body problem, capturing our overall ignorance about mind~matter exchange (involving conscious beings), is at the core of our (true) uncertainty. Specifically, the role of knowledge in economic growth has remained what Simon Kuznets called “a measure of our ignorance”. But trying to ignore this by adopting unrealistic beliefs does not solve the problem. While statistically one such a belief may lie in its ‘transformation’ of subjective

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probabilities into the objective probabilities, this is just a symptom of the deeper underlying flaw of believing in a mechanical world. Epistemology facilitates critical examination of what the bases are for beliefs. Whether it is cognitive ability, mental causation, or other instances of mind~matter exchange, we find the REH seriously wanting in offering knowledge, or even “reasoning” for that matter. Of course, the broader issue, central to this book, is that we should not focus on “the way reasonable people think” but on the way reasonable people experience. To believe that you can isolate thinking (fast or slow) is to believe you can answer: “Where does thinking end and the rest of the mind begin?” That is simply silly. Finally, if the MMH is correct and markets are indeed about collective mentality, then Lucas’ idea of understanding them by simulation (“something that can be put on a computer and run”) will remind cognitive scientists of Searle’s lingering criticism that: the idea that computer simulations could be the real thing ought to have seemed suspicious in the first place because the computer is not confined to simulating mental operations, by any means. No one supposes that computer simulations of a five-alarm fire will burn the neighbourhood down or that a computer simulation of a rainstorm will leave us all drenched. Why on earth would anyone suppose that a computer simulation of understanding actually understood anything? (Searle, 1980, p. 423)

Again, I realise that the previous subchapters were probably tough going. The next two subchapters try to tell more intuitive tales of how epistemology (indirectly) plays a role in market understanding.

4.2 Is it Safe? ‘Is it safe?’ This can be a torturing question, particularly if you don’t understand what it refers to, as was quite literally the case for Dustin Hoffman in the infamous dental scene from Marathon Man, the 1976 film. Similarly, it frequently leads to confusion among investors (including bankers). Assets which were previously regarded as ‘risk-free’ can suddenly lose their safety blanket, leaving investors in the cold. In the 1990s, Japanese investors who had put their money into property because it seemed safe—after all, its value was assumed to always go up—soon regretted that decision. During the GFC it became painfully clear that supposedly ‘safe’ triple-A rated packaged mortgages included toxic subprime loans. And without unlimited support from central banks, some of the safest sovereign bonds would have defaulted. Add to this the confusion caused by arbitrary (i.e. politically motivated) haircuts, so-called collective action clauses, and other legal challenges. Apart from the pain of financial loss, this raises deeper concerns, not least for

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mechanical economics which places the ‘risk-free rate’ at its very foundations, indeed, as the key input of its models. In the context of the MMH, what are the consequences of being denied the usual relief in knowing that an investment is safe, whether through lack of transparency, redrawn rules or shifting regulations? What if, for example, the market can no longer make sense of how the properties of one type of security—such as a credit default swap—relate to the properties of another—such as sovereign debt? By ‘sense’ I mean not only the physical characteristics, such as legal ownership, but also literally the sensations involved in holding these securities, i.e. having positions in them. This is associated with what cognitive science calls ‘the binding problem’, which is a coordination issue, basically on how to assemble the properties that are detected in separate specialised detection centres in the mind into a coherent whole or—as in portfolioism—a balanced portfolio. In terms of the market, we can think of those detection centres as the individual markets for equities, bonds and other assets, spread around the world. These centres, through their participants, determine the risk and return profiles of those assets, which include the inherent and idiosyncratic qualities attached to owning the assets. Like an overlay (i.e. S3) these qualities of assets reach over and above their quantitative characteristics, as well as any rational assessment of their utility. Specifically, the feeling of trust is what makes safe assets ‘safe’. In the words of Mervyn King, former Governor of the Bank of England, “trust . . . makes the world go round” (King, 2016, p. 83). It also hints at the system’s vulnerability. What is generally not well understood about the collective and frequently contagious psychological states, like mood, is how and why they escape traditional investment research methods. The type of knowledge one gains by experiencing trust, despair or exuberance is different from the type of knowledge one gains by fundamental, technical or quantitative analysis. As discussed in Appendix 1, market sentiment indicators such as the bull-bear spread, put/call ratio or the ‘fear gauge’ VIX, do not convey the inherent qualities we try to uncover, just as technical indicators of a piece of music (e.g. frequency, pitch, tempo and so on) fail to convey how it sounds.20 What is relevant in answering our torturing question is that ultimately prices are not just information carriers but also the conductors of qualitative market states. Just as musical instruments not only carry frequencies and the like, but also conduct the sounds into music, particularly when played together. It is through prices that the market mood is meaningfully expressed and experienced by those who “play and listen”. Crucially, prices convey—symbolically and ineffably—how we deal with hazards and threats that are immeasurable (Knightian or true uncertainty), not only rationally but also emotionally.

 This will be discussed in more detail in Chapter 9.

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And here we have arrived at the Marathon Man as a metaphor for the debt dynamics that we observed over recent years. Policymakers are using very crude tools to extract information from the markets on a question that is no longer clear. Moreover, the cognitive attempts to make sense of, and possibly answer, the question are accompanied by the sensation of drilling for that information. Whereas Laurence Olivier’s Dr Szell drills without an anaesthetic, arguably central bankers have been providing too much of the stuff. One of its side-effects has been a relentless search for yield in safe assets, also to compensate for the ‘negative’ safety of sovereign bonds. Showing nothing has been learned, the financial industry has put some new lipstick on one of its infamous pigs. Recently ETF (exchange traded fund) and other structures have been created that will hold CLOs (Collateral Loan Obligations). These are similar to the notorious CDOs (Collateral Debt Obligations) that contributed to the GFC. Still, one change is that CLOs often consist of tranches of floating-rate loans rather than fixed-rate mortgages, exposing investors to a different kind of risk. Supposedly things are different this time around because the CIO of one firm confidently stated that the highest-rated tier of CLOs held by its ETF offered “a safe space”. And then there is gold, or rather fake gold, in another case that beggars belief or rather a false belief. The Chinese media group Caixin reported in June 2020 that 83 tons of gold bars used as loan collateral by Nasdaq-listed Wuhan Kingold Jewelry, Inc. turned out to be nothing but gilded copper bars. Roughly a dozen Chinese financial institutions had issued more than RMB 20billion (US$ 2.8billion) in loans backed against it over the previous five years. Amazingly, asked by Caixin if the pledged gold was indeed fake Kingold chairman Jia Zhihong replied: “How could it be fake if insurance companies agreed to cover it?” What should add to anybody’s disbelief is that the amount of fake gold (weight-wise) was equivalent to 22% of China’s annual output and nearly 5% of its 2019 gold reserve. Fraud is typical during bubbles, particularly when cheap money is being thrown around and moral hazard is rife. What is arguably the safest physical asset is not immune to this. This case especially brings in stark contrast the mental world of belief and the physical world of reality. To conclude, years of bailouts, guarantees, subsidies, off-balance sheet transactions, and other distortive therapies have been joined by Zero/Negative Interest Rate Policies (respectively ZIRP, and NIRP), QE, forward guidance, currency interventions and trading bans. Without much consideration for the unintended consequences this is bad economics: A main factor that spawns new economic fallacies everyday . . . is the persistent tendency of men to see only the immediate effects of a given policy, or its effects only on a special group, and to neglect to inquire what the long-run effects of that policy will be not only on that special group but on all groups. It is the fallacy of overlooking secondary consequences. In this lies the whole difference between good economics and bad. (Hazlitt, 1946, p. 4)

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The most damaging consequence of such price interference is that it violates the psychophysical laws, in particular the law on discovery. Under free and open conditions, the latter allows true exploration with prices bridging the mental domain of our collective psyche with the physical domain of the real economy, while accepting uncertainty. Comedian Stephen Fry’s comment on limiting free speech arguably applies to limiting free prices as well, making it very appropriate to capture what I mean by violating psychophysical laws: “To be forced to feel other than we do [e.g. via the wealth effect] is manifestly an impossibility, therefore what is really being asked is a pretence” (Woolcock, 2020). Healing the mind of the market looks a long way off when you don’t recognise it. In the next subchapter I will explore the principle of blind spots further.

4.3 An Invisible Gorilla as (Another) Elephant in the Room Consciousness is the elephant in the room, the blind spot of mechanical economics. However, there is more to what is overlooked. We live in a world of big and growing data, where investors are ‘forward guided’ to focus on particular areas like unemployment, corporate earnings and regulations. But are we at risk of missing something else, exactly because we have confidence that we have it all covered with our advanced analytical tools? Suppose you watch a video of a basketball game and suddenly somebody dressed as a gorilla would appear, dancing around the court. Would you notice it? Roughly half of the subjects in a famous experiment, called The Invisible Gorilla, did not. There were mitigating circumstances though. Specifically, before watching the video subjects were instructed to concentrate on carefully counting the number of passes between members of one particular team. This and similar experiments confirmed a psychological condition called inattentional blindness: the failure to see an event because all your mental resources are heavily allocated to paying attention to a particular activity. In mind-asmarket terms, you made a concentrated bet. In some situations that can turn into an expensive exercise. In broad terms, the relevance of this phenomenon was already highlighted by the earlier example of her Majesty the Queen pointing out economists had not “seen it coming”. Moreover it impresses the risk of ‘overpaying’ attention to single events, the mental equivalent to having ‘all your eggs in one basket’. Shortly I will list a number of overlooked gorillas that either are already dancing on the economy’s lawn (where some see green shoots) or may trespass later, possibly disrupting the party inside (inside the financial markets, that is). First, at least as interesting is the realistic assumption that subjects in the experiment actually did perceive the gorilla, but at a subliminal level. This leads to the broader issue of the role of the unconscious, a.k.a. System 1 (or S1):

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If until recently academic psychologists have been reluctant to accept the power of the unconscious, so have others in the social sciences. Economists, for example, built their textbooks theories on the assumption that people make decisions in their own best interests, by [deliberately] weighing the relevant factors. If the unconscious is as powerful as modern psychologists and neuroscientists believe it to be, economists are going to have to rethink that assumption. (Mlodinow, 2012, p. 22)

Let’s think of the unconscious as an invisible gorilla in investment research. At the same time, it is treated as yet another elephant in the room. Following Mlodinow’s point, it is important to break this taboo. We need to complement our investment analysis to get a balanced view on our big data. Exclusive reliance on analysis and ratio involves risks, including an inflated ego which, in turn, is the source for overconfidence (e.g. ‘Of course there is no error in my spreadsheet!’). Instead, we should aim to be more modest by respecting the unconscious and how it has served humans across all time, not just our time. If you agree that you may have overlooked something and that there is a possibility that somebody or something ‘uninvited’ can spoil the game, you better take your exclusive focus off the ball. Relax a bit more, look around the audience and take in the atmosphere. To get a sense of what this would entail, the BBC Horizon documentary Out of Control (2012) discusses the influence of the unconscious on our behaviour and decision making. Towards the end of the documentary, one of the neuroscientists discusses the generation of, what he calls, “a-ha signals” which are realised (and valued) in S3. He describes the non-analytical state of mind which creates space for the unconscious to facilitate such signals: “when looking at these images, the best thing to do is relax; you’re getting into a zone”. In our case, such an approach does not mean that we replace investment analysis but rather that we complement it by using data in a different format. I have more to say about this in Chapter 9. It is also important to not ‘mechanise’ this process with incentives or punishments for meeting or missing deadlines (see Kounios and Beeman, 2015, p. 206). Another strategy altogether, of course, is to be sceptical upfront of the instructions provided by the supervisors of our investment game. In fact, some of them have been moving the goal posts, almost like an open invite to angry gorillas. On that note, and as promised, my candidate for the scariest gorilla out there is misallocation. We generally do not see it yet, but it has already been on the court and will spoil the game for a long time to come. It is closely followed by the ‘damaged’ gorilla, representing the cracks in the foundation for the recent build-up in asset prices despite dismal fundamentals. That foundation is the trust in central banks and their policies but its cracks, appearing in currencies as the last bastion, are difficult to discern by most. Finally, the price for the ugliest gorilla goes to financial repression which is gradually encroaching in the global economy and will likely enter centre stage once those cracks open fully. Some stampede that could turn into! (See Chapter 11).

Chapter 5 On Methodology: Am I Healthy? 5.1 Introduction I call this a chapter on methodology because, like the CVC, testing and treating the health of mind~bodies involve methods. Some methods are good, many others are bad. This chapter is about the methods of central banks, including their policies, which have had such an extraordinary impact on markets’ mind~bodies over the past decades. They represent mechanical economics most prominently, not only in theory but also in terms of how it is put into practice. To wit, and contrary to prevailing theory and practice, there is no separation (nor isolation) between the effects of monetary and macroprudential policies. As we have seen with the banking crisis in 2023, the monetary policy of raising rates impacts the macroprudential domain of financial stability. Contrary to physics, there is nothing “spooky” about such “action at a distance”. It is all consciously connected via exchanges. As I’ll explain, and in the spirit of Erasmus, Musil, and others, I could thus also have called this chapter “On Stupidity”. Central bank policies and actions do not purely belong to either the real economy or the financial economy. Their so-called ‘transmission mechanisms’ intend to affect the former by manipulating the latter. We can interpret this in psychophysical terms. In Europe in particular transmission has not been working properly because animal spirits remained muted for a long time. Moreover, these policies have taken one-sided measures to quantitatively ease the pain of uncertainty, taking it out of the equation of qualitative experiences. In the final analysis, central banks aspire to be the market’s homunculus and Laplace’s demon combined. As we discussed, there are many problems with assuming any success of some omniscience central command in minds. Throughout the book I regularly touch on this and related issues, but here I want to focus on two particular aspects that affect the market’s mind, namely dependencies and biases. I will close the chapter with a comment on supermen.

5.2 Dependencies Everybody would like to find a way out of large-scale QE because it’s obvious that the economy can’t function like this forever. But after a junkie gets hooked on various substances, he finds it hard to come clean and this can only happen gradually, in fits and starts. Adam Glapiński, President central bank Poland

So, at least some doctors, with PhDs in economics, seem to realise that they are dealing with an animate being, which means dependence, drugs and therapies are more than metaphorical. Throughout history, countries (e.g. Argentina), companies (e.g. airhttps://doi.org/10.1515/9783111215051-005

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lines), and investors (e.g. hedge funds, and recently pension funds) have seen their share of relapses in that regard. Dependence can be a dangerous condition because the sequence of cause and effect becomes blurry. An entity suffering from it loses free will and exhibits compulsive behaviour often fuelling the factors that cause the dependence. Its general meaning varies from subordination to addiction. For example, a dependency can be a territory subject to a state on which it does not border. In the EU this seemed to describe the relationship Greece had with Germany during the euro crisis. Another example is the dependencies on global supply chains which became contentious during the CVC. Usually, however, we think of physical dependence. This is of a different kind and involves addiction to substances, including medicines. These have negative economic consequences as well. The opiate crisis in the US demonstrates this. It is at least as bad when the addiction is to drugs as these can inflict varying levels of harm. We could also judge society’s debt overhang as a symptom of dependency. Mr Market shows symptoms of addiction and Governor Glapiński’s comment above suggests we should admit that we are dependent on cheap credit from central banks. Fair enough. But isn’t this overlooking a bigger problem? What about the dependency of the doctors themselves, especially considering their incentives? In mind~matter terms we can make a distinction between material monetary policies, e.g. buying bonds, and mental monetary policies, e.g. forward guidance. In analogy with medical treatments, the former resemble medicines whereas the latter are more like therapies. Of course, any reader is free to decide whether monetary policies are healing or harmful. However, it is crucial to realise that in our reflexive economic world dependence is an appropriate concept because the distinction between cause and effect of a financial crisis is even less clear compared to an emergency in the medical world. Still, there is pretence in policy circles of knowing this. Specifically, take the US Federal Reserve (Fed), for example. It is tasked to secure full employment, price stability and financial stability. Is unemployment the ‘true’ underlying economic factor on which monetary policy depends, in the sense that, e.g. based on the Phillips curve, it will cause the Fed to taper? Similar questions can be asked for the other two tasks. All three, explicitly or implicitly, form part of the Fed’s mandate but it remains unclear where they are along the fine line of cause and effect of monetary policy. More importantly, how do they relate to the tools the Fed has at its disposal? Apart from this operational aspect there is the broader institutional issue concerning the Fed’s (in)dependency, which is regularly challenged from many sides. Formally, it is allowed to function (by US Congress) if it operates within, and achieves the goals stated by, its mandate. But who sets these goals, or in the words of CNBC’s Rick Santelli: “Who does the Fed really work for?” Taking these leading questions I will try to make clear that the Fed’s ‘dependency’ problem is twofold.

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First, viewed from an institutional point of view, the Fed is part of the financial system. Within this system it serves two masters, namely the US Treasury and the private banking sector. Figure 5.1 illustrates central bank ownership: Government

Other central banks

Belgium Japan

San Marino Turkey Switzerland

Other private shareholders

Private banks Greece South Africa

Italy US Fed

Figure 5.1: Central Bank Ownership. Source: Bank of England

Ben Bernanke is on the record admitting that the Fed and the US Treasury are in tandem to manage inflation if this is required for price stability (see Subchapter 5.4). In addition the Fed serves the private sector banks, which was formalised in the most recent addition to its mandate, namely maintaining financial stability. Specifically, the Fed makes sure the private banks that are TBTF (Too-Big-To-Fail) do not do so. In short, the Fed does not work in isolation within its modus operandi nor is it independent. Second, from an operational point of view, the Fed uses monetary tools, the most important of which are interest rate policy and QE/QT. The dependency between these tools is easily identified: the risk~return profile of the Fed’s balance sheet is heavily skewed because its bond holdings, which have grown in size to roughly US$ 8trillion, only ‘balance’ positively if interest rates do not rise. Apart from holding the bonds to maturity, and ignoring mark-to-market, the only way for the Fed to hedge this risk is if the US Treasury takes it on (again, see Subchapter 5.4). What is more worrying, but also more difficult to grasp, is how these policies are actually causing the circumstances that trigger their use. The short summary is that the Fed has been explicitly targeting the financial markets with these policies. Consequently it has interfered in price discovery, going against psychophysical laws. Fed

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policies are likely to have contributed to the distorted allocation between minds (human capital) and machines (inanimate capital), thus skewing the distribution of income and wealth, exacerbating inequality. In turn, and combined with other misguided policies by fellow men-of-system, this contributed to real economy effects, like inflation of physical necessities (including energy) which hurt the poor the most and sustains the gap. To wit, the wealth effect mostly benefits the rich. As a side-effect, they continue to spend, whatever the costs, thereby becoming the marginal consumers who keep inflation stubbornly high. The problem, again, is that central bankers in general subscribe to the automaton doctrine of a mechanical worldview and do not give the impression they appreciate the complexity involved—via feedback loops, etc.—in their mind~matter manipulation. Among the main consequences is that we got saddled with the wrong type of inflation. By manipulating interest rates, i.e. keeping them artificially low, the Fed promotes debt-fuelled risk taking (leveraged speculation) in financial assets. The flip side of sustaining these booms is the postponement of their busts. This kicking-the-can is required under such circumstances because those same assets constitute the collateral for the debt, while being the holdings in pensions. This strategy, ironically, jeopardises financial stability. During a June 2022 seminar, organised by the investment firm Gavekal, I submitted that in our leveraged economic system financial stability would dominate price stability if push would come to shove. This is exactly what subsequently happened during the UK’s LDI-crisis and the SVB and First Republic bank failures in the US a few months later. The BOE and the Fed (if only temporarily) abandoned their inflation fight and switched to bailing out, respectively, pension funds and banks. Mind you, UK pension funds had been ‘nudged’ for many years into the mechanical investment strategy of LDI by regulators, eagerly facilitated by investment firms. In general, as the late Sir Andrew Crockett of the Bank for International Settlements (BIS) warned: The tools of prudential regulation are themselves based on perceptions of risk which are not independent of the credit and asset price cycle. If prudential regulation depends on assessments of collateral, capital adequacy and so on, and if the valuation of assets is distorted, the bulwark against the build-up of financial imbalances will be weakened. (Crockett, 2001, p. 4)

Until the recent bout of inflation, various central banks were aiming to fuel consumption by generating a wealth-effect from (inflated) assets rather than from (inflated) income. The latter could be achieved if the policies would explicitly target the real economy by promoting hiring, which would lead to rising wages (as well as “real” economic activity in the form of increased production). This could well become necessary when the next recession occurs. For example, to lower the costs of hiring as well as stimulate corporate lending, the Fed (in cooperation with the US Treasury) could design a policy which links employee costs related to government claims—like payroll taxes and medical costs—to bank loans. Banks could lower the rate of their loans to companies if these payments, acting as collateral, are guaranteed by the government. Of course, the banks could then securitise and package these loans with the Fed providing additional liquidity by becoming a ‘guaranteed’ buyer of the resulting “employment bonds”.

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Don’t get me wrong: I would prefer that the Fed and the US Treasury (or any man-of-system, for that matter) stay out of markets except to secure price discovery. But as an interim step towards an eventual exit this and similar measures could provide a major improvement in the type and quality of the collateral underlying new debt: from speculative (financial) assets to productive (human) assets. It would be in the spirit of The Tenth Annual Report of the Federal Reserve Board which stated that the Fed was supposed to extend credit only for “productive” and not for “speculative” purposes (1924, p. 5).1 Then again, I could be wrong and current policies will eventually work. Other analyses assume the Fed is a kind of ‘independent’ bartender who can refuse serving alcohol to (drunken) pub goers, or a responsible parent who can take away the punchbowl at his teenage children’s party. However, in light of the above I would argue that the Fed itself is very much involved in keeping the party going and has become dependent on the punchbowl. In fact, the dependencies of the Fed are inherent to the nature of its party, both in terms of partygoers and the type of consumption. The dynamics involved are akin to medical conditions that concern both the physical and mental states. The real economy is physically addicted to cheap credit while it binges on debt. The financial economy is mentally addicted to dovish promises while it binges on risk. In short, the Fed itself is dependent on using medicines and therapies which lead to the symptoms that these measures are supposed to suppress. In turn, the economic system shows ever clearer signs of dependence, in particular tolerance and withdrawal (e.g. bouts of market tantrums). All in all, the often-used analogy which compares the market’s random behaviour to the walk of a drunk gets a whole different meaning. To conclude, the deeper problem is not recognising that dependence within the economic system is a consequence of the flawed mechanistic worldview and its treatments. So, when Soros (2010-ii, p. 11), explaining reflexivity, states that “treating drug addicts as criminals creates criminal behaviour”, I would extend this to the economy and markets. This was also a key message of Chapter 2: if you treat them as machines, you will create mechanistic behaviour that damages their health.

5.3 Biases I trust that many of you are familiar with the story of Peter Pan, in which it says, ‘the moment you doubt whether you can fly, you cease forever to be able to do it’. Yes, what we need is a positive attitude and conviction. Indeed, each time central banks have been confronted with a wide range of problems, they have overcome the problems by conceiving new solutions. Haruhiko Kuroda, former Governor of the Bank of Japan

 https://fraser.stlouisfed.org/files/docs/publications/arfr/1920s/arfr_1923.pdf (Accessed 12 August 2021).

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The Bank of Japan (BOJ) surprised markets in early 2016 by joining other central banks2 in dropping its deposit rate into negative territory (−0.1%). This move came despite Governor Kuroda’s assurance, expressed only the week before, that the BOJ had no intention of doing so. What made him change his mind (assuming that’s what he did)? Are central banks showing herding behaviour and had Kuroda succumbed to the comfort of ‘groupthink’ (perhaps after a few drinks with fellow men-of-system in idyllic Davos)? Behavioural economics has highlighted numerous, often subliminal, biases in the decision-making process of investors. These biases result from heuristics which are rules of thumb or mental shortcuts. Central bankers have their own psychology (Haldane, 2014) but are not immune to this. So, in what follows I will highlight the main biases among central bankers. They should be of serious concern to investors, just like their own biases are. The negative rates—which, at least until recently, extended both regionally and further along the yield curve—are a characteristic consequence of central bank policies. The total amount of investment grade corporate and government bonds with a negative yield reached a record US$ 17trillion in early 2020. Negatively yielding developed-market government bonds made up a significant proportion of that, representing roughly a quarter of the JP Morgan Global Government Bond Index. In Europe more than half of all government bonds were trading with a yield below zero. Although we remained some way off from seeing triple-A corporates issuing new debt on negative yields, clearly the universe of safe securities that offered any positive yields was getting smaller. Investors chasing the same investments are a direct consequence of central bankers chasing the same policies, with complex feedback loops between these actions. As discussed, central banks are using monetary tools to manipulate prices, interest rates in particular. They combine it with communication strategies, like forward guidance, intended to make their decision-making more transparent and predictable. This is all aimed at influencing investor behaviour. In behavioural economics parlance, central banks try to ‘nudge’ investors into ‘desirable’ behaviour, implicitly assuming that investors suffer from biases which interfere with rational decision making and prevent outcomes that are ‘optimal’ for the economy. However, central bankers are also subjected to biases. Failing to recognise this is a bias in itself, called the blind-spot bias. In the case of central bankers, biases are manifested slightly differently; they are much more prone to influences from group dynamics. This is due, first, to the academic culture in which central banks are embedded, something Jim Grant calls the “PhD standard”. Academia in general is vulnerable to groupthink (e.g. Kay, 2016). In this case, it leads to the fairly dogmatic adherence, across

 In particular, the Danish, European, Swedish and Swiss central banks which had already introduced negative rates.

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‘the board’, to mechanical monetary theory which frames problems in a much more uniform way than is usually the case. Notably, the DSGE modelling approach is the first port of call that immediately anchors central banker views. Related to this is the Fed’s “dot plot” which contains policymakers’ forecasts that, combined with other inputs (like the elusive r*), morph into their expectations, anchoring decision making and enforcing the illusion of knowledge. Second, groupthink is also caused by the isolated and fairly secretive way (justifiably or not) in which their meetings take place, including those between central banks. In short, central banks are a breeding ground for biases. With this in mind, let me list my selection: 1. Overconfidence, also known as the hubris bias, is generally accepted to be an overarching bias. It is closely related to over-optimism and is supported by the illusions of knowledge and control, particularly mind control. In this case, overconfidence is about failed mental causation: the mistaken belief that one’s beliefs cause a desired change in the world. For starters there was Lucas, as if speaking on behalf of central bankers, five years before the GFC: “the central problem of depression-prevention has been solved, for all practical purposes, and has in fact been solved for many decades”. Admittedly, recalling Kuroda’s earlier words, central bankers need to project a certain amount of confidence in order to maintain the credibility and trust that our fiat monetary system requires. But they can overdo it. My argument that central bankers, broadly across their membership, are suffering from this bias is predicated on the fact that they have consistently failed to reach their targets, in particular inflation. The difficulty in predicting it was already admitted by Milton Friedman who stated that it tracked money supply with a “considerable lag” that was also “rather variable”. Additionally, central banks’ forecasts on interest rates have often been off the mark leading to, what I called elsewhere, “Long Grass, Blowing in the Wind” type of charts. Overconfidence implies the risk of not delivering on your promises. Continuing with the Peter Pan metaphor, it is the risk that their Tinkerbell effect—central bank control because people believe in it—will wear out. 2. Cognitive dissonance is the uncomfortable mental state of having conflicting thoughts, beliefs and feelings which can negatively influence decisions. It is the typical disposition of the two-armed economist: “on the one hand . . . but on the other hand . . .”. An example among central bankers was the Fed’s recognition that, on the one hand, their policies are beneficial for Wall Street but, on the other hand, that they were concerned with the fate of Main Street. In other words, the Fed acknowledged that inequality is a problem and worried that ‘their’ monetary policy may have worsened it. 3. As discussed in Appendix 1, prospect theory suggests that people value losses and gains differently in decision making. Loss aversion is its particular manifestation, and one variant applicable here is the sunk cost fallacy. Also known as irrational escalation it is the tendency to continue to stick to a decision because of the existing costs (risking reputational loss) committed so far to that decision, even if

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there is evidence that the decision is wrong. Some readers will immediately recognise that this applies to QE. Self-attribution is the tendency to judge successes as being based on one’s own hard work and skill, whereas failures are due to others’ incompetence or bad luck. It often occurs in combination with hindsight bias. I only need to refer to the various biographies of past central bankers to make this point. However, I also consider the liberal use of data-dependency as an excuse to take credit when the economy does well and blame outside events for negative developments. A good example of hindsight is Mario Draghi’s counterfactual statement to the European Parliament regarding the ECB’s quantitative easing package: “Without these measures, the euro area would have been in outright deflation last year and growth would have been significantly lower”. Herding occurs in collective settings and is the tendency to think and act similarly to the majority. Imitation is the cognitive driver, as it increases trust which, in turn, improves cooperation. It is often reflected in the popularity of the latest gimmick (or hype) which, initially, is picked up by a critical minority. The growing acceptance of NIRP is an example of the bandwagon effect, in that regard. A variation is competitive imitation. Examples are the so-called currency wars where the common strategy is to devalue currencies in a ‘race to the bottom’. It is a titfor-tat, reflecting the fear of missing out, i.e. being too late to the party. Helicopter money could be a future candidate. House money effect is the tendency to take more and/or greater risks with profits that were generated on previous trades. This bias falls into the category of mental accounting whereby the money at stake is considered ‘extra’ or ‘not yours’. Central banks broadly have no profitability requirements, nor liquidity constraints. Still, the Fed, for example, has been extremely profitable over the past decade (Figure 5.2). The temptation to ‘bet the house’ (of future generations) is thus large.

Many biases overlap in definition and effect. For example, in light of unprecedented policy measures and their outsized effects, investors may now be suffering the illusion of control by central banks (Daníelsson, 2022). Also, the list above is not exhaustive, neither in the type of biases nor in the ways that they manifest themselves. To select biases from central bank behaviour is like picking sweets from a candy store: the choice is almost overwhelming. I could also have mentioned that Mario Draghi likely suffered from the hot-hand fallacy, that Bernanke’s wealth effect is a placebo effect (at best; money illusion at worst), and so on and so forth. In their defence, central banks will argue that due to structural factors they all faced the same threat of global deflation (and now inflation) and thus needed to act in a similar fashion. They will also counter that they have implemented so-called debiasing measures to diminish these biases. For example, decision-making by way of committee voting limits the influence of biases emerging from individuals, particularly dominant leaders. The BOE has created a blog on its website as a forum for open

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Figure 5.2: House Money: Growing Profits and Growing Risk. Source: US Federal Reserve

discussion. All these initiatives are commendable but do not address the main underlying issues raised in this book. Based on the biases that dominate the list above, particularly overconfidence and the sunk costs, investors should expect that central bankers will continue to show ever more extreme behaviour. Eventually, it will likely include a retooling of monetary policy to finance fiscal policy, i.e. some variation of helicopter money, as well as an abolishment of cash. This prospect makes investing even more difficult, although maintaining a well-diversified multi-asset portfolio, with a growing weight to real assets, is probably the right approach. But I may be overconfident. We can rightfully be critical of central banks. This particularly applies to the first bias of overconfidence, especially in being able to control and shape beliefs which I’ll further clarify in the next subchapter.

5.4 Supermen As long as you’ve done everything you can, Clark, you’ve nothing to regret. And you always do everything you can. Always. You know that . . . don’t you? Lois Lane

Lois Lane believes Superman can fly but she does not believe Clark Kent can. There is something about Superman that changes her belief. Investors believe the Fed Chair can print money, but they do not believe Jerome Powell can. There is something about the Fed Chair that changes their belief.3 They are both seen as heroes, trying to

 Note that Lois Lane does not know Clark Kent is Superman whereas investors know Jerome Powell is the Fed Chair. That difference in knowledge does not make a difference in them forming similar

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save society from bad things happening. But our heroes face potential threats. We know that Kryptonite, the “K-metal from Krypton”, is a threat to Superman and weakens his powers. What about the threat to the Fed Chair? Is it also a metal? First, a small Economic Note (Central Bank Independence). Economic Note Central Bank Independence Ben Bernanke implicitly called himself a “modern alchemist” in his infamous speech on deflation. It is worth quoting: “Suppose that a modern alchemist solves his subject’s oldest problem by finding a way to produce unlimited amounts of new gold at essentially no cost . . . What has this got to do with monetary policy? Like gold, U.S. dollars have value only to the extent that they are strictly limited in supply. But the U.S. government has a technology called a printing press (or, today, its electronic equivalent), that allows it to produce as many U.S. dollars as it wishes at essentially no cost. By increasing the number of U.S. dollars in circulation, or even by credibly threatening to do so, the U.S. government can also reduce the value of a dollar in terms of goods and services, which is equivalent to raising the prices in dollars of those goods and services. We conclude that, under a paper-money system, a determined government can always generate higher spending and hence positive inflation”. (November 2002, emphasis added) Bernanke basically admits that printing money is monetary policy and does not even pretend that the Fed is independent. In fact, the “100% confidence” that he later expressed4 in his ability to manage inflation relies on cooperation with the US Treasury. On a related note, for a long-time central bankers complained that they could not do the job of supporting the economy all by themselves. In other words, monetary policies needed to be complemented by fiscal ones. Since 2020, that is what they got. In spades. In hindsight, this was another “be careful what you wish for” situation. The current phase of inflation is a result of excessive loose monetary policy, combined with largesse in fiscal policy, exacerbated by various men-of-system playing geopolitics. Fighting that inflation can thus never be successful using purely monetary policies. Then again, what do I know? In 2022 Bernanke won the Riksbank’s Nobel Prize in economics.

Let me make clear that I am not a gold bug. Nevertheless, I do believe that, from a mind~matter perspective, gold plays an important role in our monetary system as the ultimate physical asset. That angle also offers the argument that gold will likely be the threat to the Fed Chair, like Kryptonite is to Superman. In fairness, not all central bankers are alike. Contrast Bernanke’s comments, for example, with those of former Bundesbank president Jens Weidmann. By invoking another alchemical story, namely Goethe’s Faust, he clearly reminded his audience in September 2012 of the importance of separating central banking from government in light of our historic experiences with inflation. And Mervyn King argued for The End of Alchemy (2016).

type of beliefs. Even if Lois Lane does know, as possibly hinted at in the above quote, it arguably still makes no difference to her beliefs.  Interview “60 Minutes”, CBS, 6 December 2010.

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Bernanke’s comments on “value of . . . paper-money” and “credible threat” highlight the psychological rather than the physical dimension involved in this process of monetary alchemy. Other economists have reflected on the psychic value of money, such as Schumpeter who considered it as “all that a people wants, does, endures, is”. Complex psychology explains how traditional alchemy was more about psychology than chemistry. Specifically, alchemic success was not measured physically, by turning lead into gold, but psychologically, involving the transformation of the alchemist. That clearly never applied to most of the central bankers. I have already extensively criticised central planning in general, and monetary policies in particular. But allow me to summarise it in this context. Central banks are, in the words of Richard Feynman, “tickling the dragon’s tail”. They are modern alchemists (as admitted by Ben Bernanke, Mervyn King, Jens Weidmann, and others) but do not seem to be aware of the deeply metaphysical context of their experiments. Ignoring the complexity of mind~matter exchange becomes stupidity as referenced in the beginning of this chapter when it results in a lack of sense of what is happening, especially when events, instead, are interpreted to forcibly fit a worldview. Specifically, central banks numb economic sense, often in the name of ‘rationality’ and ‘stability’. They ignore Cattaneo’s “psychology of wealth” in that ultimately sensations (i.e. S3) complete agents’ experiences of wealth accumulation, and subsequent annihilation, thereby enriching our understanding of economic cycles. Anxiety, like uncertainty, should be embraced for authentic understanding of existence, as Heidegger and others emphasised. The purpose of discovery and exploration is universally exemplified by the myth of the (true) hero, showing that only by exceptional effort combined with accepting uncertainty in the region of danger (watery abyss, cavern, forest, island, castle) can one find treasure.5 Instead, over the past few decades free explorations have been constrained, while others were enforced. Overall, policies of central banks have led to the back-stopped search for yield, the negation of fear, and the numbing of pain. For now, their failure is reflected in the deflation~inflation swings that they are supposed to prevent. Remember, printing money would lead to “managed” inflation, according to Bernanke. From a mind~matter perspective, deflation is a symptom of economic cooling. In other words, furious printing is compensated by timid activity. Helicopter money is then comparable to the physical act of Jay Powell flying as Superman and dropping (financial) drugs instead of delivering (real) food. Central banks are in their final stage of experimenting to create and sustain this make-believe world. Many are worried about the risk to the Fed’s ballooned balance sheet of increasing interest rates (now that inflation is swinging for the fences). They wonder how the Fed will deal with this threat. SVB, First Republic and other banks

 See also my quote from Hayek at the start of Subchapter 7.1.

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also reflect this stress. Let’s revisit one of Bernanke’s other speeches. In May 2003 he addressed an audience in Japan and shared his thoughts on Japan’s monetary policy. He stated that he was “intrigued by a simple proposal” by the Japanese Business Federation on how to insulate the Bank of Japan’s balance sheet against the risk of increasing rates (which would harm the value of its holdings in Japanese government bonds). The proposal involved a fixed-floating interest rate swap agreement between the Bank of Japan and the Ministry of Finance. A similar agreement is perhaps possible between the Fed and the US Treasury. Or the Treasury can offer the Fed a put option on its bonds which never needs to be exercised because it only serves as a hedge for mark-to-market purposes. Problem solved! Like Paracelsus, Wei Boyang, John Dee and the other alchemists of old, the current generation of central bankers is in the process of completing their Magnus Opus. Its narrative is one of creating wealth out of nothing, a true metaphysical feat (if real). As modern alchemist Powell and his ‘subject’ merge into one, with the US dollar acting like quicksilver, transcending the physical real economy. However, just as heating quicksilver resulted in a poisonous residue, heating up the US dollar may also lead to dangerous side-effects. Still, before him, Bernanke is recorded stating that he is 100% certain that central banks can control inflation. He was joined by Mario Draghi, the former ECB chief alchemist, who assured us all that the ECB would do “whatever it takes” and that its policies “will be enough” to save the euro. It is topped by the arrogance of the CCP men-of-system to manage the whole Chinese economy. The jury is still out but such overconfidence is vulnerable.6 Yes, the herd belief in central banking power still reigns. But as the previous chapter argued, all that is required to shift this is doubt. The absence of doubt (as the twin of overconfidence) is the message from Faust and other stories of the devil’s monetary role in our MMH setting. To paraphrase Charles Baudelaire7 the devil’s best trick is to persuade you that he does not exist. Translated: there is no evil in selling your soul, just earthly rewards. A shift in herd belief, possibly triggered by somebody or something ‘pulling down the curtain’ (see Subchapter 4.1.3), will remove the aura of central banking power. Any bursting of the “Everything Bubble” by cracking faith in central banks will (finally) impact the fiat currency system, with fiat currencies as the ‘trust-based’ asset class that underlies all others. For those who prefer more concrete indicators for this, I suggest closely watching vulnerable currencies; currencies of countries with high levels of debt/GDP (and/or high interest payments/GDP). In terms of the ‘mechanics’ involved think of the growing (automatic) need to hedge currency exposure by foreign investors holding such debt

 Jung identified good alchemists as those who show “visible mental struggles’ and labelled those who do not express such modesty as “charlatans”.  Echoed by Roger “Verbal” Kind, in the movie The Usual Suspects.

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as their levels rise, leading to a mechanical feedback loop between a country’s debt and its currency. (Implied) currency volatility should increase too. And gold will likely, in parallel, provide an early signal when its price starts to rise. The next chapter will point out that all these things are complex, while attempting to simplify them.

Chapter 6 On Complexity: Am I Emerging? A completely unfree society (i.e., one proceeding in everything by strict rules of “conformity”) will, in its behavior, be either inconsistent or incomplete, i.e., unable to solve certain problems, perhaps of vital importance. Both of course, may jeopardize its survival in a difficult situation. A similar remark would also apply to individual human beings. Kurt Gödel

6.1 Understanding Complexity Man has been impelled to scientific inquiry by wonder and by need. Of these, wonder has been incomparably more fertile. Friedrich Hayek

As we saw in Subchapter 1.2, when Hayek was asked, freely interpreted, about the parallels between the human mind and the market he started his answer with the following: “In both cases we have complex phenomena in which there is a need for a method of utilizing widely dispersed knowledge”. The MMH builds on this by highlighting the principles and dynamics of complex adaptive systems that minds and markets share. So, to enhance the collective aspects of cognitive science the MMH draws on insights from complexity science. These two—cognitive science and complexity science— meet in the social space where individuals interact. In the generalised words of Prigogine and Stengers (1984, p. 203): “Each individual action or each local intervention has a collective aspect that can result in quite unanticipated global changes”. Of particular relevance is the field of Coordination Dynamics and related areas, like social neuroscience (see Subchapter 3.2). Complexity science, or complexity theory, is the interdisciplinary study of complex systems, in particular those that adapt.1 If we untangle the complexity of such a complex adaptive system (sometimes shortened to CAS),2 we can identify two essential characteristics that help to understand complexity, especially the phenomenon of emergence: exchange and creativity.

 Good introductions include Holland (2014), Miller and Page (2007), and Dodder and Dare (2000). An early economic perspective is by Hayek (1967), an interim view is by Buchanan and Vanberg (1991), and a modern update is by Arthur (2005). Johnson, Jeffries and Huil (2003) discuss complexity of financial markets. For a more general view see Mitchell (2009). Finally, a rich source of research is the Santa Fe Institute: http://www.santafe.edu/research/.  While I will continue to use this term, I would argue that we need another variation which we may call complex intelligent system (or CIS). This is particularly applicable when consciousness is involved. See my points below. https://doi.org/10.1515/9783111215051-006

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Let’s start, however, with the term “system”. A system basically stands for a whole made up of components (its structural combination) and connected via their exchanges (its relational combination). The components form subsystems that are simple compared to their combined constellation. For example, neurons are the components that together, e.g. via neuronal networks, make up the brain and the nervous system which, in turn, form part of the body. Similarly, securities are the base units of specialised market subsystems. Viewed vertically, these include sectors or industries, representing numerous underlying real economic activities, varying from extracting oil to brewing beer. Viewed horizontally, they include so-called risk factors, like dividend yield for equities, or credit rating for corporate bonds. Portfolioism neatly fits into this view: complex adaptive systems form portfolios, and vice versa. The first characteristic of complexity is the dynamic exchanges between the components which are responsible for the complex in complex adaptive systems. In isolation one exchange is usually very simple. Together, however, they create the famous synergy that differs from and exceeds the sum of the parts. In other words, a complex adaptive system not only comes into existence because of the structural combination of its components but is also enforced because of their relational combination. Explicitly, their exchanges enrich the entity with additional properties that the components cannot contribute themselves individually. As we saw, in general the exchanges involved in our world—at least how we perceive it—are essentially those between the mental and the material domains. Narrowing it down in the case of the market, an exchange involves a trade. While it involves mental justification and preparation (e.g. a decision), any trade itself is a simple, almost mindless action. But it has consequences which, together with other trades, result in complexity. When we look closer, we see an exchange of one form of contract (e.g. money) for another (e.g. a share), between two parties. The ratio between these contracts is the price which informs the wider system. Crucially, the price is the emerging property because it cannot be contributed by any party alone. Higher up, the interactions between multiple securities facilitate the global transfer of capital leading to positive, negative, or neutralised exposure to, and impact on real economic events. These exchanges are non-linear (e.g. leveraged), involving derivatives with complicated (e.g. convex) payoffs, etc. This means that the corresponding market subsystems behave as collectivities over and above the simple aggregates of their individual components. Let me state an important interim conclusion that is twofold: 1. Embodying investors’ minds, the market is a special complex adaptive system. It is special because of consciousness. Prices are its primary emerging properties.3 They, in turn, can instil market behaviour that, as downward causation, can guide (including constrain) the behaviour of the components that make up the

 I will not discuss here of what type this emergence is.

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market. Stated differently, the relational can impact the structural. The market’s essence is in the exchanges, not the exchangers. The market solves a problem that none of its components can. Specifically, price discovery distributes and shares knowledge that allows society to (fairly efficiently) allocate its resources, particularly towards its own discoveries~inventions. Moreover, that process is further enriched by the intersubjectivity of discovery, which we generally label as mood.

This brings us nicely to the second characteristic that clarifies complexity. Both minds and markets are in constant dynamic flux, particularly continuous discovery. It is the only true ‘equilibrium’. Specifically, what makes a complex adaptive system adaptive and sustain itself is its endogenous ability—driven by exchange—to generate internal surprises to respond to external ones. It is its sine qua non. That is, a complex adaptive system is able—by exchanging DNA, ideas, etc.—to spontaneously produce bespoke novelty to deal with external challenges. It creatively comes up with innovative solutions to problems that its environment throws up and, subsequently, can change that environment. If it cannot solve a problem it can seek solutions by (further increasing) exchanging with other complex adaptive systems. It does not need central planning or control. I will discuss this later in more detail. First, some more background. Complexity science grew out of the earlier Systems Theory, including the wellknown Dynamic Systems Theory (DST), a mostly mathematical discipline focussed on abstract dynamical models and their properties.4 A particularly important angle for complexity science in that regard is the Gödel-Turing5 framework. In spirit it is antimechanism but its most important insight of relevance to this book is the limitation of computation concerning market states. For our purposes this is epitomised by my variation to Berry’s Paradox (in turn, a particular interpretation of the Liar’s Paradox): “ineffable experience”. This statement describes experience as indescribable, thus forming a contradiction. It applies particularly to the A-ha experience of discovery, as we will see. Complexity science in general, and the Gödel-Turing framework in particular, offer a robust platform to derive a more abstract interpretation and understanding of the human mind, both at the individual and collective level, as a complex adaptive system. Again, the MMH focusses thereby on the process of discovery. What makes complexity science attractive for both cognitive science and economics is the formal acknowledgement and treatment of ‘elusive’ macroscopic properties involved in the

 Another closely related field is network science (for a general overview, see Havlin et al., 2012; for an economics overview, see Kenett and Havlin, 2015). On producing novelty from an evolutionary perspective, see Witt (2003).  Also known as the Gödel-Turing-Post framework (see Markose, 2005), which sometimes is extended to also include Lucas and/or Church.

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coordination of a complex system. This relates, for example, to the ‘hidden’ causes of mental, respectively, market events. In an overview, Markose points out that: In all variants of complex systems theory it is held that macroscopic properties cannot be formally or analytically deduced from the properties of its parts. Methodologically, it is precisely this that distinguishes the sciences of complex systems from the bulk of traditional science which relies on deductive formalistic and analytical methods. (Markose, 2005, p. F161)

Progress has been made along various lines. Specifically, whereas the original result of Gödel’s Incompleteness Theorem was the proof that mathematics was larger than logic, its principles have since been applied elsewhere, including economics. In light of my earlier critical remarks on the flawed mechanistic approach in economics it is important to explain why this framework is helpful to expose the flaw. As a reminder, mechanical economics assumes—for making predictions—that the computability of equilibrium due to prespecified mechanical rules is applied to predetermined (historic) data. In Gödel-Turing terms, the market is viewed as a formal axiomatic system with a finite set. Lewis (1987) and Spear (1989) already showed the non-computability of fixed-point mappings that represent equilibria in markets, challenging these assumptions. This is, for example, why Coordination Dynamics is so promising: “In Coordination Dynamics . . . —where a whole is a part, and a part is a whole—there are no equilibria, no fixed points at all” (Kelso and Engstrøm, 2006, p. xiv). And from a cognitive angle, Dennett argues that when prediction becomes “undecidable” we need to drop rationality. He then criticises economics, in that its “talk of signals and commands reminds us that rationality is being taken for granted, and . . . shows us where a theory is incomplete” (1971, p. 96, emphasis added; see full quote in Appendix 1-Preparations). To further explore this we start with a summary of the crucial comments by Gödel (1995) on Turing’s deliberations. First, Gödel agrees that Turing had established the correct definition of mechanistic computability, namely in terms of a Turing machine which executes a finite number of procedures. At the same time, Gödel states that Turing had made a “philosophical error” (Gödel, 1972). Basically, Gödel disagrees with Turing’s perceived extension of mechanistic computability and its applicability to the human condition. Specifically, whereas Turing implied human states of mind are boundedly fixed, Gödel argues that “mind, in its use, is not static, but constantly developing”. In physical terms, besides the brain’s plasticity the body generally is not a fixed substrate, with cells continuously being regenerated. In the final analysis Gödel concludes that the human mind “infinitely surpasses the powers of any finite machine” (Gödel, 1951, p. 310). In particular, he assumes there are non-mechanistic mental procedures that help the human mind to transcend such machines. This has been further explored by Hofstadter (1979) and Penrose (1995), for example. This does not mean that we fully understand it. In addition to both being (closet) dualists, what links Gödel to Hayek (and others) is their agreement that our understanding of the mind’s complexity is limited due to reflexivity, when acting as both the observer and the observed:

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Mind must remain forever a realm of its own which we can only know through directly experiencing it, but which we shall never be able fully to explain or to “reduce” to something else. (Hayek, 1952, p. 194)

In other words, one cannot comprehend one’s mind over and above its experience which, as a reminder, is the dualist realisation of information. Let’s interpret the mind’s S1 and S2 systems, for the sake of argument, in the Gödelian terms of complete and consistent. It is not just that the rules6 which coordinate the mind’s dynamics cannot be articulated because they remain largely unconscious. It is also, and more so, that each time we try to “complete” the mind by adding new axioms, we end up with a new system containing yet more unprovable statements. All we can say, in Gödelian terms, is that the ultimate test occurs in subjective phenomenal space when proof that emotions, thoughts and other products from S1 or S2 are true or false statements is experienced (as information being realised in consciousness). In other words, S1 and S2 statements can only be proved outside the dual-process system, namely in S3 (for more details, see also Subchapter 7.2). Such realisations particularly apply to the sensations that irreducibly accompany discoveries. These discoveries again? Yes. Besides benefitting from contraries (like those between S1 and S2; see below) human progress has largely been achieved by imagination. So, in addition to the creative tension between contraries, it is imagination that leads to innovation and technological breakthroughs. Imagination involves not only thinking about what could be, in a conceptual way, but also—and more importantly—how that might feel like. This involves both space (a new environment) and time (a new era). It is this experience of a possible future as distinct from the current ‘world’ that motivated people to reach beyond what was available at the time and facilitated them to focus on interim targets and goals. Crucially, imagination often occurs without external inputs. In other words, not only does it lack physical ‘big [future] data’, there is also no sense organ for time like there is for sound or touch.

6.2 The Case of Mind as Complex Adaptive System To paraphrase Hayek, Gödel-Turing’s broader relevance is that not assuming limits on computation implies that there is in principle no reason why all observed price patterns and other economic forms (like innovations) cannot be achieved by a central command with a powerful computer. Instead, it is in the true nature of economies as complex adaptive system to evade capture by any algorithm or mathematical model.

 Hayek actually calls this “meta-consciousness”. As an aside, I will not discuss the potential of contemplative practices, like meditation, to increase our understanding of mind, but see my comments in the Introduction.

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Any complex adaptive system has to produce genuine novelty, with insights as internal surprises (even to itself), to ‘unpredictably’ deal with external ones, including threats by adversaries. And for that it needs the freedom to discover. In that regard, although this matter is not settled and divergent views remain, three clear understandings have emerged in the field of complexity science over the last few decades.7 First, that the sine qua non of a complex adaptive system is discovery. Specifically, it generally is not, as is often assumed, the non-linearity or chaos it exhibits but the aforementioned endogenous ability to internally produce surprises to deal with external surprises. In the case of a conscious system, this can be to fight (via “A-ha” insights that can lead to innovations or novelty) or flight (via “Oh-no” realisations). This lies at the core of such systems’ self-organisation: to coordinate behaviour and realise order in the face of chaos. Second, that the dynamics involve both competition (like the Red Queen principle; ‘running to stand still’) and cooperation (like alliances and assemblies), ultimately realising a unity or even synergy of opposites. Such conjunction reflects that the very opposing of—say, by tension between—its elements is a diversifying strength at the systemic level. Progress by innovation thus comes with leaps and bounds—widespread acceptance and adaptation versus fierce rejection and disruption—produced by parallel and counter moves by other parties. We can observe this both in nature and the economy. In short, our conjunction requires a yin~yang kind of dualist structure that is seemingly oppositional but practically complementary. Specifically, the conditions of such opposition, combined with the recognition of its existence by each opposing force, can be shown to be logically necessary for “emergence” as Kauffman points out: “coevolution of entities which interact with and know one another. The laws which govern the emergence of knower and known . . . lie at the core of the science of complexity” (Kauffman, 1991, p. 1, emphasis added). Third, as a consequence of their emergence, certain properties of this system escape reduction into an axiomatic description and its resulting innovations are beyond algorithmic enumeration or computation. This emergence points back to reflexivity. In a system where the analysis of a situation is a function of exactly that analysis— e.g. an expectation of its outcome determines that very outcome—there is no logical or deductive way to settle this, and some form of meta-cognition is required to break this loop. For example, how did Soros know, i.e. recognise patterns, when he was participating in shaping them? He experienced back pain: “I basically have survived by recognizing my mistakes. I very often used to get backaches due to the fact that I was wrong. Whenever you are wrong you have to fight or [take] flight. When [I] make the decision, the backache goes away”.8

 My interpretation, but see in particular Markose (2003) and Markose (2005) and the references therein. See also Witt (2003).  Interview with Greg Ip in the Wall Street Journal, 21 June 2008.

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At this point you may wonder what the argument is to consider the mind as a complex adaptive system in the first place. In particular, what makes up the required oppositional structure and how can the mind endogenously generate novelty, the precondition for adaptive self-organisation? I hinted at the answer before which lies in the intrinsic opposition between the unconscious (S1) and deliberate (S2) forces which is played out in symbolic dynamics. The relationship between these can be seen in terms of the complex dynamics between competition and cooperation. Complex psychology emphasises the autonomy of the unconscious and assigns intelligence to it which feeds intuition and even embeds an element of prognostication.9 Similarly, Gigerenzer starts his bestseller Gut Feelings: The Intelligence of the Unconscious as follows: We think of intelligence as a deliberate . . . activity guided by the laws of logic. Yet much of our mental life is unconscious, based on processes alien to logic: gut [instincts], or intuitions . . . We sense that the Dow Jones will go up . . . Where do these . . . come from? (Gigerenzer, 2007, p. 3)

Consequently, my proposition is, first, that the unconscious and deliberate forces can be considered as ‘intelligent’ agents’10 in terms of the Gödel-Turing framework. Second, the opposition and resulting tension between these two subsystems, combined with their mutual recognition at their own respective level of this opposition (that is, they agree to disagree), are the necessary conditions for innovative outcomes to adapt and progress in conjunction, particularly in times of crisis. However, that conjunction and the eventual release of the tension never take place at their own respective levels but occur in S3. As discussed in Appendix 1, the feeling system S3 complements and feedbacks to the unconscious system S1 and the deliberate system S2. This feedback links nicely, first, to Soros’ reflexivity which recognises that statements about reality are false, true, or reflexive. In turn, we can judge this in our Gödel-Turing framework. For arguments sake, and to keep it simple, suppose we agree with the consensus in behavioural economics: S1 generally makes ‘false’ statements about reality (bad decisions) and S2 makes ‘true’ statements (good decisions). Here we invoke Gödel’s second incompleteness theorem which tells us that the provability of these statements can only be done outside this dual-process system. “Duh”, I hear you say: their proof requires facts from the real world (which may be delayed). So how does this fit within our mental triple system? S3 makes reflexive statements in terms of experiencing such proof: as information (i.e. facts) dually realised. These experiences are fresh and novel. They are a form of discoveries and insights. They are, in Gödelian terms, new

 Jung (1964, p. 66).  To be clear, what I am interested in here is not the substance or form of these agents but their strategies: intelligent agents execute strategies (or fulfil mandates).

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statements added to the existing axioms of our dual-process system. S3 fills the gaps left by the ‘inconsistent’ S1 and the ‘incomplete’ S2. I fact, I submit that the proof of emotions, thoughts, and other statements as realisations in S3 has two phases: 1. In consciousness, by being aware of them, they become true in the sense of being ‘switched on’ (True = 1) and ready for step 2. For example, a foreboding emerges from your unconscious (S1). Until then it was not available for provability. Or your recently produced analysis (S2) only now makes sense, i.e. it feels right, making it ready for provability. 2. In consciousness, by sentience, they are ‘fact’ checked against the reality of the outside world. These can be powerful feelings. For example, Bob Geldof felt elated after his emotional appeal for help resulted in the unique Live Aid success (proving his S1 was right). And Nikita Khrushchev felt relieved after his rational backing-down in Cuba prevented a war (proving his S2 was right). In short, S1 and S2 statements can only be proved outside their system, namely in S3. Only S3, by way of our senses, is in direct contact with the real world to realise facts. It is there where knower and known meet, e.g. via our reality checks. And only then can we fully learn. Intuition (S1) is gut instinct rather than brainy thought (S2). By becoming aware (S3) of bodily feedback and/or brainy signals the self-referential loop is transcended: you now know in your heart. By recruiting the gut, intuition is the mind’s primary tool to extend the brain, reach out and invite surprises,11 the unexpected unknowns, through discovery. It includes inspiration, for example to deal with true uncertainty, as expressed for example by poet Wisława Szymborska in her Nobel acceptance speech: “Whatever inspiration is, it’s born from a continuous I don’t know”. It also includes, as discussed, imagination with which the mind extends into possible futures. This leads to the insights the mind is searching for while exploring the unknown. They are the A-ha Erlebnisse in the eureka moments (see Harman and Rheingold, 1984; Klein, 2013; Kounios and Beeman, 2015). These are not exclusive to entrepreneurs or scientists. Dutch actor Rutger Hauer, famous for playing the robot Roy Batty in Blade Runner, stated it as follows: “I have that with all important things in life, I get like a flash of lightning, a dazzling insight” (Van Basten Batenburg, 2008, p. 53).12 They are crucial as they form the phenomenal overlay which enriches (i.e. values) dualprocesses with (e.g. aesthetic) meaning, reaching beyond their initial impressions. Ultimately, in the words of Derman, echoing Kauffman, such experience “is a merging of the understander with the understood” (Derman, 2009, p. 5).

 These surprises are not by definition immediately positive or optimal: they include mistakes and errors. I would also include, for example, fantasy and slips of the tongue.  My translation from the Dutch original.

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I have more to say on this in the next chapter, including a deep dive into price discovery. First, we need to explore the philosophical territory of numbers as symbols. Not so much in terms of Gödel’s numbers but much more in terms of “mathematical primal intuition which expresses itself, among other ways, in arithmetic, in the idea of the infinite series of integers, and in geometry, in the idea of the continuum” (Pauli, 1954, p. 149). Crucially, the numerical ‘coordinate’ (or scale) dimensions in which complex dynamics appear (e.g. in space-time: length, breadth, width, and duration) are important philosophical considerations when problems of complexity in general are analysed from a mind~matter perspective. I realise that the next few paragraphs will make some readers (even more) uncomfortable, if not disagreeable, but they are a necessary prelude to Chapter 7.

6.3 Symbols In his discussion of value and prices, Simmel makes the following Pythagorean observation of economic exchange: Only if there is a second object which I am willing to give away for the first, or vice-versa, does each of them have a measurable economic value. There is originally in the world of practice no single value, any more than there is originally in the world of consciousness a number ‘one’. It has often been asserted that the concept of ‘two’ exists prior to the concept of ‘one’. (Simmel, 1907, p. 89)

This leads us into our discussion of numbers (especially as symbols) in the economic system. Let’s start with digitisation which is based on the core digits 0 and 1. On the plus side, with numbers as symbols digitisation could offer ways to help bridge mind and matter. However, as the culmination of mechanisation it has practical implications and could backfire. Digitisation creates an intangible space, be it online or virtually. As closet dualists we perceive our reality to consist of the mental and the physical which complement one another. Still, as soon as causation is expected to rely on mentalities (including intangible tools used by those mentalities) we are moving closer to, what Knight (1921) calls, “true uncertainty”. As discussed, true uncertainty is due to the mind~body problem that extends into the economic system. For example, a tech venture that is intangible by operating online (in digital space), primarily relies on mentality (hope and hype) and operates via intangible tools (like digital apps) to achieve something is beyond Knight’s “risk”. So is investing in or lending to such a venture, especially if the lender is, similarly, a fintech bank whose business consists of electronic deposits and withdrawals of money. On that note, and importantly, if money itself has no longer any physical property (e.g. as bill, coin, let alone gold) all that remains as ‘real’ is its number, namely as price. Similarly with algos and tech which both revolve around those core digits 0/1 as their essence. Combined with banking and investment apps this means that ex-

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changes (like a central bank ‘printing’ money, a retail investor trading, or you withdrawing your deposit) are just clicks away as the only physical effort. And a bank run thus no longer consists of physical queues at branches but has become a digital event. FTX13 and SVB are cautionary tales for this. Specifically, what they have in common is that they operate in the extremes of this intangible space. An important lesson in that regard is the connection between price discovery and the discoveries more widely in the economic system. They form a reflexive chain of creativity. All are made in minds—often socially, perhaps after collaboration—and initially appear symbolically.14 Here is Pauli sharing another thought: When one analyzes the pre-conscious step to concepts, one always finds ideas which consist of “symbolic images”. The first step to thinking is a painted vision of these inner pictures whose origin cannot be reduced only and firstly to the sensual perception but which are produced by an “instinct to imagining” and which are re-produced by different individuals independently, i.e. collectively . . . But the archaic image is also the necessary predisposition and the source of a scientific attitude. To a total recognition belong also those images out of which have grown the rational concepts. (Pauli, 1948b; emphasis added)

In his advocacy for the Extended Mind Theory, including criticism of individualism/ internalism, Wilson makes two cases: “the first involves the causal integration of explicit symbols located in an organism’s environment into that organism’s cognitive regime; the second appeals to the cognitive incorporation of non-symbolic aspects of that environment” (Wilson, 2010, p. 181). In turn, these support the MMH, in that the first underlines the role of prices (in the external pricing system) as symbols for investors, whereas the second underlines the role of the market’s exchanges. In this subchapter, as well as in the next chapter, I will primarily focus on the first case to support the idea of the market mind as extension. Specifically, Sutton (again, underlining the mind-as-market by casually using economic terms in describing cognition) argues that: External symbol systems . . . are not always simply commodities, for the use and profit of the active mind: rather, in certain circumstances, along with the brain and body that interact with them, they are (part of) the mind. (Sutton, 2010, p. 190).

The mind as complex adaptive system is creative. It has to be, particularly in situations that are uncertain (which most are in our daily lives), where analytic problem solving (via S2) doesn’t apply. Symbols can help, often in a dynamic and iterative way, to generate the required insights. In their excellent book The Eureka Factor (2015), neuroscientists John Kounios and Mark Beeman relate the following experiment by the social psychologist Michael Slepian on the difference between insight problems and analytic  Ignoring possible fraud which, by the way, is usually aimed at keeping the hype going.  Like some form of symbolic interactionism (see MacKinnon, 1994), whereby its focus on language is judged as too limited/limiting. For a classic work on symbolism, especially myths, see Campbell (1949).

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problems. Ahead of a lab test, consisting of two groups of subjects, the experimenter apologised that the room was a little dark, so he switched on a light. For one group the light was a traditional incandescent bulb. They subsequently solved more insight problems but not more analytic problems. For the other group the light was a fluorescent tube. This had no impact on their performance in solving either of these types of problems. “Only the classic symbol of creativity [i.e. a lighted bulb] spurred insight” (Kounios and Beeman, 2015, p. 187). The mind generates novelty in the form of insights which results in a vast array of symbols. And as we saw: “opposites never unite at their own level . . . since the symbol derives as much from the [deliberate] as from the unconscious, it is able to unite them both, reconciling their conceptual polarity through its [physicality] and their emotional polarity through its [phenomenality]”. (Jung, 1951, para. 280). In their very competing the healthy mind’s agents offer balance and diversity, uniting like a robust portfolio in the system’s broader adaptive purpose to produce novelty. The intercourse of the unconscious and the deliberate produces (realised via S3) their conscious child, the insight, “the birth of a third and new thing, a son who resolves the antagonism of the parents and is himself a ‘united double nature’” (Jung, 1956, para. 22). I will shortly zoom in on this “third” (which, of course, is a symbol itself). With the Taoist yin~yang symbol as an example, the complementarity of cognitive opposites more generally (e.g. Atmanspacher and Primas, 2006) is closely associated with concepts in complexity science. My focus here will be on (numerical) symbols and information. There is the limited case of the Boolean True/False logic (1/0) of the Liar’s Paradox. Another is from Algorithmic Information Theory where it concerns the signal-noise dichotomy as captured in the symbol which embodies both. There are two important points to highlight in this regard. First, information is always intentional (about something) and implicitly dynamic (it arrives, is produced/consumed). A signal, in that respect, is the intermittent alerting message of a pattern which is ‘in formation’. For example, the message to ‘pay attention’ or ‘be aware’ is one of the signals of the symbol as it emerges in consciousness. Noise, on the other hand, is the ever-present infinite ‘background clutter’ of the unknown, entropy’s disorder if you will. Although a symbol contains some information, primarily its signalling property, its wider meaning is discovered, a process which reflects a large part of uncertainty (i.e. chance encounters). In the context of evolution, Damasio (2004) argued that the discovery of new things by chance is required before selection can take place. We can therefore state that a symbol is a signal enriched by noise, in the sense that the informational tendency—as in ‘probability’—reflexively emerges from indeterminate randomness.15 To further clarify what I said before, Nietzsche famously remarked that certainty is what drives one insane. Translated in terms of the requirement as a complex adaptive system, the healthy mind must strate-

 Whereby we always risk being “fooled by randomness” (Taleb, 2001).

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gically use, almost embrace, indeterminacy to ‘surprise’ itself (with insights) in order to surprise (hostile) others. In short, inner surprises are a quid pro quo to outer surprises. Second, although a symbol cannot be fully reduced to a signal (see also Subchapter 7.2), we could view the signal as the format with which the symbol is realised physically. Specifically, the physical properties of the symbol include the bio-electric signal which accompanies its emergence in consciousness. This also involves the physical route via the neural circuitry along which the symbol’s emotional charge will build. In terms of (innate) affect—like that of the light bulb—let’s also recall what Bateson stated about his own “difference that makes a difference” in the context of Kant’s “idea”: It is able to make a difference because the neural pathways along which it travels and is continually transformed are themselves provided with energy. The pathways are ready to be triggered. We may even say that the question is already implicit in them. (Bateson, 1972, p. 459; emphasis added)

This ‘road-map’ is similar to what neuroscientists call “connectivity patterns” which contain “latent knowledge” (Dehaene et al., 2006, p. 209). Regarding the dynamics, a symbol is signalling information whose content is nonexhaustive as far as meaning is concerned because that meaning continues to be shaped out of (the interaction with/exploration of) the unknown. This process of discovery, while the symbol is being shaped, is experienced phenomenally and takes place as ‘life at the edge of chaos’. Living symbols combine both familiarity and novelty. The selection of such contents is echoed in neuroscience, here emphasising the complementary market forces (in italics) at play:16 The mistake made by many cognitive scientists is to view symbolic content as static, timeless entities that are independent of their origins. Symbols, like the vortices of the river, may be stable structures or patterns that persist for a long time, but they are not timeless and unchanging . . . the processes that govern how a pattern is selected from myriad possibilities must be incorporated in any set of organisational principles for living things. These processes often involve cooperation and competition, and a subtle interplay between the two. (Kelso, 1995, p. 1 and then p. 6)

This dynamic setting, involving ranking, scoring, selecting, and the like, allows us to explore the special symbolic nature of numbers.17 Here I would like to apply it metaphorically to complementarity which can manifest itself at various levels. I build on what I discussed elsewhere in this book, including in Appendix 1. In general, so stripped down of any specific characteristics, how should we see contraries, and how do they complement. In other words, how does that ‘magic’ synergy ‘emerge’ from the tension between two contraries? In the case of the mind the

 The Ecological Dominance-Social Competition (EDSC) theory emphasises the role of competition and cooperation in the evolution of human intelligence at the social level.  For a specific view of language and symbols, see Clark (2006).

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complementarity between (the unconscious) S1 and (the deliberate) S2 involves a tension which drives the dynamics. In symbolic numerical terms: There emerges a tension of opposites between the One and the Other. But every tension of opposites culminates in a release, out of which comes the “third”. In the third the tension is resolved and the lost unity is restored . . . There is an unfolding of the One to a condition where it can be known—unity becomes recognizable; had it not been resolved into the polarity of the One and the Other, it would have remained fixed in a condition devoid of every quality. (Jung, 1938, para. 180; emphasis added)

The polarity of deliberate manifestation as the “Other” (or the Two) opposite from the unconscious source of “One” generates the novelty as the “third” (or Three), that is S3, with the “release” being the discovery which sustains the mind as a complex adaptive system.18 It can be applied to other (meaningful) experiences, like win (birth) and loss (death). The Three uniting the One and the Two, in the process enriching the former with “quality”, leads to my “strange loop”19 depiction of a meaningful experience as an ‘Ouroboros chain’ of three sections:20 1 = One => Unconscious origin (e.g. intuition) ≈ S1 2 = Two => Deliberate function (e.g. logic21) ≈ S2 3 = Three => Phenomenal culmination (e.g. A-ha)22 ≈ S3 The Ouroboros (Figure 6.1), the serpent or dragon eating its own tail, has been an ancient symbol for reflexivity, a concept that existed long before Soros applied it in investing. Specifically, it symbolises self-generation by way of the ultimate form of creative destruction. It also captures renewal by re-entry. Biologist Francisco Varela, for example, adopted the Ouroboros as a symbol for re-entry in his calculus of self-reference. In his discussion of symbol-mediating loops, Clark states more generally:

 To be complete: metaphysically the zero stands for the unified order or reality underlying the material and mental worlds according to monist and dual-aspect worldviews. See also Bateson (1972), to be quoted in Subchapter 7.2.  Cue: Hofstadter (2007).  I basically equate the phenomenal (e.g. A-ha) sensation as the culmination of the tension between the unconscious and the deliberate. The dynamics between these three “mentalities” is a continuous self-reflexive (i.e. discovery) process, akin to an Ouroboric loop, that sustains the mind as a complex adaptive system. Still, I acknowledge that some readers may not like this interpretation of the first three stages of the Axiom of Maria.  “What we properly call instincts are physiological urges, and are perceived by the senses.” (Jung, 1955, p. 58).  The resolution means that the condition where the symbol initially becomes known (in S2) is promoted to where it (particularly its meaning/outcome) is felt (S3). Until then it is “devoid of any quality” and, whatever potential gem it may be, remains in the null state, or zero. That is, unknown, not even unconscious.

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Figure 6.1: Ouroboros. Such loops are effectively enabling new forms of re-entrant processing: They take a highly processed cognitive product (such as an idea . . .) cloth it in public symbols [e.g. prices], and launch it out into the world so that it can re-enter our own system as a concrete perceptible . . . and one now bearing highly informative statistical relations to other . . . perceptibles. (Clark, 2013a, p. 185; emphasis added)

In the above, the fringes of an experience’s strange loop (i.e. One and Three) both remain outside (as in ‘escape’) the deliberate domain but meet, like the head and tail of the Ouroboros, in the non-computable ‘discovery’ space to convey the meaning.23 The importance of the exchanges between these systems in their creations while crossing boundaries is echoed by modern insights: “In nature’s pattern-forming systems, contents aren’t contained anywhere but are only revealed by the dynamics. Form and content are thus inextricably connected and can’t ever be separated” (Kelso, 1995, p. 1). As I mentioned, this was a prelude to the next chapter which will relate this to the main numbers in markets: prices. But first an intermezzo.

 Arguably, apart from first person qualia the quality of consciousness as a shared experience, i.e. intersubjectivity, also escapes reductionism. See elsewhere in this book.

Intermezzo: Parallels Between Mind and Market. What is Mind? What is Market? Years ago I lectured to young economists at an Economics School in Trieste, introducing them to complexity science and the concepts, methods, and tools of Coordination Dynamics. It was up to them, of course, to determine if any of it was useful. The economist Axel Leijonhufvud (and indeed Ken Arrow at an earlier event at UC Irvine) expressed a lot of sympathy with Coordination Dynamics, as did his colleague at the time, Vela Velupillai. In my book with David Engstrøm, The Complementary Nature (MIT Press, 2006), I actually used buying~selling in the market economy as an example of the complementary nature of Coordination Dynamics (pp. 231–234)—cycles, bistability, phase transitions, decisionmaking, fluctuations, etc. So, when Patrick invited me in 2010 to become one of his external PhD advisors, I was intrigued about his idea of formalising the “market mind”. The first time we met was at an interdisciplinary conference at the University of Essex, organised by economist Sheri Markose. We kept in touch and in May 2022 I spoke at the MMH inaugural symposium in Panmure House in Edinburgh, Scotland. The present book is a beautiful realization of Patrick’s ideas, at the same time providing a stimulus and an agenda for future work. Supported by references from Hayek, Knight, Sornette, and others, Patrick suggests the two-legged premise of mind-as-market and market-as-mind, both culminating in consciousness. Considering that we don’t properly understand markets and minds, let alone consciousness, between ourselves he has his work cut out for him. Still, though not for faint hearts, it is a worthwhile challenge from my point of view, which is that coordination is crucial for both consciousness and markets. What follows are some constructive remarks, including a few specific questions, intended to help shape the research agenda that Schotanus is building with a growing team of collaborators. For starters, which mind are we talking about? The market mind certainly doesn’t look and sound like the mind of ordinary cognitive (neuro) science, with its neat compartments and connections that correspond to mental categories such as thinking, feeling, deciding, and moving. Instead, MMH’s market mind looks more like the brain~mind of Coordination Dynamics which studies the whole system as its various parts synergistically interact. This means identifying relevant collective variables or order parameters for particular functions and their dynamics—how brain states evolve and change–without necessarily tying a piece of anatomical structure to a function. (That’s because the same piece of anatomy is often involved in multiple functions, and different pieces of anatomy can realize the same function). In economics, collective variables could be defined in the space of buyers and sellers, whereas prices qua coordination device (and synergetic indicator) could be seen as an outcome variable that acts to couple things, like goods and services with securities. Careful attention, of course, must be paid to the correspondences involved. https://doi.org/10.1515/9783111215051-007

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Coordination Dynamics considers the concept of “intrinsic dynamics” essential, e.g. for understanding learning. New information has been demonstrated to cooperate~compete with intrinsic dynamics and determine the rate and character of learning. An obvious area for future research is that individual (within-brain) and social (between-brain) learning appear to be governed by the same dynamical principles. What corresponds to “intrinsic dynamics” in the Market Mind (Hypothesis?) If it is an element of price discovery, like discounting news, this should be further formalised. For example, there seem to be similarities between active inference (i.e. limiting free energy) and active investing (i.e. limiting free lunches), in the sense that in both cases hypotheses get continuously and reflexively tested and updated. Regarding the emergence of consciousness in the market, to me this is no different than ‘free will’ or consciousness itself as emergent. That is, the complex assembly of simpler elements generate behaviour that is not predictable from the individual components and is describable by rules that are independent of those components (though constrained by them, i.e., as ‘upward causation’). In Coordination Dynamics (CD) this ‘complex assembly’ is called a synergy, which (along with nice properties like degeneracy and multifunctionality, common to markets) is governed by a principle called circular or reciprocal causality. Whereas synergies may be viewed as natural compressors of information, the nonlinear synergy dynamics expands the range of possibilities endowing a system with autonomy or choice (‘downward causation’). At the same time, dynamics offers a natural way to explain mood swings, like that between euphoria and despair. In CD, qualitative changes in thought and affect correspond to phase transitions triggered by specific and non-specific changes in control parameters. Compression~expansion is just one of the complementary pairs of Coordination Dynamics. In my view, the MMH needs to better integrate the source of complementarity, namely metastable coordination dynamics, the synergic tendency toward dependence (integration) that coexists at the same time with the anti-synergic tendency toward independence (segregation). Cooperation~competition, individual~collective, inter~intra, micro~macro, etc. are all complementary. In the context of ‘free markets’, I would call this freedom—not being attached to one or the other, but to see their complementary relation. This brings me to Adam Smith and his “invisible hand”. One of the most characteristic though underrecognized features of complex biological systems is their ability to spontaneously (and simultaneously) recruit and annihilate whatever degrees of freedom are needed to accomplish goals and tasks. Due again to their nonlinear coordination dynamics, patterns of coordination are assembled and disassembled with ease to accommodate functional demands. This indeed echoes Smith’s division of labour in his “butcher, brewer, and baker” sense. Not surprisingly, there are several MMH topics and views I do not feel entirely comfortable with. For example, borrowing Hayek’s “practical dualism” is risky because any dualism implies separation. Hayek’s own emphasis on having to use it for

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language purposes—due to our ignorance of the “unitary order” to which the mental and physical both belong (which he acknowledges)—only compensates so much. I welcome Patrick’s attempts to further improve on it via his portfolioism as well as via optimising such language, for example, by using “exchange” instead of “interaction”. For me, however, the implication of the squiggle for mind~matter interaction can hardly be overstated, namely—that for a nonlinear, symmetry breaking metastable coordination dynamics—mind and matter are complementary. A mouthful, but true. Regarding ‘physics envy’, if economics has slavishly followed physics, the question (as previously for that of mind) is which physics? Mechanical worldviews based on Newtonian physics will not work for these kinds of complex systems. What will? There is a “mindless” (not intended to be derogatory) physics of self-organisation which does not require any external or internal “homunculus-like” agent. That’s great but it’s not going to be enough for MMH, in which market dynamics refer to processes that are universal and shared between minds and markets. The reality is that no physics exists (yet) of ‘intelligent’ self-organisation, in which intelligent agents and collectives of agents figure prominently. This, I would say, is a major challenge for MMH and the rest of us. To conclude, the particularly compelling aspect of the MMH research programme is its potential to set the scene for a necessary paradigm shift in economic thinking. Despite the many challenges, I see the possibility of a remarkable scientific reconciliation between the market-as-mind and the mind-as-market that will embrace recent theoretical and empirical developments in the brain and cognitive sciences, for example in so-called 4E Cognition. I am particularly excited that my field of expertise, the science of coordination (i.e. Coordination Dynamics), with its key notion of the metastable brain~mind, may contribute to, or even play a significant role in, understanding the market mind, both in terms of individual and collective experience. Patrick Schotanus has made a creative step. This book can be our guide for making that shift. Scott Kelso Glenwood and Martha Creech Chair in Science, Professor of Complex Systems and Brain Sciences at Florida Atlantic University, and co-author of Dynamical Systems and The Complementary Nature.

Chapter 7 On Discovery: Am I Free? Indeed from the ontological point of view we must as a general principle leave the primary discovery of the world to ‘bare mood’. Martin Heidegger

7.1 Introduction Man is not and never will be the master of his fate: his very reason always progresses by leading him into the unknown and unforeseen where he learns new things for. Friedrich Hayek

Discovery is a reflexive “mind~body” loop, both at the individual and collective level.1 The MMH focusses on discovery because it is a spontaneous, unpredictable process that is exemplary for the mind’s complexity, in this case by producing knowledge while remaining itself unknown. In fact, discovery is the structural part of the explanatory gap. There are two important components to discovery, namely its process and its outcome (or content). While you may have a clear idea what you would like to discover—which is about content—what you’ll encounter along the way, when that comes along, and how it will appear will be more ambiguous—which is about process. To further clarify I connect discovery and the explanatory gap to both true uncertainty and incomplete knowledge. True uncertainty—the impossibility to know the future2—is a consequence of the explanatory gap. We do not fully understand mind~matter exchange, like the occurrence of (outside) material events and our (inside) mental responses to them.3 But we are curious and try to by gaining insight. Therefore I related it earlier to ontological uncertainty: by inserting time. This is about the process of (self-) discovery, whereby the A-ha experience of an insight itself, including the timing of its eureka moment, remains a mystery. So, true uncertainty does not prevent us trying to ‘predict’ the future by inventing it, driven by such discovery. This reduces our incomplete knowledge which I related earlier to epistemological uncertainty. In other words, epistemological uncertainty reduces when gaps in our knowledge are filled by insights, the content of discoveries. This is in contrast to irreducible ontological uncertainty.

 This chapter is inspired, among others, by Knight (1921, particularly pp. 198–201), Popper (1979, particularly p. 344) and Shackle (1972).  In one particular sense, true uncertainty is about supernatural cognition: only God knows the future. Then again, while we do not know the future, we are—via actions, imagination, etc.—shaping it and thus may have an inkling.  This includes issues like mental causality and unknown (e.g. unconscious) mental events. https://doi.org/10.1515/9783111215051-008

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Closing the gap, incentivised by A-ha experiences,4 is the (subliminal) motivation that drives our enquiries and research, in all fields of life. In mind-as-market terms, an insight is a mental reward, a profit, often only received after investing heavily in both intuition (S1) and logic (S2). Importantly, an insight has its own feeling value as it is coloured by the (S3) sensation of such an A-ha experience during the eureka moment as an epiphany. It is awakening and enlightenment, which is more than simple awareness. It conveys novelty in a qualitative sense, including hurt if the discovery is painful (e.g. a shock). This sensation is thus not relevant in terms of the self-evidence of a proposition (i.e. the deductive result). Rather it is relevant as a phenomenal payoff (return quality) from the beliefs, biases, doubts, thoughts and so on invested through S1 and S2. As mentioned in Appendix 1, I like to call insight the purest alpha among the returns in consciousness space; an impression that makes an impression, to paraphrase Bateson. The importance of discovery in society, in particular as a conscious experience in economic agents, is one of the MMH’s overarching messages. Earlier I discussed consciousness as the dual realisation of information bridging mind and world. Discovery is the moment these two connect and a (knowledge) gap is closed. This embodied engagement starts at an early age, initially involving shaping the self: Analysis and dynamical modeling of experiments on human infants suggest that the birth of agency is due to a eureka-like, pattern forming phase transition in which the infant suddenly realizes it can make things happen in the world. (Kelso, 2016, p. 1; emphasis added)

We can relate this, for example, to the importance of curiosity, creativity, imagination and inspiration for human progress. As cognitive systems become more complex, their awareness of discovery itself (and its importance) grows, both in terms of event and purpose, thereby growing our collective intelligence. We humans realise that, quantitatively, discovery leads to growth in knowledge which, in principle, should increase our overall awareness of being in the world. Qualitatively we (e.g. as investors) consume information and news and we can think of consciousness as ‘tasting’ it.5 If ordinary news ‘tastes’ like a sandwich, spectacular news, by way of a discovery, ‘tastes’ like a delicacy. It is valued more. This is why great minds in all kinds of fields seek discoveries which contribute to closing the explanatory gap. Unfortunately, in economics it is at risk of being corrupted by a cabal of forces, including concentration, mechanisation, and repression. Specifically, for many vested interests discovery is seen as a threat. It is important to emphasise the qualitative impression of human discoveries as intimate revelations (e.g. “entdecken” or uncovering, Heidegger, 1927). As Kant reminds us  Because partnering S1 and S2 concerns engaging the (external) environment, you can think of the A-ha experience (in S3) as the orgasm from the mind’s intercourse with the world/universe, with the discovery’s content as their baby. In neural terms, it is enjoyed via the reward system. See also Mlodinow (2012).  See Appendix 1-A4.

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in his Critique, such encounters with the “manifoldness, order, purposiveness, and beauty” of the world leaves us with “a speechless, but nonetheless eloquent, astonishment”. The experience of discovery remains ineffable even if its content can be expressed and shared through peer-review for instance. Still, to emphasise again, what makes discovery in general so elusive is that both its exact content and timing are unpredictable. In the wider scheme of society, this is why no one “can predict the future states of knowledge” (Popper, 1957, p. xii). A discovery is original and cannot be forced. Its process cannot be captured algorithmically. To the extent, for example, that it is random its sequence “is one that cannot be algorithmically compressed: the shortest description of a random sequence is simply the sequence itself” (Davies, 1988, p. 49).6 For the individual a discovery is truly an internal surprise. Crucially, although many people state that they ‘don’t like surprises’, it is, in fact,7 our minds’ ability to endogenously generate these internal surprises in response to external ones that helps us adapt. In the words of the Spanish philosopher José Ortega y Gasset: “To be surprised, to wonder, is to begin to understand” (1932, p. 12). In contrast, (the assumption of) knowing everything ends the sense of wonder. It is also important to view our acceptance of uncertainty, tolerance for surprises, willingness to take chances, and risk anxiety from the angle of incentives. First, there is the motivational case of desires that incentivise us to take actions. Contrasting desire~behaviour (viewed as mind~matter exchange) with the clearcut cause-effect in physics, Knight stresses that economics must distinguish between suggestive desires as physically expressed (externally) and desires as phenomenally experienced (internally), which can lead to (positive and negative) surprises: the acts most directly prompted by desire do not exactly express the desires as felt and often diverge grotesquely from them. No experience is much more common than surprise . . . at the things we find ourselves doing. Likewise our communication [e.g. exchanges] with other human beings acquaints us with the same situation in regard to them. (Knight, 1925a, p. 382; emphasis added)

More generally, what incentivises us to discover? In portfolioism terms, what drives us to be ‘active’ and ‘seek alpha’? Above all, it is our curiosity which acts as ‘demand’: we want to experience, we want to know, etc. In making subsequent choices, Keynes saw being ‘active’ as an “innate urge”, emphasising its non-axiomatic nature, as well as echoing Evolutionary Rationality: We are merely reminding ourselves that human decisions affecting the future, whether personal or political or economic, cannot depend on strict mathematical expectation, since the basis for making such calculations does not exist; and that it is our innate urge to activity which makes

 Although this quote is usually attributed to Gregory Chaitin, I could not find an original source, whereas Davies does not credit Chaitin when stating it.  See my earlier comments on the mind as a CAS (complex adaptive system). From a Predictive Processing Theory standpoint, see Clark (2018).

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the wheels go round, our rational selves choosing between the alternatives as best we are able, calculating where we can, but often falling back for our motive on whim or sentiment or chance. (Keynes, 1936, p. 162)

On ‘seeking alpha’, biology calls it exactly that, namely the “seeking system” which involves the chemical dopamine. Discoveries have a risk~return payoff, both material and mental, in that they can be expensive (profitable) and painful (joyful). In our exposure to her, Mother Nature made sure that our fear for prediction errors (Oh-no surprises) does not constrain our search for novelty (A-ha surprises): The lucid courage for essential anxiety assures us the enigmatic possibility of experiencing Being. For close by essential anxiety as horror of the abyss dwells awe. (Heidegger, 1943, p. 234)

Specifically, learning in and of itself generates intrinsic rewards and, as we saw, from a young age we value experiential surprise (i.e. as sudden realisations of insights/ news): “The rush of a sudden insight is usually reward enough” (Kounios and Beeman, 2015, p. 206). It not only explains our drive and motivation but ultimately codetermines our success in dealing with uncertainty, including generating profit opportunities in competition. Alan Watts was right in that respect, sounding as if he just had enjoyed a chat (and a drink or two) with Gasset (“wonder”), Heidegger (“boredom”)8 and Knight (“uncertainty”):9 If the game of order-versus-chance is to continue as a game, order must not win. As prediction and control increase, so, in proportion, the game ceases to be worth the candle. We look for a new game with an uncertain result. (1969, p. 40)

This is also, for instance, how I like to interpret the key message from Taleb’s Antifragile: “Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty” (Taleb, 2012, p. 3; emphasis added). This applies broadly. For example, denouncing wokeism includes accepting Rowan Atkinson’s argument that “insults” strengthen society. Watt’s quote is not just a throw-away comment. Think of Tic-tac-toe where experience eventually leads to boring inevitable draws, like an eternal equilibrium. Or think of chess and my comments (see Appendix 1) on the game of Go regarding the role of automation. In many games AI and other computer programs are now predictably superior to humans. Lee Se-dol retired in late 2019 from professional Go play for the following reason: “With the debut of AI in Go games I’ve realized . . . even if I become the number one, there is an entity that cannot be defeated”. In other words (echoing Watts), what is the point of playing? Consequently, people disengage and lose attention. While hybrid combinations of human-machines offer tantalising possibilities, for  If “mystery is lacking” it leaves us with a “fundamental emptiness that bores us” (Heidegger, 1983, pp. 163–164).  “[I]f all changes . . . could be foreseen for an indefinite period in advance of their occurrence . . . profit or loss would not arise” (Knight 1921, p. 198).

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now it makes the human-human competitions the only game worth watching for normal players, i.e. non-programmers. Or people simply move on to “look for a new game”. For those AIs which are discretely meant to control and keep people’s attention this is undesirable, and it backfires. Although it may be liberating at first, it could lead to worse situations when those who control the AI start to enforce compliance with its use. Think of the arbitrary trading restrictions activated during the GameStop saga in that regard. Also related are situations where disengagement has a mixed form, like outsourcing (e.g. pension) investing to mechanised strategies such as passive investing because they are more easily monitored and/or are preferred by menof-system. Supposedly we are ‘predictable’, but our discoveries and surprises certainly are not. Discovery as internal surprise remains a mystery, both in the scientific sense of unpredictably ‘emerging’ from the unknown and the phenomenal sense of what that feels like. Whereas the eureka moment points to the timing (e.g. sudden), the ‘A-ha’ Erlebniss is the quality (e.g. painful, as in ‘Oh-no’). This also returns us to the mind~body problem which, as I have argued, should be viewed much more widely, including from a practical perspective: it has a purpose. Specifically, mind~matter exchange—especially between us and rest of the world—is our main source for true uncertainty. It drives our curiosity, and we would not want it otherwise: “a life with uncertainty eliminated or perhaps even greatly reduced would not appeal to us” (Knight, 1921, p. 348). In other words, our mind’s complexity—especially its dualist perception of reality as the key contrary pair—and the resulting unpredictability is the biggest trick Mother Nature played on us, for our own good, and unlikely to be solved, i.e. replicated. From an evolutionary perspective this gives our mind a necessary competitive edge in a world that challenges us with its own surprising behaviour. Pointedly, consciousness evolved because awareness and (self-)reflection speed up the understanding of (mind~matter) problems, and thus the process to tackle them. Our consciousness is also what sets us apart from other species, as well as from AI. On that note, discovery is the first step in understanding the world and its events in human terms. A discovery fills a particular gap in our understanding and tightens the explanatory gap more generally. Prices play a particular role in that regard.

7.2 Price as Numerical Influence In the mathematical projection of Nature . . . what is decisive is not primarily the mathematical as such; what is decisive is that this projection discloses something that is a priori. Martin Heidegger

McCloskey, in The Rhetoric of Economics (1983), was an early explorer of the influence of metaphors and myths in economics. They offer symbolic meaning when all other explanations fail. Such influence can be generalised for all symbols, including numer-

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ical ones. I already touched on this in Subchapter 6.3. Here is Bateson (with echoes from Wheeler) in a paragraph where he explains how the base or binary numbers of information (0/1) can make a psychological difference: In the world of mind, nothing—that which is not—can be a cause. In the hard sciences, we ask for causes and we expect them to exist and be “real”. But remember that zero is different from one, and because zero is different from one, zero can be a cause in the psychological world. (Bateson, 1972, p. 459)

We can combine the symbolism of economic myths with Hayek’s view of price as an influential numerical symbol. Because whatever path got us here, along the way we seem to have forgotten that the investor doesn’t live in a mechanical world. Due largely to true uncertainty, (s)he lives in a symbolic world, the world of prices and the myths that surround them: Symbols—in the proper sense of this term—cannot be reduced to mere signals. Signals and symbols belong to two different universes of discourse: a signal is a part of the physical world of being; a symbol is a part of the human world of meaning. Signals are ‘operators’; symbols are ‘designators’. Signals . . . have . . . a sort of physical or substantial being; symbols have only . . . value” (Cassirer, 1944, p. 320).

We can push this further. If a symbol “is an expression of something that cannot be characterised in any other or better way” (Jung, 1971, par. 816), then this not only applies to single symbolic numbers, like 0 for interest rates, 666 for the S&P500 Index, or 10,000 for gold. It also applies to patterns of random prices because—as AIT argues, echoing Jung—the shortest description of a random sequence is simply the sequence itself. Interpreting symbols as signals, as per mechanical economics, gets us in pretence (of knowledge) mode. Price, as the ratio of the respective number of units of the items that are exchanged, is also an example of Popper’s “abstract relationship” that affect us and subsequently initiates “physical causal chains which have no sufficient physical causal antecedents”. Abstract does not mean cold and distant. Far from it. Numbers can invoke strong passions. Pauli, for example, was obsessed with the number 137 of the fine structure constant, and fractals include patterns based on 1.618, the golden ratio, that we see in many art forms. Not only investors but also consumers know this all too well. In periods of inflation the influence of prices as numbers is especially clear: “What? The price of bread is now 2? It was half that a year ago!” The movement of prices affects, to the point of impacting the (physical) fundamentals of the real economy. It is economics’ version of the general “claim that something abstract—something non-physical, such as the knowledge in [e.g. prices] . . . —is affecting something physical” (Deutsch, 2011, p. 114). Looking for the origin of such influence, often there is nothing but the number itself. We can clarify this by borrowing the domino thought experiment from Hofstadter (2007), in which a large network of dominos operates in a certain way, de-

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pending on whether its input is a prime number or not. In his example, Hofstadter uses 641. The relevance for prices is that Hofstadter makes the following observation about the influence of specific characteristics of numbers. We are then: not talking about anything physical at all. Not only has the focus moved upwards to collective properties of the chainium, but those properties somehow transcend the physical and have to do with pure abstractions, such as primality . . . 641’s primality is the best explanation, perhaps even the only explanation, for why certain dominos did fall and certain other ones did not fall. In a word, 641 is the prime mover. (Hofstadter, 2007, p. 38–39)

Deutsch explores this further by linking it to the mind~body problem (Deutsch, 2011, p. 117). The point for our purposes is that prices have similar intrinsic properties which affect events. While we do not know these, we like to label them anyway, often as “value”, narratives or even confabulations. So, while the comparison only goes so far, investors’ “expensive” or “cheap” is like Hofstadter’s “primality”. And the S&P’s Devil’s Low of “666” in 2009 is like Hofstadter’s “641”. Coming back to Bateson, prices and their changes, i.e. returns, reflect information that is dually realised. They form his “difference that makes a difference”, first and foremost to investors’ awareness, starting by attracting their attention once change occurs. By including returns as Bateson’s first “difference”—thereby emphasising the dynamic aspect of price discovery (e.g. in contrast to stale prices)—I am more specific than some. Ayache, for example, states that “price is a differential, not a splendid present value as equilibrium theory holds. To price something . . . is to make a difference” (Ayache, 2010a, p. xx), yet he never refers to Bateson’s concept in his otherwise excellent book. Regarding awareness, Soros—as if taking his cue from his mentor’s “abstract relationship” while thinking of an incomplete market (mind)—connects abstractions (in our case prices) to awareness: “Awareness of change is associated with a mode of thinking which is characterised by the use of abstractions; lack of awareness involves the lack of abstractions” (1962, p. 1; emphasis added). There is a vast literature on the philosophy and psychology of numbers. Early reflections include those by Danzig, Frege, Hadamard, Husserl, Kepler, Leibnitz, Plato, and Pythagoras. Husserl (1891), who was Heidegger’s mentor, discusses in detail the “metaphysics” and “transcendence” of numbers which he considered to be ideal and independent from physical objects. To wit, he submits that higher numbers (i.e. > 12) can be thought of only by mastering a certain numerical symbolism that relates them to intuitively graspable mathematical concepts. Marie-Louise von Franz is one of my favourite sources. In my PhD thesis I argued for the first time that price discovery—as the dualist realisation of information—is what she had been looking for in terms of measuring psychic intensities numerically, in this case at the collective level, culminating in rhythmical, seasonal, and other patterns: Number is bound up with the latent material aspect of the psyche and with the latent psychic aspect of matter. Up to the present time, however, no means of measuring psychic intensities numerically has been envisaged, although I believe such a possibility exists because of the fact . . . that

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all emotional, and therefore energy-laden, psychic processes evince a striking tendency to become rhythmical. (Von Franz, 1974, p. 157; emphasis added)

Among her most memorable statements is her assertion that numbers “possess a dynamic, active aspect which is especially important to keep in mind. It is not what we can do with numbers but what they do to our consciousness that is essential” (Von Franz, 1974, p. 33; emphasis added). Referring to the work of Kepler, Pauli agrees and noted that “It is that ancient psychic ‘dynamic’ of number which is still effective today” (Pauli, 1954, p. 122). More recent research by contemporary cognitive scientists connects numbers to the extended mind: In accordance with Clark (2006), I argue that external media together with the internal cognitive processes involved in number form a hybrid cognitive process. Next to this, I make a relatively strong claim for the interaction between internal and external cognitive resources. The enduring use of external media results in structural changes in the brain: the cognitive scaffolding we use . . . is recruited in numerical cognition alongside the number-sensitive neurons (De Cruz, 2008, p. 486; as well as the references therein).

Others (e.g. Rolls, 2007; Levy and Glimcher, 2012; Kennedy and Hill, 2018) confirm, for example, the interaction between emotions/feelings and values/numbers. This supports investors’ claims that prices embody an emotional charge and by themselves possess the potential to trigger instinctive behaviour, independent of fundamentals. Whether via screens on their smartphones, terminals on trading floors, the ticker tape in New York’s Times Square, or billboards in other city centres, people are mesmerised by market numbers. In other words, to paraphrase Von Franz, it is not what we can do with prices but what they do to us that is essential in our understanding of markets. Here too the literature on investor psychology offers some insights: Numbers have often been considered elements of knowledge production that increase objectivity and certainty. The fluid numbers of [financial] markets invite us to examine the consumption of numbers more closely. Traders look for clues to the direction of the market by observing the numbers. At the same time, the short time frames of . . . trading introduce a fundamental instability and uncertainty into economic judgments based on these numbers. The provisional nature of market numbers and the approximate character of traders’ conclusions suggest that traders’ practices are best characterized as interpretation rather than exacting calculation. But scholarly theories of numbers and quantitative representation are insufficient to provide a full reading of the power of numbers in financial markets. (Zaloom, 2003, p. 2; emphasis added)

Together with other aspects previously discussed, their “provisional nature” means these numbers (i.e. prices) are emerging as symbols which is where their “power” resides. Such symbolic meaning of prices can be concentrated in their numbers as such, and particularly applies to ownership. The novel Cosmopolis captures this (admittedly a bit over the top, but it exemplifies what happens in bubbles and hypes):

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Property is no longer about power, personality and command. It’s not about vulgar display or tasteful display. Because it no longer has weight or shape. The only thing that matters is the price you pay . . . What did you buy for your one hundred and four million dollars? Not dozens of rooms, incomparable views, private elevators . . . Not the swimming pool or the shark. Was it rights? The regulating sensors and software? . . . You paid the money for the number itself. One hundred and four million. This is what you bought. And it’s worth it. The number justifies itself. (DeLillo, 2003, p. 34; emphasis added)

I previously discussed reflexivity: prices affect fundamentals which, by way of adjustments in participants’ expectations, re-affect prices. Viewed from a complexity angle reflexivity distinguishes between statements. For individuals, true and false statements originate in S1 and S2. Building on reflections in Chapter 6, as well as von Foerster’s “undecidable questions” (von Foerster, 2003, see also Appendix 1-Preparation), we can reformulate these in market terms, namely in the form of a question. Price— personified in Mr Market—continuously (implicitly) asks you: “Am I True?” It consequently invites or teases out investors’ statements, namely about P (Price) relative to V (Value). Take a simplified example that regularly confronts portfolio managers. Say your S1 states V > P (= buy recommendation), whereas your S2 cautions instead that V < P (= sell recommendation). This internal competition eventually leads to a trading decision where correctness is judged via the experienced outcome (in S3), when knower and known merge. Put into action this reaches wider into the investment community. A buy order states that the price will go up. We will call it statement B. Except for making such statements there is no further discussion between market participants. That is, the only way to agree or disagree with statement B is to submit a buy order (i.e. another statement B), respectively a sell/short order (i.e. statement S). Those who trade thus clearly make statements. But even those who do not, and remain ‘flat’, are often still made to think by prices. Next, depending on the investment horizon at some point the verdict comes in: say, statement B is true (buying was correct) and there is a profit. What is important, again, is to view this in a Gödelian setting with the proof of such statements always delayed and, for humans, dually realised as ‘fact’ in S3. Formally, realising the profit as fact is by experiencing (in this case experiencing a positive return, or Pclose/Popen—1, but see the general consciousness case in Appendix 1-A4). In turn, this is informative which leads to new statements in S1 and S2. Again, this is how we learn. In short, it is the experience of prices that is the true reflexivity. Also, to the extent that this was not clear yet, the difference with the EMH is that the latter suggests price is not asking a question but is confronting the investor with its own statement, pretending to be a fait accompli, to be ‘passively’ accepted: “I am True” (i.e. P = V). And as von Foerster, Spear, and others, each in their own terms, tried to explain, this forces an internal proof which cannot be provided. In other (Gödelian) words, a market cannot be complete and consistent at the same time. The positive feedback loop implied by reflexivity can, under certain conditions, cause financial markets to reach extreme disequilibrium, contrary to the equilibrium

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assumed in mechanical economics. Compared to the EMH, Soros argues that financial markets cannot correctly discount (nor predict) the future because they are shaping it. Andrew Smithers agrees: Asset prices have an important impact on the real economy, and one which has often been denied, partly because they have no place in the neo-classical model and partly because asset prices cannot become overvalued according to the EMH. But, once their importance is accepted, it explains why economic forecasts are not just fallible but must be so. (Smithers, 2011, p. 3; emphasis added)

More broadly, the mind~body perspective recognises that price discovery plays a delicate, and sometimes painful, role in the psychophysical bridging in our economic system. As already mentioned, and to be discussed in more detail in the next subchapter, price discovery connects the physical real economy with the mental markets thereby coordinating and bringing order to the modern global society. It provides the numerical measuring for the collective mind’s state and intensities. In the dynamics between order and chaos “the primary means for ordering something in the chaotic multiplicity of appearance is therefore number” (Von Franz, 1992, p. 36). In the most objective shared sense of their symbolism—their just-so-ness—numbers reveal the hidden order underlying chaos. This “sense” in terms of meaning was expressed by Von Franz as follows: “man possesses an unconscious ‘numerical sense’” (Von Franz, 1992, p. 256; referring to Kreitner). This description is remarkably similar to number sense, a subsequently developed and well-researched hypothesis within mathematical cognition which focuses on how the mind gives rise to mathematics.10 Neuroscientist Stanislas Dehaene, one of its leading experts, states that number sense “provides animals and humans alike with a direct intuition of what numbers mean” (Dehaene, 1997a, p.5): These empirical results tend to confirm Poincaré’s postulate that number belongs to . . . the innate categories according to which we apprehend the world . . . Intuition about numbers is thus anchored deep in our brain. Number appears as one of the fundamental dimensions according to which our nervous system parses the external world. (Dehaene, 1997a, p. 5; emphasis added)

Based on a growing amount of convincing proof (see also Butterworth, 1999; Brannon, 2005; Gilmore, McCarthy, and Spelke, 2007; and the numerous references therein) there is a broad consensus on this ability. Moreover, Sklar et al. (2012), in a series of experiments, reported that effortful arithmetic equations can be solved unconsciously. In similar vein, the following description of the instinctual catching of a ball is by Richard Dawkins: When a man throws a ball high in the air and catches it again, he behaves as if he had solved a set of differential equations in predicting the trajectory of the ball. He may neither know nor care what a differential equation is, but this does not affect his skill with the ball. At some subconscious level, something functionally equivalent to the mathematical calculation is going on. (Dawkins, 1989, p. 96; emphasis added)

 See also Bueti and Walsh (2009).

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But it is not just the unconscious which is at play in prices’ numerical affects and the meaning they convey. The following description by Dehaene of the phenomenal experience of numbers could have been written about passionate investors in their relationship to prices, immersed in the market which is: a landscape of numbers . . . within which (they) can move freely. These people claim to experience numbers in a phenomenal way, often within a spatial setting, and they claim that numbers and their properties immediately pop to mind. Furthermore, many claim to experience strong pleasure associated with this—some go as far as to prefer the company of numbers to that of other fellow humans! (Dehaene, 1997b, p. 14)

At the same time there are important limitations to number sense that are relevant for our case, especially regarding debt. One of the consequences of our brain’s ‘wiring for numbers’ is that beyond a certain size, numbers become meaningless. Hofstadter warned about this in general terms, arguing that there is “no excuse for not being able to understand—or even relate to—numbers whose purpose is to summarise in a few symbols some salient aspects of . . . huge realities” (1985, p. 117). Global debt is such a huge reality. So, allow me to give a simple example of how to let large debt numbers make a “salient” impression that is more meaningful than their naked quote. Suppose you agree to pay back your (e.g. US$) debt at a frequency of 1 dollar per second (or around US$ 86,000 per day). If you have a debt of US$ 1million, it will take you roughly 12 days to repay this. If you have a debt of US$ 1billion, it will take you approximately 32 years to repay it. And if you have a debt of US$ 1trillion, it will take you 32,000 years to repay it. These intuition pumps are about increasing awareness, the ‘easier’ part of consciousness. Additionally, research by Burr and Ross (2008) extends the concept of qualia to numbers. What about prices? As previously discussed, prices are the signatures of the market mind’s impressions, reflecting the consumption and production of information. To wit, they are dualist information concentrates in the form of numerical symbols that provide meaning relevant for decision making. To help with the intuition, compare the qualities of prices to those of words. Whatever the formal definition or intended meaning of an expression used in an exchange, it carries qualitative value for the receiver. For example, what was meant as a compliment can be felt as hurtful. Similarly, whatever their quantitative reading, prices carry qualitative value for investors. Price qualia overlay investors’ deliberate participation in the market and complete the latter’s state. Besides market mood generally, the quality of the experience of price dynamics includes its duration (e.g. intrinsic time) as well as its uniformity (e.g. the shared comfort of the crowd). In Bateson’s terms, we rely on price qualia to notice the “difference”, that it “counts”. Price qualia convey what it feels like to participate in the market, when prices make that impression, particularly at ‘meaningful’ turning points. They contribute to experiential knowledge and cannot be left out if

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prices are (assumed to be) informationally exhaustive.11 Stated differently, a market’s state exhibits a collective allocation to material as well as mental resources, with prices as bridging symbols. Only the totality of market experience, including the phenomenal, exhausts the possible information about this state. To paraphrase William James, it is this “radical empirical” shared participation of investors in the market that makes the market’s mind reflexive and its state meaningful. But there is something peculiar about prices compared to other ‘carriers’ of qualia, beyond their intersubjectivity. Let’s compare price-conveyed moods with other phenomena to which we attach qualia, like smell and colour. Coffee, for example, is a substance which remains fairly static when it releases its smell. This is even more evident for an apple or a tomato when they reveal their colours. In my PhD thesis I explained how, at first sight, experiencing coloured prices on screens and monitors by millions of investors can be compared to experiencing the greenness of an apple or redness of a tomato. Prices are, of course, linked to securities and the physical assets underlying these.12 However, as ‘qualia-carriers’ prices are not like coffee, apples, or tomatoes. They do not exist as physical substances independent from subjects who discover them and experience their qualia. At those moments prices appear as numerical carriers of, say, an emotional charge where valuation of that charge only applies in the phenomenal sense (see Appendix 1). Collectively and recursively we dis- and recharge by buying and selling, maintaining the “strange loop” of price discovery which involves the three chains of the unconscious, the deliberate and the phenomenal. In short, what makes prices unique is that they are psychic self-references rather than physical originators of qualia. It is the reflexive dynamics involved in their discovery process which drives this by making prices living symbols chasing values in the “I’s” of multiple beholders who are all engaged in economic survival. To conclude, numbers receive their affective powers as symbols. They preconsciously create order in the mind by facilitating the dynamics of symbolic mapping as the mind attempts to make sense of what it senses, bridging the imaginative with the real. This autonomous and often dominating influence manifests itself as follows, both supported cerebrally: – In individual consciousness via numerical intuition. – In crowd consciousness via imitation which underlies its intersubjectivity.13 Problems start when manipulation leads to distortions in these cognitive processes.

 In the sense of Bohr (1949, p. 210): “only the totality of the phenomena exhausts the possible information about the objects.” See full quote in Subchapter 9.3.4.  As discussed, ultimately prices are units of currencies and in our modern times of fiat money currencies have no intrinsic value. As Jung stated: “Money becomes paper and everybody convinces everybody else that the little scraps are worth something because the State says so”.  Not only in terms of safety in numbers, but also in terms of building trust.

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These cognitive findings are highly relevant for investing as a discipline dealing with numbers. The intersubjective nature of the market’s mind, with prices as the symbolic signatures of the dual realisation of information, make it an exemplary phenomenon to be researched from a cognitive science perspective. The question is, of course, what recent developments in markets mean for number sense in the context of price discovery. As Tesla, bitcoin, and the GameStop sagas have shown, prices can act as the Pied Piper, mesmerizing crowds to march to the same music, back into the caves. Finally, any claim that numbers affect humans is controversial. This certainly is the case when such affect is viewed as occurring autonomously, i.e. by numbers themselves (as, for example, Plato and Pythagoras believed). But even if numbers are considered our own mental constructs, such affect implies mental causation—making us physically act—which will be questioned. However, this claim becomes more reasonable when such affect is considered inherent to the creation, or rather discovery, of such numbers by other humans collectively (i.e. without any identifiable single causal origin). This seems to occur in markets, exemplified by the phenomena of momentum and reflexivity. Nevertheless, if we accept this claim, it must be clear that it has major implications for several of the core assumptions in mechanical economics, most prominently equilibrium and fundamental value.

7.3 Price Discovery I am convinced that if it were the result of deliberate human design, and if the people guided by the price changes understood that their decisions have significance far beyond their immediate aim, this mechanism would have been acclaimed as one of the greatest triumphs of the human mind. Friedrich Hayek

Spontaneous price discovery via the price system “is a scientific mystery” (Smith, 1982; see full quote in Appendix 1-Preparations). We viewed this more broadly, namely for markets and minds. To recap, economic allocation (for markets), respectively metabolic allocation (for minds) of physical and psychological resources is largely in the pursuit of discovery—thereby gaining knowledge/novelty—to benefit mankind. The MMH suggests thinking of discovery and exchange (say, of ideas14) in portfolio terms: the more diverse the sources of discovery are, the more robust the economic system can handle shocks, that is adapt to surprises. Randomness and true uncertainty mean we should expect anything from anywhere at any time. It thus demands a wide mandate: freedom (no constraints) and transparency (nothing hidden) to pursue discoveries in a diversi-

 E.g. I trade my idea in return for your feedback/enhancement/etc. Brainstorming and brainwriting are formal techniques to achieve this in groups.

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fied portfolio sense. All this is relevant for practical investment themes like productivity, passive-active investing, Big Tech, and TBTS Banks. I will return to these later.

7.3.1 Society’s Chain of Discovery Discovery~invention. More than anywhere else, it is in the economic system where these apparently contradictory (e.g. in mathematics) phenomena become a complementary pair. Minds and markets are continuously discovering, inventing, and innovating. Specifically, society’s chain of discovery—forming a reflexive loop, here viewed in one direction (clockwise; Figure 7.1)—starts, roughly speaking, with insights in individual minds, like those of scientists. Two recent promising examples in energy space are the (alledged) breakthroughs respectively in nuclear fusion by scientists in the US and in seawater electrolysis by scientists in China. This start-up phase is often funded by both public and private sources. Frequently involving an interim step of inventions, the insights are then shared and turned into innovation (like new technology) by entrepreneurs in the real economy. Emphasising this collective dimension in innovation, Muthukrishna and Henrich argue that the combined brains of those involved already “beget a collective brain” (Muthukrishna and Henrich, 2016, p. 1). Confirming my earlier comments on the importance of idd-minds, they further argue that the rates of innovation are heavily influenced by “cultural variance” (Muthukrishna and Henrich, 2016, p. 1), among others. This chain then culminates in valuation via price discovery in markets which can fund further insights.15

Funding

Insight

Valuation (via price)

Invention

Innovation

Figure 7.1: Society’s chain of discovery.

 This echoes the concept of entrepreneurial discovery of Austrian economics. See Kirzner (1997). It also relates to the work of Hidalgo and Hausmann (2009) on economic complexity and, re gaining knowledge, their concept of “personbytes” (see also Chapter 9). In broader terms it touches on the knowledge commons (Frischmann, Madison and Strandburg, eds, 2014).

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Innovation implies novelty, new ways to use our resources, be they natural or human. As economic Nobel laureate Edmund Phelps states: “Innovators are taking a leap into the unknown”. By definition, the unknown is virgin territory that needs to be explored. It is the space where discoveries take place. Crucially, growth in the economy is generated by ‘physical’ discoveries—like technological gadgets, manufacturing improvements, and medical breakthroughs—that allow us to adapt to our environment and improve our lives. These are then valued by way of the discovery of prices in financial markets which completes the circle, thereby bridging the physical (e.g. innovations) and the mental (e.g. insights). Ultimately these discoveries originate with individuals who, both individually and collectively, require the freedom to make them. While purposeful, neuronal activity and price activity are largely random.16 Still, in both domains spontaneous patterns emerge from such randomness. Sometimes these are similar, including those resulting from autocorrelation in noise ahead of voluntary decisions (see Schotanus and Schurger, 2020). Generally, economic agents try to make sense of price discovery and attach narratives to patterns. It is exactly at critical turning points, when price discovery is of the breakthrough kind, that such sensemaking is sought. It is crucial for the self-maintenance of (entities that include) organisms which: regulate their interactions with the world in such a way that they transform the world into a place of salience, meaning, and value—into an environment (Umwelt) in the proper biological sense of the term. This transformation of the world into an environment happens through the organism’s sensemaking activity. Sensemaking is the interactional and relational side of autonomy. (Thompson and Stapleton, 2009, p. 25; emphasis added)17

The problem is that the moment you remove the element of surprise, whether by policy or mechanisation,18 you start to limit and distort discovery. There are often (e.g. lobbied) political reasons to remove surprises, mainly to sustain or strengthen the status quo. Unfortunately, this manipulation subsequently infects the whole of society’s discovery loop and replaces spontaneous patterns with induced ones. The following chart (Figure 7.2), of the annual (CPI) inflation rate in the US, exemplifies this for the US economic system. Over most of recorded economic history inflation was fairly random, with spikes during wars. The ‘engineering’ of the economic system, and by extension the manipulation of prices (here reflected in inflation), started shortly after WW2, particularly through the use of monetary policies. It went into overdrive from the 1960s onwards when the ‘equilibrium’ theories of mechanical economics became dominant and provided academic cover for the mechanical approach in policymaking. Some now see the danger of this. Former Fed Chair Paul Volcker, who was never really a neoclassic

 It is generally assumed, for example, that equity returns show negative autocorrelation.  Clearly echoing Mises’s purposeful action. See also Madsbjerg (2017).  Again, mechanical (IF-THEN) rules are, by definition, predetermined in that regard.

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40

30

20

10

0

-10

-20

-30 1774 1784 1794 1804 1814 1824 1834 1844 1854 1864 1874 1884 1894 1904 1914 1924 1934 1944 1954 1964 1974 1984 1994 2004

Figure 7.2: US Inflation Engineering. Source: Robert Sahr, Political Science Department, Oregon State University.

believer anyway, criticized the Fed’s approach, in particular ridiculing the “degree of precision” implied by its 2% inflation target. We know what happened next.

7.3.2 Distortions, Interferences, and Consequences What do the iconic speeches by Winston Churchill and Martin Luther King Jnr, Mahatma Gandhi’s civil disobedience, the “tank man” at Tiananmen Square, and the fall of the Berlin Wall have in common? Despite differing in format their symbolism affects us in that it represents the struggle for freedom. They appeal to deeply ingrained psychological modules that move us. Freedom, in that regard, does not equal libertarianism, just like spirituality is not the same as religion, and science differs from academia. Mr Market has his own struggle for freedom and many of his prices have become false symbols. In Chapter 2 I listed some of the consequences of economic engineering/mechanisation in general. Here I will go into more detail and list a few specific additions, concerning the repression of surprises in particular: – Moral hazard, as predictability removes responsibility and caution. For example, the predictability of the central bank’s put/taxpayers’ bailouts/government’s guarantees, has led to excessive (debt-fuelled) risk taking. – Passive investing (or index tracking) follows a simple algorithm that freerides on the discovery by active investors (who, arguably, get paid too much). The growth in passive investing reflects the popularity of the EMH, to the point that, ironi-

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cally, its key argument is at risk. Originally, this argument was that the market was efficient because it aggregated the numerous independent opinions and diverse sources of information of the investor community. However, index investing only has one EMH-inspired opinion: price equals value. Relative to market capitalisation, this makes price its single source of information to which investors become insensitive, i.e. they become price takers. That consensus means that the advance of index investing diminishes the diversity of opinions and increases inefficiency, to the point that eventually the diminishing minority of active investors can no longer correct it. Passive’s growth also leads to concentration of ownership with the risk of biased proxy voting, for example. However, as I indicated in Chapter 1, the issue is not between passive and active per se. The real problem is the growing imbalance between mechanised and discretionary investing. Mechanised ‘active’ investing, via smart beta for example, still (wrongly) implies that pre-determined algorithms can somehow ‘discover’ in an economic meaningful way. Combined with central bank machinations, mainly price interference, this turns the default negative feedback into positive feedback, thus sustaining, rather than reversing, the momentum in capital appreciation. This is compounded by the fact that most of these strategies only use internal market data, not external economic data. Worse still, the underlying ‘momentum’ that drives it is the positive feedback-loop between the theory of the market as machine (‘somebody will fix it’) and the practise of employing algorithms and other mechanical rules that exploit it. Allied to this are legal and ethical issues of outsourcing decisions to (e.g. deep learning) AI, which arise because there is no human understanding of those decisions. Investment Note Antitrust Applying practical dualism is often helpful to judge economic issues, by dissecting and explicitly considering both material and mental aspects of such issues. One example is antitrust, the principle to prevent the concentration of market power by blocking monopolies and promoting competition. Currently legislation and policies are only based on criteria applied to, or derived from, the (physical) real economy. Examples are market share and consumer prices. However, because the two interact antitrust should also be judged in terms of the (psychological) financial economy. One situation concerns the concentration of share ownership among large passive institutions. For example, through its many funds BlackRock was one of the top three shareholders of more than 80% of the companies in the S&P500 in 2021, according to S&P Global Market Intelligence. Not only is it impossible to properly represent its many diverse shareholders in its funds. Such concentrated power also risks the biased proxy voting I mentioned earlier. Concentration of financial-economy power is also reflected in measures like market capitalisation. Such concentration occurs across many indices but is more pronounced in some. At the time of writing, just seven companies make up more than fifty per cent of the market capitalisation of the Nasdaq 100 index. Other measures of concentration include wealth and capital structure, reflected in the hierarchy and covenants of issued securities. Such power can not only subsidise real-economy power, but can also detrimentally affect investors, or even taxpayers (think of the bailouts of too-powerful banks).

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So, even if today’s conglomerates comply with the rules in the real economy, antitrust benefits accrue asymmetrically and do not benefit, in a more balanced way, the overall economic system. Until recently this situation had grown worse with the engineering of the economic system, particularly since credit became very cheap, due to lowered interest rates. For example, many companies that offer consumers low prices, or even free services, nevertheless enjoy relatively high stock prices, concentrating market values and wealth. These and other companies have no pricing power in the real economy (and are often loss-making) but do have pricing power in the financial market by being able to issue low-covenant debt. One interpretation is that the financial economy is judging those companies, despite being lossmaking, to have a moat that acts like a barrier of entry and prevents competition in the real economy. That gives them the ‘benefit of the doubt’ and allows them to survive in an engineered economy.

Over time a number of cognitive research experiments showed how vulnerable people are to various forms of manipulation, varying from well-meant nudging and incentives to enforcing group think. Such vulnerability among investors, grouped as a collectivity, can influence markets. I will list a few seminal ones, whereby the common denominator for those with group dynamics, at least for our purposes, is the ‘mechanical’ conditions of the situation and its subsequent treatment by those involved. It specifically occurs by way of mindless copying and repetition, leading to a litany of ‘more of the same’ practices to keep control of the situation: 1. The Asch Conformity Experiment (1951): Social psychologist Solomon Asch gathered groups of participants. Each group had one true subject. The others were actors who were fully aware of the real focus of the study: how many subjects would change their answer to conform to those of the actors in their group, despite those answers being wrong. Specifically, the group was shown two cards. Card A had a line on it, while Card B had three lines on it. The participants were asked to speak out loud which of the lines on Card B matched the line on Card A, with the real subject always replying last. To cut a long experiment short, the level of conformity by subjects to the group’s opinion, that of the actors, turned out to be surprisingly large. ‘More of the same’ applied to the number of wrong opinions by the actors, adding power, as it were, to group think. 2. The Festinger Cognitive Dissonance Experiment (1959): Social psychologist Leon Festinger performed an experiment regarding cognitive dissonance in which he asked the participants to execute boring mechanical tasks, such as repeatedly turning pegs in a peg board. Half of the participants were paid significantly less than the other half. Another part of their participation was to prepare the next ‘subject’ (who was actually a researcher) by telling them about the task, truthfully or not. This experiment showed how incentives can control behaviour, e.g. ‘telling the truth will cost you’. 3. The Milgram Obedience Experiment (1963): Psychologist Stanley Milgram had a person of authority instruct subjects A to test the memories of other (but hidden) subjects B and administer electric shocks in case subjects B erred. What he did not tell subjects A is that subjects B were actors who never physically received those shocks

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but acted out realistic responses with cries of pain or for help. The message of this notorious experiment (although later criticised) was that people would generally follow the orders of authorities and have various ways to excuse, justify or otherwise rationalise doing harm to someone—here interpreted as ignoring that they are conscious beings. Specifically, ‘more of the same’ consisted of agreeing to increasing the voltage of the electric shock. 4. The Stanford Prison Experiment (1971): psychology professor Philip Zimbardo recruited a group of volunteers who, all unbeknownst to them, were randomly assigned the role of prisoner or prison guard. After the prisoners were ‘arrested’ and delivered to the prison by real police, the experiment continued by having the prison guards establish a prison regime. What emerged was an increasingly brutal regime, reflecting the ‘more of the same’, to keep things under control. 5. National Geographic’s Peer Pressure Experiments: More recently, in a series called Brain Games, the National Geographic team performed a number of experiments, like the Waiting Room Conformity Experiment, all showing various aspects of conformity, peer pressure, and group think. My point is that many of us have become ‘comfortable’ and—be it by incentives or peer-pressure—are going along with doubling down on more of the same mechanisation in recent monetary and investment ‘experiments’. However, we are left with an increased vulnerability of the economic system due to diminished diversity in the sources of discovery and numbed awareness of agents. True discovery is a delicate and often painful process. The latter aspect makes some people uncomfortable and motivates them to interfere to avoid such pain. However, interference, particularly of the ‘politically correct’ kind, distorts prices and weakens individual responsibility (e.g. Böhler, 1970). Other forms of manipulation, like the Libor and Forex rigging scandals or the pre-release of inside information,19 have similar detrimental effects. The problem is that moral hazard and other (unintended) consequences of these policies may not always be apparent on the cognitive surface which we explore with our analytical methods. In general, interference leads to the unhealthy over-reliance on such analysis: “Every act of interference with the course of nature changes it in unpredictable ways . . . The more one interferes, the more one must analyse an evergrowing volume of detailed information about the results of interference on a world whose infinite details are inextricably interwoven” (Watts, 1966, p. 40). A number of researchers have pointed out that repressing naturally occurring behaviour, for example because it is unpleasant, will exactly cause it to build up, some-

 See https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1901.en.pdf?ca0947cb7c6358aed9180 ca2976160bf. The BBC recently reported on (Libor) interest rate rigging during the GFC, involving central banks (Verity, 2023).

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times elsewhere in the system. On that note, the link between emotions and volatility is an obvious one (e.g. Howard, 2014). Hedge fund manager Kyle Bass stated it succinctly: “if you suppress volatility long enough, then when the ‘event’ happens it is greater than the sum total of all the suppressed vol over time”. To learn more about volatility as a phenomenon, I particularly recommend the work by volatility pioneers Christopher Cole of Artemis Capital Management and Mark Spitznagel from Universa Investments. (See Spitznagel, 2013, for example). More philosophically minded readers should consult Ayache (2010a) and Taleb (especially 2012). Ultimately prices and their patterns20 are the market’s symbolic reflection of such tension.21 Although causality in this composite complex adaptive system remains elusive, its mind~matter dynamics can lead to the build-up and shattering of investment myths which has been clearly shown in the many bubbles and crashes over the past centuries (e.g. Kindleberger and Aliber, 2011). As discussed earlier, the unconscious and the phenomenal both escape scientific capture. So, neither I nor anybody else can analytically convey the combined primal awe and sensational intimacy of personally experiencing the market as it makes up its mind, sometimes violently so (see the earlier quote from Hassoun in the Introduction). In an ideal world price discovery orders the market’s mind, whereby the individual investor’s search for ‘peace of mind’ is a search for ‘true value’ while engaging the collective market psyche. At that level, price discovery is most powerful when it is experienced as an intuitive insight, in the immediate moment of a trade ‘aligned with the market’, while often going against the crowd. There are suggestions that there may be a way to more directly tune in to this collective consciousness in order to understand the symbolic meaning of prices. I will discuss this in Subchapter 9.3. Over the past decades we have become further removed from that world. As an example of the ills this leads to, I will explore the issue of productivity in the next section.

7.3.3 Price Discovery, Innovation and Productivity Productivity is hot. But only as a topic. As a measure it has cooled. For the G7 countries it has dropped from roughly 4% in 1970 to less than 1% in 2020. In the US, the Bureau of Labor Statistics reported a while back the first sequential drop in productivity since 1993. Its trade deficits built up because the US let other countries produce physical stuff which it consumed, financed via recycled US dollars mediated by its

 Among these are so-called technical patterns which have repeated themselves over the history of capital markets. They have exotic names like ’head and shoulders’, ‘island reversal’, and ‘saucer bottom’, each with a story to tell. They also include cycles and waves, often scaled according to the Fibonacci ratio.  In case the reader has not recognised these already, additional examples of opposing (but complementary) forces in the market’s mind are sellers~buyers, bulls~bears, longs ~shorts, but also regulators~speculators.

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booming but non-producing financial sector. And the ‘production’ by some of its tech giants, whose CEOs recently had to testify in Congress and explain their oligopolies, is dubious. From a practical dualism perspective, we particularly care for productivity because it results from the mental and physical ability of workers to produce goods and services relative to production costs, like the amount of energy and material used for that production. Flattening productivity is worrying for a number of reasons, but mainly because of the links between productivity, wages, and consumer spending. Specifically, higher productivity increases wages and leads to higher spending. So when George Osborne, former UK Chancellor of the Exchequer, referred to productivity and warned that “Our future prosperity depends on it” he was not exaggerating. Moreover, there are demand and supply issues involved, and I will argue that most are symptoms of economics’ hard problem. How did we get here? There was a time, not so long ago, when things were looking better. In the build-up to, as well as aftermath of the internet bubble labour productivity, particularly in the US, grew much faster than over the previous three decades. In one of his more memorable speeches Alan Greenspan even talked about the “productivity feast”.22 That didn’t last. So what went wrong? In February 2015, the Fed published a report23 arguing that we are possibly witnessing “a pause in—if not the end of—exceptional productivity growth associated with information technology” mainly caused by “slower growth in innovation”. Going forward, a relatively slow productivity pace is their best guess. Smithers (2015) has a different take on it. He argues that poor productivity is a consequence of low investment. The latter is caused by the corporate incentive structure, specifically paying executives large bonuses for short-term success. In addition, being rewarded in (options on) shares favours tactics to influence the share price, for example via buybacks, consistent with my earlier comments on manipulation. The bonus culture is detrimental to long-term investment, and this is not only negatively impacting productivity, but is a problem for the entire economy and possibly society. Why? Because of a related hot topic, namely inequality which I touched on before. If top management (representing those exclusive “1%-ers”) has largely benefited from delaying or diverting investments, a logical question is whether this also goes for average workers. This is relevant for the broader economy because final demand, i.e. consumption, by these workers is ultimately fuelled by their income. A lively debate has followed early research, centred on comparing growth in productivity to that in compensation. The flat lining of the real value of family income started even before

 It became known as his “(internet) productivity miracle” speech. In it he also referred to a “productivity famine”. https://www.federalreserve.gov/boardDocs/Speeches/2002/20021023/default.htm.  https://www.frbsf.org/economic-research/publications/economic-letter/2015/february/economicgrowth-information-technology-factor-productivity/.

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the internet peak and is among the standard bearers of the ‘demise of the middleclass’ argument.24 It counters the general assumption that faster productivity growth leads to higher real wages and improved living standards. If this has shown to be elusive when productivity was still ‘okay’, then we should indeed worry when productivity growth drops even further, particularly in those developed markets experiencing demographic pressures. Productivity clearly matters to the private sector. Depending on the broader demand outlook, a company hires employees on the expectation that they can improve its productivity. By hiring the firm believes it can optimise the balance between its investment in capital (plants, machinery, etc.) and that in humans, allowing its workforce to work its capital harder so to speak. It hopes that this will lead to cheaper production of better goods which, in turn, should lead to higher demand and profits. But when productivity disappoints while employment and other (e.g. interest) costs increase, pressure starts to build on margins. Soon a vicious circle can develop where hiring stalls, unemployment increases, consumer spending is cut, and final demand drops. Acknowledging the limits of stimulating demand, there are ways to improve the supply side that can support productivity: – Restructuring the economy more broadly (e.g. the so-called ‘third arrow’ of Abenomics in Japan). – Investing in infrastructure (e.g. China’s new Silk Road [a.k.a. Belt and Road Initiative], US road network); and – Boosting trade (e.g. the various trade-agreements). These measures aim to increase competition~cooperation and raise productivity thereby lifting the potential growth rate of the global economy. However, various initiatives along these lines are ‘pending’ or have ceased and progress is slow. And obviously the recent crises and (trade) wars are not helping. A related issue, particularly in light of the debt overhang, is whether the financing of supply initiatives is actually prudent for countries that have ample fiscal space. Surprisingly, the IMF25 has argued it is, suggesting that reducing debt in such cases is undesirable because the costs involved, such as potentially jeopardizing an early recovery, can be larger than the benefits. Their pro-stimulus stance further weakens

 However, it is also a contentious issue. A number of economists have argued that it requires adjustment, and that hourly compensation has actually tracked productivity over the past 65 years. As far as US consumption is concerned, the IMF has argued that it is the rich who are now the “marginal buyer” of goods and services and, via their savings rate, have contributed most to the recent consumption boom-busts. This also has implications for (stubborn) inflation. Available from https://www.imf. org/ external/pubs/ft/wp/2014/wp14225.pdf.  Available from https://www.imf.org/external/pubs/ft/sdn/2015/sdn1510.pdf.

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whatever little is left of the austerity camp. The caveat, of course, is that increasing interest rates make such debt funding less attractive, if not unsustainable. In summary, both global economic growth and productivity had been disappointing and received further blows during the CVC. Various fiscal and monetary policies like ZIRP/NIRP and QE have attempted—but so far failed—to address this. Instead, as we have experienced, the multitude of distortions in demand and supply have resulted in inflationary pressures. At the time of writing, these are not yet fully reflected in higher wages (as usually happens), but rather seem to be particularly fuelled by companies using the excuse of ‘inflation’ (e.g. due to ‘supply-chain issues’) to raise prices and/or to turn it into shrinkflation. Evidence for this is the sustained level of record profit margins. Such squeeze of consumers is eventually going to negatively impact profit margins and interest rates. For this, and other reasons, demand initiatives are now being supported by attempts to strengthen supply via onshoring for instance. It remains to be seen if these are more successful. Longer term I am sceptical for reasons I have shared previously. What both demand and supply policies have in common is the misbelief that the economy is mechanical whereby these two key economic forces can somehow be engineered and controlled. The implications of this negatively impact both the demand and supply side of growth in general and productivity in particular. Nowhere is this clearer than in the creative processes that ultimately underlie innovation. Apart from (ironically) the Fed, several other experts (e.g. Gordon, 2012) have voiced concerns that innovation, as a driving force for growth through renewal, is diminishing. I believe there is more than a grain of truth to this but that it should be viewed in the broader framework of “society’s chain” of discovery that I have described. Bloom et al. (2020) find that ideas are “harder to find”, with research productivity falling across the board. And Nature (Park, Leahey and Funk, 2023) reports that science has become less disruptive in that the proportion of publications that shift a field has plummeted over the past half-century, with the biggest decline occurring in the social sciences. While the paper leaves this open, my suspicion is that this is due to the growing mechanisation and specialisation which leads to repetitive silo-thinking. It diminishes meaningful and spontaneous interdisciplinary exchanges between conscious minds. This includes diversity and intersubjectivity which, especially in terms of creativity, is pivotal. Using fascinating experiments Daniel Richardson of University College London and his colleagues showed that groups of people become more creative after undergoing spontaneous collective experiences, like an underwater rave party. Investors don’t need a rave party. For them there is nothing more spontaneous than freely exchanging in markets. In the final analysis mechanical economics leads to centrally determined policies to financially engineer demand and/or supply funded with immaterial money via the non-producing banks. The structural issue over the past decades has been the repression of the market rate below the so-called natural rate of interest. This fuelled such engineering, leading to increased borrowing to capture the spread which pushed up

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prices of those assets which yield more than the market rate. In the real economy (where we all participate) it demotivates investing by companies in human and fixed capital, while instead promoting the buyback of stocks in the financial economy (which mainly benefits the select few). It also contributed to lower productivity. I already listed other negative consequences and symptoms. Perhaps most worryingly, it forces people into a ‘required’ behaviour, like chasing yield rather than discovering value. A mechanical worldview that justifies centralised manipulation chokes the individual creativity that underlies innovation. No wonder productivity slows.

7.4 The MMH as Price Theory Let me summarise what the MMH says about prices.26 For arguments sake, I start with underlining money’s symbolism (especially its numerical expression). Money is “a symbol of economic value, because economic value is nothing but the relativity of exchangeable objects. This relativity, in turn, increasingly dominates the other qualities of the objects that evolve as money, until finally these objects are nothing more than embodied relativity” (Simmel, 1907, p. 125). Prices thus symbolise relative information. But the relativity of information is more general and applies to scientific information, for example. Think of the information contained in or provided by theories. Theories are relative, especially to each other, as well as to themselves over time. One theory contains more information because it covers more phenomena than another. And a third delivers better information in terms of predictions. In physics there is even a theory called “relative information theory”. Again, relative information is more complicated in the case of conscious humans who dually realise it relative to one another. In all cases, “truth is valid, not in spite of its relativity but precisely on account of it” (Simmel, 1907, p. 115). Next, let’s state money’s mind~matter in black-and-white terms. On the one hand, suppose Marx (see fn. in Subchapter 2.1) is right for once: as fiat currency denominations prices are just mental (“ideal”) constructs. Ironically, libertarians who are critical of fiat currencies would have to agree with him: there is nothing physical such as gold backing prices nominated in such currencies, only promises. Consequently we are just left with numbers. How can they have such influence? On the other hand, there are those who counter by insisting that prices are eventually backed by physical assets. Yes, in the first instance currencies (as well as sovereign debt) only receive the “full faith” of their issuing government. But in practice this translates in the backing by physical labour which, via production, makes physical collateral available. All the while the currencies are accepted as income and paid as taxes. Fiat,

 There are many alternative views of the pricing system, including interpretations critical of classical physics and inspired, instead, by quantum physics (Bouchaud et al., 2018).

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they say, can be sufficiently enforced, by physical incarceration if need be. However, all this relies heavily on conscious mind~bodies. How can this be reconciled? Supported by number sense, the nature of information (bits), and other arguments, the MMH submits that prices have both physical and phenomenal aspects. As information-concentrates they can thus form the empirical building blocks of our psychophysical bridge in modern society. Specifically, they are the numbers with which we match the allocation to physical assets with the allocation to psychic ones. Both are scarce resources which enable us, at a massive collective scale, to adapt by gaining exposure to, or protect against global circumstances. Prices are the numerical meeting points in the market’s mind where buyers and sellers collectively agree on quantity but disagree on quality. They capture the information that is doubly realised and, as numerical ratios, have mental efficacy as Popper’s “abstract relationship”. Prices offer us a way to deal with and to recognise: both sides of reality—the quantitative and the qualitative, the physical and the psychical—as compatible with each other. It would be most satisfactory of all if [matter and mind] could be seen as complementary aspects of the same reality. (Pauli, 1948a, p. 164)

They and their patterns form the elements of Pauli’s “psychophysical unitary language”. Price discovery, as the search for shared values, is the markets’ self-organising principle operating at a global scale. By extension, it guides the other innovations in the economic system, the surprising novelty and creativity that make it a complex adaptive system. Von Franz points out that the mind’s unconscious operations are symbolised in numbers. Paraphrased for the market’s mind it is not just what we can do with prices but also what they do to us. This collective unconscious dynamic of price formation at the macro (i.e. market mind) level is echoed in the words of Hayek when, talking about the “formation of abstractions” as “discovery”, he states that it: ought to be regarded not as actions of the [individual] human mind but rather as something which happens to the mind . . . [and seems] . . . not something at which the mind can deliberately aim, but always a discovery of something which already guides its operation. (Hayek, 1967, p. 43; emphasis added)

In that process, ultimately of connecting physical and psychic events, the market’s apparent randomness is regularly transformed into numerical patterns reflecting, what seem, piled up coincidences, like the ‘unlikely’ events during the GFC and the CVC. More generally, these also include trends, reversals, and other formations. Pathdependency plays an important role and can be viewed as the ‘memory’ of the market mind. The affect from prices cannot be un-experienced, after all. On that note, in a recent paper Guyon and Lekeufack (2022) show that volatility is largely explained by the historic price path, i.e. via trends. Among others, it means that:

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There is no need to add an artificial stochastic variable to account for volatility’s dynamics. The assumption in mechanical economics that volatility only depends on external events and the existing price is misguided and can lead to erroneous conclusions, e.g. about option (volatility) smiles. Consistent with some of the findings on excess volatility (see Subchapter 9.2), volatility is mostly endogenous whereby fundamentals do not act as input but, instead, can be impacted by volatility.

To conclude, the MMH is also a theory about prices. Its premise is that they are the informational signatures of a collective mind which are dually realised. It means that market dynamics is similar to mental dynamics (for some preliminary empirical back-up, see Subchapter 9.2). This goes deeper and further than mechanical economics’ central theme that ‘prices are rational’. Whereas the MMH’s psychophysical worldview considers discovery, creativity, and surprises as central, mechanical economics (due to its mechanical worldview) has no room for them.27 Nor does mechanical economics properly consider true uncertainty.28 In contrast, the MMH grasps the origin of our struggle to deal with it. Perhaps ironically, it instils a specific awareness that events are unpredictable exactly because we are (still) ignorant about the relationship between mind and matter. This is also why the MMH adopts portfolioism: a practical way to see these domains combined. In the economic system, which emerged exactly to facilitate this at a large scale, prices form the transmission between those domains. Their discovery is, as I have just described, rather delicate and cannot be forced. Impeding discovery is like preventing us crossing the bridge between mind and matter and thus blocks further understanding.

 Again, others have made this point before (see Frydman and Goldberg, 2011).  The miserable failure of VaR and DSGE models exemplify these blind spots in that regard.

Chapter 8 On Portfolios: Am I Balanced? I am large, I contain multitudes. Walt Whitman

8.1 Introduction The previous chapters focussed mostly on the market-as-mind aspect of the MMH. This chapter will discuss the other aspect: mind-as-market. I will explore this in more detail, while using arguments via portfolioism which I introduced previously and discuss in Appendix 1-C. My motivation to do so is that cognitive science, broadly speaking, misses a clear framework regarding the exact dynamics between mind~body in general, and between various domain-specific psychological adaptations in particular. This especially applies when considering extending minds into collective settings. In terms of neuroscience, this was raised by a team of behavioural economists, headed by Colin Camerer and George Loewenstein, who echo what I mentioned at the beginning of this book, namely that there are plenty of references in the cognitive literature to economic concepts to help describe mentality, but that this remains largely unformalised: Neuroscience is shot through with familiar economic language—delegation, division of labor, constraint, coordination, executive function—but these concepts are not formalized in neuroscience as they are in economics. There is no overall theory of how the brain allocates resources . . . An “economic model of the brain” could help here. (Camerer et al. 2005, p. 56)

The MMH considers this more widely and offers a market model of the mind. Regarding division of labour, extension of the mind involves delegating and outsourcing the burden of cognition to various tools that can become part of the physical realisers of consciousness. In terms of 4E cognition more generally, as raised by Rowlands earlier, it is left with questions on “ownership” of cognitions, for example. Add other economic terminology used in its literature—like “currencies”, “cooperation”, “competition”, “rewards” and “values”—and it should be clear for cognitive science to see what is right in front of its eyes: market~portfolio dynamics offers such a framework through portfolioism. To recap quickly, an investment portfolio is a collection of assets, mostly in the form of securities, which can benefit from or hedge against economic, political, weather, and other developments in the wider environment, a.k.a. states of the world. Depending on the latter the overall value of the portfolio can go up or down. People manage such portfolios to achieve certain financial goals, like retiring in case of a

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pension fund. Through an investment portfolio people participate in the financial economy, increasing their exposure more broadly to the economic system. Portfolioism states that the management of mind~body assets (like S1’s instincts and S2’s thoughts) can similarly be viewed from a portfolio perspective, i.e. a cognitive portfolio. In their most basic form these assets are called psychurities (derived from their financial cousins, securities). A domain where the management of an investment portfolio and the management of a cognitive portfolio particularly overlap is trader psychology. This field includes the work of trading coaches, like Flavia Cymbalista and Denise Shull. I refer the reader to the literature for more details, including references in Subchapter 1.2 as well as Fenton-O’Creevy et al. (2011), Lange and Von Scheve (2020), and Steenbarger (2003). The portfolio management perspective of mentality is consistent with the MMH’s broader principle, namely that markets and minds share the same dynamics—by way of competition~cooperation, demand~supply, and other complementary market pairs —ultimately aimed at coordination via the discovery of value. In this case, it starts with the view that the process underlying the allocation of monetary resources is not just an analogy for, but a reflection of, that underlying the allocation of mental resources. Portfolioism’s perspective can provide an insightful angle on mentality and possibly offer suggestions for modelling both market and investor minds.1 In the next subchapter I will focus on S1 in particular. Notably, I will discuss portfolioism as applied, primarily, to emotions. Emotions are important cognitions of S1 that also feature prominently in investment management. Still, my general arguments apply to other cognitions (e.g. beliefs, thoughts, and delusions) as well. (You may want to read Appendix 1-A first, especially if you are not a cognitive expert. It also has sympathy with and echoes some of the arguments of Evolutionary Rationality, summarised in Appendix 1-B4). Valuation is central in portfolio management and offers a connecting principle between minds and markets. Still, it is not a cure all. Despite the similarities there remains—as part of the larger explanatory gap—some friction between monetary values of assets and the mental values of cognitions which these assets invoke. Although the numbers—represented by prices— form, the MMH submits, Pauli’s “common language” to bridge the physical with the mental, something gets lost in translation.

 In the following I therefore assume that the reader is familiar with the basics of portfolio management. For those wishing to learn more, Burton Malkiel’s classic A Random Walk Down Wall Street is a useful introductory text in the tradition of mainstream finance, especially Modern Portfolio Theory. Again, portfolioism keeps some of its concepts, while dismissing the underlying mechanical worldview.

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8.2 Emotions as Portfolios of Psychurities First, we need to make the distinction between an investment portfolio or IP (which contains securities) and an emotion portfolio or EP (which contains psychurities). Specifically, emotions can be viewed as portfolios of domain-specific psychurities. Each psychurity is a single psychological adaptation: its activation or trigger is eventdependent or, in finance terms, ‘state contingent’. As such (and just like finance’s “pure securities”), a psychurity embeds a contingent claim, or payoff. These can be held individually or can be combined. In the latter case, your EP can contain those proverbial ‘mixed emotions’. In general, each EP is structured to replicate a strategy to respond to an event. Such strategies are ultimately aimed at gaining a reward or hedging a risk, especially if the latter cannot be quantified, i.e. is more about uncertainty. Portfolioism suggests that the mind forms a multi-layered complex of EPs (a ‘fund-of-fund’ if you will) which, via allocation and dynamic rebalancing, allows it to seek or avoid exposure to a multitude of circumstances, sometimes unexpectedly. At the same time, just like the fund-of-fund leads to a stream of payoffs, the combined EPs result in a ‘stream of consciousness’ for experienced emotions. The values of these EPs are determined across various mind markets. First, they are appraised by way of the exchanges through cortical centres, and other exchanges between the subcomponents in the individual mind. Specifically, the competition for neuronal resources in the brain between emotions and thoughts is an extension of the broader polarity between the unconscious respectively deliberate forces. Second, they are also appraised via the exchange with other minds. Here mirror neurons, for example, seem to play a physical role in the shared unconscious assessment of emotions (Gallese, Eagle and Migone, 2007). We can consequently view this as a global market of emotions. Together with the local (personal) market, emotions are valued according to their fitness to a certain situation: an emotion becomes more valuable if its payoff, implied by the strategy as a response to the situation, increases the mental (e.g. epistemic) utility of the overall complex of portfolios (see Evolutionary Rationality in Appendix 1B4). Also, due to their ancient existence (making S1 much older than S2), the history of emotion valuation is much longer than that of, say, logic valuation. (For a financial comparison: the history of gold valuation is much longer than that of oil). There are four arguments why the EP interpretation is useful: 1. Focus on the leading indicator of cognitions As so much of our behaviour originates in the unconscious (S1), we are mostly interested here in clarifying the earliest phase of the emergence and impact of cognitions, in particular instinctive (or “fast”) ones. In other words, we are interested in the leading indicator of cognitions. How is this mirrored in the collective world of economics? Financial markets are generally considered the leading indicator for the real economy. As discussed, they are an economic meta-adaptation in that they solve a crucial economic problem: to allocate capital, in a reasonably efficient way, to investments

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which eventually find their way into physical assets, (the output) of the real economy. And like the personal unconscious, much of market cognition remains hidden which, depending on your preferences, you can view in terms of “supra-conscious” (Hayek, 1967), “macroscopic intelligence” (Sornette, 2003), “cognitive non-conscious” (Hayles, 2014), or “collective unconscious” (Jung, 1934), for example. But in both cases, of the mind and the market, emotions are the early expressions of such non-conscious activity. Again, this interpretation is part of the general connection between the economic mind~body and the mind~body economy. That also includes the sector-specific securities in the economic mind~body which can be equated to domain-specific psychurities in the mind~body economy. Both have a risk~return profile reflecting simultaneously historic experiences and implied expectations. And whereas individual cognitions are correlated to (historic) collective cognitions (like myths, stories, and themes), individual assets are correlated to the risk factors of the broader markets (like momentum, quality, and value). In short, both IP and EP reflect historic experiences, future expectations, and intersubjectivity. This makes portfolio management so appropriate as a shared and mutually applicable concept between securities and psychurities. 2. Focus on valuation I argued in Appendix 1 why the mind is about valuation. Like the value of a portfolio of securities, the value embedded in an emotion, as a portfolio of psychurities, is not always clear-cut and can fluctuate. In particular, it emphasises that there is always a flipside to the return of the psychurity, namely its risk, like the cost of selecting it. This is consistent with the original meaning of responses to stimuli, for example in terms of foraging. The attractive prospect of the stimulus food may not outweigh the cost of obtaining it as it can be bad or poisonous, too risky to pursue, and/or not adding (diversification) value (because similar food is already available or stored). More importantly, as aforementioned, the value of cognitions is ‘above all’ assessed collectively. This not only concerns ‘nature’s cognitions’ which are the primordial instinctive cognitions which reflect values with a rich history that we commonly share with our ancestors. It also includes ‘nurtured cognitions’: those more social and culturally biased cognitions, such as morals, which we commonly express in our exchanges while we simultaneously experience the same situation (for instance a family mourning the death of a child). Embodied simulation via mirror neurons suggests that the capturing of an expressed emotion by the senses, e.g. via observation, unconsciously triggers a similar emotion in the observer. In short, cognitions may be experienced privately but, like portfolios, they receive their ‘objective’ appraisal collectively. On that note, a bubble is not only an excessive valuation in financial wealth terms, but also in terms of collective narrow-minded emotional values. 3. Focus on dynamic rebalancing Dynamic rebalancing of the weights of the constituents of a portfolio enables the replication of an unlimited array of payoffs. In other words, the concept of portfolio management includes a principle which can explain the flexibility and versatility of

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cognitions in terms of handling the multiple psychurities to react to changing circumstances. 4. Price dynamics in financial markets differs from those in the real economy Price discovery in financial markets is different in nature from that in the real economy. To wit, and in the words of Jim Grant: “In almost every walk of life, people buy more at lower prices; in the stock market they seem to buy more at higher prices”. Consequently, the relationship between emotions and financial assets is different than between emotions and consumer products. Specifically, due to reflexivity, the dynamics of supply and demand in response to extreme price action in financial markets is counter-intuitive from an economic perspective. Reflecting Grant’s observation, in the build-up of a crowded trade demand, in the form of volume, frequently goes up as prices increase. This leads, among others, to the phenomenon of momentum. Another example is inflows in passive indexing funds. These funds subsequently need to immediately buy the components of an index according to market capitalisation, regardless of value, making them price insensitive. This often means that, on an annual relative basis, they then buy (sell) more of those stocks that have gone up (dropped) the most over the past year. To summarise, portfolioism’s approach to mentality (here emotions) is consistent with and fits most of the foundations of cognitive science, including evolution (see Chapter 1). Evolution, via natural selection, has resulted in psychological functions or capabilities directed at solving specific adaptive problems, mainly those that existed among our hunter-gatherer ancestors. The problems that confronted our ancestors basically involved the questions of how to gain rewards (pleasure) and/or how to avoid penalties (pain). In other words, the evolved capabilities have economic characteristics: they enable responses to stimuli which suggest a return (profit) or a risk (loss). These capabilities are adaptations which enhance survival by assessing the risk~return profile of situations and structuring an appropriate payoff. What differs, among others, with a purely economic assessment is the role of epistemological utility. Consequently, placed back in the settings of our contemporary ‘financial jungle’, these adaptations enhance, in principle, economic survival and value creation. However, there are two reasons why they do not always lead to optimal economic solutions or enhanced economic fitness: 1. Because they also contain a strong historic/primordial bias this frequently does not fit the current circumstances. In other words, the ancient ‘nature’s jungle’ of our hunter-ancestors may not always properly reflect our modern-day ‘financial jungle’. Whereas nature’s jungle led to losses of lives, the financial one leads to losses of livelihoods. 2. They also include adaptations which attempt to optimise an individual’s well-being, irrespective of the individual’s wealth. An example is charity which attracts volunteers from both wealthy and less wealthy backgrounds. Translated in utility terms,

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emotional utility maximisation is not always aligned, and in fact frequently inconsistent with, economic utility maximisation. From a statistical perspective, this is due to issues such as bounded rationality (i.e. incomplete information about the likely outcomes of a series of actions) and the “inability to use optimal algorithms when combining conditional probabilities” (Rolls, 2007, p.410).2 Although the level of specialisation of these psychological capabilities varies, each reflects its characteristics in the form of a risk~return profile and can thus be considered as psychurities. Combined they require some form of management as argued, for example, by Tooby and Cosmides (2005). Emotions are among the high-level functions which achieve this by way of portfolio management: they form portfolios (EPs) of lower-level, or domain-specific, psychurities. These portfolios, in turn, can become part of higher-level portfolios. In this case, the mind manages multi-layered emotion portfolios that consist of psychurities, each characterised by a particular risk~return profile, replicating a strategy with an implied payoff.

8.3 Valuation of Emotion Portfolios According to the view described in the previous sections, an emotion—both its expression (as behaviour) and its impression (as experience)—is reflecting the weighted value of a portfolio of psychurities. This is dynamic in the sense that the overall mind~body portfolio is dynamically rebalanced in order to benefit from/hedge against an (emerging) situation, thus reflecting fluctuating values. It means that the various S1 and S2 portfolios are exchanging in the wider mind~body (market) portfolio.

8.3.1 Quantitative Valuation Quantitative valuation involves planning or syntactic operations. In short it manages symbols. This takes place in the linguistic centre which is part of a larger higher-orderthought system (HOTs) located in the cortical area of the brain. It enables deliberate reflections on emotions. The linguistic centre tries to quantify emotions, including those expressed by other minds, by modelling their sensitivities, their fitness, to an emerging situation. However, like the CAPM in finance, its main assumption is rational behaviour. The implication of this assumption is, in simple terms, that the variables are assumed to be linear and lead to stable relationships with predetermined outcomes. To interpret this in terms of a metaphor, think of running a regression of emotions versus

 These conditional probabilities are implied, via their so-called Environment of Evolutionary Adaptiveness (EEA), by the selected psychurities in the EPs.

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a similar historic situation; the linguistic centre uses an ordinary linear (or least squares) equation for its model. In this process of symbol manipulation it translates, as it were, the pre-rational representations into rational predictors of future behaviour. In fact, Rolls argues that in order to avoid inconsistencies in behaviour, the “explicit system” processing all this has to have the belief that it is in control, even if it is an illusion: When other brain modules are initiating actions (in the implicit [unconscious] systems) . . . the explicit [deliberate] system may confabulate and believe that it caused the action, or at least give an account (possibly wrong) of why the action was initiated. The fact that the [explicit system] may have the belief even in these circumstances that it initiated the action may arise as a property of it being inconsistent for a system which can take overall control . . . to believe that it was overridden by another system. (Rolls, 2007, p. 410)

8.3.2 Qualitative Evaluation The qualitative dimension of valuation, on the other hand, concerns the semantic representations, or symbols, themselves and is performed by the non-analytical capabilities of the mind. The qualitative meaning of the symbol emerges in consciousness as an emotional charge or feeling-tone which influences the overall value of the cognition. This value is felt which, if rationalised, becomes a confabulation: the story to explain the emotional response. In the words of Jawaharlal Nehru “a man of action in a crisis almost always acts subconsciously and then thinks of the reasons for his action”. This emotional charge consists, firstly, of its symbolic impact due to the shared meaning recognised (‘appraised’) by all agents in the ‘cognitions market’. Back in the financial markets, cognitions are collectively expressed in price patterns, including correlations and trends. Each pattern is symbolic for the behaviour of a fictional composite investor. Physically, once subliminally recognised, it could trigger cross-brain synchronisation, perhaps involving mirror neurons, leading to contagion. This part of the emotional value is mostly uniform or non-personal because the valuation follows the same path, culminating in what Vittorio Gallese—one of the discoverers of mirror neurons—calls the “same body state”. It goes through the limbic system, containing the more archaic parts of the human brain, specifically the amygdala, which particularly deal with instincts, the most uniform cognitions. Secondly, the charge includes a personal impact in that it has subjective meaning. The evaluation at the individual’s level is influenced by the subjective perception of a pattern, biased by the accumulation of previous personal experiences concerning similar patterns.

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Investment Note Valuations3 Stocks aren’t cheap and popular at the same time. Anonymous. Robert Shiller is a Nobel laureate and professor in economics at Yale, where he is developing his heterodox theory of “narrative economics”. He largely built his reputation by presciently predicting the bursting of the internet bubble in 2000 and the financial crisis in 2008. In both cases he identified overvaluation as a key contributing factor. Shiller is also famous for developing the Cyclically Adjusted Price/Earnings ratio, popularly known as CAPE, a valuation tool for equities. Calculated as the price divided by the 10-year average earnings adjusted for inflation, it showed relatively high readings over the last few years. It thus came as a bit of a shock when Shiller announced in early 2021 that “sky high stock prices” actually “make sense” if using a further adjustment to his CAPE indicator. Renamed into “Excess Yield CAPE”, the adjustment consists of inverting the CAPE, which turns it into a yield, and subsequently deducting the 10-year bond yield. This justification of high stock prices was met, to put it mildly, by surprise from many in the investment community, including Albert Edwards and Jeremy Grantham. The general criticism varied, roughly, from ‘Shiller is changing his narrative’ (pun intended) to ‘Shiller is moving the goal posts’. So, how should we view this question of valuation? Are stocks fairly valued or in a bubble when using this measure? For clarity, what follows is a discussion of this issue under the conditions that prevailed at the time (2021) and may return in the future. Stocks are popular . . . I like the opening quote (even if we do not know who said it). It combines the issue of valuation with that of crowd psychology. Regarding the latter, seminal works, such as Kindleberger (2011) and Mackay (1841), have identified common characteristics of bubbles, hypes, and manias. Those traits, updated for our modern times (at the time of writing), include: – Central banks making credit easily available, which allows leveraged bets. That includes record volumes of traded call options, often short-dated and on highly speculative stocks. – Growing retail investor participation, now facilitated by user-friendly mobile apps created by zero-commission brokers, often colluding with HFT firms. – Lack of breadth in the market characterised by a concentration in a few stocks or sectors that move broader indices. FANG (Facebook, Amazon, Netflix, and Google) and its variations are the most recent reincarnations of this phenomenon. – A frenzy in IPOs, nowadays involving loss-making companies (including some “unicorns”). Until recently it was accompanied by a craze in Special Purpose Acquisition Companies (SPACs), which are corporate vehicles established to make acquisitions. In crypto space we had Initial Coin Offerings (or ICOs) for funding projects that mostly never materialised. – Narratives and stories whereby popularity is correlated with the level of implied ‘disruption’ or ‘transformation’, not its probability. In other words, the more outrageous the claim, the more popular the theme and the stocks playing it. AI is at risk of joining this. – Exponential price patterns without significant corrections. Examples include semiconductor stocks and Tesla. Anecdotally, the always original Jesse Felder asked rhetorically on Twitter (7 January 2021) what the Scott McNealy of 20 years ago, when he was CEO of internet darling Sun Microsystems and

 The original version of this note appeared as an article for Jackson Hole Economics on January 25, 2021. It has only slightly been updated.

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criticised investors for paying high multiples for his company’s stock, would think about Tesla trading at 30 times revenues. He quickly got a response from the real McNealy: “I want to be Elon Musk”. Still, the bubbly nature of the market is not the main topic of this note. Instead, I would like to return to the question of valuation. . . . but are they expensive? Reviewing numerous articles and comments since Shiller’s flip-flop, it has become clear where the disagreement on valuation hinges. Let me start by removing one technical misunderstanding. Some commentators incorrectly equated the Excess CAPE Yield to the infamous Fed-model. A key critique of the Fed-model is that it is flawed because it deducts a nominal bond yield from a real earnings yield. However, the original CAPE already corrects for this error by adjusting the earnings for inflation. In short, the Excess CAPE Yield is not the same as the Fed-model. Other commentators make a more valid point. They argue that bonds are in a bubble and massively overvalued owing to extraordinary monetary policies. With yields at, or in some cases below, zero, they have a strong argument. Accordingly, the Excess CAPE Yield remains high—and thus seemingly attractive—only because bond yields are absurdly low. The Excess CAPE Yield would be much lower, making stocks more expensive, if a ‘fair-value’ or historic average bond yield were employed. But that does not mean that artificial valuations cannot endure for longer, which is a common argument from equity bulls. It goes something like this: “based on policy makers setting the rules, monetary policy and regulatory financial repression guarantee that bond valuations will remain way above what we consider ‘fair value’ for many years to come. This keeps equity valuations attractive”. That claim, however, requires qualification involving its underlying assumption of ceteris paribus. Any “guarantee” depends on policymakers not making any mistakes and not losing (e.g., yield curve) control. Further, and more relevant here, is the duration of valuation regimes. After all, nothing lasts forever, so at some point some kind of mean reversion in bond valuation will occur. Importantly, this is implicitly also acknowledged in the claim above. Which brings us to the crux of the debate. While there is little disagreement on valuation—everything is overvalued—there is considerable difference on timing. Specifically, those reducing equity exposures today based on valuations do so because they (implicitly) admit that they cannot time mean reversion. Those who accept the risk of current valuations by staying fully invested do so because they (implicitly) believe they can time it and get out ahead of everyone else. For what it is worth, traditional valuation approaches take risk into account, exemplified by the principle of “margin of safety” popularised by Benjamin Graham and Seth Klarman. We term it an equity risk premium for a reason. From that perspective, valuation is not intended for timing purposes. To conclude, the new CAPE is appropriate as a valuation tool provided its advocates make clear that bonds are assumed to be fairly valued. Once this assumption is relaxed, the valuation question turns into one of timing mean reversion. Today, a fair value assumption is foolhardy. Mean reversion is more probable, if even with uncertain timing. Moreover, as I have previously expressed, mechanical economics, and especially policy makers’ overconfidence in exerting control over markets and the economy, is flawed and could lead to disruptive future adjustments. In short, economic, financial and heuristic risks do not justify prevailing valuations. More likely, they convey a dangerous ‘priced for perfection’ situation. For now, the valuation debate is likely to continue. It relates to that other familiar question which is always looming in the background: will history rhyme or is it different this time?

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8.4 Summary and Conclusion The title of this chapter refers to being balanced. To see more clearly how a mind’s portfolio can be risky and become unbalanced, we can review the warning (for example by Nietzsche) for the opportunities~threats of nihilism. In this case the psychurities involved are not emotions but beliefs. Suppose you consider your life to be meaningful and to have great purpose. It is based on beliefs about individuals (e.g. your heroes) and/or causes (e.g. the movement you have joined). Can you see the risk here? You have invested heavily in supposedly ‘purposeful’ assets, but what if they become ‘bankrupt’? For example, what if your hero turns out to be a cheat and/or your cause has devastating consequences on your family? You become demoralised and demotivated. Nihilism takes over. Your purposeful life becomes empty, and you risk a (potentially existential) psychological crisis. (Nietzsche, of course, has his own recommendations for how you should protect your portfolio, but we leave that discussion to others). Whatever situation you are in, its perceived image emerges as a result of the exchanges between and integration of the various subcomponents of the mind’s portfolio. This emerging: – Is embodied by way of an expressed cognition which reflects: – A quantitative value according to an objective appraisal via access processing; depending on the source of the cognition (say, S1 or S2), the appraisal can be a confabulation. – A qualitative value according to a subjective appraisal via affective processing. – Is dominated by the influence of the unconscious: “The struggle between rapid unconscious pattern-detection processes and their slow, effortful modulation by deliberation is not a fair contest: so automatic impressions will influence behavior much of the time” (Camerer et al., 2005, p. 21). Table 8.1 encapsulates what this chapter discussed, as well as earlier portfolioism reflections. Table 8.1: Securities compared to psychurities. The securities market:

The psychurities market:

Demand and supply of securities

Demand and supply of psychurities

Allocation (e.g. via competition and cooperation) of Allocation (e.g. via competition and cooperation) of monetary resources mental resources Securities allow economic adaptation to environmental conditions

Psychurities allow psychophysical adaptation to environmental conditions

A security has a monetary risk~return profile

A psychurity has a mental risk~return profile

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Table 8.1 (continued) The securities market:

The psychurities market:

Securities form investment portfolios

Psychurities form cognitive portfolios

Value reflects economic utility

Value reflects epistemic utility

Value embeds historic fitness to previous monetary Value embeds historic fitness to previous mental conditions conditions Investment analyses are quantitative valuations. They explain value.

Investment moods are qualitative valuations. They feel value.

Mechanical economics assumes that valuation is largely rational and, eager to be consistent with the natural sciences, that the investor can be separated from the market to make objective observations. Instead, the MMH argues that the movements in price and related data within the financial system reflect a collective consciousness that is intersubjectively experienced by investors: whatever rational deliberations lead to buying and selling, they often originate in the unconscious and are enriched by sensations (especially as part of the subsequent realisations of their outcomes). The above makes clear that the value of a cognition, for example an emotion’s charge, is the result of a valuation of patterns: situations are assessed in order to deploy the most efficient portfolio of domain-specific psychurities for the confronting situation. In many cases, particularly instinctive cognitions, this originates at the unconscious level with subliminal ‘regressors’ or ‘factors’, because such cognitions proceed along a path that is similar for all healthy humans. If we ignore cultural and other influences, emotions are uniformly mapped because the statistical structure of ancestral situations, e.g. their distribution of historic occurrences, and their relationship with the evolved specialised psychological functions is the same for our species. Translated in terms of portfolio management, this means, for example, that for each situation the portfolios for different individuals will contain many similar psychurities. The extent of similarity between the portfolios depends on the common value, or shared meaning, of the confronting situation. Some events and symbols reflect more uniform meaning than others. They broadly evoke the same emotional response and thus lead to more instinctive responses. This manifests itself most clearly when individual consciousness is subsumed by crowd consciousness: the comfort of the crowd which allows an emotional escape valve from personal doubts. It also requires a non-conceptual numerical template to enable the numerical operations involved, in particular counting, scaling and trigger setting, as well as to translate various emotional exchange rates into a “common (neural) currency” (e.g. Rolls, 2007; Levy and Glimcher, 2012). Cognitions deal with a large variety of psychurities, reflecting the immense arsenal of stimuli. Their resulting values not only need to be ‘expressed’ in a common currency in order to make ‘fair’ comparisons possible,

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i.e. benchmarking, but also in the right ‘weight’ to trigger a bodily response. The mind discovers these values as ‘relativity’ numbers similarly to price discovery in markets (even though the ‘I’ remains unaware of most). Eventually this is shared with other minds (either deliberately or subliminally) by mutual exchanges with recognition involving number sense and related capabilities. In markets this takes place at a large scale. Numerical symbols, in their appearance, reflect the quantitative aspect of any emotional value immediately. Liverpool FC winning by 7–0 against Manchester United is a clear statement in that it invokes extreme emotions in fans on both sides. Numerical symbols provide the most objective form of communication, a non-verbal language. It is most objective because, compared to words, numbers undisputedly express this aspect of reality and consequently directly focuses individual consciousness. Stripped of every other characteristic, number is what remains. On the other hand, the qualitative aspect (i.e. what it’s like to experience them) is more elusive and its tension with the quantitative aspect is the cause of nuance leading to the change in emotional charge, namely the dynamics in emotional values. Google and Amazon’s names are considered symbols for the internet. We could endlessly debate whether Google is a better symbol for an internet company than Amazon. However, if Amazon is quoted at, say, $200 compared to $100 for Google, we all agree that we need to pay more for an Amazon share. That is the reality. But this quantitative aspect has a qualitative flipside and the tension between the two leads to even more heated debates about values such as “is Amazon worth $200?” In other words, due to their immediate reflection of values the meaning of numeric symbols, in terms of their emotional impact, is more concentrated than names, slogans or logos. In the next chapter I will introduce some of our empirical work and potential practical applications for the MMH.

Chapter 9 On Empiricals: Am I Verifiable? No amount of experimentation can ever prove me right [whereas] a single experiment can prove me wrong. Albert Einstein

9.1 Introduction This chapter will share some completed as well as ongoing research, aimed at putting empirical meat on the theoretical bones of the MMH. Admittedly, this contains some technical material that will not appeal to all readers. However, I feel it is important to make this connection to inspire future research, including our own research agenda which I’ll introduce in Appendix 2. Subchapter 9.2 will discuss our pilot project “Spontaneous Volatility” which was part-funded by the Edinburgh Futures Institute. It combined economic and cognitive science by partnering, respectively, noise trading with the readiness potential. Subchapter 9.3 will discuss one of our pending projects, the AVIR-project. AVIR stands for Audio-Visual Investment Research aimed at developing and testing new software.

9.2 Spontaneous Volatility; Fooled by Reflexive Randomness The noise that noise traders put into stock prices will be cumulative. Fischer Black

This subchapter is an amended and shortened version of an earlier paper (Schotanus and Schurger, 2020) based on our pilot project. That paper also contains all the references used here.

9.2.1 Introduction The market is 17 times more volatile than is justified by the clairvoyant stream of dividends. So, we have designed a marketplace to be more a measure of hysteria than a measure of value. It is completely overwhelmed, on occasions, by the psychology of crowds. Jeremy Grantham

In intriguing research Petra Vértes and her colleagues compared time series of stock prices with fMRI (functional Magnetic Resonance Imaging) brain data. They concluded that “the conceptual connections between brains and markets are not merely https://doi.org/10.1515/9783111215051-010

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metaphorical; rather these two information processing systems can be rigorously compared” and “share important topological properties” (Vértes et al., 2011, p. 1). In that spirit, let’s agree that any serious market hypothesis must be able to relate market phenomena to mental phenomena. We believe a key connection in that regard lies between stochastic fluctuations in markets (basically noisy price data) and stochastic fluctuations in brains (basically noisy neuronal data). This is relevant for the ongoing debates in finance. As discussed, active investors and behavioural academics question the EMH, characterised by its emphasis on the randomness in prices. However, what all its critics have overlooked is, ironically, something the EMH itself also has ignored: the potential link between accumulating randomness in traders’ brains and the accumulating randomness in prices (see Fischer Black’s comment at the beginning of this subchapter), as well as the (anomalous) patterns it may result in. In other words, apart from the arrival of external news a potential additional and endogenous source of randomness in markets is internal: the collectively embodied investors’ brains that occasionally synchronise. To investigate this, we partnered the topic of the readiness potential (RP) in cognitive science with the topic of noise trading (NT) in finance. Our particular interpretation of RP is primarily based on Schurger, Sitt and Deheane (2012; See also Schurger, 2018) and suggests accumulation of neuronal noise as a factor in the timing of spontaneous voluntary actions, for example responses to random events. Our particular interpretation of NT— following the pioneering work by Shiller (1981), Black (1986) and others—suggests accumulation of price noise, measured as excess volatility, around random market events. Combining these two interpretations leads to our working hypothesis that the noise build-up observed in NT is, to some extent, the market’s collective reflection of the underlying build-up observed in RP. Among others, it adds a specific insight to the (generally positive) relationship that has previously been identified between volatility and volume. This insight is that trading activity, triggered by technical events and measured in terms of volume, trade-size, and other trading statistics, can be linked to the build-up in excess volatility preceding such events. In terms of the angle and scope of our research, we did not engage for this pilot project in direct measurement of brain data in connection to trading (although we may do so in our follow-up project; see Appendix 2). Rather, using existing (analyses of) high-frequency RP data we point to the similarities with excess volatility, derived from high-frequency market data we analysed. Neither did we develop a formal mathematical framework, although we do point to similarities between the (leaky) stochastic accumulator models used in RP research and the volatility models (both stochastic and trend) used in finance. Also, we argue that the RP-framework is particularly applicable to the behaviour of a sub-population of noise traders, namely discretionary technical (retail/day-) traders, who have previously been identified (e.g. DeLong et al. 1990). They make subjective trading decisions based on technical analysis, a method to time trades via patterns and indicators, using past prices and other market data. Specifically, they are primed to react to prices crossing their moving averages, a selection of which we analysed. Our analysis

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includes the calculation of a proxy for excess volatility, called Variance Difference (or VD for short), which we used as an indicator. Connecting the two, we were particularly interested in the distribution of VD (e.g. peaks in VD) around moving-average crossings. All our analyses were derived from high-frequency data of two (US) ETF securities that are popular with such traders: SPDR S&P500 ETF (Exchange Traded Fund; ticker SPY), as well as the United States Oil Fund ETF (ticker: USO) over the period April 2007 to December 2017.

9.2.2 Noise Trading Noise trading (NT) involves so-called ‘noise traders’ whose trades are primarily based on market internals, for example, technical patterns like price trends, which the noise traders (mis)interpret as information. In contrast, so-called ‘informed traders’ base their trades on fundamental data and ‘liquidity traders’ are motivated to trade due to exogenous, perhaps hedging, reasons. The behaviour of noise traders is generally considered irrational, certainly in comparison to that of the other trader types. Noise trading has become a burgeoning topic in the academic literature, covering various sub-topics. We particularly highlight Black (1986) who framed excess volatility in the broader debate of noise trading, which is of interest here. As just mentioned, for this pilot project we had a particular category of noise traders in mind, namely (discretionary retail) technical traders, about whose behaviour we make a few reasonable but simplified assumptions and measurements. Specifically, we assumed that the triggers for the timing of their trades regularly involve prices crossing their moving averages (either up- or downward). Based on the literature and industry practice we selected a number of popular (e.g. Fibonaccibased) moving averages. The technical events we thus investigated existed of prices crossing these moving-averages, both on an intraday and closing basis. It meant, for example, that we analysed price bars of various frequencies, e.g. minute and hourly price bars. Apart from being able to better time-lock with other data (in our case excess volatility and volume-indicators), this pattern’s objectivity is also more likely to lead to correlation across noise traders. This is important as noise trades “will only matter if they are correlated across noise traders. If all investors trade randomly, their trades cancel out” (Shleifer and Summers 1990, p. 23). Related to such copied trading behaviour, cognitive science has shown that (experiencing) imitation of behaviour in general impacts the quality of human exchanges generally. In particular, it increases trust which facilitates trade (e.g. Van Baaren et al. 2003). Our focus was on excess volatility (via our VD indicator) as the measure of market noise and its relationship with volume and other trading statistics. We used these indicators as rudimentary reflections of trading behaviour (no doubt, more advanced analyses are conceivable with additional datasets).

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According to the literature, a positive relationship generally exists between volatility and trading activity. In the case of volume, for example, this is theoretically explained via the mixture of distribution hypothesis (MDH) and the sequential information arrival hypothesis (SIAH). This literature also includes research on microstructure. An early source in that regard is Tauchen and Pitts (1983). Referring to noisy personal information, they requested from future research insight into “the stochastic specification” of the dynamics involved “as more traders enter the market”. We hope to provide this via the stochastic interpretation of RP by showing that volume-indicators, reflecting traders’ reactions, change according to the accumulation in market noise around noisy informational events. This suggests, we propose, that increased market noise ‘invites’ on occasions more (smaller) traders because—combined with their own build-up in neuronal noise—it makes them sensitive, i.e. trigger happy, in their discretionary trading around such events. Again, inter-brain synchronisation due to shared attention and intention seems to play a role. 9.2.3 Readiness Potential Readiness Potential (RP) is the English translation of the original German Bereitschaftspotential, suggesting a “readiness” to act. Technically, RP reflects cortical brain activity (measured via EEG), in particular a build-up of neuronal activity, ahead of voluntary action or movement. Tests of RP are generally done in a laboratory setting. Subjects, whose brains are monitored, are asked to (repeatedly) perform an instructed movement spontaneously without any forethought or pre-planning. Researchers are particularly interested in the brain activity leading up to those movements which they measure in narrow windows of a few seconds. Researchers are also interested in the timing of the movement itself and the perceived timing of the decision to move. The RP has a rich history going back to the 1960s when it was first discovered. In 1983 neuroscientist Benjamin Libet (with colleagues) published seminal research showing (presumably) that the feeling of having decided to act, the so-called conscious decision, emerges long after the neuronal decision has already been made in the brain. Libet’s account emphasised the role of the unconscious, while the conscious decision is tacked onto the decision-making process after-the-fact without playing any causal role. His argument rested on the time course of the RP, whereby the gradual build-up of neuronal activity in the pre-motor area precedes not only the onset of movement, but also the conscious decision to initiate the movement. Since then the RP has always been presumed to reflect a process of planning and preparation for movement. More recently, however, Schurger, Sitt and Deheane (2012)1 challenged this presumption by applying a stochastic accumulator model,

 For background and an introduction, see “A Famous Argument Against Free Will Has Been Debunked” in The Atlantic, September 10, 2019.

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including a so-called Libetus-interruptus task: subjects perform the standard Libet task, but are told that they may sometimes, i.e. randomly, be interrupted by an auditory “click,” in which case they are to perform the movement (in this case a button press) as quickly as possible. The authors found a slow build-up of neuronal activity preceding fast reactions to the click, that began long before the click itself. In short, they argue that RP might reflect ongoing stochastic fluctuations in brain activity whose crests tend to coincide with the onset of movement. This latter interpretation has since received additional support. Of particular interest is a study by Murakami et al. (2014) which found evidence for an accumulator process in area M2 of rats (homolog of human premotor cortex) performing a task where the rat could spontaneously abandon waiting for a large reward and instead opt for an immediate and certain, albeit smaller reward. The gist of the argument from this stochastic RP interpretation is the following: when the imperative to act is weak, for example due to lack of evidence, the decision’s timing that leads to movement is partly determined by ongoing sub-threshold fluctuations in brain activity. To be clear, this timing is the precise moment at which the decision threshold is crossed. Time locking to movement onset ensures that these slow fluctuations are recovered in the event-locked average in the form of a gradual build-up or accumulation. By this account the RP does not reflect a goal-directed process and the real “decision” to initiate action is a threshold crossing event that happens very close in time to the onset of the movement itself. We translated this in the collective setting of NT.

9.2.4 Parallels Between NT and RP The stochastic RP interpretation suggests that the process of neuronal accumulation preceding the onset of a trade may involve autocorrelated random fluctuations that influence the decision and thus the tipping point of execution. In particular, the precise moment, i.e. timing, at which the decision threshold is crossed is largely determined by spontaneous sub-threshold fluctuations in neuronal activity when the imperative for a trade, i.e. information, is weak or absent. Arguably this is often the case in an investment environment characterised by uncertainty and incomplete knowledge (or even lack of efficiency, for that matter). But it seems especially applicable to technically oriented noise traders. Why? Because timing is key in their motivation. In addition, the evidence to back-up their trade is, well, noise. In short, this is why we stated earlier that, like subjects receiving the Libetus-interruptus in a laboratory RP-setting, technical traders are primed to respond to the market’s version of random interruptions in the form of moving-average crossings. Of course, not all moving-average crossings result in a ‘noisy’ trade. But those that do trigger it do so because some decision variable has already been building up to threshold. To wit, based on our working hypothesis we forecast that the specific profile, i.e. steepness, in the build-up of excess volatility (again, quantified as VD) can

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explain some characteristics in trading behaviour, as captured in the variance in volume-indicators. Moreover, in markets uncertainty (or rather risk) is generally expressed in volatility, with higher volatility reflecting more uncertainty and thus, arguably, less evidence. So, increasing volatility weakens the (informational) imperative to trade, thereby increasing the susceptibility of trading decisions to subthreshold neuronal fluctuations. And here we arrive at the potential reflexive connection we mentioned before between markets and minds. Not only does this connection suggest a possible neuronal explanation for a positive feedback-loop, connected (cross-brains) through traders, between noisy prices and noisy neurons. It also provides us with a testable hypothesis. In short, we propose that noise trading activity following technically significant events, i.e. (Fibonacci-based) moving-average crossings, is related to the accumulation of excess volatility ahead of these events and has an internal, i.e. neuronal, contributor. We summarize our interpretation of RP translated into a NT setting in Table 9.1. Table 9.1: Readiness Potential (RP) versus Noise Trading (NT). Item in RP setting

Item (proxy) in NT setting

– – – – –

– – – – –

Neuronal noise (build-up) Event, i.e. interruption Decision Action/movement Subject type/behaviour

Excess volatility (build-up) Price crosses its MA Trading decision Execution of trade Trader type/behaviour (via volume and other trading statistics)

For example, RP’s “voluntary self-initiated movements” are translated in the NT setting as pushing buttons on a keyboard, mouse clicks, or other actions that execute trading decisions. The broader “imperative to move” in RP we interpret in the NT settings simply as the urge to trade, possibly motivated by active management, including the drive to reach a particular target or outperform some benchmark.

9.2.5 Findings from the Pilot Project Here I will summarise our main findings from our investigations. The first focused on determining the “general profile” of the build-up in excess volatility around movingaverage crossings to see if, pattern wise, it approximately reflects that of RP’s neuronal noise. The second focused on the relationship between build-up in excess volatility and changes in trading statistics around moving-average crossings, which we then interpret in terms of trader type/behaviour. Here we allowed ourselves some leeway,

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for two reasons. First, there is no practical comparison for RP’s ‘subject type/behaviour’ in the NT setting. Second, to infer trading behaviour we had to make do with the dataset we were given. In Figure 9.1, the chart at the top (1A) shows the general profile of build-up in neuronal noise, culminating in a spike, around an RP-event. Ignoring different timescales, etc., this is roughly mirrored by the two charts below it, reflecting the general profile of build-up in price noise around moving average crossings (both up [in blue] and down [in red]) for both the SPY (1B) and the USO (1C).

Figure 9.1: Neuronal and price noise. Source: Schotanus and Schurger (2020, p. 207)

In conclusion, both data sets—neuronal (1A), respectively price noise (1B and 1C)—obviously result from different measurements and, consequently, show variations in profile. The clearest differences are that the build-up in price noise is less gradual and that it lingers. This is probably partly due to the relative lower frequency of the price data and thus in measurements. Additionally, we also relate this to our overall hypothesis and see it as a consequence of the non-lab collective conditions. Whereas the EEG Build-up (1A) is an aggregation of EEG measurements on isolated individuals, in markets (1B and 1C) individual neuronal noise and collective price noise reflexively interact, possibly including contagion (see the discussion in the next section). Despite these differences the overall similar shape of their related graphs broadly confirms what we hoped to find.

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Our second investigation consisted of running a large number of regressions of VD Build-up against trading statistics around moving-average crossings. Our general finding was that VD Build-up could explain some aspects of the (assumed) typical behaviour of discretionary technical traders. In other words, the data showed footprints of these traders as being active around ‘default’ moving average crossings.

9.2.6 Discussion of Results Fischer Black famously stated that “noisy trading puts noise into the prices” (Black, 1986, p. 532). Inspired by this we applied the RP framework of decision and action to NT. As we hypothesised, the variance in volume-indicators following our choice of technical events often suggests a trading behaviour that is characteristic for discretionary technical traders whose timing is sensitive to a combination of weak evidence and accumulating neuronal noise. We submit that the latter’s footprints are, collectively for this group, reflected in excess volatility. In other words, we have potentially identified an ‘internal’, that is an embodied, contributor to Black’s “cumulative noise” (Black, 1986, p. 532). Implicitly therefore, market noise and mind noise could reflexively feed on one another, with noise traders particularly susceptible to participate. This could help explain, for example, bubbles and crashes in stock prices, which are patterns emerging from, in our case, (accumulated) randomness. The earlier comment on ‘correlated’ noise traders is relevant in that regard—especially considering the possibility of cross-brain synchronisation—and we would like to make one further clarification of the type of similarities between the mind and the market, this time metaphorically and anecdotally. Specifically, we link spontaneous volatility to the EMH, in the sense of clarifying how a pattern of sustained buildup during major turning points challenges market efficiency. Initially it seems that the self-interest underlying choices by individuals naturally results in those choices being independent. This then supports the argument of the random walk leading to equilibrium. How could spontaneous volatility disturb this? In other words how could randomness turn into a pattern which shifts equilibrium? Dehaene (2014) describes a cascade of neuronal activity as an “avalanche” (p. 131) culminating in a “global ignition” (p. 135), with neurons bursting into widespread coordinated activation, leading to the emergence of consciousness. Echoing some of Kelso’s work, he compares this to the way an audience begins with a few random claps and then erupts into synchronous applause. In similar terms, but now implying such cross-brain synchronisation, here is a quote from Sornette (in Bastiaensen et al., 2009) characterising the build-up of market activity, when a crash is spontaneously emerging from randomness: The audience expresses its appreciation with applause. In the beginning, everybody is handclapping according to their own rhythm. The sound is like random noise. There is no imminence of

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collective behavior. This can be compared to financial markets operating in a steady-state where prices follow a random walk. All of a sudden something curious happens. All randomness disappears; the audience organizes itself in a synchronized regular beat, each pair of hands is clapping in unison. There is no master of ceremony at play. This collective behavior emanates endogenously. It is a pattern arising from the underlying interactions. This can be compared to a crash. There is a steady build-up of tension in the system . . . and without any exogenous trigger a massive failure of the system occurs. There is no need for big news events for a crash to happen. (Bastiaensen et al., 2009, p. 1; emphasis added)

In that regard, our proposition provides cognitive support to Black’s assertion that “noise in the sense of a large number of small events is often a cause factor much more powerful than a small number of large events can be” (Black, 1986, p. 529). Of course, by “jumping on the bandwagon” (DeLong et al., 1990, p. 379) rational speculators could also contribute to the accumulation of excess noise ahead of the technical events we described. Even if their ex-ante motivation seems rational, our argument applies to them as well: the exact timing of their ‘front-running’ trade is likely to be disproportionally affected by their respective neuronal noise, particularly when excess volatility is increasing, weakening any evidence of actual technical events (let alone any fundamental information) that acts as the ‘rational’ imperative to trade. In other words, as long as the reason to trade is motivated by an information source that is noisy (in our case a crossing of a moving average) and coloured by uncertainty (reflected in volatility), all discretionary traders, including rational frontrunners, are susceptible to RP’s accumulation breaking the threshold that triggers the decision to trade. This argument, however, does not hold for so-called mechanical (or system) traders whose decisions are objective, in the sense that they are coded and outsourced to computers, with execution fully automated (but see our comment on machines later). In many respects, this project was speculative. Still, both NT and RP are important topics in their respective fields with, we think, potential linkages that deserve further research. Appendix 2, for example, contains a brief description of a potential followup project that will involve human traders in a real-life setting so that we avoid artificial laboratory conditions. For that purpose we will use non-intrusive tools to gather both high-frequency market and behavioural data that will help to expand upon the thesis of this pilot project. Our goal is to build a (machine-learning) model that shows how human decision making together with modern information delivery systems contributes to reflexive market dynamics. We anticipate that it could contribute to improving risk management. Hopefully it can also inspire others to follow-up in similar research directions. Obvious suggestions include using different securities, frequencies, and/or events (e.g. based on fundamental data). Also, our research could potentially be enriched with data from (retail) day-trading accounts, whereby technical market events can be timelocked to actual trades.

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Finally, the big elephant in the room: what about ‘the machines’? As we already pointed out, we know that accumulation of randomness, i.e. the build-up of noise, toward a tipping point is a phenomenon that occurs elsewhere in nature. However, we prefer to leave this for others to contemplate.

9.3 The Market Speaks its Mind I need to know what is happening in the markets . . . I hooked up a music synthesizer to the computer, linked it to the interface between the computer and quote screen, and generated a program that would give a musical summary of the markets. I used piano tones for stocks, strings for interest rates, the cello for short-term rates, and the violin for the 30-year bond. The Japanese yen was registered with the high flute, corresponding to the favorite instrument in Japan, the shakuhachi. The English horn, the French horn, and the Alpenhorn stood in for the other currencies. Victor Niederhoffer The Education of a Speculator

9.3.1 Introducing AVIR For many years, neuroscientists have treated the brain as a mechanical, and later digital device. The Geiger counter was among the first mechanical devices it was compared to because the firing rate of neurons—as a measure of brain activity—seemed similar to the click rate of a Geiger counter—as a measure of radiation. Subsequently the brain was considered to be some kind of computer, consisting of hard- and software. In contrast, my overall case is the mind-as-market embodied in the market-asmind. Currently, via analytical methods (e.g. regression), prices are treated in a remote and sterile manner which reflects the physical aspect of their dual realisation. But what about their phenomenal aspect? How could this be investigated? Here the MMH’s potential for novel empirical research truly comes to the fore. For inspiration we first look at quantum mechanics. Quantum mechanics teaches that understanding light—as particle-like or wave-like—depends on how we investigate it. What if prices in markets are, metaphorically, like the photons in light? In other words, what if Bohr’s complementarity applies to investment research, in that prices have complementary physical and phenomenal aspects whereby understanding depends on the method of research? This brings us to the second inspiration from music: [Non-lyrical] music does not depict knowledge but embodies it. Instead of showing or telling us something, the music simulates a vital experience. The result can feel almost shockingly firsthand, as if it were happening to you . . . For the listener, this often means coming to feel what it is like to understand something—even if one cannot always say just what that something is. (Kramer, 2023)

Among others, it underlines the importance of flow, frequency, patterns, and rhythm in minds which makes a comparison to musical instruments seem obvious, with an

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orchestra as the collective grouping. In terms of such dynamics think, for example, of playing the piano: besides the number of times you hit the keys, the force and timing of those hits determines the sound, while in a group the other instruments complete it. Utilising James’s radical empiricism, it highlights the relationships and transitions (pauses) between data points. As I will show, a number of cognitive and investment experts have seen this similarity too. What follows is my crude and preliminary attempt to suggest a complementary investment research method in that spirit. This section describes a skeletal framework for our Audio-Visual Investment Research (AVIR) Project. AVIR is a new investment research method2 we are developing, including software, which puts some of the theoretical concepts discussed in this book into practice. Specifically, it contains a suggestion or proposal for an experiment (Subchapter 9.3.7) that would test a few of the (implied) sub-hypotheses of my book. AVIR is meant to complement existing investment research methods and most clearly aimed at (clarifying what I mean by) “investigating market data in a psychophysical perspective”. Various other sources provided inspiration for AVIR.3 For a start, it tries to help in answering Gigerenzer’s earlier question in a practical way: We think of intelligence as a deliberate . . . activity guided by the laws of logic. Yet much of our mental life is unconscious, based on processes alien to logic: gut feelings, or intuitions . . . We sense that the Dow Jones will go up . . . Where do these . . . come from? (Gigerenzer, 2007, p. 3)

In the spirit of Niederhoffer (see opening quote), Ciardi (2004, p. 1) argues that “stock market environments, in which large numbers of changing variables and/or temporarily complex information must be monitored simultaneously, are well suited for perceptual research in sonification”. Marcovici (2014) showed something completely different. By

 In the following, AVIR refers to the combination of method and the software tools used for this purpose. Earlier market sonification software tools have not made the connection to advanced visualisation.  Apart from those mentioned and the literature on data sonification and visualisation, they include Coates (2012), Cymbalista (2002), Gigerenzer (2007), Klein (2013), Kounios and Beeman (2015), Sacks (2007), and Schwager (1995). Moreover, in an earlier version I had peppered this book with many music lyrics as quotes. Like much of art generally, music reflects the Zeitgeist of society. It impresses or translates its meaning, whereby history rhymes. “Musica è” as Eros Ramazzotti so memorably wrote. It is inspiring and keeps you going (particularly important for writing a book, which is a labour of love). However, as a first-time author I was a bit naïve: my publishing advisors told me to remove all lyrics because it would be a nightmare—and an expensive one at that—to get permission. So, instead and in acknowledgement, I offer the following selection of complementary pairs, many of whom are still with us. Unfortunately, as you’ll notice, some have turned into truly contrarian pairs: Beethoven~Mozart, Lindsay Buckingham~Stevie Nicks (Fleetwood Mac), Jimmy Page~Robert Plant (Led Zeppelin), Miles Davis~John Coltrane/Herbie Hancock~Wayne Shorter (Miles Davis Quintet), James Hetfield~Lars Ulrich (Metallica), Philip Bailey~Maurice White (Earth, Wind and Fire), Kiri Te Kanawa~Luciano Pavarotti, Stone Gossard~Eddie Vedder (Pearl Jam), David Gilmour~Roger Waters (Pink Floyd), Andrew Lloyd Webber~ Tim Rice (Jesus Christ Superstar), and Rick Davies~Rodger Hodgson (Supertramp). Finally, Boudewijn de Groot and Bløf: for keeping the link to the home country alive.

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sonification of prices and exposing them to the resulting sounds he trained rats to trade forex and commodity futures.4 A common thread for others was that they emphasised the role of the body, feelings, and intuition in decision making. For example, Borch, Hansen and Lange (2015) explored “the relationship between bodily rhythms and market rhythms in . . . the open-outcry pit (prevalent especially in the early 20th century) and present-day high-frequency trading”. They show “how traders seek to calibrate their bodily rhythms to those of the market” (see also subchapters 1.2 and 3.2). Accordingly, AVIR is about questions like ‘how to get in sync with the market’s rhythm?’ and ‘how to sense its emotional excitement?’ These questions need to be answered both from a theoretical perspective, as well as from a practical one. Specifically, what tools are required to mediate this in a proper format, that is a format that improves the investor’s understanding of market movements? Moreover, AVIR is backed by insight such as that provided by Bruguier, Quartz and Bossaerts: Our findings should also inspire research to improve visual representation of order and trade flow. Since humans often are better at recognizing the nature of intention in moving (animate or inanimate) objects (Heider and Simmel (1944), Castelli et al. (2000)), we suggest that traders may be more likely to successfully detect insider trading when order and trade flows are presented in a moving display, as opposed to the purely numerical listings commonly found in the industry. (Bruguier, Quartz and Bossaerts, 2010; emphasis added)

In a broader context, AVIR is prompted by the fact that we currently do not treat our unconscious (S1) as a system of abilities we can train to use in a disciplined way, like we do our deliberate system (S2). For S2 we use analytical methods with the support of powerful external tools but we have no such approach for S1. In the context of this chapter, it is like testing a subject on their overall understanding of music by providing sheet music that allows an analytical interpretation but denying them an instrument for an intuitive interpretation. It is thereby important to remember that both S1 and S2 inform S3 where their information (or knowledge) is realised in consciousness. And it is the transmission from S1 to S3—by raising awareness—that is particularly lacking. To be more specific, whereas we use Excel, Matlab, Python, R, and similar software for our rational analysis until now we have had no such tools available for our intuitive synthesis. Part of the reason was the lack of a proper format with which to appeal to our S1 capabilities. As I will show shortly, by identifying audiovisuals as such a potential format, appropriate tools—albeit not originally meant for investment research— also become available as a result. These consist of advanced data-converters, so-called DAWs, combined with audiovisual software. With AVIR’s planned software (see section 9.3.6) the MMH thus suggests a somewhat contrarian approach. First, it is contrarian in that it goes against the current fashion of relying on S2 and only using analytical (e.g. AI) tools. But it is also contrarian in that it enhances awareness of feelings which helps, for example, to enter trades  Although his motivation was different, i.e. whether rats can replace human traders.

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that make one feel uncomfortable. An investor is confronted with a psychological challenge at two integrated levels in that regard. First, via ToM he has to read other investors’ minds, collectivised in Mr Market. In brief, he has to deal with collective psychology and, particularly, resist herd mentality. Second, he is confronted with the personal psychological issues, like biases, that are raised by investing while being part of this collective. Sometimes these issues have nothing to do with investing itself. In short, he has to deal with the emotions, varying from stress to relief, to commit money to convictions. This challenge requires ways of overcoming it, apart from simply exiting or not entering the market, i.e. not participating. Overall, AVIR software will be developed to (further) strengthen the ‘moat’ of mental capabilities that are uniquely human. We are particularly strong in contextual assessment, intuition, and pattern recognition. Still, it is often argued that we overdo the latter, in that we see patterns where there are none. But what if there is a flip side to this: we cannot see patterns because we do not have the tools to support us in finding or recognising them. Overall, successful tests would support that AVIR could improve the investment performance of subjects according to the test criteria. In other words, such an outcome would not only confirm the theoretical sub-hypotheses of my thesis, in particular regarding embodied cognition. It would also suggest AVIR as complementary to existing investment analysis (i.e. fundamental, quantitative, and technical). We plan to set-up and complete the experiment as part of our research programme (perhaps in combination with other [extended] tests). In the meantime I hope this proposal clarifies what I have in mind (and may perhaps inspire somebody else to perform it). Ultimately AVIR’s central aim is twofold. First, help investors make better investment decisions (although, as I will explain below, it is unlikely that everybody will benefit from AVIR equally). Second, by providing a complementary, more contemplative, method of research AVIR can compensate for the current overreliance on, including overconfidence in, quantitative investment analysis, thereby possibly contributing to a healthier, more balanced market mind. AVIR software complements S2’s existing analytical software exactly because it enhances S1 capabilities rather than suppresses them. In my view, this form of augmented intelligence is where true synergy between ‘man and machine’ will occur. Before discussing AVIR from section 9.3.4 onwards, I will provide some background and further clarify my motivation.

9.3.2 Background and Motivation Like any conscious entity the market expresses a broad range of behaviours, varying from rational to emotional. We are particularly interested in those mental categories that originate in the unconscious (S1) and/or culminate in the phenomenal (S3) domain. As discussed in the Ouroboros metaphor, as endpoints of our mind’s “strange

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loop” both remain outside the deliberate domain, are irreducible to deliberate functions, and escape axiomatic capture. They thus make traditional investment analysis methods inappropriate. The prime example of such a category is mood. Specifically, market moods are not contained in a representational format (in contrast to, for example, memories or thoughts). Consequently, they do not become available to deliberate functions like examination and reasoning. MMH’s psychophysical premise of the market not only throws up thorny philosophical issues but also practical challenges in that regard. The latter concern the research methods with which we could approach the elusive sensations that complete market states. Current practice, dominated by analysis, prefers not to deal with the S3 phenomenal overlay of market conditions. In contrast, the MMH sees S3 as the twilight zone where S1 and S2 are actually realised, mostly distinctly (‘I am emotional’/‘I am rational’) but sometimes complementary (‘This analysis confirms my earlier intuition’). Crucially, like its physics’ cousins, the particle and the wave, the unconscious and the deliberate both contribute to our overall understanding but manifest differently as distinct sensations in S3.5 There are a few important points to repeat and further clarify here, also aimed at other researchers of markets: 1. To recognise the market as a collective animated entity and to appropriately interpret its communication accordingly is the message that this subchapter is trying to bring across. Specifically, 4E cognition suggests that price qualia are also intersubjective. 2. On that note, the U.S. sociologist Robert E. Park coined the term collective behaviour and defined it as “the behavior of individuals under the influence of an impulse that is common and collective, an impulse, in other words, that is the result of social interaction (emphasis added)”.6 Translated in terms of the MMH, market behaviour is the behaviour of market participants under the subliminal influence of prices that are the result of price discovery by way of exchanges (i.e. trades). 3. Subliminal because some of these behaviours, including embedded nuances and intentions, are not immediately picked up consciously by the participants (let alone observers, for that matter). Specifically, market data can contain patterns which are non-random and have tacit meaning. That is, patterns can at a subliminal level reveal information about the more primordial expressions of the mentality of a market state, e.g. emotions. The subsequent revelation, let alone epiphany, is an experience which is dynamic: as trend, reversal, squeeze and so on. This dynamic character is important: market data, either recorded or live, needs streaming to convey such meaning. It is part of the overall discovery process, while distinct from  The great minds of physics became comfortable with Niels Bohr’s complementarity, originally inspired by William James’ mental concept of it. In light of physics-envy it is thus somewhat ironic that the consensus in behavioural finance is very rigid in separating S1 from S2.  For an overview of collective behaviour, see Goldstone and Gureckis (2009).

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any cognitive deliberations about the market state that take place in analysis. Such revelation also influences subsequent behaviour. More broadly, primordial expressions are largely uniform across cultures and generations and therefore instantly recognised at the subliminal level. They particularly occur in forms of art because art is the epitome of such communication which can help to coordinate and shape society, especially via culture (Klamer, 1996; Mulgan, 2023). This is one reason that most economic narratives contain key elements of myths and similar eternal themes that art and other creative forms express. The ability of art generally to convey meaning depends on the objectivity of the symbolism used, thus underlining the power of numbers and their relationships as they are the ultimate objective symbols. An obvious example of this power is the use of the Fibonacci sequence and its resulting golden ratio (e.g. via fractals) in architecture, music, paintings, etc. (e.g. Hofstadter, 1979). Above all, comprehension of tacit meaning adds to experiential knowledge, gained through a qualitative synthesis rather than a quantitative analysis. Again, this is conveyed when market patterns are experienced dynamically with ‘live’ prices (i.e. for historic time series this means bringing recorded prices back ‘alive’ by streaming them).

In short, to comprehend the market’s full state we deal, first, with prices as the symbolic expressions of its mind. Being numbers they are the most objective symbols available for shared meaning across cultures and generations. Second, we focus on the qualitative aspects of these symbols via a non-analytical technique, aimed at: grasping the total situation . . . For obvious reasons, a cognitive operation of this kind is impossible . . . Judgment must therefore rely much more on the irrational functions . . . that is on sensation (the “sens du réel”) and intuition (“perception by means of subliminal contents”) (Jung, 1955, p. 49; emphasis added)

It is clear that this interpretation of market dynamics is a far cry from the rational point-estimates and random patterns which the EMH advocates. By the same token, it should also be clear that both the method and the tools with which we traditionally research markets are inadequate to reveal tacit meaning in a format that appeals to the psychophysical functions associated with such understanding. Therefore I will also explain in this subchapter which method and which type of tools could potentially be used to achieve this: appeal to intuition and other S1 abilities to reveal in S3 the market’s subliminal messages, as well as its phenomenally manifested mood. If we accept that this is a potential capacity, then it can perhaps be nurtured and trained in order to develop it (as only a few seem to have a natural talent for it): You are part of the market, you notice every small shift, you notice when the market becomes insecure, you notice when it becomes nervous . . . All this (amounts to) a feeling. When you develop this feeling, and not many people have it, the capacity to feel and sense the market . . . then they can anticipate (it) and can act accordingly. (Anonymous investor; in Knorr Cetina and Bruegger, 2000, p. 153; emphasis added)

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Again, such undertaking—of developing and applying new methods and tools— should be viewed as complementary, not contrary, to analytical methods and tools of investment research. Perhaps unusual for a proposal for an experiment, I regularly quote various experts because, as discussed, I have no empirical proof yet for my proposal. All I can do at this stage is to use these quotes to weave my arguments together. They support and clarify both the motivation and approach for AVIR as well as the proposed experiment to test it. Here I would like to use a quote from Damasio, which I freely interpret by replacing his “(living) organism” and “biological systems” with “market”, respectively “assets”: The miniconcert of fear is ready to be played whenever the situation demands it . . . It may be helpful to think of the behaviour of [a market] as the performance of an orchestral piece whose score is being invented as it goes along. Just as the music you hear is the result of many groups of instruments playing together in time, the behaviour of [a market] is the result of several [assets] performing concurrently. The different groups of instruments produce different kinds of sound and execute different melodies. They may play continuously throughout a piece or be absent at times, sometimes for a number of measures. Likewise for the behavior of [a market]. Some [assets] produce behaviors that are present continuously, while others produce behaviors that may or may not be present at a given time. The principal ideas . . . here are: First, that the behavior we observe in [a market] is not the result of one simple melodic line but rather the result of a concurrence of melodic lines at each time unit you select for the observation. Second, that some components of behaviour are always present, forming the continuous base of the performance while others are present only during certain periods of the performance; the “behavioral score” would note the entrance of a certain behavior at a certain measure and the end of it some measures later . . . Third, that in spite of various components, the behavioral product of each moment is an integrated whole, a fusion of contributions not unlike the polyphonic fusion of an orchestral performance . . . something emerges that is not specified in any of the parts. (Damasio, 1999, pp. 87–88; emphasis added)

Building on this, and by considering markets to be animated, especially rhythmical entities, we can use and interpret the below in our search for an answer to the question of how to sync with markets via audiovisuals: The answer, oddly enough, can be found in music . . . The melding of sight and sound generates a powerful set of memories . . . Lives are conducted to a musical score, proceeding to a beat and a rhythm that operate below usual human awareness. Music provides the moods, the emotional texture . . . In music, a pivot chord is one that contains elements of several different keys, providing a natural transition to a new key. The point at which the pivot chord is struck is one of maximum ambiguity, as the score could proceed in any of several directions. Composers [e.g. Mr Market] often sustain a sense of anticipation and drama by prolonging pivot chords, creating a build-up of tension to be released in the subsequent key shift (Steenbarger, 2003, pp. 35–36; emphasis added) . . . Mood will swamp any message that is offered. The first step, then, is to achieve a mood shift. That is the purpose of the pivot chord (Steenbarger, 2003, pp. 37; emphasis added) . . . Evoke an enhanced state, and a pivot becomes possible—a new melody, a new rhythm . . . people will process information more deeply and more enduringly when they are in such enhanced states . . . in routine states of mind, people can only see things in routine ways and

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behave according to routine. It is when they shift their musical scores that they become able to process even the thorniest emotional patterns in new and constructive ways. (Steenbarger, 2003, p. 38–39; emphasis added)

The topic of “pivot chord”, for example, is echoed by trader Howard Abell as he reflects on his transition from floor to screen trader: sights and sounds, more importantly, lead to your feelings . . . I rely on my intuition. As the market moves and I watch the screen, I monitor my own feelings and mental images. With each price movement, I can see and hear and feel what’s going on as if I were still on the floor. I can literally hear the sound of the ticks being made on that screen. I can visualize . . . Based on all those sights and sounds and intuitions, if you will, I decide where to enter and exit the market . . . When you’re sitting in front of a screen, if you think about it, you can see and feel the climax that takes place, which is to say, the sudden cessation of emotionality in the market. You can “feel” that the market is at a turning point. (Koppel, 1996, p. 150)

Again, others need more help with such imagination which is where AVIR comes in. What does this ‘market mind reading’ using AVIR mean for the EMH which argues that prices contain all information? The question we should ask—keeping in mind Bohr’s complementarity argument—is whether the analysis of prices transmits all this information. As discussed here and in Chapter 7, prices contain information over and above this, and their qualia result in experiential knowledge. Price qualia cannot be reduced to the functions or processes of the market. The latter are the focus of analysis via traditional methods of research. Those methods rely on representational content via access consciousness. They only reach part of the information, leading to analytical knowledge. However, the traditional view on how this phenomenal aspect could be appreciated and comprehended is from the first-person perspective. Critics of a first-person perspective will argue that it is not objective. Still, some have argued that, in studying consciousness, data that are accessible through first person methods should be put out for intersubjective validation (e.g. Blackmore, 2005, p. 224). Dennett has coined such an approach heterophenomenology. Kahneman and Krueger, on subjective reporting of well-being, seem to agree: the data are a valid subject for study in the sense that they capture at least some features of individuals’ emotional states . . . Acceptance of self-reported measures . . . subject to the many caveats that subjective measurement requires, could have a profound impact on economics. (Kahneman and Krueger, 2006, p. 22)

Still, the difference with the traditional first-person perspective is that the phenomenal in the case of markets extends to intersubjectivity. Prices, as pointed out before, provide objective testable evidence for intersubjectivity, the phenomenal essence of the market’s mind. To uncover these qualitative patterns requires a different methodology and there clearly is no tradition in finance for researching market states from this perspective. AVIR attempts to fill this gap.

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9.3.3 AVIR In the following quote the neurologist Oliver Sacks points to a phenomenon that is at the heart of AVIR: The almost irresistible power of rhythm is evident . . . Rhythm and its entrainment of movement (and often emotion), its power to “move” people, in both senses of the word, may well have had a crucial cultural and economic function in human evolution, bringing people together, producing a sense of collectivity . . . (Sacks, 2007, p. 246; emphasis added)

This has been researched thoroughly and widely (including on synchronised finger tapping and hand clapping). In fact, the historic roots of AVIR start in the 1970s. Richard Voss, who worked with Benoit Mandelbrot at IBM’s Thomas J. Watson Research Centre, may well have been the first to experiment with sonification of financial time series, using IBM stock prices. Voss also played a seminal role in the visualisation of data, using fractal patterns.7 Other sonifications, some in combination with visualisation, included sonification-mapping, sonification to support trading, and multi-modal sonification. In his bestseller, The Education of a Speculator (1997), Victor Niederhoffer spent a whole chapter on music and markets. AVIR takes this into a different and potentially more practical direction by combining sound and visuals, supported by recent insights in cognitive science. It facilitates pattern recognition in market data by appealing to the mind’s S1 capabilities, but in a structured and disciplined way, using advanced software tools. If nothing else, it helps S1 to ‘compete’ more fairly against S2 in any ‘judgement contest’ (e.g. Kahneman and Klein, 2009) on their competence to contribute to, for example, investment decision making. The creative dimension was recognised, for example by BenTal and Berger: Our line of work reveals interesting glimpses of creative processes. We propose that listening, in itself, is a creative process, in that, by identifying patterns and detecting trends, a meaningful mental image is elicited from a stream of air pressure changes. (Ben-Tal and Berger, 2004, p. 5)

The importance of a balanced mind and allowing creativity was also acknowledged by trader Ed Seykota: “if I didn’t allow myself the freedom to discharge my creative side, it might build up to some kind of blowout. Striking a workable ecology seems to promote trading longevity, which is one key to success” (Schwager, 1993, p. 154). We need to perceive this in the broader context of price discovery as the creative collective self-organisation that orders the market’s mind. As discussed, market mood is the most elusive part of its state. Traditional analytical attempts to ‘measure’ it completely fail to convey its essence, namely what it is like to be in it, i.e. how it feels.

 A term first introduced by Mandelbrot. A fractal pattern appears the same on multiple levels, i.e. it is scale free.

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Crucially, what these attempts are missing is the ineffable sense of (rhythmical) ‘animation’, mediated primarily via moving prices, which is an intricate characteristic of the experiences associated with (swings in) market moods. As we saw, many traders realise this and, instead, rely on their own intuition, whereas others may need some help. By appealing to the creative functions (in S1) of the user, via sonification and visualisation of market data, AVIR attempts to improve sensing the market’s mood. This brings us, finally, to music. Music is unique among the arts for numerous reasons, but for our purposes the following is of particular relevance, whereby the reader should contrast it to static analytics: – It allows large amounts of data to be efficiently combined (e.g. a song can be incredibly complex without sounding as such). – It is dynamic and has/conveys duration. – It has an aesthetic, specifically mathematical, order (e.g. fractal patterns). – It facilitates anticipation.8 – And most importantly, it can express/instil/invoke emotions and feelings. Allow me to let others provide additional arguments. First, Sacks who is an expert on music therapy: We humans are a musical species no less than a linguistic one. This takes many different forms. All of us (with very few exceptions) can perceive music, perceive tones, timbre, pitch intervals, melodic contours, harmony, and (perhaps most elementally) rhythm. We integrate all of these and “construct” music in our minds using many different parts of the brain. And to this largely unconscious structural appreciation of music is added an often intense and profound emotional reaction to music . . . Much that occurs during the perception of music can also occur when music is “played in the mind”. The imagining of music, even in relatively nonmusical people, tends to be remarkably faithful not only to the tune and feeling of the original but to its pitch and tempo. Underlying this is the extraordinary tenacity of musical memory, so that much of what is heard during one’s early years may be “engraved” on the brain for the rest of one’s life. Our auditory systems, our nervous systems, are indeed exquisitely tuned for music. (Sacks, 2007, p. xii; emphasis added)

Many have argued more broadly to use art forms in general, and music in particular, to broaden our scientific research of the human psyche. Like others before and after them, physicists David Bohm and David Peat have been reflecting on what makes the material and mental complementary, i.e. what coordinates a shared order. They discuss the implicate, explicate and generative (e.g. fractal) orders in their book Science,

 “Research has shown that so called responses to rhythm actually precede the external beat. We anticipate the beat, we get rhythmic patterns as soon as we hear them, and we establish internal models or templates of them. These internal templates are astonishingly precise and stable; as Daniel Levitin and Perry Cook have shown, humans have very accurate memories for tempo and rhythm.” (Sacks, 2007, p. 240). By the way, anticipation does not mean pre-determined, so music can still surprise.

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Order and Creativity. Of particular interest to us in light of the animated and temporal nature of experiencing price patterns, e.g. trends, is the following (notice their reference to path-dependency): the implicate order can be experienced directly, not only in connection with the fluctuating background of consciousness but also in relationship to perception of certain kinds of well-defined forms. Consider, for example, how music is comprehended. At any given moment, a particular note may be sounding in awareness, but at the same time, a kind of “reverberation” of a number of earlier notes can also be sensed. Such reverberation is not the same as recollection or memory. Rather it is more like a part of an unbroken enfoldment and unfoldment of the notes concerned into ever subtler forms, including emotions and impulses to physical movement, as well as a kind of “ethereal” echo of the original notes within the mind . . . This suggests that, at any given moment, a number of notes are present in awareness in various degrees of enfoldment. The simultaneous awareness of all of these is what constitutes the sense of unbroken flow that has been described above. But this means that it is possible to be directly aware of an implicate order as a set of similar differences that are present simultaneously in different degrees of enfoldment of successive notes. (Bohm and Peat, 1987, pp. 187–188; emphasis added)

AVIR aims to achieve the intuitive attunement of the individual mind to the market’ mind by representing market data in the form of sound and visuals. In Subchapter 9.3.5 I will provide a link to a Vimeo file that contains a very rough proof of concept. The contemplative method, consistent with mindfulness/mind walking research (see below) and the (e.g. relaxation) techniques it suggests, appeals to the non-analytical and creative capabilities of the mind. Its aim is to trigger the “enhanced state” to get “in sync” with the market by dynamically displaying market data, creating a multimedia experience of engagement with its mind: a sense of total absorption in the market. In the “zone” conscious thought disappears and an ultimate sense of presence takes over . . . senses are heightened to the rhythms and sounds of the market and the flow of trades. Achieving oneness with the market can wipe away thoughts beyond the moment . . . Joshua Geller attributed the success of one of his traders to his musician’s access to the rhythmic flow of the market; . . . “He sways with the market”, Geller said. He followed the market cadence, switching his positions with the changing tempo of trading, moving his positions in and out with an improvisational technique. (Zaloom, 2006, p. 136)

By transmitting as audiovisuals the mesmerising ‘beats’ of the market, e.g. surrounding hypes, we can achieve an effect similar to that of trance music. Although some users will be content to have others create such audiovisuals and to receive them as research material, the biggest impact will be for those users who—following the advice of Seykota—get themselves involved and create their own audiovisuals. In short, the method, which will require training, may help investors to understand market dynamics at another level than the analytical. Longer term, and depending on its pickup, AVIR also opens the door for future investigations into its own effect—by changing its users’ behaviour—on market states overall. One final comment on the additional visualisation: it further emphasises the qualitative characteristics of market moods as captured in the sound. Kathryn Coe’s

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definition of visual art is appropriate in this context: “the modification of an object or body through color, line, pattern and form that is done solely to attract attention to that object or body” (Coe, 2003, p. 76). The motivation for such multisensory display is to widen the computer~human bandwidth, which is the amount of information, displayed by the computer, that users can perceive through their senses. This can be achieved by mapping different data attributes to the different senses (Nesbitt and Barass, 2004, p. 45). In short, audiovisuals can project an enormous amount of the market’s big data immediately in concentrated format. Next, I will discuss the methodology AVIR subscribes to.

9.3.4 Methodology Traditional research methodologies in economics vary from empirical, through experimental, to behavioural. Examples include statistical analysis of time series (e.g. regressions), investment games in simulated markets, and investment questionnaires. What they have in common is the general objective of identifying whether the results show any anomalies to existing theories or confirm them. Moreover, although they may state that they test (the flaws in) S1 capabilities, they all use tools and methods among subjects (including researchers themselves) that are tailored to and facilitate mental capabilities associated with S2. Talk about biases! If nothing else, this is unfair. And if it turns into systematic suppression, it becomes unhealthy: [The deliberate] and unconscious do not make a whole when one of them is suppressed . . . If they must contend, let it at least be a fair fight with equal rights on both sides. Both are aspects of life. [The deliberate] should defend its reason and protect itself, and the chaotic life of the unconscious should be given the chance of having its way too—as much of it as we can stand. This means open conflict and open collaboration at once. That, evidently, is the way human life should be. It is the old game of hammer and anvil: between them the patient iron is forged into an indestructible whole. (Jung, 1934, par. 522; emphasis added)

Based on the earlier discussed complementarity, an improvement would be to try to bridge the two (with S3 as their pinnacle), starting with acknowledging in more detail that: 1. The current approach for comparing the unconscious with the deliberate system is not like comparing apples with pears but rather like comparing an oenophile with a chemist. Their relative ‘performance’ is thereby measured by using research methodologies associated with chemistry. Specifically, both the oenophile and the chemist are judged by how they convey the quality of a wine when using a microscope, tubes, and a distiller. 2. Consequently, it is no wonder the chemist (i.e. S2) is considered ‘superior’ according to consensus. Back to our mind, it is unfair to judge the unconscious as inferior if that judgment is based on using methods and tools that are not suitable to

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it. Specifically, we use analysis and spreadsheets to bring out the best in our deliberations, but they fail to do the same for intuitions because the latter have a different modus operandi. This means the standard comparison and related judgment are biased and incomplete. We need to develop new methods and tools which specifically appeal to the psychological functions involved in S1, thereby facilitating transmission of intuitive research in a disciplined way. Only then can we round off a fair test with the actual tasting of any wine (via S3).

Various disciplines have a long tradition in such methods although they and the tools that support them will need to be tailor-made for investment research. The new methodology I propose stimulates and guides subjects’ intuition by using AVIR. Specifically, the aim of the empirical experiment proposed in section 9.3.6 is to test some of my claims, in particular whether AVIR improves investment decision making. Although different in set-up and purpose, it builds on previous research in: – Market sonification, particularly by Janata and Childs (2004) as well as Worrall (2009).9 The fact that sound has duration and can thus portray changes over time, that it can efficiently contain large amounts of different data sets, and that it dynamically conveys the underlying patterns and structure of that data are among the arguments used for sonification.10 – Market visualisation, particularly by Nesbitt and Barass (2004) as well as Hasanhodzic, Lo and Viola (2019).11 Here the main argument is twofold. First, the human eye is exceptional in detecting complex and meaningful patterns in market data (like price charts).12 Second, and related, performance depends on the way data is visually represented and thus can be improved by changing it, e.g. adding other visualisations of the same data. Compared to these sources, the format with which the data is transferred and transformed via AVIR is expected not only to add to the computer~human bandwidth but also improve the attunement to the subject’s intuition. Specifically: 1. It is in the dynamics of prices and other market data where the qualities of rhythmical patterns are embedded and AVIR thus advocates streaming any (e.g. historic) data ‘live’ to convey the overall sense of rhythm. This is in contrast to the static graphs and tables used in analysis. 2. In order to appeal as much as possible to the primordial recognition capabilities in S1, AVIR advocates creative forms of sounds and visuals. The emphasis is on

 Specifically, regarding tools for market sonification, see for example Ciardi (2004) and Van Ransbeeck and Guedes (2009).  Here is a website about market sonification: http://www.sonification.com.au/markets/.  In the broader context of data visualisation they are in the spirit of Edward Tufte’s work.  So, for clarity, we are not talking about recognising cats, for example.

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the surreal and irrational, away from, while simultaneously complementary to, the analytic. The multimedia result can best be described as ambient and early trials communicate ‘trance’ audio accompanied by ‘psychedelic’ visuals. My short answer to Gigerenzer (i.e. “where does it come from?”) should be clear by now. Market data, i.e. prices, capture collective human mentality in concentrated numerical format that reflects qualitative (e.g. rhythmical) patterns. These resonate with S1 functions we share in our (collective) unconscious, possibly neuronally supported by mirror neurons, and eventually get mediated into feelings and senses. But there is currently no systematic way to support this process and make it a robust method of investment research. In his own research Gigerenzer discusses framing of empirical data and, referring to physicist Richard Feynman, he observes that different representations of the same information “helped Feynman to make new discoveries, and his famous diagrams embody the emphasis he placed on representation”. Specifically, in terms of the dual-system theories of mind Gigerenzer argues that intuition is richer than logic. Once again it echoes complementarity: entertaining different representations of the same data can appeal to a broader spectrum of our mind’s abilities, ideally enhancing our insights. In the words of Niels Bohr, and viewed in the context of the ‘informational exhaustive’ assumptions underlying the EMH: Evidence obtained under different experimental conditions cannot be comprehended within a single picture but must be regarded as complementary in the sense that only the totality of the phenomena exhaust the possible information about the objects. (Bohr, 1949, p. 210)

In short, the role of intuition as a dynamic gateway between the unconscious and the phenomenal is crucial because the former is the shared uniform space where discovery starts for interacting minds. The representations which appeal to intuition, i.e. symbolic imagery, are often familiar but not always comprehensible. In the case of AVIR it consists of dynamically synchronising sound and visuals derived from prices and other numerical market data. Visualisation software has more generally been used to represent complex data in unconventional formats in order to facilitate pattern recognition. As mentioned previously, music visualisation has been used to enhance the experience of the music’s mood, for example playing video projections at house/trance parties. In general there is a large and growing literature on representing data by way of audio and/or visuals.13 Bettner, Frandsen and McGoun (implicitly) make the case to combine them: the mental pathways for the creation of auditory patterns and visual patterns are different. One forms anticipations of events in time; the other forms structures of points in space. One engages

 For an overview, see Pauletto and Hunt, 2005. See also Wolfram, 2002, specifically https://tones.wol fram.com/about as far as sonification is concerned.

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the emotions more directly than the other. Each employs different parts of the brain. There are indeed reasons why we might hear something more or at least something else in the music generated by an algorithm than we might see in a picture that was created from the same data. (Bettner, Frandsen and McGoun, 2010, p. 294)

Finally, although historic data is crucial for training purposes for all time frames, the frequency of data in combination with the look-back period defines the forecasting period, e.g. current intraday real-time data is the only format to assess ‘today’s mood’, but one needs more data to compare it to other periods like ‘this week’s mood’. The next section will describe examples of the type of software tools that can be used (and/or should be further developed) for the proposed experiment.

9.3.5 Software Tools Earlier, in Chapter 4, I mentioned the BBC documentary Out of Control (2012). Towards the end of the documentary (at time: 54:50), one of the scientists, Professor Scheider, discusses the generation of, what he calls, “a-ha signals”. He pinpoints nicely the non-analytical state of mind which AVIR, with the help of the software I will discuss in this subchapter, attempts to establish: “when looking at these images, the best thing to do is relax, you’re getting into a zone”. This is to be achieved by transforming market mind data. As discussed, art is generally the form of representation used to appreciate the qualitative aspects of other minds. In the case of music, modern “house” and particularly “trance”, by DJ/VJs like my fellow Dutchmen Tiësto, Armin van Buuren and Ferry Corsten, comes close to the repeated beats I have in mind with regard to market rhythms. They also often have their music accompanied by dynamic visuals. The tools I will describe shortly—while not originally meant for this—can transform market data into the proper audiovisuals for our purposes. The intuitive capabilities of the human mind, namely, to perceive what is not immediately obvious analytically, are related to crowd thinking and creativity. The type of creativity we are after in markets is “grasping the total situation”, namely the market state in its rhythmical sense, particularly ahead of when the mood changes. Such creative breakthroughs at the thresholds of swings, i.e. at peaks and troughs, require contrary thinking: It may sound peculiar that contrary thinking is required to achieve creative thought . . . This, however, becomes self-evident when we realize that thinking the way someone else thinks results in mimicry—a “copy-cat” requires the minimum of creative thought . . . Therefore, the inference is that to achieve any creativeness, some change has to be made. From this, it stands to reason that the optimum in creativeness must approach the maximum change . . . and the maximum change must be close to the opposite. (Neill, 1954, p. 148)

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Weinberger, in an overview of research, concludes that “the findings to date provide solid support for the claim that music increases creativity . . . That creative potential can be increased is of great importance. That music appears to be an effective means of accomplishing this goal should be glad tidings for everyone”. (Weinberger, 1998, p. 39). It is but a small step to aim for the type of multimedia experience of markets I have in mind. For the proof-of-concept I used three advanced and specialised software tools: 1 Compose14 transforms any numerical data, including time series, into audio signals, i.e. music.15 Files can then be saved in the industry-standard MIDI-format. 2 In turn, this format can be imported and played by FL Studio,16 a so-called Digital Audio Workstation (or DAW) to compose, arrange, record, edit, mix and master music. It is a powerful tool which, among others, can digitally synthesise numerous instruments, including whole orchestras. 3 Finally, via embedded links the resulting audio signals are dynamically, i.e. responsively and in real-time, visualised into animated spheres and other (threedimensional) shapes via Magic.17 Combining these tools can create a format of market data which enables the user to experience this data dynamically as an audiovisual extravaganza. For the proof-ofconcept I turned data (prices, returns, volatility, and other time series) from a number of benchmark assets into MIDI-format (via Compose). This was subsequently imported into FL Studio to create the musical composition. The latter was exported as a collection of WAV-files which, in turn, were imported into Magic. The result is only a very rough proof-of-concept which captures a specific period in finance history. It took me a long time to create because, as mentioned, these software tools were not meant for time series. We therefore need to create bespoke new software to make this process more efficient and user-friendly. I have uploaded it to the Vimeo platform for viewing (available here: https://vimeo.com/462307807), so please let me know what you think. In terms of types of research, another example would be when investors perceive the recent market action as similar to a previous historic period (or periods). By creating audiovisuals for both periods, the current and the historic, these can be compared by running them simultaneously. It will show any differences audiovisually in patterns which can enrich any insights from, say, a regression analysis of the underlying data.

 Alternatives include Audacity and Sonification Sandbox.  In fact, it even translates (EEG) brainwaves into music. This application may be of use to test separately the state of mind of investors while experiencing market data in audiovisual format, e.g. to look for any correlation, synchronised in the respective audio signals.  Alternatives include Pro Tools, Live (Ableton), Cubase, Logic, and Studio One.  Alternatives include Acrobat After Effects, ZGame Editor and Resolume. Less advanced visualizers include Aeon, Morphyre and Plane9.

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Depending on how the data was transformed into Compose I would argue that AVIR can induce Professor’s Scheider’s “zone” in order to achieve the qualitative “rhythmical” pattern recognition, in other words, the market’s a-ha signals that we are after. To conclude, I let Linda Bradford-Raschke, another well-known trader (who majored in music), emphasise the associations between market rhythms and music: A musical piece has a definite structure: there are repeating patterns with variations. Analogously, the markets have patterns, which repeat with variations. Musical pieces have quiet interludes, theme development, and a gradual crescendo to a climax. The market counterparts are price consolidations, major trends, and runaway price moves to major tops or bottoms. You must have patience as a musical piece unfolds and patience until a trade sets up . . . In both music and trading, you do best when you’re relaxed, and in both you have to go with the flow. (In Schwager, 1993, pp. 306–307)

9.3.6 Proposed Format Experiment This section contains a proposal for an experiment to be conducted sometime in the future. To recap, my reasoning for the proposed experiment is that current investment research methodologies exclusively focus on analysis. They are not suited to address the interiority of market states, as acknowledged by the MMH. Reducing this qualitative experiential level of a market state to the analytical quantitative level destroys the patterns we hope to account for. To understand this level we need, instead, to bring market data back into the interior (i.e. the mind~body) of the investor in a format that appeals to the mental abilities which facilitate the qualitative (conscious) recognition of such patterns. The points below describe the basic version of the experiment, but there are many possible extensions (see next subchapter). The proposed format for such a ‘hypothetical’ experiment consists of the following: – A group of subjects (e.g. students) are invited for an investment experiment that is spread over 3 half-days. The main requirements are that they are proficient with using computers and are interested but have no professional experience in investing.18 – In a briefing prep section, subjects will get background information and instructions about the experiment in general. These subjects are asked to manage a portfolio which can contain (any combination of) an equity index, a bond index, and cash. Subjects are free to decide the respective weights of these assets in their

 An example of a potential extension to the basic set-up is to get an equal number of female and male participants who will be submitted to a Myers-Briggs test (see next section). A totally different set-up would be an online test with numerous anonymous testers but I haven’t given this much thought.

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portfolio.19 The experiment is divided in three phases, each taking place in either the morning or afternoon of a different day, whereby subjects are exposed to data provided in: – an analytical format (phase 1), – an audiovisual format (phase 2), – both analytical and audiovisual formats (phase 3). The setting is a laboratory-type environment. The test would be whether any of the formats, again in isolation (phase 1, respectively phase 2) or combined (phase 3), is superior in terms of the performance of the subjects as expressed in the returns of their portfolios. The subjects will not themselves use the actual tools with which the data was prepared in the respective formats because it would require too much preparation time to train them in using the software. Also, the priority for this experiment is to see how subjects perform using the same data in the same standardised format for each phase. Finally, the difference in capabilities of using the tools might skew the outcome of the test too much. Instead, they will all receive exactly the same formats and all they are required to do is to interpret the data (in that uniform analytical, audiovisual, and combined format) in order to make investment decisions. In each phase the underlying market data is in the form of time series. Specifically, three samples, each containing a sufficient amount20 of consecutive (and normalised) daily data, will have been randomly drawn from a full history of data from 1970 to the most recent date of available data. Called data-set, it consists of realised historic prices, rolling monthly (20-day) returns, and monthly (20-day) volatility of benchmark indices representing the three main asset classes, from a particular country or region: equities, bonds, and cash. At each stage each participant will receive one sample, again randomly selected from the three samples. It means that each subject will eventually receive the same three samples over the full experiment, but the order of the data will be different across the subjects. This removes any potential bias from momentum or reversal patterns in the samples to sneak into any particular phase. In the first phase of the experiment, at T0, the subjects will all receive a read-only spreadsheet with the first batch from the dataset of their selected sample represented in tables and charts, including summary statistics. They will be asked to invest a hypothetical GBP£10,000 in any ratio across the three assets on a (simulated) monthly basis, initially and during the next twelve months, i.e. they can re-allocate part or all of their capital each month. They will have an hour to prepare for their start allocation by the end of which they enter their decision at T1. Subsequently, every 15 minutes the dataset will expand with updated daily data for a new month (roughly 20

 Long only portfolio, so no shorting.  Sufficient in the sense of a base history to allow both analysis and listening. Personally I would think 5 years of daily data as a starting history, e.g. related to the length of a business cycle, is enough.

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additional new data points per time series). It will be provided in similarly formatted spreadsheets which now contain the previous and the new data. This means that the subjects have 15 minutes to update their portfolio, based not only on new additional data but also knowing their investment performance. With this set-up, the first phase of the experiment will take a total of 4 hours (T0 to T13). The argument for using a 15minute interval is to allow enough time for analysis, the latter being the prime cognitive ability of interest in this phase. In the second phase, subjects will again be asked to manage a hypothetical GBP £10,000 portfolio across the above-mentioned assets. They now receive a different sample but transformed into an audiovisual file they can play on their computer.21 Again, they will have an hour to prepare for their start allocation which they enter into the computer at T1. The subsequent steps will be exactly the same as in phase 1, except that the format which contains the previous and updated data will be audiovisual. The final phase will see the remaining sample, determined per subject, represented in both formats: the spreadsheet and the audiovisual file. Subjects can now analyse the spreadsheet while viewing/listening to the audiovisuals of the same data. The goal remains to manage a portfolio of GBP£10,000 based on this new set of regularly updated data. In light of the voluntary nature of the experiment, subjects can earn incentives (for an overview of the impact of incentives on experiments, see Camerer and Hogarth, 1999). Small prizes (£50) are rewarded to the top performer in each phase, as well as a higher prize (£200) to the top performer on average (but adjusted for the spread between high and low scores) across the phases. The latter prize is significantly higher to control the house money effect, namely to motivate subjects not to make extreme bets during any of the individual phases. The main goal of the experiment is to test a number of forecasts based on my hypothesis. Specifically, I hope to show that across the group of subjects, on average, phase three shows a statistically significant improvement in performance versus both phase one and two. I call this excess return R(3–1), respectively R(3–2). It is expected that phase one shows an overall better performance than phase two. However, I expect R(1–2) to be less pronounced, in absolute and statistical terms, than R(3–2). Other goals, in the extended variations of the experiment (see section 9.3.7), relate to differences in investment style/horizon, respectively investment personality. Specifically, I expect confirmation of the forecast that a change in the interval of investing, e.g. using a real-time feed of the data, will significantly impact the results. I also expect confirmation of the forecast that certain psychological types score statistically different across the stages.

 A variation would be to use the same dataset, unbeknown to the subjects. This would help in contrasting the performance across the two phases as the same data is involved. The obvious risk is that subjects will discover this and thus distort their performance.

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This set-up, specifically the random selection of the samples per subject, counters some of the likely criticisms of this experiment, for example the claim that the periods are incomparable in terms of the statistical characteristics. The claim that the subjects in phase three have become much more used to the audiovisual format which may explain the improvement in performance can be countered by randomly changing the order of the phases, e.g. have some subjects start with phase 3. In the next section I will briefly discuss extensions (as variations) of the basic proposal for the experiment.

9.3.7 Extended Versions of Experiment If this empirical test confirms my hypothesis, the method could potentially help investors to improve their assessment of market states, adding to the theoretical relevance of my thesis. However, this benefit is unlikely to be universal, both in terms of investment style (i.e. trading vs. investing) and investment personality (i.e. psychological types). 1. Investment Style An obvious variation to the above basic approach is to have a ‘real-time’ feed of the data. This does not necessarily require the use of high-frequency (intraday) data. In this particular case, the subsequent 12 months of daily data could be streamed in a much shorter interval, say every minute. The argument that daily data can continue to be used is that the price data is fractal in nature as far as the rhythmical aspects, via trends and their reversals, are concerned. So, although daily frequency contains less noise than intraday frequencies it still contains the basic patterns, i.e. rhythms, we are after. Nevertheless, using intraday data could of course be another variation to the basic test. Overall, it is to be expected that such a continuous feed of data will have a statistically significant impact on the results. Clearly it simulates more closely the circumstances under which traders make their decisions, rather than those of longer-term investors.22 2. Investment Personality Another interesting extension of the experiment would be, first, to choose an equal number of female and male participants in order to assess any gender bias in the eventual results. Next, to dig even deeper in the results one can submit subjects to a Myers-Briggs test ahead of the actual experiment. The aim would be to see whether certain psychological types score statistically different for this

 We would need to source the advanced trading-simulation software required for this variation.

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experiment. This would, in turn, be suggestive for the usefulness of the tools across these types, i.e. who would potentially benefit most from using AVIR? Jung concluded that differences in behaviour between people originated in differences in dominance of the four psychological functions: thinking, feeling, intuiting and sensing. How does this apply to investing? Type theory claims that the best decisions are made by a balanced mind. In other words: – Both perception functions, i.e. sensing and intuiting, are used to ‘record’ all data. – Both judgement functions, i.e. thinking and feeling, are used to ‘assess’ this information. For many years Van Tharp has applied this framework, including the Myers-Briggs typology, to trading. He has discussed this extensively and I therefore refer to his work for more details. For other research on personality and investing, see for example Richard Peterson’s website, Market Psych: http://tests.marketpsych.com/personal ity_test.php. Derived from my hypothesis is the expectation that certain types will score better in the test so the software will be most beneficial to them. Specifically, although the longer-term benefits of using my method are potentially greater for Extraverted/Sensing users, I predict that, for this test, those who score “IN” in their typology will show the biggest improvement because the tools appeal to the functions they already feel most comfortable with, i.e. are dominant. Moreover, even the general effects of music on behaviour are quite different in different people. For example, the effect to which background music affects learning and recall depends on this personality dimension (Furnham and Bradley, 1997).

9.3.8 Summary, Conclusion, and Future Vision At the start of Subchapter 9.3 I indicated that, apart from describing a framework as a proposal for an experiment to test sub-hypotheses of my thesis, I would explain the method and tools with which the market’s subliminal messages could be better received. The method, AVIR, entails a dynamic and creative representation of market data via audiovisuals. Earlier reflections by others discussed such an approach along similar lines: Another more anachronistic (the use remains limited) example of traders’ apparent loss of a feel of ‘the market’ is software that simulates sounds of a virtual open outcry floor based on the information from the electronic system. This allows traders to react to the roar of the market which often signals volatile shifts in the market. The following is taken from an ad for a product . . . : ‘Hear your electronic market in real time and real voice . . . Connect your mind to the market, not your eye to the screen.’ The quote shows how the intention is to ‘re-embody’ trading and to recreate ‘connections’ to the market (Arnoldi, 2006, p. 389).

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My proposal takes this to the next level. The tools include advanced software packages that could be bespoke retooled for investment research. The following quote from Brett Steenbarger is in line with how Marcovici trained his rats and in the spirit of how I see these tools being used, namely repeatedly playing the audiovisual files to train attunement: What traders can do to accelerate their learning is increase the intensity of their practice chapters, as an athlete would. Maybe practice trading in more than one market or simulate a whole day’s worth of trading in 15 minutes . . . after looking at pattern after pattern, decisionmaking becomes second nature. (In Stewart, 2002, p. 6; emphasis added)

In terms of the market’s ‘big data’, although noise to some is music to others, my argument is that more data is not necessarily better in understanding the market. Instead, the challenge lies in how we represent and interact with its data due to the fact that our current tools are limited, and often cause confusion rather than clarity: Statistical innumeracy is often attributed to problems inside our minds. We disagree: the problem . . . lies in the external representation of information . . . Every piece of . . . information needs a representation—that is, a form. Some forms tend to cloud minds, while others foster insights. (Gigerenzer and Edwards, 2003, p. 741)

Many resources are currently expensed on gathering ever more data to add to the already big data of markets. Although I am doubtful this will bring the rewards its advocates list in their promotions (if only because they focus, again, exclusively on analysis), I do not think it necessarily hurts. On the other hand, we ignore another and complementary approach: to use that data in a different way. This is what AVIR is all about. The MMH speculates that dynamically representing market data by streaming it in audiovisual format, improves our understanding of the market speaking its mind. In 4E cognition terms, the movements act like the market’s intonations or even gestures, similar to those we make when we speak. Specifically, I predict that systematically using a non-analytical research method to complement traditional (e.g. spreadsheet) analysis methods will improve the forecasting ability of the average user of this method, compared to exclusively using analysis methods. The method I envisage is aimed at systematically building tacit knowledge which would complement any analytical knowledge. Nesbitt and Barass argue that adding audio and deepening the visual experience of data increases the human~computer bandwidth. While in agreement my method hopes to add to this by maximising the investor’s “personbytes”, in this case the amount of market knowledge that they can embody. Specifically, the aim is to optimise tacit knowledge by improving the attunement of the human mind to the rhythm of the market by presenting it in rhythmical format, that is, as audio accompanied by responsive visuals. Both audio and visuals transform the market data in dynamic qualitative formats far removed from tables, charts and other quantitative, mostly static, research formats.

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These formats of prices and other data add meaning to our understanding of market states, over and above any knowledge derived from analysing them, because they help convey how those states feel like. These are collective feelings although their uniformity is not always strong, as they may not necessarily correlate with each and every subjective feeling. In that respect, there is potentially a deeper consequence of using this methodology. Through experiencing prices audiovisually the aimed-for knowledge of ‘the state of the world’ is as Watts says: “in one sense, self-knowledge. For knowing is a translation of external events into bodily processes, and especially into states of the nervous system and the brain: we know the world in terms of the body, and in accordance with its structure” (Watts, 1966, p. 100). This, I believe, is what is behind Soros’ famous back pains (see also Cymbalista, 2002b). It also links more broadly, of course, to 4E (in this case embodied) cognition. Assuming tests confirm my hypothesis, and we will be able to properly develop such tools, I would like to see them being used on trading floors across the world, next to analytical ones. Such rebalancing, consistent with modern mental health views, is not only beneficial to our individual investor mind, for example by improving our decision making. It may also contribute to correct some of the major imbalances in the financial system, i.e. improve the health of the market’s mind which is being ‘brainwashed by algorithms’. In the context of society’s chain of discovery, how can we expect our creativity to generate the innovations in the real economy that we rely on for our progress and adaptation if these eventually are priced in a mechanistic way by a market that has become an automaton? Worse, accepting the market as an animated collective entity with a mind also means that the growing influence of these algorithms has a parasitic impact, causing the market to become increasingly lifeless. Due to the reflexive nature of price discovery, where the individual mentalities fuse into the collective market mind via a dynamic feedback loop, the growing overreliance on quantitative research and investment methods has already resulted in dangerous conditions. A rebalancing is long overdue and such levelling of the playing field could make the overall market more balanced and dare I say, lead to a healthier market mind? Ultimately the question is not whether the market exhibits ‘narrow-minded’ states which can be captured by mechanistic (‘mindless’) algorithms which are basically context-free rules. Like all of us, of course it does. But that does not make the market an automaton, just like we are not robots. The question is rather what triggers the market state to shift whereby investor behaviour becomes sensitive to context (e.g. contrarian versus crowd mentality) and what this implies in terms of research methods and tools. This keeps economics’ hard problem at the epistemological level, which is something we can handle. Instead of making a drastic ontological commitment, i.e. ‘the market is different from us’, we accept that the market’s mind puts limits on our understanding. The aim thus becomes to explore and possibly push the boundaries of these limits. AVIR is part of that goal.

Chapter 10 On the Hard Problem: Am I Conscious? Consciousness is what makes the mind~body problem really intractable. Thomas Nagel

10.1 Addressing the Critics There are various criticisms that can be aimed at the MMH. Before I list and address some of these, let me recap first. Following the recent reality checks, confronting it with its multiple failures, economics is introspectively, through its enlightened critics, looking for answers. In reference to the Prologue’s opening quote by Soddy, the MMH argues that this soul searching should be more than just metaphorical. In fact, the role of mental causation in the economic system—correctly identified by Mises, Akerlof and Shiller, and others—points to the core underlying problem which should be the focus of economics and its research efforts. The market’s mind~body problem asks why, in confronting uncertainty, the quantities involved in physical processes and cognitive content give rise to the qualities of the market as felt by agents. In other words, how does the interaction between the physical properties (e.g. real assets) and access-conscious properties (e.g. expectations) lead to market states as we experience them in the shared phenomenal sense (e.g. moods)? Moreover, how can something mental—like mood—impact something physical—like labour, inventory, and transport? Alternatively, stating it from the angle of physics envy, why has the physics approach of mechanical economics not only failed to explain and account for the reality of market phenomenality and its influence but has itself caused dangerous side-effects? This problem is at the centre of the MMH’s research agenda. To start addressing this I previously quoted Chalmers regarding consciousness as the dual realisation of information. I repeat the key part: It may be that principles concerning the double realization of information could be fleshed out into a system . . . connecting the physical and phenomenal domains. We might put this by suggesting as a basic principle that information (in the actual world) has two aspects, a physical and a phenomenal aspect. (1996, p. 286; emphasis added)

In this book I have “fleshed” this out for the above terms in italics as, respectively, market “principles”, economic “system”, and prices as dual-aspect “information (in the actual world)”. The phenomenal is manifested as mood which exists because most market participants are humans, and their consciousness extends into the market that embodies them. Specifically, the market mind is a complex adaptive system which exhibits shared cognitive properties that emerge from the (e.g. technology enabled) exchanges between participants while simultaneously not exclusively belonging to any of those participants individually. Notably, a market state involves qualities https://doi.org/10.1515/9783111215051-011

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that not only complement any quantitative conditions of that state, but also are intersubjectively experienced by participants over and above their individual subjectivity. The challenge—as acknowledged by Marks, Powell, and others—is thus to understand this market mood. As I have argued, that will also require new research approaches. In addition, and more particularly, the MMH states the following: – The relationship between the real economy and the financial economy is the collective version of that between the bodily and the mental economy of the individual. Specifically, what drives the economies in the external world is the same as what drives the internal economies, namely optimising allocation whereby cooperation and competition coordinate behaviour so that demand for physical and psychological resources can meet supply via exchange. In the process values are discovered. As explained, other market dynamics such as currency, profit, and randomness also play their role. – By extension, the issues involved in the distinction between the mental and the material are relevant in both domains. True uncertainty largely concerns our ignorance about (the complexity of) mind~matter exchange at these multiple levels. To the extent possible, these (metaphysical) issues—raised implicitly via assumptions, narratives, and statements—should be made explicit in discussions, papers and proposals in order to improve our understanding of the economic system. Unambiguously, the MMH assumes that most agents have an aspect-dualist view of reality which they act upon, thereby inviting the mind~body problem more broadly into the economic system. – Just like the EMH, the MMH invokes the “as if” assumption. But whereas the EMH states that prices “are determined as if all investors are rational” (Rubinstein, 2001, p. 15), the MMH proposes that “prices are determined as if all human investors are conscious”. For one research approach we can limit the state of being conscious to access consciousness. This allows us to focus on attention (span) as a criterion for the allocation of mental resources in parallel with the allocation of money while trading. This approach can help, among others, to establish the link between (relative) price moves and (relative) brain activity, in particular the impact of changes in volatility on allocation of attention.1 To investigate phenomenal consciousness we need more innovative and radical approaches. Next, this section addresses some of the scepticism and criticism regarding previous suggestions that markets manifest collective mentality. Overall, many critics—who often seem not to have properly experienced markets with skin-in-the-game—oversimplify what a market is, overlooking its complexity in general and its non-linearity in particular. They offer anecdotal, sometimes naïve comments but no convincing arguments.

 This is the focus of our main “Spontaneous Volatility” research project.

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The first oversimplification equates a market to an index. The related argument is that the market is nothing more than an aggregation of its constituents, for instance, “the stock market” is simply “the S&P500 index”. As a reminder, the latter is a (market capitalization weighted) average of its constituents, the stocks of the 500 largest US corporations. Consequently, all computations related to those constituents result in the value of the index. However, investors do not consider any index to represent a market.2 An index certainly cannot convey what it means to be in a market. But there are more problems with this naïve assumption of aggregation in the market: The computations carried out by the stock market are merely aggregative; this is demonstrated by the fact that the addition of a trader will have the same effect on the computational processes that are carried out by a stock market regardless of whether there are twenty traders or two hundred. So, while the properties of a market may be descriptively interesting, they do not offer any new explanatory resources beyond those that could be acquired on the basis of information about the individual traders and their decisions. (Huebner, 2014, p. 86)

There are many errors in this reasoning. First, it completely ignores emergent phenomena in markets, whereby Mr Market’s behaviour and mentality are both more than and different from the (aggregated) behaviours and mentalities of market participants. Next, its emphasis on computability exposes it to the previously discussed limitations of computability in markets (see also comments further below). Third, this statement denies the very real issue of liquidity in markets, that is, whether there are twenty or two hundred traders does often matter when adding another trader. This is especially the case when it is a marginal trader (i.e. the Marginal Trader Hypothesis) or an institution (like a central bank) with a different objective than profit. Fourth, there is no way to acquire “information about the individual traders and their decisions” except via properties of a market. Specifically, it completely overlooks the signalling function of prices which actually goes beyond the information of individual traders. Bettencourt—indirectly highlighting the relevance of Coordination Dynamics—shows mathematically why “we can say that knowledge of a set of variables may yield more insight than the sum of the information in its parts . . . From this perspective the price of an asset is potentially a synergetic indicator of the information manifested by all active traders in terms of their offers to buy and sell” (Bettencourt, 2009, p. 603, 616; emphasis added). Let’s explore this some more. Distribution of knowledge and availability of information is also directly related to the issue of efficiency in markets. Apart from the fact that testing market efficiency is notoriously difficult, market crashes suggest that price discovery does not occur via some fixed law of computational aggregation. In particular, it is very hard to determine what the underlying mentality (e.g. S1 vs S2) of

 Let alone, the market in the CAPM sense, i.e. Rolls’ second critique.

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the individual agents are. This is closely related to that famous statement by Graham that I referred to previously: “In the short run the market is a voting machine, but in the long run it is a weighing machine”. Simplified, think of it in terms of the scale shown in Figure 10.1 with weights expected to be dispersed like a normal distribution.

A few behaving irrationally one way

Most investors acting rationally

A few behaving irrationally the other way

PRICE OF ASSET EFFICIENTLY PRICED

Figure 10.1: Theoretical market (static equilibrium). Source: Connected Wealth (via Valuewalk)

In this first instance, the majority of investors are then occupying the middle of the scale (holding ‘rational’ expectations). A few occupy the left side of the scale (holding ‘irrational’ pessimistic expectations), and a similar few occupy the right side of the scale (holding ‘irrational’ optimistic expectations). The EMH argues that this is the ‘normal’ situation, where the two ‘irrational’ camps at both ends cancel each other out exactly, while the majority occupies the middle. A further consequence of assuming the normal distribution is that the mean represents the ‘average investor’. Market practitioners know that, instead, all it takes to tip the balance and create disequilibrium is for a few irrationals to move places (Figure 10.2): In terms of duration, borrowing the famous (paraphrased) words of Keynes, “the market can remain irrational longer than you can remain solvent”. More broadly, it also ignores the convincing evidence that the market is a complex adaptive system (e.g. Johnson, Jeffries and Hui, 2003) that achieves targets, i.e. efficient allocation of society’s resources, that are unachievable by aggregated individual efforts. This, of course, follows the more general reflections by Smith, Hayek and others on the ‘composite method’ of markets whereby exchanges between individual intentions lead to social, collective outcomes that were not part of (the design of) those individual intentions. Emergence concerns the synergistic mental state of the market. Critics, in particular those from an intentional stance (like Huebner, 2014, p. 72), argue that market

10.1 Addressing the Critics

Many behaving irrationally one way

A few behaving irrationally the other way

Most investors acting rationally

PRICE

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ISPRIC

ET M F ASS

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Figure 10.2: Real world market (continuous disequilibrium). Source: Connected Wealth (via Valuewalk)

mentality is simply the aggregation (or summation) of the mentality of its individual participants. In other words, these critics state that there is nothing over and above, nor different from, the aggregated mentality, where we also take into account the average of such aggregation (again, based on the assumed normal distribution). We can respond to this, first, by looking at decision making. If we focus on rationality, it can be violated by two types of decision errors: occasional random errors from mistakes and systematic errors from heuristics and biases. Even though the EMH admits that individual investors are not always rational, it emphasises that the market, viewed as a composite agent, nevertheless is rational (e.g. Rubinstein, 2001; again reflected in Figure 10.1). In technical terms this is called minimally rational. It means, for example, that although prices are not set as if all investors are rational, there still are no abnormal profit opportunities for the investors that are rational. In fact, most investors would agree that the market is more rational than the average investor (‘but not me’), with academics arguing that the market, over time, is more rational than any individual investor (thereby advocating passive investing). So, both investors and (the EMH) academics point to a mathematical conundrum in answer to the aforementioned critics: if the market results from aggregation, how can it be more rational than the aggregated level of rationality of its agents? In popular parlance: how can the market know more than the average investor? For the sake of argument, suppose we agree with the critics that aggregation is indeed applicable to the first type of error, in the sense that market rationality results from random errors being aggregated away. But this does not apply to systematic errors. Something else occurs that may push market rationality ‘above the aggregated average’. A neat set of experiments from ecology makes this case. Sasaki and Pratt (2011) analysed the behaviour of ants and their colonies. Whereas previous studies

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showed that distributed decision making can filter out random errors from individual irrationality via aggregation, their results highlight that collectivities can also suppress systematic errors if allowed so: This contrast between group and individual reverses a traditional view that collectivities are prone to amplification of individual irrationality. Our results instead suggest that an appropriately structured collective can prevent irrationality by avoiding the overburdening of individual cognitive abilities . . . Other cases of irrational choice involve systematic preference changes that cannot be cancelled out by summing the choices of many independent decision makers. Collective choice can only limit these choices. (Sasaki and Pratt, 2011, p. 279; emphasis added)

What seems to be happening is that in the right collective setting, which we assume a market generally is, systematic errors are suppressed (compared to individual usage) because of competition between heuristics. It is discussed as Evolutionary Rationality (see Appendix 1-B4), whereby only beneficial heuristics and biases survive. To some extent this is in the spirit of Lucas’s critique of behavioural economics, in the sense that it questions the sustainability of systematic errors. Overall, it is another example where the forces of competition and cooperation combine into coordination. In particular, whereas trading is competitive from the bottom up, it creates a top-down synergistic cooperation for the market as a whole. The final element of a defence against the critics on this point consists of the reality of market mood. And mood cannot be addressed via the intentional stance (as others have also argued). I will return to mood shortly on another point. This leaves aside general criticism of the intentional stance in explaining intentionality by other cognitive scientists such as Seager. A second oversimplification, often exhibited in papers on agency-modelling which pretend to realistically simulate markets, are the assumptions regarding the ability to learn rational expectations and the computability of current and forecasted equilibrium prices. Lewis (1987), Spear (1989), and others showed the non-computability of fixedpoint mappings that represent equilibria in markets. Specifically, Spear invoked Gödel to argue that unless agents have perfect information about the state of the world rational expectations equilibria cannot be learned. Related research has also shown, for example, that such markets cannot be both complete and consistent. A third, and related, oversimplification is the assumption that intrinsic value is (algorithmically) computable and that the outcome (i.e. price level) is competitively determined by predictive strategies. In particular, passive strategies (which mechanically follow past price levels) are not considered differently from active ones. According to these arguments there is, by extension, no difference between a market involving only human traders and one involving only computers. However, the biggest flaw in mindless (e.g. agency-type) assessments of markets is ignoring the reflexive nature of price discovery, in particular the influence from experiencing price moves. To repeat the earlier quote from Soros: “markets are not supposed to have moods. Yet they do” (Soros, 2010a). As I mentioned, moods are not representations nor representational. This

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makes it hard to maintain that whatever market mentality is, it can be reduced to the computations of its agents. Instead, a market mind is highly complex (see Chapter 6). Physically speaking, and per Algorithmic Information Theory, if complexity increases by the bit size of the underlying information processing, then the market is more complex than the individual brain because it involves multiple interacting brains. On that note—as argued, ironically, by both the EMH and its Austrian critics—the market performs calculations that exceed the capacity of any individual agents. More generally, according to mainstream complexity theory complexity results from simple components that interact. New properties emerge from this interaction, whereas properties owned by the individual components can be affected by such complexity. Deniers of market mentality seem to reverse the argument: humans have consciousness, a highly evolved adaptation, but this is not manifesting itself in a more complex system, like the market. However, they do not explain how it somehow disappears.

10.2 Meeting Conditions of Collective Consciousness The previous rebuttal may still not satisfy every reader, so I will strengthen my arguments further. First, we can apply folk psychology. To judge whether we consider some entity to be conscious, it is often useful to reflect on how we feel when it will be destroyed. Even if you are a panpsychist I doubt you object principally to a perfectly running car being turned to shrapnel, or your colleague smashing his computer out of frustration. But you may start to feel uncomfortable about your company doing a hostile, assetstripping take-over of another company, or your government’s decision to attack a ‘rogue nation’. Now, think how you feel when you see Mr Market being poisoned by toxic instruments (CDOs), manipulated by price fixing (Libor), and becoming an addict to cheap credit (ZIRP) while being pushed around by nudges, repression, and other interferences. It should be clear by now that such mistreatment has real impacts that affect us.3 More formally, we can apply the framework provided by philosopher Kay Mathiesen (2005) to clarify further why and to what extent financial markets manifest collective consciousness. She defined three reasonable conditions that any account of collective consciousness needs to meet. It allows me to promote the market as a strong contender.

 Again, for intuition pumps please see my earlier comments in the Introduction by way of describing scenes from the movie The Big Short.

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– Plurality: a collective consciousness “must be composed of a number of separate centres of consciousness, which are not directly accessible to each other” (Mathiesen, 2005, p. 237; emphasis added). In the market the multiple conscious subjects consist of investors. At the macro level all investors together make up the market. At lower levels of aggregation, investors form groups (or crowds, or herds), often identified by colourful terms like ‘bulls’, ‘bears’, ‘hedgehogs’, ‘sheep’ and, more recently, ‘WallStreetBets’. In both instances they do not have direct access to each other’s consciousness. Investors form each other’s (“indeterminate”) “Other” (Heidegger, 1927, p. 164) whose presence is ultimately felt via price dynamics, which is particularly relevant in an existential sense. – Awareness: a collectivity that manifests consciousness “must have collective awareness and genuine intentionality” (Mathiesen, 2005, p. 240). In terms of the specific philosophical meaning of intentionality, the power of the market’s mind—its ‘aboutness’— is to represent an (expected) economic state of affairs. Explicitly, by way of securities that are valued and expressed in prices, the market mind is continuously occupied with such a state, which the constellation of prices discloses. (Changes in) prices reflect (changes in) awareness about that state. Earlier, Subchapter 7.2 connected prices (as abstractions) to awareness by freely interpreting Soros: “Awareness of change is associated with . . . the use of abstractions; lack of awareness involves the lack of abstractions”. In terms of goal-directed behaviour, collectively investors have the same intention, namely to grow wealth. Or, to put it more bluntly, to make money, i.e. to trade profitably. However, since every trade has a buyer and a seller, not all will achieve this goal (at least not viewed on a per-trade-per-term basis). Goals or wishes can also differ between groups, e.g. bulls (bears) want to see the price going up (down). In addition, investors invest in a variety of securities within and across markets, as well as over time. Securities are the shared objects of attention, whereby their relative price dynamics reflect the relative extent of awareness across these, and this can consequently differ.4 Still, and importantly, price moves are suggestive for the growth and decline in overall wealth as well as the overall broader intentionality of markets in terms of resource allocation,5 for instance whether there is a preference for gold over silver. In markets collective awareness and intentionality is thus reflected in prices which investors observe together, both historically and in real time. In brief, although no individual investor is completely knowledgeable about the underlying drivers, they are all aware of the (intentional) state of the market as reflected in these prices, whereby their numerical symbolism is their market mind representation. Of course, one important feature of representations is that they can misrepresent, e.g. symbolism can lead to excesses. Financial history is full of exam-

 This is why, at the beginning of this book, I stated that I consider securities to be the ‘neurons’ of the market mind. As an aside, shared attention extends to other aspects of social cognition like intentional attunement (Gallese, Eagle and Migone, 2007).  Again, supposedly efficiently but see Farmer, Nourry, and Venditti, 2012.

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ples of these. There is also an implicit acknowledgement of superior knowledge at the market level as far as intentions for the system as a whole is concerned: “You know it’s an invisible hand, the market is always right, it’s a lifeform that has being in its own right. You know, in a sort of Gestalt sort of way (. . .) it has form and meaning . . . a greater being”. (Knorr Cetina, 2003, p. 12). Combined with violent moves from (non-representational) mood shifts, this can be awe inspiring. In their classic paper on awe, Keltner and Haidt highlight its extraordinariness via two appraisals: “perceived vastness, and a need for accommodation, defined as an inability to assimilate an experience into current mental structures” (Keltner and Haidt, 2003, p. 297). – Collectivity: “In order for collective consciousness to be genuinely collective, it must be something that persons share and that ties them together” (Mathiesen, 2005, p. 241). This relates back to intersubjectivity. Investors share the market state, its complete mentality (expectations, emotions, etc.), as reflected in the constellation of its prices. This state is a composite state, different and independent from the mental state of any individual investor, although they can correlate depending on (the holdings in) the portfolio of the individual investor. The uniformity of feelings is strongest in cases of extreme price moves. For example, in March 2009 all investors shared in the move towards the symbolic 666, the “Devil’s Low”, in the S&P500 index. It indicated, among others, a deteriorating outlook for the US economy. Although the subjective feelings varied across investors, again depending on how their portfolios were made up, the overarching mood was one of extreme worry because of the potential implications of a complete collapse of the global financial system. I refer to my earlier comments in the Introduction on what this felt like. Interestingly, on this last feature of collective consciousness, i.e. collectivity, Mathiesen refers to Edmund Husserl and wonders: While Husserl does say that these social subjectivities arise out of the ‘intercommerce’ between the individuals, he does not describe exactly how the attitudes and activities of individuals mesh to form such personal unities. How do the separate individual subjectivities coalescence to produce a shared social subjectivity? (Mathiesen, 2005, p. 243; emphasis added)

In answering this question she overlooks the hint she herself gives in the quote above which leads us to the MMH (especially its Market Mind Principle): Husserl’s “intercommerce” is very appropriate in general because, as I have argued, complementary market forces underlie general collective mentality. In the case of markets as collective consciousness intercommerce consists of price discovery (i.e. Mathiesen’s “attitudes”) and trading (i.e. “activities”). It leads to the composite expression of individual mentalities, the “mesh” that forms Mr Market, the moniker investors give to Mathiesen’s “collective subject” (Mathiesen, 2005, p. 235). The reflexive phenomenality of this intersubjectivity is nicely captured in the earlier words of an anonymous investor:

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You are part of the market, you notice every small shift, you notice when the market becomes insecure, you notice when it becomes nervous . . . All this (amounts to) a feeling. (Knorr Cetina and Bruegger, 2000, p. 153; emphasis added)

Reflexivity (which has already been discussed extensively) should be added as another criterion to this list. What I would like to emphasise here for this condition is that discovery is an important aspect of reflexivity. I mentioned this previously in the section on “society’s chain of discovery” and in Subchapter 3.3 on Predictive Processing. In short, and ceteris paribus, an entity manifests collective consciousness if there is some similarity to the individual mind’s discovery process in order for reflexivity to transition as discovery through the whole system. This, for example, makes the accompanying phenomenality (via intersubjectivity) go beyond pure supervenience. On the other hand, it seems to me that the explanation of causal inference, i.e. how a collectivity handles information about the likely causes of sensory signals without direct access to their source, has to be comparable to that of the individual mind. Again, the market meets this criterion based on its price discovery, whereby prices handle information without direct access to their fundamental sources while reflexively affecting those (as well as other prices). Finally, at first sight there seem to be other candidates for accounts of collective consciousness. Perhaps some will argue that the internet in general and social media in particular form more convincing cases. However, compared to markets they miss a clear and objective expression of the qualities which make the “psycho”-part complete in terms of phenomenology. Albeit in varying shades of uniformity, those qualities are properties of shared experiences. They should particularly convey a shared meaning in the context of (economic) survival of the collective subject; felt qualities in an existential sense. This is crucial and the reason why I emphasised the message from the reality checks as lived experiences. The expression should also be in a format that is uniformly understood, ideally reflecting values which allow scaling of the shared mentality concerning the overall state of the collective subject, for example, from exuberance to despair. As said previously, such an expression should also indicate intentions, particularly in terms of a commitment to (as in ‘valuing’) a resource in order to survive under circumstances which mostly are constrained. So, whereas ‘tweets’ on Twitter and ‘likes’ on Facebook remain largely individual expressions of emotionally charged events, (‘I am afraid’), we are instead looking for a collective expression which genuinely captures the intersubjective intensity of a feeling (‘we are afraid’). It should not be limited to a predefined group but potentially involve the human race while stripped of as much individual subjectivity as possible. I hope I have made clear that financial markets by way of pricing do meet these requirements which make them unique. “Fine”, some will say, “but so what? Even if the market is conscious what does that tell us about it that we did not already know?” To answer this we can ask how far does the mind~body perspective reach: is the market ‘alive’? Although the market

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does not procreate in the biological sense, it does spawn the mental insights which facilitate the creations that support our own biological procreation. It achieves this by way of price discovery which, as we have seen, is a creative process in and of itself. If we include the narratives, by distributing cognition the market spreads memes instead of genes (see also Blackmore, 1999). The ones that are superior survive. Investing, as Soros and others have pointed out, is like continuously testing hypotheses characterised by uncertainty. Trades are the creative acts in the market. The American painter Richard Diebenkorn advised in one of his rules for creative acts: “Attempt what is not certain. Certainty may or may not come later. It may then be a valuable delusion”. Those creative acts spawn prices as symbols of awareness. Specifically, when Jung states that “every act of dawning consciousness is a creative act, and it is from this psychological experience that all our . . . symbols are derived” (Jung, 1968, para. 30) we can similarly interpret his “creative act” in terms of price discovery, with prices as the numerical “symbols” of markets. It involves a collective effort by humans, supported by physical tools, which extends their individual mentality. It leads, in particular, to a valuation of resources that cannot be computed by individuals. Creativity is a dynamic that is difficult to explain from the mechanistic, pre-determined perspective taken by mechanical economics. As far as the “so what?” of economics’ hard problem is concerned, let me recap by rephrasing why identifying this problem is critical: it zeroes in on mind~matter exchange which is the endogenous transmission process of the economic system. This is what Soddy, Mises, Akerlof and Shiller, Sornette, and others recognised but did not explore sufficiently. It matters whether we consider or treat something as mental or physical, especially in a world of conscious humans who (mostly) are closet dualists. Soros (1987) implicitly raises this in the Epilogue of The Alchemy of Finance when he distinguishes between “ideas” and “material conditions”. It also leads to the practical dualism reflections by Knight and Hayek. To conclude, the market mind is a strong candidate for meeting the conditions of collective consciousness. The collective mentality of the market, like individual mentality, includes sensations. These infuse the experience of a market state in a qualitative sense and particularly impress the collective nature of that state by way of a mood that is felt over and above any individual mood. In other words, intersubjectivity is an irreducible property of the market’s mind. Combined with shared unconscious drivers that are particularly attuned to collective settings, this makes the market’s mind conscious in a more complex way than that of the individual minds that compose the group. Finally, it urges us to look at the market’s mind for guiding us more broadly in cognitive matters.

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10.3 In Sum: Investing = Dealing Together (With Our Hard Problem) This section will gather all the main points of the previous chapters. It will centre on the unique properties of markets to manifest consciousness via prices as intersubjectively and dually realised information. Solving the original hard problem is not (yet) on the table, as Chalmers, Hayek, Soddy, and others previously warned. But acknowledging it via the MMH, recognising that, beyond the explanatory gap, its practical implications extend into the economic system, and collectively dealing with it by investing is an important part in addressing it. Unfortunately the circumstances are far from ideal: I will also summarise how mechanical economics has distorted Mr Market’s mind and deteriorated his functioning. First, the central role of information is where the EMH and the MMH can agree on. Here is Hayek to remind us how prices carry information: In our whole system of actions, we are individually steered by local information—information about more facts than any other person or authority can possibly possess. And the price and market system is in that sense a system of communication, which passes on (in the form of prices . . .) the available information that each individual needs to act . . . (Hayek, 1982, p. 326; emphasis added)

How information is consumed and produced by conscious agents, and what it is like for them to be in markets, is where the MMH excels and parts ways. For ease of reference (i.e. at discrete time points), prices capture a market state. In reality (i.e. continuous time), “state” should not be interpreted as static but rather as a condition that has internal coordinating dynamics. Certain characteristics of a market state concern physical processes, involving physical parts. Others concern cognitive processes. Although these processes can be analysed, they do not describe the full market state. As we discussed, there is something in addition, namely the lived experience of what it is like to be in that state for investors as (part of) a collectivity. In other words, as a result of their exchange there is something over and above buyers and sellers, symbolised in the squiggle that pairs them: buyers~sellers. Accordingly, market states, over time and expressed in prices, reflect intersubjectively realised information in both the physical and phenomenal sense. While echoing the practical dualism of Hayek and Knight, this is particularly important in the context of Chalmers’ statement concerning the double realisation of information in consciousness generally (see particularly Appendix 1-A). In this case, each investor reflexively experiences that market state by participating in exchanges while owning a portfolio that changes value. In short, prices and mood affect each other. Due to their nature prices also solve the issue of “the lack of data” in consciousness research which Chalmers and other cognitive scientists complain about (see the Introduction). By connecting the real and financial economies they form, simultaneously, the informational building blocks for our bridge between the physical and psychological shores of consciousness. In fact, the practical message of this book for

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cognitive science is that financial markets provide insights in the elusive but irreducible collective dimension of human consciousness, backed up by large amounts of empirical data waiting to be further (and freshly) explored from this new perspective. That is, market data has so far primarily been analysed from mechanical economics: the perspective of the market as a physical mechanism using the natural sciences, particularly physics, as a template. For decades mechanical economics has justified policies, practices, and products that have had disastrous consequences for our economic health, many unintended and most unexplainable. It makes excessive ontological commitments: to the economy as machine, the market as automaton, humans as robots (apart from making other costs, including technical ones like turning subjective probabilities into objective probabilities). Growing empirical evidence, particularly recent alarming events, has shown that these commitments have become too expensive, to the point of bankrupting both the economy and its theory. It is not as if we weren’t warned. Previously many were already critical of this template and approach.6 Mises, for example, declared that: What differentiates the realm of natural sciences from that of the sciences of human action is the categorical system resorted to in each in interpreting phenomena and constructing theories. The natural sciences do not know anything about final causes; inquiry and theorizing are entirely guided by the category of causality. The field of the sciences of human action is the orbit of purpose and of conscious aiming at ends. (Mises, 2007, p. 240)

In his Nobel-acceptance speech Hayek highlighted the risk of making an “outright error” which, I submit, has now materialised: It seems to me that this failure of the economists to guide policy more successfully is closely connected with their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences—an attempt which in our field may lead to outright error. (Hayek, 1974, [n.p.])

Soros (1994) added his verdict on using physics: “Applied to events which have thinking participants, it provides a distorted picture of reality”. Namely, these participants collectively are the market while being in it. And as Knight so brilliantly pointed out, they are conscious. This raises many questions, especially philosophical ones, which require a different perspective, away from physics and the mechanical worldview. Backed by 4E cognition, the MMH offers this as the mind~body perspective personified in Mr Market. This book is about this genderless collective entity, and I previously detailed how ‘he’ has been misunderstood and mistreated based on mechanical economics. The abuse committed by the mechanics-cult consists of a form of brainwashing, with both physical and psychological elements. For example, Mr Market’s brain has been rewired using high-frequency connections to exchanges. Its damaged neurons

 Also, see Knight (1921) for early critical comments ‘. . . all organic readjustments would become mechanistic, all organisms automata.’ (III.IX.7; p. 268). In a biology context, see Ho (1998).

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now include executive options with automatic resets, Pay-In-Kind loans (PIKs), Special Purpose Acquisition Companies (SPACs), Contingent Convertible bonds (CoCos, a.k.a. AT1s), and other heads-I-win-tails-you-loose securities. This parasitic engineering exclusively benefits certain ‘insiders’ and incentivises them to keep manipulating Mr Market’s mind. Other ‘neuronal’ areas which are likely to see major ‘haemorrhages’ include (ETFs of) CDOs and other highly leverage securities due to deteriorating credit ratings. While enriching the various dealers and doctors, their monetary and fiscal drugs as well as therapies further distort and numb Mr Market’s mind. And finally, like with any cult, freedom is slowly but surely removed. Following the wipe-out of Credit Suisse’s CoCo bonds in March 2023, investor Louis Vincent Gave considers this in a larger context by connecting the men-of-system of the West with those in the East: Western economies keep on undermining their main comparative advantage, namely, the rule of law and sacrosanct property rights. After all, when China was accepted into the World Trade Organization in 2001, the hope was that as trade grew, China would become more rules-based, democratic and civic rights-minded. Instead, the reverse has occurred, with Western countries following China to permit less free speech and impose more government interventions that include directed bank lending policies. The West embraced stupid Covid restrictions, imposed vaccine mandates and repressed demonstrations of dissent . . . In the battle between “individual rights” and the “common good”, the West could usually be relied on to strongly favor “individual rights”. But can one believe that today? The Credit Suisse take-under shows that, given a chance, policymakers will trample all over “individual rights” in the name of promoting the “common good”. (Gave, 2023, p. 2)

It translates into trading and financial repressions in both the real and financial markets. They constrain, if not prevent, wider discovery, the self-organising principle for a healthy economic mind~body. We identified the cult leaders as the main culprits earlier, men-of-system who include central bankers, financial engineers, systematic traders, and totalitarian regimes. Sadly, we must conclude that Mr Market and his extended family have become zombie addicts who are disconnected from the real world, the world inhabited by ordinary citizens. By tolerating the abuse of markets we abuse ourselves, as we continue to painfully experience. Fortunately we know that brainwashing can be cured and the MMH offers an antidote. While I use its language to make complex issues more intuitive, especially in contrast to the mechanical view, the MMH’s view is not anthropomorphic. Rather it is about recognising Mr Market as a unique, extended 4E mind~body which deserves mindful treatment. So, just like you need your embodied mind to feel, with bodily parts transmitting information, Mr Market needs: – our minds, extended within it, to feel (via freely moving prices) – and our bodies to purposefully act (via free buying and selling). Now, why take this perspective of markets? Specifically, why formalise the concept of the market’s mind and raise it to the next level? There are three main motivations.

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1. Removal of the blind spot Something is lacking in the current paradigm. The market is largely made-up of and meant for us, humans. Even though, as Hayek reminds us, we did not design but “stumbled upon” its pricing system. Although there are arguments that we are, to some extent, machine-like our core self does not operate mechanically, but rather as a complex adaptive system of three tiers that compete, cooperate and show other market-type activities in their internal and external exchanges. Specifically, apart from rational analysis (S2) our evolutionary success relied on intuition (S1). Creativity and non-routine changes, which mechanical economics either ignores or assumes can be automated, culminate in consciousness (S3), the feeling-overlay for S1 and S2. It is likely that the traditional reputation of this final mental frontier has prevented formalising the market’s mind so far. However, this antipathy is more a result of ignorance than of valid arguments, as Knight already concluded in 1925. Others expressed similar sentiment (I particularly refer to comments by Kim and Harman, elsewhere in this book). Fortunately, Tononi and Koch posit that things are rapidly changing. It is in consciousness where discoveries culminate that eventually lead to inventions and innovations which change economies. In consciousness the novelty of insight, as information from the unknown, is dually realised, both physically and phenomenally. Discovery is our endogenous ability to generate internal surprises to deal with the external ones, the sine qua non of all complex adaptive systems. It also leads to mental causation. For example, the valuation of new experiences, phenomenally realising their meaning, literally reshape our brain and body to adapt to and act in the (new) world. That valuation reaches out into the markets of our economic system. 2. Keep surprising The unconscious and the phenomenal are the fringes of the full “experience” loop in general, and the “discovery” loop in particular. They also happen to escape, especially in their collective manifestations, mechanical capture and it only makes evolutionary sense that they will continue to do so. Because nothing would make us more vulnerable than to become predictable, so Mother Nature acted to prevent that.7 Consequently, because discoveries and the knowledge they produce cannot be predicted, neither can the changes they cause in the economic system. In short, it is mind~matter complexity that leads to uncertainty whereby the MMH suggests seeing mind and market alike (via portfolioism), for example by viewing experiences as pay-backs, just like profit and loss: Profit arises out of the inherent, absolute unpredictability of things, out of the sheer brute fact that the results of human activity cannot be anticipated and then only in so far as even a probability calculation in regard to them is impossible and meaningless. (Knight, 1921, p. 311; emphasis added)

 Although I’m not convinced by quantum approaches to consciousness, I have sympathy with those who like to see it as a variation of Heisenberg’s uncertainty principle with, for example, position replaced by matter and momentum by mind.

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3. Need for new tools Future research from the mind~body perspective should not be limited to obvious areas or experiments, such as fMRI scans of traders. For guidance on where to look we can start by considering the role of numbers in the internal and external markets. Stripped to their core, the numerical nature of experiences (just like price dynamics) has both quantitative and qualitative properties. Experiences are specific but produce a mixed return when entangled in our mind~body portfolio. So, although each attribution can be quantified (by visual, auditory, and other readings), the overall portfolio return can only be designated qualitatively, i.e. what it is like to enjoy (or suffer) such return. On that note, I deliberately distinguish between mood and sentiment as forms of ‘market psychology’ because they are different. I argued before that ‘sentiment indicators’ simply fail to convey what the essence of mood is, namely what it feels like to be in it, its interiority. Earlier I gave more arguments for why I think that the main cause for this failure is the exclusive use by mechanical economics of analytical methods and tools. In other words, apart from framing this dimension appropriately in a theoretical sense, we need to develop new methods and tools to practically enhance our understanding of it. The mind~body perspective, with its acknowledgement of consciousness, is the logical apex of behavioural economics. It emphasises that it is the full spectrum of our faculties that, collectively, achieves the allocation of physical and mental capital and gives rise to market states as we experience them. Portfolioism is the MMH’s cognitive economic framework for dealing with mind~matter exchanges and related issues. In general terms, it views a portfolio as an interconnected hierarchical but also dynamic structure. From the bottom-up single assets provide local, specialised returns while, from the top-down, they are integrated in the broader portfolio allocation, affecting diversification, tilts, and so on. Portfolios are exposed to the world whereby exchanges (e.g. trades) are actions of adaptation. Portfolioism states specifically that we should view the mind and body8 as two complementary aspects, like two investments that make up a portfolio. In technical investment terms, the mind~body forms a complex balanced fund-of-funds, with two core funds: one mind-fund and one body-fund which reflexively correlate. Each fund holds multiple smaller mental, respectively bodily portfolios which, in turn, contain many individual mental, respectively bodily assets, which I have called psychurities. The performance of this portfolio not only depends on outside influences (like world events), but also on the exchanges between its many layered components. Moreover, the causes of that performance can, in turn, be material or mental events. The performance is, first, distributed across the portfolio rather than physically located in any one of its holdings. In other words, we won’t find Descartes’ “seat of the soul” in the pineal gland that is held in the body-fund, nor

 Or mind and matter generally.

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will we be able to identify a homunculus in the mind-fund. Second, the performance extends outside because the portfolio forms part of the all-encompassing ‘market portfolio’ of human society (and beyond). Finally, there is no fixed one-to-one correlation between mental and bodily assets, at any of the layers. This is similar to those slippery correlations between financial securities and economic fundamentals, for example. Now, imagine that the MMH becomes accepted, thus changing the paradigm: academia, industry and policymakers recognise the market as our composite mind~body instead of an inanimate machine that stands in isolation. Accepting the MMH has many implications. Key among them is the removal of the separation condition of the mechanical worldview (see Appendix 1). Once you acknowledge consciousness as fundamental and pervasive there is namely (see Harman, or Kirchhoff and Kiverstein) no clear separation between mentalities. It means that issues that are of concern to a free and healthy human mind are suddenly of concern to the market mind, and vice versa. In other words, the collective market mentality includes the social, in terms of human values. This particularly applies to ESG and related issues. To paraphrase Clark and Chalmers: where does individual morality end and market morality begin? Coming back to my earlier comments in the first Economic Note in this book, promoting yourself as a leading ESG investor while stating that: “We are not trying to set the moral compass” (as an ESG fund manager said to the Financial Times) is thus utterly inconsistent, naïve, and unsustainable (pun intended). Adam Smith would be disappointed. It is through collective consciousness, the intersubjectivity consisting of shared experiences, that physical boundaries disappear, particularly those between minds and the wider world. Of course, there will still be parties who would want to manipulate the economic system (perhaps because they continue to believe that it is a machine, or because their authority or salaries depend on it). However, what would happen if our view changed in the manner that Thomas Kuhn describes? Assuming such a change would include the clearer conviction of society’s self-hurt caused by such manipulation, I submit that we will punish them much harsher than we have done over the past few years with the bank bailouts and the FX/Libor scandals. It also raises the bar for central bank policies in that decision makers will feel much more responsible for and sensitive to their actions and will be scrutinised more closely. I suspect they will revisit and likely revise the monetary toolbox. I am well aware that I risk sounding like a broken record player. Still, I cannot emphasise this enough: that what makes us human is—more than anything—our consciousness. We have the ability to be aware of and reflect on experience itself. Consequently, we can appreciate the redness of a tomato, the woody smell of freshly brewed coffee, and the joy of a winning trade. More importantly, that appreciation extends into intersubjectivity, for example with the migration of butterflies between stomachs of lovers culminating in a shared orgasmic climax, the exhilaration shared with other fans when your favourite sports team wins the title, and the intense moods in markets. Consciousness values but is itself invaluable and precious.

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I will now summarise some of the above into the MMH’s, admittedly preliminary, psychophysical principles:9 1. One important question in cognitive science revolves around the self. Is there a notion of self in the MMH, i.e. the market’s self? The answer is twofold. I have argued that—while investors form part of groups—ultimately Mr Market is us, leading to a shared identity. Still, at this stage we have to agree with Hayek, Sornette and others that any market self-awareness likely occurs top-down and at a macro-level that individuals cannot fully perceive. For evolutionary and other reasons it is probably better that way. We should thus think about phenomenology in terms of intersubjectivity, and all we can sense is Mr Market’s moods. 2. As far as prices are concerned the MMH submits that, at any point in time, they reflect an information state that is simultaneously realised materially and mentally. Apart from its quantitative characteristics (e.g. number of bits), such a state has qualities (e.g. economic meaning) to which investors, collectively in the market’s mind, have direct access. Market states, via moods, feel different and investors can make these qualitative distinctions. It is just that they have no knowledge about how they do this. This makes any theory which assumes complete knowledge, like the EMH, incomplete. 3. In terms of the mind’s dual-process system cognitions are formed subliminally or deliberately. They are true or false statements about the world but cannot all be proven. Moreover, whether they are subliminal or deliberate—which is another true/false statement—can only be determined outside the dual process system. For example whether a decision—regardless of whether it is produced by emotion (S1) or rationality (S2)—is correct can only be determined after the fact. In awareness they get valued phenomenally. This subjectivity is self-reflexive which, in Gödelian terms, escapes axiomatic capture. 4. In the external (real) economy money = attention. In the internal (mind~body) economy attention = money. The next chapter will speculate what may happen when the message of this book is dismissed, and we do not mend our ways.

 It will take time to turn them into laws.

Chapter 11 On the Worst Case: Am I Breaking Down? I do think we’re much safer and I hope that [a financial crisis] will not be in our lifetimes and I don’t believe it will be. Janet Yellen (2017) Après moi, le deluge. King Louis XV

11.1 Introduction Sooner or later everyone sits down to a banquet of consequences. Robert Louis Stevenson

In a way we profiled Mr Market during the previous chapters, asking some tough questions in the process as highlighted by the chapter titles. The impression we are left with is concerning. If we do not address it, his state could become worse. We know from history that economic systems can collapse, and we came close on a few recent occasions. What if this and other messages of this book are ignored, with TFTC (Too Few To Care)? What is a possible worst-case scenario of the evolution of markets in light of recent reality checks? In the spirit of the footnote in Subchapter 9.3.1— knowing that music captures our Zeitgeist and that history rhymes—this is my modest attempt to pay homage to Supertramp’s epic Fool’s Overture. On that note, “When the music stops, in terms of liquidity, things will be complicated. But as long as the music is playing, you’ve got to get up and dance. We’re still dancing”. This was the notorious comment by Chuck Prince, then CEO of Citibank, just ahead of its 2008 troubles and that of the market. These are the main characteristics of (most) financial crises: 1. They are seen as ‘black swans’ (e.g. Taleb, 2007) or ‘outliers’: they are rare, both in time and shape. 2. In their bubble build-up, they suggest ‘it’s going to be different this time’ (e.g. Reinhart and Rogoff, 2011): they are mentally projected as an imagined new reality, shaped by the financial economy, that everybody buys into with cheap money. In short, it is a bubble in false promises and misplaced trust. 3. They are causal via: – ‘tail-wagging-the-dog’ effects, including those fuelled by self-reinforcing hedging strategies. These can have a disproportional impact on the real economy, (dualistically) varying from abandoned and derelict factories and homes to suffering by consumers and producers.

https://doi.org/10.1515/9783111215051-012

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‘butterfly effects’—via derivatives in markets and contagion in minds—are non-linear and involve radical and uncertain change, both in terms of chaos and transformation. After all, a butterfly can cause a storm (chaos), yet is born from a caterpillar (transformation).

The reality checks I discussed qualify for such crises. They could have ended much worse. In fact, future reality checks are likely to get worse if economics does not change. Edward Chancellor, Jeremy Grantham, John Hussman, Sandy Nairn, and others have called the latest incarnation the “Everything Bubble” (or used similar terms) and argued that it is bursting. While I obviously have sympathy with their views, and there are some troubling signs, it is still early days. The men-of-system will undoubtedly attempt to kick the can some more. This will likely mean complete repression of discovery, as Russell Napier has argued, to the point where Mr Market ‘can’t take it’ anymore. So, continuing to treat the Markets as these men-of-system have done over the past decades will have dire consequences. It means that the turmoil over the last few years will turn out to have been a bit of a breeze, forewarning that a perfect storm is brewing. But there is a lot of confusion about how this is going to play out, mainly due to our duality. I submit that a bursting of such a bubble will likely (and finally) involve fiat currencies which, so far, have largely escaped the turmoil. In that case, the bursting bubble will become a flood and the core mentalities of confidence and faith that support the monetary system will flush away. Moods will drive this. They will shift and overwhelm to such an extent that they will affect physical stuff in the economic system beyond what we saw in the GFC. At the same time it will debunk the myth of the omniscient men-of-system and their mechanical worldview. At least for a long while. In short, this chapter will be about that final phase when the “Everything Bubble” bursts.

11.2 Of Wetlands and Debtlands As already begun, I will continue to use metaphors and analogies to clarify the main issues. Among them are solvency and liquidity. These are terms with which we describe financial states as if everybody understands them. But because their dynamics primarily involves mind~matter exchanges, many don’t (e.g. Minsky was largely forgotten before the GFC). Here I will focus on liquidity. Draining or increasing it—be it by printing money or by market making—has become mechanised. However, as discussed in Subchapter 6.3 for example, digitisation—as the climax of mechanisation—can have detrimental impacts when money is nothing-but digits and capital can flow in and out with a few automated clicks. Mechanisation has artificially changed the mind~matter balance of the economic system thereby interfering with its spontaneous self-organisation. It increases the potential impact (and thus relevance) of mood even more. Be careful what you wish for, men-of-system. For inspiration on liquidity and other issues we are going to look at nature, specifically climate change which has become so clearly manifested in our weather over

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the past few years. Consequently, analogies with water and man-made impacts on the environment inform my story. In short, by comparing Mother Nature to human nature I will tell a tale about wetlands, debtlands, and caterpillars. Normally, nature determines the boundaries between land and sea. Dunes, riverbeds, and cliffs are examples of natural boundaries. But we humans have not always been content with nature’s laws and decided to move the boundaries or create our own. A case in point: The Netherlands, where I was born, is a country of canals, rivers and wetlands. A significant part is below sea level, exemplified by polders. We reclaimed much of our land from the sea and then had to build protection by way of dikes and dams. Although they were aware of the risk, the Dutch intentionally moved to the polders and built their homes ‘under the water’. Specifically, they trusted the dikes, believed in their design, and had faith in the engineers who built and maintained them. This episode has a rich but also tragic history, made famous by the story of Hans Brinker (Figure 11.1), the boy who plugs a hole in a leaking dike with his finger while yelling for help.

Figure 11.1: Statue of Hans Brinker in Harlingen.1 Source: By Uberprutser—Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid= 33780559

But why was he sounding the alarm? Well, once water starts to seep through a dike it eats away at the fundamentals, the sand and stones that make up the dike. Moreover, one hole is a warning for the dreaded drip effect, suggesting that there are likely others in the making. Once they connect, the structure of the dike is compromised. This risks a devastating flood and Hans was doing his part in ‘saving’ his community. Something similar is happening in the economic landscape. Normally, the real economy occupies the land with physical ‘stuff’ and other fundamentals. Capital, primarily in the form of money, streams in the rivers and seas of the financial economy,

 Harlingen is the birthplace of my father which we used to visit regularly. It’s the key harbour for ferries to the Frisian Islands, for example.

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to evaporate and rain down again for growth on fertile lands. Savings and loans, credits and debits, all meet and flow together, like sweet and salt water. Crucially, the majority of the population wants capital to flow appropriately and freely and to fairly benefit all parties. Finally, like water, being liquid is one of capital’s prime properties. However, a fast-growing region in the global economy has become a cause of concern. These debtlands are like polders where savers and spenders seemingly go about their lives and businesses. But theirs is not a natural ‘business as usual’. It has been artificially created, claimed and maintained. Whatever inhabitants of the debtlands own (a home, a car, a degree, a pension, or an oil-well), it is often structurally ‘under water’. It is safeguarded by monetary dikes and sustained by other ‘waterworks’ like monetary canals, plumbing, and sluices. These form the institutional rules and infrastructure that support the modern monetary system at the centre of which is money creation via fractional reserve banking.2 As we saw, modern money is immaterial and involves fiat currencies. Consequently, monetary dikes are ultimately made up of non-physical constructs which are designed and manufactured by financial engineering. The latter is informed by mechanical economics and delegated to men-of-system. Their designs and constructions ultimately depend on legitimacy, credibility, and trust. Overall, this makes the dikes more like ‘halls of mirrors’. Any cracks are warnings, and any holes need plugging (e.g. by the savers.) Crucially, in terms of how mood affects the economic landscape, please think along the lines of how nature’s elusive forces—via gravity, the seasons, the weather— move land and water. As a consequence, prices fluctuate, making capital (in markets) rise and fall. Financial ripples, waves and tides are formed. Specifically, mood accompanies booms and busts. Booms experience high tides that can eventually burst, flooding the landscape with deflation and default. In such instances they turn into busts which equate to low tides or even droughts. This is what monetary dikes are supposed to protect against, according to its engineers. However, whereas engineering Mother Nature is clearly challenging, it is dangerous and ultimately futile in the case of human nature. The butterfly effect, which culminates in a hurricane, applies to mood in that it is sensitive to small initial disturbances that can turn it into an overwhelming force. Artificially creating land is a fight against nature and its laws. Similarly, artificially creating wealth (the wealth effect) is a fight against economic laws which, in turn, supervene on psychophysical laws. That fight may be ‘politically correct’, but it is not sustainable. It is more than ironic that this was acknowledged by Summers: “there are economic laws like there are physical laws and, as with physical laws, economic laws do not yield to political will” (2015). Still, that hasn’t stopped politicians

 Among the technical elements of our modern monetary waterworks are concepts like loan creation, the real bills doctrine, inside and outside money, and repo and securities lending. I will not discuss these here.

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from trying. In Europe, that means maintaining the union at any cost. In the US it means maintaining superpower status at any cost. And in China it means maintaining party rule at any cost. However, they all have something in common. Not only do they need to keep the debtlanders happy, including newly recruited inhabitants. They also need their support to help maintain the landscape, including plugging holes. They achieve this via ‘politically correct’ fiscal and monetary policies, for example aimed at channelling capital into centrally planned directions (e.g. mortgages) while offering insurance (e.g. central bank put) against damages. Enter climate change.3 Climate change reflects unintended consequences of manmade policies. The main result is a general rise in sea levels, among others putting pressure on the dikes. In the debtlands’ case, man-made policies4 vary from zero/negative interest rates (ZIRP/NIRP) and quantitative easing (QE), to currency interventions and biased regulations. Unintended consequences include leveraged misallocation (e.g. debt-funded buybacks), moral hazard (e.g. bailouts), and uneven playing fields (e.g. high-frequency trading). The pressure is in the form of financial imbalances and instability. Under fiat conditions it is crucial that the dikes are perceived to be safe and, at least as importantly, that the financial engineers of the dikes are perceived to be in control. So, we need to look for holes in the dikes as warnings that this is an illusion and that we face the risk of a proper flood. That is, a mass panic. Arguably, holes have appeared, and we already need a growing number of fingers to plug them: – Inflation; regardless of the level (which recently set multi-decade records in many countries), generally the world experiences the wrong type of inflation, namely under conditions of debt-fuelled growth. This risks stagflation (lite). – Markets; renewed stress in credit, repo and energy markets. – Citizens; populism, strikes, and civil disobedience/protests reflecting dissatisfaction and changing mood. Remember, men-of-system need their tax payments. As indicated, we should particularly watch currencies. While there were several incidents over the past few years (e.g. euro, Swiss franc, and Turkish lira) it was relatively contained. Still, there are other structural issues. Despite the reality of privatised profits and socialised losses, the economic predicament is projected as ‘we are all in this together’. We might call it our Keynesian inspired variation of the Dutch polder model, a socialist consensus framework for governance. One of its pillars is the belief that all liabilities are assets, justifying the ‘creative accounting’ routine to turn liabilities into assets. The engineering part consists of securitisation that turns loans into securities  Apart from its metaphorical use here, climate change, of course, offers a real threat to the economic system.  They can also include social engineering and other policies that have an economic impact, like immigration controls or one-child birth restrictions.

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for investors, as well as central banks. Buying by the latter occurs, for example, via QE whereby a central bank expands its balance sheet by buying bonds as assets. In our analogy we could call them ‘under-water’ land claims. But not all structures are built equally in the debtlands, with those at the periphery mostly at risk. Also, anecdotally we know that recently popular items are of questionable quality and would not stand a chance against a flood. Examples include PIKs, subprime car loans and low-covenant loans. Still, central banks have moved down the credit rating ladder and have increasingly been buying higher-risk debt over the past years. The need to keep pension funds above water is where the role of central bank engineering is most prevalent in our polder model. What would a perfect storm look like with these red flags?

11.3 Watersnoodramp: The Endgame You may know society is doomed when you see that in order to produce, you need to obtain permission from men who produce nothing; when you see that money is flowing to those who deal, not in goods, but in favors; when you see that men get richer by graft and by pull than by work, and your laws don’t protect you against them, but protect them against you; [and] when you see corruption being rewarded and honesty becoming a self-sacrifice. Ayn Rand Atlas Shrugged

Although it had been subject to flooding before, the Netherlands experienced its worst flood disaster in 1953. It became known as the “Watersnoodramp” (Figure 11.2). A perfect storm, when a high spring tide is accompanied by severe winds, broke the dikes.

Figure 11.2: Watersnoodramp Netherlands. Source: https://www.isgeschiedenis.nl/toen/watersnoodramp-van-1953/

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It also affected other countries, including the UK. The US has had its own disasters and recently commemorated Hurricane Katrina which struck in August 2005. So have other countries, including China. The damage from a flood is not only caused by the initial bursting, but also by the aftermath. Normal life grinds to a halt. The land is left barren with crops flushed away. Goods, particularly clean water, are scarce. Prices rise, often to extreme levels. The economic variation of this type of ‘liquidity event’ is a debt deflation followed by stagflation. The debt deflation starts once the debtlanders and the community at large realise that central planning not only fails but is actually damaging, with a ‘shocking’ final fix. We know now that central planning is causal in a negative way because it denies price discovery, the natural bridging between physical fundamentals and psychological capital. In terms of the market’s mind, it leads to a mental breakdown with physical consequences. There are two phases to this breakdown, the first showing the aforementioned cracks and holes. In situations where the ‘powers that be’ are starting to lose that power, they take desperate measures, the kind that ‘move the goal posts’ and jeopardise credibility, trust, and other foundations of their edifice. We have seen plenty of those over the years, including the EU Troika plugging holes while blatantly ignoring referendums. Another example is Japan introducing a new CPI measure which would make it easier for the BOJ to achieve its inflation target. And now we have a Swiss (yes, Swiss) bank’s CoCo bonds being flushed away. Holes start off as cracks. The cracks have been plastered over for a while, and the observed holes have not yet connected. Men-of-system will also make all attempts to keep the storm at bay. Their loss of control will be part of the early signs of panic, but we have not seen that sufficiently yet. Still, due to their intrinsic nature—namely going against Mother Nature— dikes are vulnerable. The trigger will occur when mood’s butterfly effect reaches its final state. It means, for example, that the savers (can or will) no longer plug the holes. This state also painfully exhibits the limitations of monetary policies. First, liquidity depends on money creation. The latter primarily occurs when the private sector has confidence and takes out loans that get recycled as deposits, i.e. money. The relatively high uncertainty in an economic environment characterised by high debt, low growth, political polarisation, and increasing interest rates means that appetite for risk is muted. With relatively high levels of debt that require servicing, this becomes risky itself from a liquidity perspective. Second, and related to the first point, the pursued policies are causing a shift in mood surrounding money and debt. This started already to change when we had low interest rates. Although one could borrow cheaply, NIRP/ZIRP means that depositing funds even if temporarily gets punished. So, when holders of cash and money-like debt no longer see them as fungible ‘assets’ and start to treat them differently, it causes shifts in capital flows to which currencies are particularly vulnerable. Third, open market operations have resulted in too unevenly distributed, that is concentrated, holdings of (public) liabilities. The JGB holdings by the BOJ are a

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prime example. As a result, the (patched-up) weaknesses in the coastal defences become exposed. The holes will multiply and grow. Which section is most vulnerable? Among the historically grown imbalances are the bailouts which were politically preferred to defaults and restructuring.5 The emerging economic conditions put pressure on cash flows and will ultimately lead to bankruptcies, particularly of ‘zombie’ debtlanders. That will increase the (credit) risk premia, even for those former ‘safe haven’ sovereign bonds. Combined with other, increasingly violent, capital flows, this will then instigate the panic mode. The panic will take hold once the dikes break which affect the inhabitants most closely located to or working on the waterworks (e.g. banks, insurers, real estate, and other leveraged debtlanders). Banks underperformance, let alone failures like SVB, First Republic, and Credit Suisse, are thus another warning signal. Men-of-system will then resort to fiscal expansion with debt monetization, capital controls, transaction taxes, and further financial repression. We are already seeing some of this now, including stagflation-lite. As history does not repeat but rhymes, current circumstances are not the same as the nineteen-seventies. Stagflation-lite is thus not characterised by the traditional high level of the so-called “Misery Index” which is simply the sum of inflation and unemployment. Rather it reflects a slow economy with such a level of inflation that it pushes real growth and real returns into negative territory which, in turn, threatens cash flows relative to interest payments (especially if rates have gone up). For investors it has implications for how to structure portfolios, as historic correlations and other relations between assets break down. Men-of-system will reach the point that their implicit admission of guilt, i.e. ‘we have been part of the problem all along and we’re now resorting to the final fix’, can no longer be ignored. In short, the final fix by the men-of-system will lead to a drought in private capital, increased risk premia, and currency crises. It will devastate everybody (so any totalitarian strong man should wipe that smirk off his face).

11.4 Painful Lessons The reader will ask, quite rightly, whether anything good came out of the Watersnoodramp? Well, better designed dikes, for one. This culminated, for example, in the Deltawerken (Delta Works), a series of multi-decade construction projects in the southwest of the Netherlands. But there were more important intangible benefits as well, especially

 Although painful, there remain misplaced fears for such an outcome. In an updated 2014 paper, Reinhart and Trebesch analysed the impact of debt relief achieved through default and restructuring. They concluded: “The economic landscape after a final debt reduction is characterized by higher income levels and growth, lower debt servicing burdens and lower government debt. Also ratings recover markedly, albeit only in the modern period”.

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the return of a healthy respect for the forces of nature, including allowing those forces to operate naturally on the Dutch landscape and waterways. In a similar vein, that is what the economic system requires. We need our version of King Canute to show the men-of-system that ultimately there are limitations and consequences to controlling tides and waves mechanically. Their overconfidence combined with a lack of respect for the delicate (and, yes, spontaneous) market dynamics, is what jeopardises our economic foundations. Thinking about migration crises, certain regions in the economic landscape have become deserted, whereas others have become overcrowded. Waterways overflow, get drained or run dry. We observed this recently in bonds: first the men-of-system forced value investors out by lowering rates to the zero bound (and below). Next, they indicated that they would force momentum investors out by pushing inflation higher. When that eventually exceeds their comfort levels, they slam on the brakes with 75 bps hikes. Similar mass movements were engineered in other asset classes and investment methods, including the migration to mindless (i.e. price-insensitive) passive investing and LDI. Finally, their regulations facilitated electronic trading which is now an oligopoly of a few HFT firms. Again, engineering begets mechanisation and vice versa. Eventually there will be ‘no body’ left. Financial engineering has weakened the market’s body and manipulation has confused its mind. To replace the market by private-public monopolies is part of the final fix in this engineering ‘feat’. Some time ago, the journal Portfolio Institutional suggested that volatility spikes “raise serious concerns about whether or not market participants really understand the liquidity environment”. To address this within our analogy setting, we first need to be clear about the role of beliefs and (experiential) knowledge in rational behaviour (see also Chapter 4). Our analogy focuses on the association between water and liquidity and how it extends to capital. There are multiple dimensions to understanding water. ‘Water = H2O’ is an example of what philosophers call posteriori necessity, a physical fact. Physical facts inform our beliefs at the cognitive or psychological level. However, philosophy has also made strong arguments against identity physicalism, the view that every physical fact is fully identical to the associated mental fact. Specifically, the physical fact of water = H2O is not identical to the phenomenal (known) fact that water feels wet (and then only in its liquid form). So the belief that water = H2O contains both a physical and a phenomenal aspect. Per the central 4E cognition theme of this book, this is extended: one may need to add a relational element, to account for the fact that certain beliefs may depend on the state of the environment as well as the internal state of the thinker. It has been argued, for example, that to believe that water is wet, a subject must be related in an appropriate way to water in the environment. This relation is usually taken to be a causal relation . . . where the causal roles stretch outside the head into the environment. (Chalmers, 1996, p. 21)

Market mood infuses these mental aspects in our intersubjective environment. It acts on capital like the elusive forces of nature that can move water, turn it from liquid to

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solid form, or let it evaporate. The consensus on market liquidity is that it is the condition where a real asset can be painlessly exchanged for capital, i.e. money. This physical fact, ‘real asset = capital’, does however not fully describe how market liquidity feels like. That experience is crucial to understand liquidity, just like you need to feel the water getting too hot or too cold to know that it is time to get out. If we include the condition that market liquidity means that you can exchange your asset without affecting the price, then—in reference to Warren Buffett’s famous quip—you need a sense of when the tide is coming in or is going out, which will show who has been swimming naked. So the belief that capital is liquid, e.g. that it is ‘freely floating’, involves sensations as part of our natural mentality. As I have argued, this phenomenal dimension is essential in price discovery. Sensations complete the market mind’s holism, the interdependence of the various aspects of its mentality. Unfortunately, abusing markets and turning them into mechanical automatons has repressed the natural sensations of freely floating capital, for example, the associated natural fear that too much of it can make you drown. Others, like the pains from creative destruction, have been anaesthetised or otherwise medicated away. More broadly, we lost the sense of what it means to be in markets because they lost much of their purpose. The perfect storm and the subsequent “deluge” are thus not triggered by some physical economic event, not even multiple ones. They are triggered by the mental breakdown, the sudden realisation of the inconsistencies in the economic worldview, the current belief system that designed the waterworks. It is captured in the age-old saying that you cannot have your cake and eat it too. It is also the culmination of our butterfly effect. To conclude, whatever you think is our caterpillar, it turned into a Death’s-head butterfly. And it flapped its wings a long time ago. The resulting perfect storm is brewing but it is currently still offshore. However, water has started to seep through the dikes and Hans Brinker has been yelling for a while now. Who is listening? Like nature’s laws, the psychophysical laws of economics will eventually prevail. Water will find its own way and the debtlands will see their inevitable cleansing.6 Sometimes floods are the only (hard) way to learn and all we can do is accept and deal with the consequences. On that note, water can also be a creative force of destruction which will enable us to rebuild and improve the economic landscape. Perhaps we should then start with a mind~body perspective of the economy and markets, and treat them in organic rather than mechanical terms? Now that is a different caterpillar all together. Finally, the worst case may not be the most likely case. So, the Investment Note (Conflicting Beliefs on “The Worst Case”) nuances it.

 To extend the metaphor, it could, of course, turn out to be a wintery storm with record low temperatures that will freeze up the sea, rivers and canals. Even then, it will pressure and likely break the faulty designed waterworks.

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Investment Note Conflicting Beliefs on ‘The Worst Case’ That mechanical economics is not only wrong but also becoming expensive, is a red thread running through this book. It is not just that some of its practices distort prices, leading to costly misallocation. The broader distortion, by way of prescribing a machine-like economic system, leads to a reality mismatch. It makes no sense to the dualist view through which most people perceive their reality. In other words, if the balance between mind and matter becomes unhinged, values (in the broadest sense) become distorted. In the economic domain, money is particularly vulnerable to this. Thus my warning that the fiat currency system will fall in any endgame of what others have called the Everything Bubble. In the interim, many readers will wonder how the regularly observed excessive divergences between the real economy (Main Street) and the financial economy (Wall Street) can exist, particularly with the debt overhang as a Sword of Damocles. Let’s assume that there will be no recovery in the former and that ‘reality’ will set in. That is, the fundamentals of the real economy will not improve and eventually will force the expectations in the financial economy to converge. Let’s call this a reset. How likely is it, and when will it occur? I don’t have an answer, but I will offer my suspicion. Maintaining this status quo is the battle between two separate pairs of beliefs which are roughly defined as follows: 1. ‘we cannot fool ourselves all of the time’ + ‘history rhymes’, 2. ‘it’s called ‘political economics’ for a reason: financial repression works’ and ‘it’s different this time’. Although we cannot dissect them fully in epistemological terms, like we did in Chapter 4, the first set of beliefs implies a disbelief in central planning which, in turn, has major implications for the trust in the monetary system, with historic evidence of previous economic collapses. The second set of beliefs basically implies that the economic mind and body can sustainably exist and operate in different worlds, facilitated by the now-familiar monetary drugs and therapies. This would suggest that the health profile of the economic system I have presented in this book may be correct but is not worrying, and that we can ignore historic evidence. Alternatively, of course, the financial economy could hold the true belief in that the real economy will come out of the doldrums. In other words, Mr Market’s discounting, and so on would in that case have worked again (with a bit of help). It would also mean that I am wrong in my assessment of his health. What this battle boils down to, particularly due to the much higher (debt) stakes, is increasingly binary: it seems ‘rational’—in a somewhat cynical way—to bet that men-of-system will be able to control this because everything will just turn null-and-void if they won’t (i.e. there is no point betting they will fail). In other words, a reset will not occur because it is in nobody’s interest. This suggests an expectation for a continuation of the divergence. Still, my worry is that growing population of butterflies out there, furiously flapping their little wings in a climate that has only gotten worse since the GFC.

Chapter 12 On Closure: Farewell and Good Luck The natural device for squeezing as much unacknowledged ideology as possible out of the subject is open professional criticism. Obviously, then, one must protect and encourage radical critics. Robert Solow

12.1 Parting Words It is one thing to show a man that he is in an error, and another to put him in possession of the truth. John Locke Remember, all I’m offering is the truth, nothing more. Morpheus (in The Matrix)

If mechanical economics is the answer, then our problems show we have been asking the wrong questions. I have tried to correct this by raising and answering the right questions in the titles of these chapters. Among my key answers is that mechanical economics, motivated by physics envy, cannot explain the psychophysical nature of market dynamics because it ignores that the minds of economic agents are conscious. According to mechanical economics, spatiotemporal embodiment, enactment, embeddedness, and extension play no role in constituting the agents in markets. Specifically, the key physical constitutions of markets are not computers but embodied brains which enable agents to experience markets. Such experiences—by being in the market—are deeply intense, multi-temporal, while unified (just like the returns in their portfolios). Mechanical analysis fails to capture this in a meaningful way. Its models—mostly via static regressions—only offer partial computational explanations based on given moments, i.e. via snapshots. These subsequently shape policies, strategies, and other practices. No wonder our economic challenges are mounting. It’s not simply a question of ‘the map is not the territory’. Rather, a map is used for something that isn’t a territory. Keeping reflections by Kant, Von Weizsäcker, and others in mind, claiming—like the MMH— that the economic system is an extension of us seems more ontologically modest than claiming it is a machine (which is different from us). As I said, mechanical economics makes an ontological error, a commitment that is becoming very expensive: We cannot impose any worldview we like and hope that it will work. The cycle of perception and action cannot be maintained in a totally arbitrary fashion unless we collude to suppress the things we do not wish to see while, at the same time, trying to maintain, at all costs, the things that we desire most in our image of the world. Clearly the cost of supporting such a false vision of reality must eventually be paid . . . [In general] the well-being of society is intimately connected with the particular worldview it happens to hold. (Bohm and Peat, 1987, p. 57)

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Underlining that the revision of economics remains topical in this tercentenary of Smith’s birth, Nobel laureate Sir Angus Deaton concluded in his 2022 Christmas reflection for the new year: We economists often assume a mantle of policy expertise for which we have no qualification, with predictably disastrous outcomes . . . is there a deep flaw in economics that continuously leads its practitioners astray? I tend to favour the latter hypothesis . . . above all, we need to spend more time with philosophers, recapturing the intellectual territory that used to be central to economics. (Deaton, 2022)

While Sir Angus may not agree with it, such new enlightenment is what the MMH is all about. Inspired by pioneers in cognitive economics like Smith, Hayek, Knight, Simmel, and Soddy our research continues with “recapturing the intellectual territory that used to be central to economics” in collaboration with leading philosophers, other cognitive scientists and enlightened economic/investment experts. Key is the recognition that something is amiss and LTCM, Lehman and the Repocalypse were wake-up calls. The question is what type of waking up followed? Many suggest we should see the Keynesian inspired rescues by men-of-system as a fairy tale, like the sleeping beauty waking up after the prince’s kiss with both living happily ever after in a new centrally planned paradise. Not surprisingly, I have a different view which I will explain by referencing the movie The Matrix: we should rather see it as a collective version of Neo waking up. With this book I am offering you the red pill of the MMH. You consequently shockingly witness what ‘the machines’ and their controllers have turned Mr Market into, manipulating the minds and abusing the bodies of the collective economic system. In short, the market is a collective mind~body but is being mistreated as an automaton, brain-washed if you will, to benefit those who view themselves as economics’ deus ex machina. That is the true lesson of the crises, our “Welcome to Reality” moment, to quote Morpheus again. Although no fairy tale, this lesson is actually hopeful because we have correctly identified Sir Angus’s “deep flaw”, namely economics’ mechanical worldview which denies its version of philosophy’s mind~body problem. Like Neo, we can now start doing something about it, including its consequences, especially the (biased) machine outsourcing which crowds out human awareness. On the other hand, such a lesson is not easy to learn, if only because we have to largely unlearn what mechanical economics has taught us. In fact, it has become even harder from a practical point of view in light of the ongoing mechanisation in the economic system—including AI, HFT, and passive investing. Worryingly, this trend is almost uncritically embraced because of special interest groups as well as a misplaced automation bias. Instead, the complex mind~matter issues it involves, including ethics and morality, should be robustly scrutinised in a proper cognitive setting. This book is just the start. Together with other researchers I hope, over time, to “show you how deep the rabbit hole goes” (another Morpheus quote). Or, taking a less dystopian tone with the prophetic words of Clark, “the frontiers of . . . economics may turn out to border rather closely on those of cognitive psychology, cognitive science,

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and the theory of complex nonlinear systems and neural networks. There is clearly much to learn. Perhaps we should learn it together” (Clark, 1996, p. 288). On that note, let me reinforce the important messages from the market to cognitive science. First, in reference to earlier quotes from Hayek, Kim, and Knight, the most powerful qualia in markets—in terms of influence—are shared and sensed as market mood. As the ‘financial Armageddon’ implications of LTCM, Lehman and Repocalypse showed, they are of life-or-death importance for society too. Second, prices form Chalmers’ information that is dually realised collectively. Furthermore, markets solve his “lack of data”: massive amounts of empirical market data are waiting to be explored further from a mind~matter perspective (rather than, so far, from the machine perspective). Clark’s suggestion also nicely completes the circle back to David Foster Wallace’s words at the beginning of this book (see the Background and Motivation section). Both highlight the importance of education and learning. This is why this book is specifically intended for the younger generation of readers, with the MMH aspiring to contribute to a new, cognitively inspired agenda for economics and investing. In a way it is my own (admittedly long-winded) version of a commencement speech for students entering those worlds. In that sense, the question asked by Wallace’s older fish to the two youngsters (“How’s the water?”) is crucial beyond the metaphor, as touched on in the previous chapter. In Wallace’s own words:1 “The immediate point of the fish story is that the most obvious, ubiquitous, important realities are often the ones that are the hardest to see and talk about. Stated as an English sentence, of course, this is just a banal platitude but the fact is that, in the day-to-day trenches of [e.g. investor] existence, banal platitudes can have life-or-death importance”. For investors, market consciousness is that reality, but it is hard to see and talk about because, apart from ‘awareness’, it is all about ‘what it is like’ and ‘how it feels’. We nonchalantly refer to its main manifestation as ‘market mood’. However just like water is not simply H2O (but rather, feels wet) market mood is not simply VIX. What it feels like, particularly whether Mr Market feels trustworthy, determines our participation and is thus the true liquidity. In the spirit of Vernon Smith’s vivid quote on the pricing system (see Appendix 1), I would even say that the discovery of trustworthy prices is an essential part of human development. Market mood is at the centre of economics’ hard problem and dealing with it is of life-or-death importance. In 2008, Mr Market behaved exactly as we would expect a conscious being to behave when confronting an existential threat. In that Minsky moment, to paraphrase Walt Kelly, we met Mr Market and he is us: collectively embodied and fully mental, warts and all. Specifically, we experienced when he almost lost consciousness, that is, what I have called, “Lehman’s Lesson”.  Combined with Wallace’s earlier comment, some readers will recognise the similarity with Michel Foucault’s sentiment on the role of philosophy: “to make visible precisely what is visible, that is, to make evident what is so close, so immediate, so intimately linked to us, that because of that we do not perceive it. Whereas the role of science is to reveal what we do not see, the role of philosophy is to let us see what we see (Foucault, 1994, pp. 540–541). In turn, this reaches back to Heidegger.

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So, what is this lesson? In Heideggerian terms, our existential awareness (in this case of financial Armageddon) gives rise to anxiety. This is an important natural response. Experienced as existential mood by investors, anxiety is a fundamental consequence of ‘Being-[in-]the-market’. We should embrace this as well as more broadly the radical uncertainty of our world. We can then gain a deeper understanding of ourselves and the world, especially regarding economic survival by way of discovery~invention. Mr Market holds up a mirror in that regard. Anxiety about his state confronts us, in a volatile inyour-face way, with our own existence and the limits of our control. It therefore opens— in that collective setting—the possibility for what Heidegger calls “authentic” selfreflection and a more meaningful engagement with economic life. In contrast, financial engineering and repression create Heidegger’s “inauthentic” mode of market existence, a pretend mode that often characterizes central planning. Long term exposure to such an inauthentic mode is mentally damaging, again especially to discovery. Creative destruction, for example, can break through such pretence. Moreover, in embracing anxiety individual agents are compelled to confront their own economic existence and take responsibility for their choices and actions, which is required to remove moral hazard. What this teaches us is that mechanisation is dangerous because we are not dealing with an automaton. That is to say, mechanical economics and the resulting removal of humans and their mentality from the investment process is a form of dehumanisation. It is dangerous as it diminishes market consciousness—the source of our sensemaking—thus drying up true liquidity. Mechanical economics promotes ‘consciousness blockers’ and we are progressively being deprived of price qualia in one sense modality after another. By numbing or otherwise not allowing (exposure to) the shared experiences they convey, even if painful, we risk not internalising Lehman’s Lesson. For example, the pain of loss is eased by a central bank put. The feeling of fairness evaporates due to bailouts. The weight of responsibility is unloaded as trading decisions are outsourced to algorithms. And the sense of safety is removed when risk-free bonds become bail-in diktats. It also distances us further from meaningful wider economic discoveries whereby it risks distorting the narratives and other socio-cognitive forms of connecting which enrich our sensemaking. The sentient fabric of humans is a crucial but vulnerable part of the scaffolding of the market’s mind. Removing it and, by extension, the “mystery” of price discovery will be detrimental to its health and our wealth. Unlike objects in nature, markets do not exist ‘when nobody is looking’.2 That is, prices are meaningless when humans are not conscious of them. Mr Market will have left the building; there is nobody home anymore. It is more than ironic that among the brilliant physicists that are envied by mechanical economists some were closet philosophers, including Bohm, Heisenberg, Pauli, and Schrödinger. Showing modesty in their writings, they nevertheless contrib-

 This is a slight variation on the classic thought experiment of the falling tree that makes no sound when nobody is listening.

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uted impressive reflections on mind and matter, often questioning physicalism. Perhaps we should take a page from their book. To paraphrase Einstein, the market may not be “a magnificent structure”, but one thing is for sure: we can only comprehend it very imperfectly, “and that must fill a thinking person with a feeling of humility”. We can start learning by making mind~matter issues in the economic system explicit, including related issues like a renewal of morals, honouring Smith. This would require asking the right but difficult questions which, as Einstein suggested,3 should take up more than 90% of our time. But for mainstream that leaves too little for modelling. Moreover, mechanical behavioural economics keeps reminding us that our “defaultsetting”, to use Wallace’s term, is flawed. Some have argued that because of this we need to further mechanise investing by outsourcing to ‘objective’ machines and AI. As I hope I have made clear, this does not help in accepting reality that, for most of us, appears dualistic. Far from it, it is more a form of denial. By pointing to our ongoing challenges—like increased levels of debt, TBTS banks, TBTC tech firms, and growing inequality—people complain that nothing much has changed since Lehman. A cynical reply—also to Summer’s comment on page XXVII— is that that is what happens when you refuse to change your thinking. That is also what you get if you deny reality and continue to treat our economic mind~body like a machine. Some will say that we avoided financial Armageddon because of the boldness of policymakers and their policies. As Einstein suggests, we should be humbler. Humans have a tendency to believe those who say they know in uncertain times and subscribe to their policies that capture that ‘knowledge’. This, temporarily, builds a feeble trust in those policymakers and policies. This book has argued that our incomplete knowledge results from a gap in the foundations of our explanations. We escaped financial Armageddon through luck, rather than wisdom. The latter is required longer term (as Chapter 4 argued). To repeat, unlike objects in nature, financial markets do not exist ‘when nobody is looking’. Financial markets exist because the collective physical and cognitive processing of our exchanges spawns a phenomenal overlay which completes their states. This realisation, committed to as a mind~body, enables us to identify questions and answers along the lines of ontology, epistemology and methodology. Consequently this clarifies our debate, which often suffers from category mistakes and other fallacies. Moreover, it judges and frames fashionable topics such as monetary policies, regulations, ethics, AI, and high-frequency trading in the proper format. For example, it has become human nature to control Mother Nature and financial markets play an increasing role in that endeavour. Longer term, it also suggests looking at this from a historic perspective. The development of modern markets covers less than one basis point of the evolution of human minds. Still, motivated by recent events, we increasingly rely on mechanistic approaches

 “If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions”.

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such as HFT, LDI, and VaR, to deal with our world when, looking back over the full sample of human events, a balanced approach seems more valid. Specifically, our imagination and creativity in discovery were crucial in spawning the intuitions and insights that inspired our knowledge and contributed to our evolutionary success in, what always has been, a complex, rather than mechanistic world. Talk about survivorship bias! Some readers will wonder, exactly because of the growing automation, whether the message of this book is only relevant in a normative sense. The answer, obviously, is that it will depend on ‘the data’ on how mechanisation is developing (measured, for example, in the market share taken in their respective domains by passive investing, automated trades, and other mechanical strategies). Due to legal and ethical constraints, I personally believe that humans will continue to have a final say in trading decisions, including those generated by algorithms. We do not live in a world where decisionmakers can fully delegate responsibility to machines. ‘The machine ruined your portfolio’ would then be investment’s version of ‘The dog ate my homework’, and just as unacceptable, including in court. Then again, this might be wishful thinking and I could be wrong. Unbridled mechanisation of the investment process could become another angle to the end game discussed in Chapter 11. You think a digital bank run or a flashcrash is scary? Wait until you see the market’s Singularity or Inversion. In any case, none of this prevents the current imbalances from growing. If they do and subsequently cause the detrimental effects I have described, then I guess the normative message will be confirmed empirically. With these caveats in mind I have a few suggestions for politicians and policy makers. Political Note Suggestions to Policy Makers I will only discuss the following suggestions briefly here, but they all deserve chapters (if not books) of their own. All suggestions are in parallel with (or assumed to follow) the recovery of Mr Market and our trust in him: 1. Hire a 4E cognitive scientist. Whether it is a neuroscientist, philosopher or other cognitive expert, they can help you view issues and problems in mind~matter terms, to complement any economic, humanitarian, political, and/or related views. Chapters 1 and 2 discussed the evolutionary origins of mind~matter exchanges and how we are conditioned to view life in metaphysical (e.g. dualist) terms. Almost all of us (subconsciously) use it as our prime filter. Portfolioism should further help in that regard. 2. Educate early and properly. Consistent with my arguments about both individual responsibility and the importance of (a diversified portfolio of) discovery throughout society, I propose another call to arms: give investing back to the people. Not via discount brokers who sell orders to HFT sharks but via education, something Didasko focusses on, for example. Whether we like it or not, we live in an economic jungle. This doesn’t mean people need to be ‘nudged’ into a cave ‘for their own protection’ with government blankets ‘for their comfort’. Rather they need to be educated (dare I say drilled?) to survive. Financial education is woefully insufficient and transforming it is long overdue. Start with ‘Cognitive Economics 101ʹ in primary school. Disintermediate and de-monopolise the actual investment processes, while strengthening the institutionalisation (culture, education, laws, norms) that facilitate people to take back their investments and manage it themselves. This would still require independent

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advisors but would dissolve the concentration in the asset management industry. Finally, and no surprise here, the lack of economic understanding among the general public is also a consequence of the flawed and deliberately complicated theory that does not make sense to ordinary humans. More mechanisation will just remove it further. 3. Protecting price discovery at the centre of regulation: economic coordination needs competition and cooperation. It means finding the right balance between freedom and regulation, between individual initiative and institutional control. If Hayek and Keynes are the traditional polar points of this discussion, Frank Knight describes the middle ground. A focus should be safeguarding the conditions that allow discovery to take place across the domains of the real and financial economies, along the path from technological breakthroughs to their valuation. 4. Transparency is in, manipulation is out: some markets have asymmetric information, e.g. insurance, structured products, etc. It means that there is no full transparency (i.e. data is missing), for example because of (legitimate) privacy reasons. A natural solution is to operate with multi-tier pricing, dependent on the level of disclosure. However, it is then crucial to make these different prices available, linked to the required disclosure. 5. Responsibility: I refer to my iatrogenesis comments earlier. If the economic oath is to do no harm to the economic mind~body, the flip side for all market participants should be volenti non fit injuria. Freely translated, once you voluntarily decide to participate in markets you must accept full responsibility for your decisions and not ask for a bailout or submit a claim against another party (except in cases of criminal acts, like fraud). 6. Fair legal playing field: see legal frameworks in terms of fair exchanges. This largely means that no ‘risk-free’ legal/regulatory arbitrage is possible. In other words, prevent parties to abuse your legal/regulatory system by raising the penalties when they do. This particularly applies to parties whose own legal/regulatory system is less robust and transparent, for example because it is blatantly biased against foreign parties. 7. IP temps: view patents as temporary monopolies of knowledge that impair discovery. Regulate and tax them in exponential terms, i.e. the longer their duration and/or the more connected to other patents, the more the underlying products/services are taxed. (There are unfortunately too many examples of products/services that are completely fortified by ‘patent estates’). 8. HFT tax: any transaction tax should not be fixed but fairly and flexibly linked to the level of activity (= # trades/time). The more trades the higher the tax level, e.g. use tax rebates when liquidity is low. 9. Capital gains tax: link it to (relative) level of markets and distribution of beneficiaries in order to avoid/correct a concentrated wealth effect. 10. Economic transmission rule: assuming the main purpose of the financial economy is to support the real economy, set taxes and interest rates based on correlates between the real economy and the financial economy. For example, taxes on the financial economy could be increased if markets are booming while the real economy is struggling, and vice versa. 11. ESG: see my earlier comments in the Economic Note “The Spirit of ESG” in the Introduction.

The opening quotes of this closing chapter refer to truth. This gets us to Schopenhauer who identified three stages for it. To paraphrase him (and others with similar observations), a heterodox theory is first ignored, then criticised, and eventually considered

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self-evident (and known all along by former critics as their idea). Dragging many readers out of their comfort zones, this book is likely to receive criticism and opposition. We welcome this, as it would mean a promotion from the initial stage of being ignored and denied. As far as critique is concerned, I realise some readers will disappointingly conclude that this book is further evidence that economics “limps along with one foot in untested hypotheses and the other in untestable slogans” (Robinson, 1962, p. 26). Others will criticise the MMH for its lack of falsifiability. In my defence I invoke the words of Hayek (who was a friend of Popper): while it is certainly desirable to make our theories as falsifiable as possible, we must also push forward into fields where, as we advance, the degree of falsifiability necessarily decreases. This is the price we have to pay for an advance into the field of complex phenomena. (Hayek 1967, p. 29)

Zhuangzi tells us that those who realise their folly are not true fools. I am the first to admit, in that regard, that the MMH is not only heterodox but incomplete. You may not agree with everything I have written; in which case I hope you will constructively contribute to the debate. However, what you cannot do is continue your old way of thinking after what has happened and the resulting ongoing plight. Nevertheless some remain in denial. This may simply be due to their incentives. As Upton Sinclair pointed out, “it is difficult to get a man to understand something when his salary depends upon his not understanding it”. Others are blunter in their ignorance. I once gave an introductory MMH-presentation to a couple of senior colleagues who essentially told me that they didn’t care about any of it, and that investing was only about “making money”. I would call that attitude irresponsible investing. Punters. Don’t be like them. I want to end on a positive note. Driven by cognitive research we are making significant progress in better understanding our mind~bodies. A key insight, also promoted by the MMH, is to recognise the benefits of bridging the (perceived) separation between mind and matter, rather than emphasising it. Remember how Clark and Chalmers beautifully answered their own profound question of “Where does the mind stop and the rest of the world begin?” Consciousness theories show that mind and matter are complementary, with blurry boundaries. They emphasise not only the need for, but also the reality of, connection, interdependence, and sharing. In the final analysis, this reaffirms the importance of free markets, trade, transparency and, ultimately, price discovery. That is also the guidance with which we will find solutions to our economic problems. I hope you have enjoyed this book and have learned something new. You decide where you stand on the issues raised here. I particularly hope that if your (grand)children ask you in due time “When did you realise?” you will reply “When I read The Market Mind Hypothesis”. And when they follow up with “What did you do next?” you tell them “I did my bit to support the MMH and put it into practice”. Your legacy then becomes mine, so thank you.

Afterword: The Market Mind Hypothesis and 4E Cognitive Science: A Post-Cognitivist Approach to Cognitive Economics Cognitive Economics is an emerging field of study that seeks to combine economics with cognitive science. The ambition of this field is for economics to learn from models of information processing in individuals, while cognitive science can learn from economic models of information processing in collective, distributed cognitive systems (see Chater, 2015; Johnson, 2019). The contribution of cognitive science to this research programme is standardly taken to be the modelling and explaining of computational processes taking place inside of the heads of individuals. Patrick Schotanus’s Market Mind Hypothesis (MMH) offers a fascinating alternative to what I will call this “cognitivist” understanding of the economic system. His MMH draws on a new paradigm in the cognitive sciences, that first began to take shape in the 1990s, which understands the human mind as embodied, embedded, extended and enacted. My aim in this Afterword is to briefly re-describe some of the themes Schotanus draws on from what has come to be referred to as “4E cognitive science” (Rowlands, 2010; Gallagher, 2017; Newen et al., 2018). I will argue that the attraction of the MMH lies in its offering a post-cognitivist perspective on what a synthesis of economics and cognitive science could look like. 4E cognitive science proposes that a science of cognition should start from principles of biological organisation, explaining cognition in terms of its embodiment in moving and sensing animals, situated in a world of meaningful affordances or action possibilities. 4E cognitive science is premised on what Clark (2001) has called a “biological incrementalism” according to which human thought and rationality are the result of small, incremental tweaks to more basic capacities for perception and action. In neurobiological terms these capacities for perception and action work through multiple coordinated neural systems that carry out fast pattern completion, along similar lines to the large language models currently taking the world by storm. 4E theorists claim that what sets human intelligence apart from other biological systems is the niche humans have constructed for themselves over the relatively short period of human history. Humans have created and maintained environments rich with symbolic, social and institutional structure. Our human minds develop cognitive structure that work in ways that delicately complement and coordinate with the social, cultural and technological environments we wrap around ourselves. Brain processes thus develop functional capacities that are tightly constrained by distributed webs of linguistic, social, cultural and technological scaffolding. The MMH explores the implications of this paradigm shift in cognitive science for economics. 4E cognitive science predicts for instance that economic models should work best when choice is constrained and limited by the larger social and institutional structures within which an individual economic agent is embedded. For instance, busihttps://doi.org/10.1515/9783111215051-014

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nesses, as large-scale organisations, make decisions with the aim of maximising profit. It is this fact about businesses as complex social and cultural organisations that explains why the models of classical economics are somewhat successful at predicting the behaviour of economic agents, and not facts about the workings of the minds of individuals. Collective institutions behave somewhat as if they were rational economic agents. It is the behaviour of these institutions, and not of individuals, that classical economics sometimes succeeds in modelling. The MMH further agrees with the enactive strand of 4E cognitive science in rejecting a dualistic distinction between the mind as object of scientific study and the mind as subjectively experienced (Varela et al., 1991). In cognitive economics, the human mind is often understood by analogy with the workings of digital computers that perform unconscious computations—rule-governed operations on internal mental representations. A computational conception of the human mind encounters the problem of how to make room for the fact that humans (and other animals) undergo first-person conscious, subjective experiences. This is also a problem for both behavioural and cognitive economics. Traditionally, cognitive economics, insofar as it has been premised on cognitivist assumptions, has taken over this computational understanding of economic agency. Economic agents are modelled as making decisions and choices through the mechanical, algorithmic like application of rules. Cognitive economics has ignored or deliberately left out that consumers, investors and other economic agents are subjects of conscious experience. There is something it is like for agents to live through economic cycles of boom and bust. MMH argues that what makes cognitivist models of economies of limited value is precisely that they fail to take into account consciousness in markets. The MMH identifies a dualism inherent in economic systems that is often overlooked between the real material economy of goods and services and the psychological economy of financial markets. Here, I suggest, a comparison with the phenomenological distinction between the lived body (Leib) and the living body (Körper) may prove instructive. The real economy in the MMH can be compared to a living organic body that is more or less healthy. The health and vitality of the real economy depends on its interaction with financial markets. Perverse incentives, gross inequalities and lack of innovation can, for instance, make the economy as a living body unhealthy and diseased. Instead of a spontaneously ordered, resilient and adaptive system the economy may become vulnerable through for instance growing inequalities, excessive leveraging and so on. Financial markets can be compared to lived bodies—it is through their embodiment that subjects experience the world. The MMH proposes to understand markets as collective entities with ‘minds of their own’ over and above the individual minds of the traders and investors that interact within particular markets. To restore consciousness to its rightful place MMH proposes to use a second core premise from 4E cognitive science—the thesis of the extended mind. The core idea behind this thesis is that minds extend beyond the boundary of skin and skull to include the many artefacts, tools, and technologies humans increasingly rely upon in their thinking and intelligent

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problem solving. The MMH claims that, through the mediation of the signalling system of prices, markets extend the minds of interacting investors and consumers. I suggest that the price discovery system in markets can be conceived of as what Richard Menary (2007) has called a “cognitive practice”. Menary argues that through the integration of public symbolic systems of representation, neural circuits become enculturated: they come to acquire new culturally specified functions that are genuinely transformative of an individual’s cognitive capacities. The individual becomes able to solve problems and make inferences that would not be possible were it not for the functional integration of such public systems of representation. The MMH can be read as claiming that the pricing system can be thought of as an external symbol system, with prices operating as symbols for markets. Computers that allow for rapid transmission of information about fluctuations in prices, comes to be functionally integrated with the neural processes of traders. The MMH makes an additional claim that goes beyond Menary’s idea of cognitive integration. It claims that the price discovery process physically realised in markets also forms the basis for a collective form of consciousness. Prices don’t only serve as a means of distributing knowledge of possible risks and rewards associated with particular assets or firms among investors. MMH claims that prices also serve to extend the consciousness of individual investors in such a way that participants in a market come to form a collective consciousness. It is often supposed that consciousness must depend on brain processes internal to individuals. Proponents of the extended conscious mind argue that there is frequently no clear well-defined line separating internal from external processes in dynamical systems that are tightly coupled to their surrounding environment (Kirchhoff and Kiverstein, 2019). More specifically, it is not always obvious that one can treat the body and everything outside of it as ‘external’, and take ‘internal’ brain processes to be sufficient by themselves for consciousness. The nervous system, the rest of the body and the environment are coupled across numerous spatial and temporal scales in such a way as to form a single complex adaptive system. Consciousness is best understood as emergent from the self-organising dynamics of this complex adaptive system the brain and the rest of the body form with the surrounding environment. MMH claims that what is true of the conscious minds of individuals may also be true of consciousness in markets. The coupling between traders that is made possible by the technologies connecting them in real time extends the minds of individual traders in such a way that they combine to form a single collective consciousness. Prices provide an information-based medium for markets to adapt to unexpected, surprising events in the real economy but crucially the MMH claims that prices also have an experiential aspect to them. There is something it is like for the market to feel squeezed, to trend upwards or to undergo a reversal. What connects consciousness in markets to the real economy is the information realised in prices. The MMH claims that the information that prices carry has dual aspects—it is both a physical quantity reflecting the market dynamics but information also has a phenomenal or

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subjective aspect. This phenomenal aspect is reflected in the phenomenon of market moods such as exuberance, depression, fear, despair, mania or euphoria that investors often report experiencing intersubjectively. Does the MMH subscribe to a view of markets as distributed information processing systems with the qualification that information has dual aspects—at one and the same time physical, reflecting the dynamics in markets, and phenomenal, reflecting the mood of the market as a whole collective entity? Such a view of the market could be interpreted to be consistent with the cognitivist understanding of economic systems by analogy with computational systems. The important addition would be that economic systems should be understood as engaging in distributed computation as famously introduced in Hutchins (1995). I will conclude my afterword by pointing to a different possibility, drawing upon the enactive strand of thinking in 4E cognitive science. Instead of thinking of economic systems as information processing systems I suggest we could read the MMH as claiming that economies are complex adaptive systems that are intrinsic sources of meaning. Markets can be productively compared to self-organising biological systems that are adaptable, spontaneous and, at least within certain bounds, robust to external perturbations. In complex adaptive systems, novel properties emerge out of the non-linear dynamical interactions between the elements that make up the system. These emergent properties are collective or macroscopic properties of the system as a whole that act as collective variables or order parameters that loop back down to reduce the degrees of freedom in the behaviour of the elements of the system. Kelso defined order parameters as “functionally specific, context-sensitive informational variables” that are “intrinsically meaningful to system functioning”. Kelso claims that order parameters are “intrinsically meaningful” for a system because they specify the coordination dynamics among the parts of a system and its environment (Kelso, 1995, p. 145). I suggest that the MMH could be read as proposing to understand prices as collective variables that become meaningful for markets in their coupling with the real or physical economy. When seen from this enactive perspective, prices do not just carry information in the sense of representing risks and rewards. The systems of financial markets that embody meaning intrinsically are temporally extended patterns of activity that can criss-cross boundaries between individuals participating in a given market. Instead of representing an external environment, information can be understood here in terms of enacting or bringing forth of an environment—a market. Moreover, this process of enaction is inseparable from the embodiment of the order parameters in the complex adaptive systems that forms out of the interactions of participants in markets, which the MMH claims combine to form a collective consciousness. Julian Kiverstein Assistant Professor of Neurophilosophy at the University of Amsterdam, and co-author of Extended Consciousness and Predictive Processing

Abbreviations and Glossary ~

Squiggle or tilde, symbolising the dynamic paring synergy that emerges ‘over-andabove’ two seemingly contrarian aspects as a result of their exchanges. Important dualist examples are mind~matter and psychological~physical.

E

Embodied, embedded, enactive, and extended. Specifically, E cognition considers the mind to be embodied, embedded, enactive, and extended.

–

AI

–

–

ML

–

– – – – –

BOE BOJ ECB Fed BIS

– – – – –

Artificial Intelligence is intelligence attributed to software programs run on computers, machines, robots and other technology. It applies to tasks like decision-making, speech recognition, translation and visual perception. The discipline studying and applying it is also called AI. Machine Learning, including Deep Learning and other advanced AI. Bank of England, the central bank of the United Kingdom. Bank of Japan, the central bank of Japan. European Central Bank, the central bank of the European Union. Federal Reserve, the central bank of the United States. Bank for International Settlements. The ‘central bank of central banks’, based in Basel, Switzerland.

CAPM

Capital Asset Pricing Model. The CAPM is derived from MPT and is the core model of the EMH.

CAS

Complex Adaptive System, comprised of components whose dynamic exchanges generate its complexity which is synergistic, meaning proverbially that it exceeds the sum of its parts. Minds are a special kind of CAS because they are intelligent and, in some cases, conscious.

CDO/CLO

Collateralized Debt Obligation/Collateralized Loan Obligation. A financially engineered (or ‘structured’) security that is backed (‘secured’) by a pool of other securities. Depending on the risks of the latter these are divided along so-called tranches, with credit ratings varying from very safe (AAA) to speculative (BBB) and lower (e.g. junk/subprime).

CD

Coordination Dynamics, one of the cognitive theories, especially regarding consciousness, discussed in this book.

Cognitive economics

An emerging field that partners cognitive science with economics. Cognitive science itself is an interdisciplinary field that studies all aspects of the mind and includes AI, neuroscience, philosophy (of mind), psychology, and sociology. It has a cognitive, and in the case of the MMH psychophysical, worldview of the economy, market, and agents.

https://doi.org/10.1515/9783111215051-015

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(continued) –

Consciousness

–

–

Collective consciousness

–

Viewed from the perspective of mind-as-market, consciousness is the mind~body’s valuation system, the (‘domestic’) market where its ‘prices’ are realised. Technically, it acts as the mind~body’s overlay portfolio (a.k.a. S3). Specifically, conscious experiences are the multisensory real-time ‘returns’ for actively ‘investing in the now’, i.e. the payoffs for paying attention while employing your eyes, ears, skin, S1, S2, etc. and committing memory. Some of those returns are positive, others negative which translate as pleasant or unpleasant experiences. Viewed from the collective perspective of market-as-mind, consciousness extends into the economic system, becoming part of a global valuation system—centred on the market—to help coordinate society.

CVC

Corona (or Covid) Virus Crisis, which started in  and, economically, ended in late .

DSGE

Dynamic Stochastic General Equilibrium, the equilibrium principle underlying mechanical economics’ main models.

–

Discovery

–

– –

Invention Society’s discovery chain

– –

Economic system = – Economy + – Market

Discovery is the experience of insight (‘A-ha’) in the eureka moment, often after a considerable period of exchanges (e.g. between S1 and S2) in the mind. Such epiphany, when part of the unknown is unveiled and becomes part of our understanding, is the most highly valued ‘return’ (a.k.a. alpha) in our mind-as-market. Invention is the (physical) application or implementation of a discovery. Under healthy conditions, society benefits from a collective and reflexive chain of discoveries and inventions that weaves through and connects the economy and the market which, in turn, value these.

The economic system is a combination of: – The physical real economy of markets in goods and services—a.k.a. “the economy”. – The psychological financial economy of markets in securities—a.k.a. “the market”. In general, economic system is meant in global terms, i.e. the global economy and markets worldwide.

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(continued) –

Economic utility

–

–

Epistemic utility

–

–

Epistemology

–

– –

Ontology Methodology

– –

Economic utility is the usefulness that individuals derive from consuming goods and services and, by extension, the value they place on them. Epistemic utility refers to the usefulness of information in general and knowledge in particular, regardless of any economic benefits. Epistemic utility is particularly concerned with the value of information and knowledge in terms of gaining a deeper understanding. Studies what and how we know. Related are concepts like epistemic luck, epistemic risk, and epistemic utility. Studies the essence/nature of phenomena. It is related to metaphysics. Studies what and how we investigate. It includes research tools, like software.

EMH

Efficient Market Hypothesis, mechanical economics’ main theory of markets and investing; specifically central to modern finance.

EMT

Extended Mind Theory, one of the cognitive theories, especially regarding consciousness, discussed in this book.

ESG

Environmental, Social, and Governance criteria. ESG-investing consists of selecting securities based on those criteria.

FEP

Free energy principle, a.k.a. active inference. FEP states that the concept of free energy stands for the amount of (expected) prediction error. This means that minimizing free energy corresponds to minimizing prediction errors of the mind~body’s model of the world. It is based on the assumption that organisms and other agents maximize the evidence for their model of the world through an active sampling of sensory information. Apart from the link to utility maximisation, the MMH roughly associates this with an efficient market-as-mind where ‘free lunches’ (i.e. pricing errors) are removed by way of arbitrage.

Forward guidance

A mental tool employed by central banks that consists purely of communicating the likely future course of monetary policy, especially about interest rates, based on an expected economic climate. Implying only a promise (threat) of loosening (tightening) monetary conditions it aims to impact the physical behaviour of businesses, consumers, and investors.

GDP

Gross Domestic Product, the standard measure of a nation’s economic activity.

GFC

Global Financial Crisis. It initially centred on the collapse of the US investment bank Lehman Brothers in  but subsequently morphed into the euro(zone) crisis which started in late .

GWT

Global Workspace Theory, one of the cognitive theories, especially regarding consciousness, discussed in this book.

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(continued)

HFT

High Frequency Trading. HFT is a mechanical, specifically algorithmic, form of trading in the market. Using advanced computers and communication equipment it is characterised by high speed and large volumes. While grown in size it remains controversial. Specifically, HFT firms pay mainstream brokers to channel orders to them, a method called Payment-For-Order-Flow (PFOF).

Idd-minds

Independent-and-differently-distributed minds. It suggests diversity in consciousness and, by extension, in thinking, decision-making, etc. It is a wordplay on statistics’ iid (independent-and-identically-distributed).

IIT

Integrated Information Theory, one of the cognitive theories, especially regarding consciousness, discussed in this book.

IOU

‘I Owe You’. It stands for (the promise to pay back) debt.

LDI

Liability-Driven-Investment, a mechanical investment strategy aimed at automatically matching assets to liabilities. It is popular with pension funds.

Man-of-system

Adam Smith’s (unflattering) term for a central planner, introduced in TMS.

–

Market-as-mind

–

–

Mind-as-market

–

–

MM Principle

–

Considers the market to be a collective 4E mind. It is one leg of the premise underlying the MM Principle. The MMH relates it to economics’ macrofoundations. Considers the individual 4E mind to be a market. It is the other leg of the premise underlying the MM Principle. The MMH relates it to economics’ microfoundations. Market Mind Principle, based on but also shared by the above two legs. It consists of intelligent, sometimes conscious self-organisation via market-type dynamics, centred on exchange and aimed at discovery, especially of value.

Mechanical economics

The combination of new classical economics and Keynesian economics. It has a mechanical worldview and considers the economy to be a machine, the market an automaton, and agents to be robots.

Metaphysical stance

A person’s metaphysical view of reality, i.e. whether they are a dualist, idealist, monist, physicalist, and so on. Often people are not aware of their stance, but it can be inferred from their behaviour and/or communication.

Mind~body problem

Asks to explain the relationship between matter (i.e. the brain) and mind (i.e. conscious experience). Another way of stating this is how (and why) our physiology gives rise to our psychology, especially its qualities known as phenomenality (via sentience).

MMH

Market Mind Hypothesis, the heterodox theory introduced in this book.

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(continued)

MPT

Modern Portfolio Theory, the main investment interpretation of the EMH. It uses a mathematical (‘mean-variance’) framework to optimise portfolios whereby the expected return is maximized for a given level of risk. Among others, it helps to create the so-called efficient frontier along risk and return axes for various assets.

Mr Market

A (gender-neutral) moniker for the (composite of) financial markets. It is an oft-used term among investors, initially popularised by Benjamin Graham.

NIRP

Negative Interest Rate Policy, whereby central banks set their official interest rate below zero.

OTC

Over-The-Counter. OTC securities are bespoke, often complex products traded between parties ‘over-the-counter’, so not via the market. Because they are illiquid, their valuation is often debatable.

Portfolioism

The bespoke metaphysical stance of the MMH, based on Hayek’s practical dualism (itself a form of dual-aspect monism in technical terms). Portfolioism considers everything in our universe—so all resources, including our mind~bodies—to consist of single or multi-asset portfolios, whereby assets (and by extension portfolios) can have monic or dualist aspects (i.e. physical~mental). Negative assets are liabilities.

PPT

Predictive Processing Theory, one of the cognitive theories, especially regarding consciousness, discussed in this book.

Psychurity

Based on portfolioism, a psychurity is the mind~body’s version of a security (see below). Psychurities include (portfolios of) beliefs, emotions, thoughts and other mental assets and instruments. They are valued within our mind-as-market based on their (e.g. epistemic) utility to benefit from/hedge against states of the world, i.e. our environment. They can become intersubjectively valued in exchanges with other mind~bodies.

QE/QT

Quantitative Easing/Quantitative Tightening. Both are monetary policies where a central bank becomes an active participant in the market by purchasing/selling securities (usually government bonds). The indirect (and desired) result—via so-called ‘transmission’—is an increase/decrease in activity in the economy due to the improved/weakened liquidity and related credit conditions (e.g. interest rates) in the market that stem from these purchases/sales.

REH

Rational Expectations Hypothesis, mechanical economics’ core assumption about rationality in the economy, market and agents.

Reflexivity

The philosophy of investor George Soros. In MMH terms, its key insight is that (mental) prices are not purely based on and do not objectively reflect the (physical) fundamentals, as mechanical economics assumes. Rather they can influence these, as well as other prices. In other words, reflexivity is about mind~matter exchange in the economic system.

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(continued)

Repocalypse

Starting in late , this was a period of several months of severe stress in repo and other interest rates that reached systemic risk levels. Repo rates are the interest rates used by central banks in their borrowing/lending (including of securities) with commercial banks.

– – –

– – –

S1 S2 S3

System 1: the unconscious system of the mind, responsible for ‘thinking fast’. System 2: the deliberate system of the mind, responsible for ‘thinking slow’. System 3: the phenomenal (overlay) system of the mind, responsible for ‘feeling’, in this case especially for experiencing/realising (the outcomes of) S1 and S2.

Security

A financial instrument, legally in the form of a certificate/contract, that provides the holder with some claim to an asset, like outright ownership or a right to buy/ sell. Examples of securities include bonds, stocks and derivatives.

– – –

– – –

TBTC TBTF TBTS

Too-Big-To-Care Too-Big-To-Fail Too-Big-To-Save

Although applicable to the concentration in other sectors as well, these acronyms have especially been used to refer to large banks. They have only grown in size since the GFC, mainly due to (arranged/forced) mergers with weaker competitors. The first acronym is most appropriate for the current state of global banking according to the MMH. TMS

The Theory of Moral Sentiments, a book written by Adam Smith and first published in .

ToM (or TOM)

Theory of Mind. ToM is a cognitive science concept that describes our ability to “theorise” about the mind, including those of other entities, and to attribute states to it.

VaR (or VAR)

Value-at-Risk. A popular quantitative measure of risk exposure used in risk management models and methods.

WN

The Wealth of Nations, a book written by Adam Smith and first published in .

ZIRP

Zero Interest Rate Policy, whereby central banks set their official interest rate at, or close to zero.

For additional terms and concepts, please see also Appendix 1.

Appendix 1 Bridging Concepts and Terms If I was a young researcher now, I would study the mind~body problem. This is the great challenge of the 21st century. Ilya Prigogine

Preparation Apart from appealing to general readers, I hope this book will trigger interest particularly among both economic and cognitive researchers. Bringing them together, academically speaking, to investigate the market’s mind would be mission accomplished. To start such collaboration I need to explain a few concepts and terms used in these disciplines, largely based on consensus mixed with personal interpretations. While necessary for many, it is fairly technical and admittedly does not make for exciting reading. Think of it as your least favourite subject at school that you have to ‘crunch’ in order to progress. Still, I assume by choosing this book you accept this. Moreover I assume that you are, at least at a basic level, familiar with both markets and minds, as well as economic and cognitive science. In fact, you may already have noticed the surprisingly large number of terms that reflect the close connection between these domains. In cognitive science we have terms like “attention allocation”, “cognitive economy”, “division of cognitive labour”, “intercommerce”, “neuronal assemblage”, “psychological household”, and “purchase of understanding”. While in economics we have “consumer psychology”, “producer confidence”, “economic depression”, and obviously “market’s mind” and “market mood”.1 In any case, the concepts and terms explained in this Appendix are not exhaustive and I refer to the respective literature for more details. Readers who feel comfortable in both fields can skip most parts, but I urge every reader to at least read the segment on consciousness in section A as well as section C, in particular the segment on portfolioism. To set the tone, allow me to make two important points. First, price discovery— via the pricing system—is not only at the heart of connecting economic with cognitive science but central to our large-scale bridging of mind and matter as a global society. It is key to the collective coordination of our affairs. This is particularly relevant in the current environment of scepticism about markets and misplaced omniscience by men-of-system (a term introduced by Adam Smith; see Chapter 1). There have been a few thought leaders who have expressed this brilliantly, no one more so than Nobel laureate Vernon Smith:

 This goes as far as an “economy of the unconscious” (Dailey, 2000). https://doi.org/10.1515/9783111215051-016

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How is it that the pricing system accomplishes the world’s work without anyone being in charge? Like language, no one invented it. None of us could have invented it, and its operation depends in no way on anyone’s comprehension or understanding of it. Somehow, it is a product of culture; yet in important ways, the pricing system is what makes culture possible. Smash it in the command economy and it rises as a Phoenix with a thousand heads, as the command system becomes shot through with bribery, favors, barter and underground exchange. Indeed, these latter elements may prevent the command system from collapsing. No law and no police force can stop it, for the police may become as large a part of the problem as of the solution. The pricing system—How is order produced from freedom of choice?—is a scientific mystery as deep, fundamental, and inspiring as that of the expanding universe or the forces that bind matter. For to understand it is to understand something about how the human species got from huntinggathering through the agricultural and industrial revolutions to a state of affluence that allows us to ask questions about the expanding universe, the weak and strong forces that bind particles and the nature of the pricing system, itself. (V. Smith, 1982, p. 952)

Importantly, Hayek emphasised its success by contrasting its spontaneous character with design: I am convinced that if it were the result of deliberate human design, and if the people guided by the price changes understood that their decisions have significance far beyond their immediate aim, this mechanism would have been acclaimed as one of the greatest triumphs of the human mind. (Hayek, 1945, p. 527)

To be clear, and lest we forget, markets evolved despite of us, not because of us. Guided by a familiar but invisible hand. What if such self-organising process—balancing competition~cooperation, demand~supply, fear~greed, and other complementary market pairs—also operates in the human mind? As within so without, so to speak. Then perhaps it is no coincidence that we, now on a massive global scale, ended up with ordering our affairs by way of price discovery. In fact, there are arguments to see nature and evolution itself as reflecting market dynamics, which I explain in the first section of Chapter 1. As we will see, the pricing system remains a mystery because discovery, which ultimately is about mind~matter understanding by mind~bodies, is a mystery. And that is how it should be, accepted with a humility expressed in broader terms by Max Planck: “Science cannot solve the ultimate mystery of nature. And that is because, in the last analysis, we ourselves are part of nature and therefore part of the mystery that we are trying to solve” (Planck, 1932, p. 217). Unfortunately, price discovery is threatened from many angles, which has contributed to our economic challenges. This leads to the second point I need to make which is about economics’ paradigm. As a reminder, a paradigm is “the basic way of perceiving, thinking, valuing, and doing associated with a particular view of reality” (Harman, 1988, p. 10). I will alternatively refer to the latter as worldview. Subscribing to a worldview implies taking a metaphysical stance, here particularly referring to mind~matter issues. Metaphysics will be discussed in detail in section A on cognitive science. For now, like

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investing, metaphysics is complicated and often speculative.2 However, as I mentioned in the Prologue, recent economic crises were reality checks and dramatically showed why metaphysics is relevant for economics. Money itself is also about metaphysics. In addition, developments like the attention/experience/virtual economy, crypto currencies, and central bank policies (see Subchapter 4.1.3) raise serious questions on metaphysics in general and on mind~matter issues in particular. While Hayek, Knight, Mises, Soddy, Sornette, Soros, and other economic experts,3 either explicitly or implicitly, shared their reflections on mind~matter issues, it is completely absent from most of today’s economic discussions, including communication by central banks. ‘It’s a slippery topic’ is no excuse. Due to this lack of mind~matter awareness and scrutiny, many economic assumptions, investment strategies, and regulatory policies are ad-hoc at best, dubious on average, and counter-productive at worst.4 It is like trying to understand, let alone play, the game of football from a hockey perspective, including hockey methods and rules. That needs to change, perhaps as part of a paradigm shift, and that is why metaphysics is important. Einstein asserts that “every true theorist is a kind of tamed metaphysicist” (Einstein, 1950, p. 13) and “it finally turns out that one can, after all, not get along without metaphysics” (1946, p. 291). More to the point, fellow physicist Carl Friedrich von Weizsäcker emphasised that “every scientist works with metaphysical assumptions, and those who deny this most usually work with the poorest ones” (Atmanspacher and Müller-Herold, p. 141; fn. 11; emphasis added). This counts even more so in fields that are dealing with, in the words of Heinz von Foerster (2003), “in principle, undecidable questions” that we, nevertheless, can and must take seriously. Economics and investment management are full of those, not only because of the metaphysical nature of money itself but mainly due to true uncertainty. The latter—also known as Knightian, or radical uncertainty—is a consequence of the fact that the future is unknowable. To emphasise, the MMH argues that we are confronted by true uncertainty specifically because of our lack of knowledge about mind~matter interaction, i.e. the mind~body problem. To continue using probability for supposedly rational decisions under those conditions—offering the pretence or illusion of understanding and control—is actually irrational. Add to the mix contaminated data—which mechanical economics, nevertheless, assumes to be proper signals and commands—and we can then end up in a situation where: Not surprisingly, as we discover more and more imperfections . . . our efforts at intentional prediction become more and more cumbersome and undecidable, for we can no longer count on the

 See, e.g., Heidegger (1929). In this work, as well as its supplement (Heidegger, 1943), Heidegger goes beyond traditional metaphysics.  John Coates (2012), for example, discusses the original mind~body problem and argues for an embodied view of cognition to replace dualism.  I will have more to say about this in Chapter 4.

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beliefs, desires, and actions going together that ought to go together. Eventually we end up . . . dropping the assumption of rationality . . . This comes out vividly if we look at theory building from the vantage point of economics [where] talk of signals and commands reminds us that rationality is being taken for granted, and . . . shows us where a theory is incomplete (Dennett, 1971, p. 95–96; emphasis added)

Such deliberations are also related to freedom and responsibility, i.e. agency, while ultimately pointing to the mind~matter dichotomy. Critically viewing separation, often between seemingly opposing domains or forces, as well as locating the implied border, is a key topic in this book to which I will return shortly. Here it is applied to two key “undecidable” investment questions that we can and must decide upon: 1. Individually: am I apart from or part of Mr Market, i.e. where does my mind stop and the market begin?5 2. Collectively: how is the market separate from or joined to the real economy, i.e. how does the tail wag the dog? Let me close by quoting von Foerster once more regarding his “metaphysical postulate”: When I invoke Metaphysics, I do not seek agreement with anybody else about her nature. This is because I want to say precisely what it is when we become metaphysicians, whether or not we call ourselves metaphysicians. I say we become metaphysicians whenever we decide upon in principle undecidable questions (von Foerster, 2003, p. 291).

As indicated, in the next sections I will introduce and explain a number of concepts, terms, and topics of both cognitive science and economics. Most will return elsewhere in the book. They also are often independent so the paragraphs may not flow as fluently as desired.

A. Cognitive Science Cognitive science is an interdisciplinary field that studies all aspects of the mind, including the brain and consciousness. It is particularly grounded in anthropology, informatics, neuroscience, philosophy, psychology, and sociology. Within these are sub disciplines and specialisations, like linguistics and artificial intelligence (AI).

A1. Cognition In this section I will explain cognition and a few terms that are directly related to it. A stripped-down definition of cognition is knowledge, and in particular the process of acquiring it via mental and physical (inter)action. For the purposes of this book I will  To paraphrase Clark and Chalmers (1998). See Section A below.

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regularly use the general term cognition (and its multiple: cognitions) to refer to both the processes and outcomes involved. Cognitions can be unconscious and deliberate. They can include emotions, intuition, perception, and reasoning. Cognitions, in turn, form part of mentalities which is a term I use frequently. A mentality is a broader mental state, like attitude or mood. An example of a mood is exuberance. Extended cognition of our mind means that supporting actions like gestures and speech, as well as physical tools like pen and paper, a smartphone, or a computer, are part of a cognitive system. Importantly, extended cognition can also include other minds and is then generally called distributed cognition (e.g. Hutchins, 1995; I discuss this in more detail in Subchapter 3.2). Extension can reach further: multiple minds can also have collective experiences, which involves intersubjectivity. Taking a cognitive stance means that you focus on the mind and how it generates cognition—in contrast to a behavioural stance which focuses on behaviour. It is often the starting point to more specific cognitive stances. With the intentional stance— which I’ll discuss later—one determines, for example, whether a cognition or a behaviour is rational or not. As indicated, we are particularly interested in somebody’s metaphysical stance which reveals their metaphysical assumptions of reality. Cognitive closure is the argument that certain aspects of cognition will remain closed for introspection and other forms of investigation. Consequently, this will limit our understanding of cognition. In other words, the mind will never know itself fully. Hayek, among others, accepted this via his practical dualism (see next section) and it motivated him, for example, to advocate imperfect knowledge in economics. Even if one disagrees with Hayek and denies individual cognitive closure, cognitive closure applies to a collective mind. It is the logical consequence of the fact that while our cognition is extended via other minds (even if it involves intersubjectivity) this does not provide full access to individual subjectivity. So, while our minds interact my cognition is fuelled and otherwise supported by, but also remains partly closed to, your cognition. This is irrespective of the condition of individual cognitive closure, i.e. whether we each know our own mind. The formal distinction between cognition (e.g. knowing) and consciousness (e.g. experiencing; see A4) is not absolute. Specifically, cognition can be conscious (but also unconscious). And some conscious states involve cognition, while others don’t. The easiest way to think about where they meet is to see cognition as processes (e.g. decision-making, speaking, memorising, problem-solving, and thinking), while consciousness is the experience (e.g. via awareness, sensations, etc.) of these processes, a.k.a. what it is like to undergo them. In this book—using the MMH’s portfolioism—cognition is specifically judged in economic terms. For example, feeling is about valuing cognition—what knowing something, or having knowledge, is worth. This is related to epistemic utility. In simple terms, that what is valued most (in real time) is paid attention and raises awareness. Because physically, the brain’s bandwidth is not unlimited, attention, as the mind’s money, is a scarce resource. For instance, social media is (sometimes desperately) trying to get ours.

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A2. Metaphysics In the Amazon Prime documentary All or Nothing Pep Guardiola, the successful coach of Manchester City football club, at one point, tells his players that winning is about mentality, not physicality. This was echoed by another sports coach, Deion “Prime Time” Sanders, the legendary former American football player (as well as baseball player). In the first episode of the Amazon documentary Coach Prime, where he was the successful coach of Jackson State University, Sanders tells his team: “Everything about life is either psychological or physical”. This dualist distinction is common, not only in sports. Our sciences are modes or disciplines for understanding. Each consists of different methods and processes with which it contributes to our overall understanding. Some (e.g. physics) can be called physical disciplines, whereas others (e.g. mathematics) are mental disciplines, while the remainder (e.g. medicine) have aspects of both. This book acknowledges that aspect dualism (the weak form of dualism) is probably the consensus worldview of the average person. It also suggests how to practically deal with such dualism, thereby starting the process to overcome the implied either/or. It implicitly raises the mind~body problem and points to the importance of metaphysics which is of relevance, for example, to Hayek’s practical dualism. For our purposes, metaphysics is not a scientific approach or a set of doctrines. Rather it is a fundamental mode of enquiry that seeks to uncover the nature and meaning of being and existence, in this case for humans and their markets. Notably, it helps— here via portfolioism—to identify phenomena in the economic system along the physical~mental scale as well as to clarify what it signifies when we recognise them as such. A key issue between this complementary pair is the contribution of capture to any explanation. In the case of physical objects, we are generally able to physically capture an object of study, e.g. in a test tube or under the microscope. This allows it to be touched, seen, smelled, etc. for further interpretation which strengthens explanation. Mental phenomena, on the other hand, can often be explained but not captured. You cannot put an emotion in a test-tube, and even a sentence does not capture the thought, thus (relatively) weakening any explanation. Consequently, prediction becomes more challenging for mind~matter exchanges. Since it largely ignores this (due to its worldview), the explanans and explanandum of mechanical economics do not correspond. This is my (simplified) interpretation of Hayek’s practical dualism for our purposes, and why we should use it. Economics cannot escape this, and in the following statement Simmel also hints at this and many related topics—like reflexivity—that feature in this book: economic forms are recognized as the result of more profound valuations and currents of psychological or even metaphysical preconditions. For the practice of cognition this must develop in infinite reciprocity. Every interpretation of an ideal [mental] structure by means of an economic [real] structure must lead to the demand that the latter in turn be understood from more ideal depths,

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while for these depths themselves the general economic base has to be sought, and so on indefinitely. In such an alternation and entanglement of the conceptually opposed [i.e. dualist] principles of cognition, the unity of things, which seems intangible to our cognition but none the less establishes its coherence, becomes practical and vital for us. (Simmel, 1907, p. 54; emphasis added)

In philosophy, the branch of metaphysics (here interpreted for our purposes) is concerned with the fundamental nature of being and reality (ontology), our knowledge about this nature (epistemology), as well as how we investigate and gather evidence for that knowledge (methodology).6 For example, they can be applied to variations in uncertainty and risk in our MMH setting:7 – Ontological uncertainty: the uncertainty related to the nature or essence of being and existence. The key question that captures this is “What is reality, i.e. what exists?” In our case, we can ask what the mode of existence is for the market. Heidegger’s Being and Time (1927) is a serious source here. Specifically, the element of time connects this to (Knightian) true, radical or fundamental uncertainty, i.e. the uncertainty of future being/what will exist. In the words of Shakespeare’s Ophelia in Hamlet, “we know what we are but know not what we may be”. What exists today may be extinct tomorrow, whereas novelty brings new existence. The irreducible chaos in complexity starts, after all, with the transformation of the caterpillar into a new being, the proverbial butterfly that causes the tornado. In our case, when mind and matter meet next as an insight it not only changes our knowledge but changes our being. In some cases this can even involve reshaping the brain with new neuronal connections. From an investment perspective, ontological uncertainty entails not just the unknown future in terms of (novel) external events, products and services. It also entails our ignorance8 about our own future being (e.g. our future feelings concerning such novelty) once we become conscious of them, particularly in an ‘experience economy’. Neither can be quantified—meaning, for example, that ontological uncertainty underlies model uncertainty—but it is the latter that has often itself been ignored in debates about uncertainty and risk. In short, consciousness realises economic discovery (e.g. phenomenally via Aha-sensations) which make its process unknown. Ultimately it means that ontological uncertainty cannot be reduced (see Appendix A4).

 All three form the headings of separate chapters. Confusing or mixing the first two amounts to a category mistake. I’ve tried to avoid it in this book. Although, arguably, the distinction between the physical and the psychological suggests I should also distinguish between metaphysics and metapsychology, I have not done so for practical purposes. In short, my metaphysics will also cover metapsychology.  Again, to keep things simple, this should be interpreted as including, respectively, ontic, epistemic, and methodical.  Largely due to underlying unconscious processes. Also, it reaches beyond future knowledge (e.g. Popper, 1957).

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Epistemological uncertainty: the uncertainty related to our (incomplete) knowledge. It applies, for example, to the (in)famous “knowns” and “unknowns” raised by Donald Rumsfeld, former US Defence Secretary. The key question that captures this is: “What, and in what sense, do we know?” Because insights can increase our knowledge, epistemological uncertainty can, in principle, be reduced. Many lucky or spontaneous discoveries mean that, in Rumsfeldian terms, previously “unknown unknowns” have become “known knowns”. Chapter 4 discusses this in more detail. Among others, I will make the distinction between epistemic rationality and practical rationality to critique the REH. Related concepts include scepticism and epistemic luck. The latter is concerned with (the implications of) accidental, coincidental, or fortuitous true beliefs. Methodological uncertainty: the uncertainty related to empirical data and research techniques. The key question that captures this is: “What and how do we investigate?” Including the question of whether we can measure something, it comes closest to statistical uncertainty, better known as risk. It is also referred to as aleatory uncertainty. For our purposes it thus is not limited to data risk (contaminations, errors, gaps, outliers) but includes also the risk of inappropriate techniques.

Metaphysics covers a vast field, including the origins of political-economic systems for example. In this book we primarily use metaphysics concerning matter and mind, e.g. whether they differ in terms of aspect, function, property, purpose, or otherwise. The main metaphysical stances include dualism, idealism, monism, panpsychism, and physicalism (or materialism). Karl Marx, for example, was a materialist who turned Hegel’s idealist account of history into his materialist version of Marxism. Moreover, in Das Kapital he judged the pricing system to be idealist: “The price or money-form of commodities is, like their form of value generally, a form quite distinct from their palpable bodily form; it is, therefore, a purely ideal or mental form”. I’ll discuss dualism and physicalism in more detail shortly. Harman (in Harman and Clark, 1994, p. 8) lists the main metaphysical assumptions which became the intrinsic premise of modern physical science: – Objectivism: the assumption of an objective world which the observer can hold at a distance and study in isolation. – Positivism: the assumption that the real world is what is physically and independently measurable. – Reductionism: the assumption that we come to really understand a phenomenon by studying the behaviour of its elementary parts. The economists Hayek (1974), Knight (1921, 1925b), McCloskey (1985), and Mises (1949) for example, provided their interpretations of this premise (which they labelled with terms like “scientism/scientistic” and “modernism”) and they criticised mainstream

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economics’ unequivocal adoption of it: “it involves a mechanical and uncritical application of habits of thought [from other fields]” (Hayek, 1974; emphasis added). They have been joined by contemporary critics, including Derman (2011), Friedman and Goldberg (2011), and Mirowski (1988). This criticism can be further specified in terms of the previously mentioned aspects of metaphysics, with claims varying in strength: – Ontologically, economics’ “scientism” is the claim that mechanical economics is the only or at least the best guide to the nature or essence of economic being and existence. – Epistemologically, economics’ “scientism” is the claim that mechanical economics is the only or at least the best form of economic knowledge. – Methodologically, economics’ “scientism” is the claim that the methods and techniques of mechanical economics are the only or at least the best ways to investigate economic phenomena. The common denominator of the assumptions of objectivism, positivism, and reductionism—and consequently the central characteristic of the premise they support—is separation, the (perceived) existence of divisions with clearly defined boundaries. Its relevance for economics starts, perhaps, with the division (and resulting specialisation) of labour as historic precedent, whereby the mechanistic bias influenced how people were organised in collectivities within the economic system. This matters especially for decision-making: the way people are rigidly (e.g. hierarchically) institutionalised and incentivised (e.g. via excessive bonuses and stock options) can distort the decision process, both individually and collectively. For domains that include conscious minds, separation is challenged by 4E cognition, exemplified by that legendary opening question in Clark and Chalmers (1998): “Where does the mind stop and the rest of the world begin?” Before I discuss the perceived separation between mind and matter, I want to make an important point regarding separation in general. The absence of separation does not mean that we recognise such absence; we do not necessarily see continuation. Neither does it mean that there are no boundaries nor that crossing those boundaries is always a good thing. Most boundaries are soft, but sensitive. Pushing, let alone removing them harden other or raise new boundaries. Also, elsewhere in the book I discuss complexity, emergence, synergy, and other phenomena which occur due to the exchange between two or more units. That exchange crosses whatever separation stands for (e.g. culture, space, time) on that particular occasion. Still, any separation often offers the (pun-intended) “boundary” conditions and constraints for such exchange. For example, strong-A and weak-B can compete as long as they can be separately identified, but unless B cooperates/merges with C, B will cease to exist, thereby terminating the A-B competition. The separation of mind and matter is the most prominent. For humans this is perhaps the natural consequence of our brain being physically separated from the world

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by the skull. That is, the brain is not in direct contact with the world and receives signals ‘filtered’ via the senses. This leads to ambiguous information and, ultimately, contributes to incomplete knowledge. Freely interpreting Jerry Fodor (as the challenge for Clark and Chalmers), there appears to be a gap between the mind and the world and we should mind the gap. Other examples of perceived separation are between man and nature, observer and observed, subject and object, and cause and effect. Free will is of particular interest in the objective-subjective separation which is considered—at least according to some interpretations—an illusion from the objective (i.e. third person) perspective but a reality from the subjective (i.e. first person) perspective. To wit, central in the (methodological) discussion about individualism vs. sociality, internalism vs externalism, etc., is—theory wise—whether the separation is real or, say, an illusion, whereas in practical terms it is about the challenge to overcome any perceived or real separation, for example by way of technology or other tools. Ultimately the above premise led to denial of the mind~body problem in that, for a long time, “consciousness became essentially absent from the scientific worldview” (Harman, 1994, p. 10). In the early days, the main motivation to stick to this premise was twofold. First, anything to do with mind, spirit, and soul was the monopoly of the Catholic Church, the dominant institution at the time. Among others, it promoted separation between God, man, and nature. Upsetting the Pope and the Vatican was just not worth the risk.9 Second, there simply was already enough to investigate in the physical domain, so why bother with the more elusive mental one? It gained further momentum with the initial successes of the natural (hard) sciences which yielded naturalism as its worldview. Naturalism states that everything that exists is natural (there is no supernatural) and all phenomena should be investigated by the natural sciences and their methods. Later this led to the term “naturalising the mind”.10 As time moved on and society became more complex and prosperous, we gained relatively less insights from the physical sciences and required more from the humanities and social sciences, including knowledge about the mind~body. Consequently, its problem received renewed attention. In fact, consciousness research is now increasingly at the forefront of cognitive science. In the words of leading neuroscientists Tononi and Koch, “the study of consciousness is becoming a science” (Tononi and Koch, 2015, p. 2). By extension this includes a reassessment of separation itself. Still—and this is crucial—most of us would agree that they experience reality in a dual sense, so both mentally and physically. Dualism was made famous, initially, by the philosopher René Descartes as expressed in his famous dictum cogito ergo sum (I think, therefore I am). In assessing what reality consists of in a metaphysical sense, it makes a distinction between mind and matter.11 Depending on the strength of the dis Some readers will recognise the similarity with today’s constrained state of the cognitive, humanities, and social studies, as well as the arts, in countries run by totalitarian regimes.  See Dretske (1997). For a critical overview, see Bishop (2014).  Descartes called these res cogitans (‘the thinking thing’), respectively res extensa (‘the extended thing’).

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tinction, this can vary from substance dualism (mental substance vs material substance) via property dualism (mental properties vs material properties) to MMHfavoured dual-aspect monism (the mental and the physical are two complementary aspects of the same underlying reality). Physicalism argues instead that not only all our reality is physical but also that nature is what physics is about. In other words, physicalism argues that physics can fully explain nature, including consciousness. More formally expressed, nature is exhausted by physics in terms of both ontology and epistemology. Applied to the social sciences, this can be questioned (as Hayek, McCloskey, and others have done). Ironically, some physicists also question this. Erwin Schrödinger, for example, argued that “Consciousness cannot be accounted for in physical terms. For consciousness is absolutely fundamental. It cannot be accounted for in terms of anything else” (Moore, 1994, p. 181). And the prominent feature of Niels Bohr’s coat-of-arms is a blue-and-red yin~yang symbol (Kelso and Engstrøm, 2006, p. 35). Why discuss these two stances in particular? As far as physicalism is concerned, it underlies the “physics envy” of mechanical economics criticised by many. More importantly, its related mechanical worldview, which Knight called “mechanistic monism” (1925b, p. 251), is the implicit justification for financial engineering and mechanisation in the economic system. By extension, it invites and promotes mechanical policies, practices, and products in a self-reinforcing loop. To round this off, mechanical economics implicitly assumes that agents are (robotic) rational physicalists. Physicalism projects a reality that does not make sense to most humans. Instead, the MMH assumes that the average person most of the time views and thus describes the world in dualist terms, recognising both its mental and physical aspects, for example, in their own mind~body. This starts at an early age: “With regard to ontology, children as young as three years firmly divide the mental and physical worlds . . . young children appropriately distinguish between real and mental entities” (Wellman 1993, p. 14; see also Bloom, 2004). Among the few economists addressing this explicitly is Hayek, who suggested “practical dualism” to deal with reality: While our theory leads us to deny any ultimate dualism of the forces governing the realms of mind and that of the physical world respectively, it forces us at the same time to recognize that for practical purposes we shall always have to adopt a dualistic view (Hayek, 1952, p. 179) . . . In discussing mental processes we will never be able to dispense with the use of mental terms, and that we shall have permanently to be content with a practical dualism, a dualism based not on any assertion of an objective difference between the two classes of events, but on the demonstrable limitations of the powers of our own mind fully to comprehend the unitary order to which they belong. (Hayek, 1952, p. 191)

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Just to be clear, Hayek rejects substance dualism as well as the existence of a Cartesian self, an internal executive which centrally organises the mind.12 His view is consistent with modern cognitive insights. Regarding “terms”, I would add—echoing reflections by Kant, Nietzsche, Wittgenstein, Zhuangzi, and others—that language has its own limitations. Much of consciousness in particular is ineffable. I will return to this later. Their difference in metaphysical stance is a key aspect of the mismatch between mechanical economics and cognitive economics of which the MMH is part. The MMH broadly subscribes to practical dualism by translating and applying it for cognitive economics in general, and investment purposes in particular. Called “portfolioism”, this will particularly be explored in more detail in the third part (C) of this appendix. Finally, metaphysics plays a role in exchanges (e.g. Simmel, 1907), which is a broad term that covers interaction, transfer, but also trade. It matters whether we consider something mental or physical in an exchange (even if it is just based on our perception). A simple well-known comparison can help to clarify: if you have an apple and I have an orange and we exchange, then you are left with an orange, and I am left with an apple. But if we each have a different idea and exchange these, then both you and I end up with two ideas which, from that moment on, join in our respective mentalities and eventually integrate. Only mentally can you have your cake and eat another too, so to speak. So, an exchange between inanimate objects only involves physical aspects. This is relatively simple, which is why we know the laws which govern these exchanges. But sometimes an exchange concerns or is mixed with something else, like ideas, looks, and words. The question of separation—as the common denominator of objectivism, positivism, and reductionism—features prominently again. In the words of Hofstadter, “you will probably come up with some criterion involving separation of the objects in space, and making sure each one is clearly distinguishable from all the others. But then how could one count ideas?” (Hofstadter, 1979, p. 56). In general, exchanges between conscious beings, including the environments they inhabit while exchanging, involve material and mental aspects. These are more complex and non-linear,13 which is why we are still in the process of determining their ‘psychophysical’ laws. In terms of exchanges with our environment per se, these often involve physical acts inspired by mental perception, in particular discrepancies between expectations and outcomes. By way of these acts we want to change the environment to better fit our wishes. It is our evolutionary drive to have mind influence matter.

 I speculate that it was largely this conviction that led Hayek to his criticism of central planning in economics. However, I could not find any reference in the literature confirming this.  The coupling between, say, system A and system B is nonlinear when at least one variable (e.g. output) of A is a parameter (e.g. input) of B and at least one parameter of B is a variable of A.

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A3. Mind When in the market place you toilers of the sea and fields and vineyards meet the weavers and the potters and the gatherers of spices, . . . Invoke then the master spirit of the earth, to come into your midst and sanctify the scales and the reckoning that weighs value against value. Kahlil Gibran “On Buying and Selling” (in The Prophet)

4E-Mind: Mental Market Portfolio Having provided the overarching metaphysical window, the earlier mention of portfolioism hints at the particular investment-inspired angle with which the MMH views mind~matter issues. Here, I will discuss this as far as mind itself goes, whereby exchanges overcome mind~matter separations, starting with subject~object: To have a mind means nothing more than to execute this inner separation . . . That there is ‘no subject without an object, no object without a subject’ is realized first within the mind, which raises itself as the knowing subject above itself, as the object known; and by knowing this knowledge of itself, the life of the mind proceeds necessarily in the progressus ad infinitum. (Simmel, 1907, p. 116)

As the table in the Introduction showed, the mind is to the body what the financial system is to the real economy. Together mind and body form a person’s mind~body economic system.14 Specifically, following Glimcher (2003), Vértes et al. (2011) and others, the MMH considers mind to operate largely like an internal (or domestic/local) asset market: [T]he person is no longer a throughput but rather a literally self-conscious agent, who makes decisions by finding equilibria among not only external incentives but also her shifting selfpredictions. She shares some of her properties with . . . financial markets, analogies that have always been thought of as more poetic than descriptive. (Ainslie, 2014, p. 1; emphasis added)

In terms of balance (which I prefer to “equilibrium”), a key goal is to maximise insight as reward while minimising error as risk. We should distinguish, in that regard, between (A-ha!) surprises and (Oh-no!) shocks as variations of novelty experience. The former are desired because they provide new knowledge. The latter are to be avoided because they go against our expectations. This is contextualised by discovery. Specifically, via discovery both markets and minds endogenously generate internal surprises (i.e. insights) to adapt to external surprises. A portfolio’s risk~safety profile is thereby the investment version of the novelty~familiarity balance in cognition: in both cases we try to hit the sweet spot. The non-linearity of consciousness as the bottom-line ‘P&L’ of mind~matter exchanges make it particularly applicable for option theory, as I’ll mention in a moment.  For a description of an economic system, please see part B.

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Minds and markets deal with information in various ways which, again, we can interpret in economic terms. In minds this particularly involves the allocation of attention. Damasio calls attention “something of a finite commodity” (Damasio, 2000, p. 128), similar to Brady’s “finite resource” (Brady, 2007, p. 280). Allocating attention has become especially prevalent in our modern world of ‘attention seeking’ information blasts. Herbert Simon highlighted this early on: In an information-rich world, the wealth of information means a dearth of something else: . . . the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources (1971, pp. 40–41).

Such allocation serves minds and markets which, as I mentioned, are in the valuation business: they attach—(sub)consciously—values (symbolically via numbers) to realised information. This goes beyond simply communication or computation. Whereas a market maximises economic value for wealth, the mind maximises cognitive (e.g. epistemic) value for well-being. Other examples of mental assets or mental capital include memory and knowledge. Like their material cousins in the real economy mental assets are scarce or at least finite. As I will discuss shortly, consciousness’ functional aspect is awareness whereby paying ‘attention’ is the currency involved in the selection of an object of interest. Mental capital, held in various portfolios, is exchanged and valued in support of the consumption and production of other assets in the wider mind~body economy. This multi-tracked dynamic is how the MMH views mind~matter interaction which, contextualised in 4E cognition, overcomes the underlying dualism, or at least makes it practical. The purpose of all this is to deal with uncertainty, i.e. to confront the unknown, which, as pointed out, originates with the mind~body split. Here are the main characteristics of the MMH’s view of mind: 1. The mind allocates mental capital via natural, nurtured, and noisy15 instruments. In order to make a distinction with economic instruments, i.e. securities, I call these mental instruments “psychurities”. Psychurities are instruments that endow neuronal goods (e.g. dopamine) and mental services (e.g. thoughts) in return for (ownership of) payoffs. Those payoffs can be rewarding or penalising. Some payoffs occur in the mind’s public market but most occur within the mind’s private (subpersonal) market. 2. This allocation is based on demand and supply via competition and cooperation between deliberate and unconscious forces, employing “division of labour” skills across emotion, intuition, reasoning, etc. Specifically, they cooperate and compete for awareness (or ‘acknowledgement’) in consciousness. 3. This top-down~bottom-up market dynamic affects the chain between thinking, decisions, and sensations (see below) in that it becomes reflexive. Similarly for per See Cepelewicz, 2020.

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ception and action (see Subchapter 3.3). Crucially, your “I” engages in active mind management. Active management of allocation is via attention, the mind’s version of money with which exchanges are ‘cash-settled’. It is only ‘legal tender’ in consciousness: by paying attention you (i) become aware in general and (ii) in particular receive experience-payoffs in return.16 To estimate their ‘net-present-values’ to justify investment consciousness more broadly discounts sensory information (i.e. psychophysical ‘news’) while signalling such realisations (“impressions”) back to the wider mind in real time. Impressions can last, forming an inventory of stored memories, for example.17 Allocations and exchanges in the mind’s private market, on the other hand, are largely passive. Also, they are ‘non-cash settled’ via ‘physical delivery’ (say, of dopamine). For example, the mind has its versions of exchanges ‘in barter’, or ‘on credit’. Metaphorically, these subpersonal exchanges can be considered Over-TheCounter (OTC) or Off-Balance-Sheet (OBS). Most importantly, they do not involve nor receive attention, unless the assets involved move to the public market, e.g. are ‘cashed-in’. Finally, and as I mentioned, at the centre of the mind’s self-organising process of discovery is valuation, serving a similar purpose as price discovery. This culminates in consciousness. Specifically, the creation of value occurs primarily with the (net-present) valuation of new experiences. The mind values these realisations of information in terms of meaning and sensemaking.18 The preservation of value, on the other hand, occurs via (re-cognising) valuable memories. As the apex, the realisation of a discovery itself, the insight (see Klein, 2013; Kounios and Beeman, 2015), is valued as the A-ha sensation in the eureka moment.

All this, again, is aimed at coordination,19 e.g. in terms of guiding planning and selecting purposeful action in confronting uncertainty. And it does not occur in isolation from the body. Whereas the mind allocates across mental capital, the body

 In Subchapter 4.3 I discuss that this is not always perfect, via the case of inattentional blindness.  Just like the physical payment system is backing the payments of money, working memory is the physical system backing the payments of attention. Still, it’s an outdated metaphor. Our storage capacity for memories is estimated to only be around one gigabyte, signifying that our mind is not a computer.  The meaning of maintenance hypothesis is one interpretation that is particularly relevant in case of the surreal. It suggests, among others, that when we are confronted with reality checks and other inexplicable events that shake our worldview and increase uncertainty we use ‘fluid compensation’. It basically means that we retreat to safe havens to restore order, meaning, and sensemaking. See Heine, Prouix and Vohs (2006).  Please see the vast literature on Coordination Dynamics, starting with the work of one of its pioneers, Scott Kelso (e.g. 1995).

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allocates across bodily resources, mainly for its metabolism. Think, for example, of your microbiome: the millions of cells, bacteria and other microorganisms who compete and cooperate, consume and produce, or act otherwise as ‘agents’ within your body. Together with other resources, like factories (e.g. organs) and commodities (e.g. water), they ‘keep your economy going’. Also, the mind~body’s capital and resources can be combined into portfolios that are held and optimised for specific purposes. Portfolioism will be thoroughly discussed in section C but I will briefly introduce it now. In one sentence: portfolioism considers everything to be a portfolio. It starts with the view that reality consists of material resources and mental capital. Both are deemed and denominated as assets, whereby negative assets are liabilities (so going forward I will generally only use the term “assets”). Alone or combined these assets form portfolios at multiple levels. All assets are exchanged and valued in markets, including internal markets, private markets and domestic/local markets. So, portfolios interact via markets by way of exchanging assets which change those portfolios, including liquidation. In some cases portfolios form the market themselves, i.e. the market portfolio. Specifically, your mind~body is a portfolio, called the M~B Portfolio. It consists of bodily and mental assets. For example, it contains physical assets like bacteria, fluids, nerves, organs, and viruses. Your M~B Portfolio is an example of a fund-of-funds, a collection of sub-portfolios, some of which (it seems) are managed by ‘you’, while others are run in the (unconscious) background via algorithms and black-boxes. Also, some assets are traded exclusively within your internal market. All contribute to the overall M~B Portfolio return, whereby their specific (e.g. random) behaviour is less important than their portfolio weight. In order to deal with uncertainty, at each moment your active investing, primarily via consciousness, positions your M~B Portfolio relative to the world and relative to other M~B portfolios. Specifically, it is a clustering of psychophysical properties with a reflexive link to that world. Also, over time your M~B Portfolio changes in terms of make-up, exposure, tilts and so on including changes made by you. There are many possible applications of portfolioism, limited only to our imagination. Portfolios are, first, about ownership: when I hold securities in my financial portfolio, say shares of Amazon, I not only own those securities but by extension (pun very much intended) also part of Amazon. Second, by holding securities a portfolio has value. It is worth something. Both similarly apply to psychurities as instruments. Here ownership via portfolioism impresses the sense of self. It thereby solves the problem, raised by Rowlands, of “explaining the sense in which . . . a subject can own . . . its cognitive processes; that is, the problem is one of explaining the sense in which a cognitive process can legitimately belong to a subject” (Rowlands, 2010, p. 136). In terms of value, portfolioism impresses self-worth (and, intersubjectively, that of others). Nevertheless, any sense of self is relative because what we assume is ‘me’ is a portfolio. Top-down, and exposed to external markets, exchanging with other

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‘me’ portfolios results in dynamics that form patterns whereby the ‘me’ regularly dissolves. Another application is the investment concept of pure securities and the rich research behind them. It allows us not only to think of mind derivatives (e.g. Spinoza’s passions) but possibly also to think about how to model them:20 The work of Spinoza . . . bears a close relationship . . . to the twentieth-century theory of financial derivatives. (Derman, 2011, p. 29)

Although I will not detail this here, one can similarly reflect on ‘credit’, ‘leverage’, and ‘interest’ applied to the mind~body economy. I cannot emphasise enough, in that regard, two key points. First, most of the mental dynamics and processes involved are, at bottom, numerical. Be it in operation and/ or outcome. Examples include categorising, comparing, ranking, selecting, sorting, and (especially) valuing. Importantly: numerical does not necessarily mean computational nor purely quantitative. Far from it. Blindly applying mechanical economics and its flawed “mathiness” to cognitive science is not advisable. Also, of course, many of the mind’s operations remain unconscious, part of your internal invisible hand, so we assume ‘as if’ they are numerical. However, their signalling still contributes to the overall information processing. So, while we should interpret such processing in a Hayekian “local knowledge” sense it is expressed in the common language of numbers. Eventually there is a final global scoring in that regard, particularly of the sensations. The senses, like smell and taste, can be expressed numerically and are valued as such, stripped of all their other qualities. This brings me to my second point. There is sufficient evidence—shared throughout this book—that the MMH is justified in considering the mind to be like a market and engaged in valuing, when all relevant information is dually realised in consciousness. For example: The rudiments of consciousness were probably built upon neural systems that symbolize . . . values [to] inform organisms how they are faring in the game of survival. (Panksepp; in Carter, 2002, p. 186; emphasis added)

And: It is the resolution of uncertainty that we associate with the intrinsic value of behavior, which we assume is synonymous with epistemic value . . . casting rewards and value as probabilistic beliefs means that intrinsic and extrinsic values share a common currency. This means one can express (extrinsic) reward in terms of (epistemic) information gain and quantify their relative contributions to behavior. (Friston et al., 2015, p. 188; emphasis added)

 Part of the modelling could include things like the Lyapunov function which is also suggested by some cognitive scientists (e.g. Friston, 2018).

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A key cognitive ability involved in valuation is number sense, the intuition of what numbers mean. I say more about this in Chapter 7, but for now this “Intuition about numbers is . . . anchored deep in our brain. Number appears as one of the fundamental dimensions according to which our nervous system parses the external world” (Dehaene, 1997, p. 5). In short, by valuing the mind goes deeper than ‘just’ evaluation because it deals with its numerical fundamentals. While being a private market in many respects, the individual mind forms part of the wider global 4E21 economic system, whereby its continuously shifting exposure impacts the mind’s allocation, discounting, and action. Specifically, per 4E cognition the mind is: – Embodied, meaning that the purpose and significance of psychological states is grounded in properties of the whole embodied organism, not just the states and properties of its brain (e.g. Varela et al., 1993). One example is the way in which bodily gestures both help constitute the meaning of speech, and feed back into the cognitive capacities of the cognizer (see Clark, 2011, Sections 6.7–6.9). A more pertinent example is George Soros’ famous back-pains which, acting as warnings, supported his investment decision-making (see Cymbalista, 2002b). Embodiment can be considered both a causal and a constitutive claim. – Embedded, meaning that mind is situated in and influenced by its environment (e.g. Clark, 1997). An example is the role of culture and institutions—including their biases, habits, laws, and rules—in shaping the kinds of cognitive capacities available to human agents. Embeddedness is generally considered a causal claim. – Enacted (or enactive), meaning that mind emerges from patterns of physical interaction with the world (e.g. Stewart, Gapenne and Di Paolo, 2014; Ward et al., 2017), not just neuronal processes. There are variations of enactivism, like sensorimotor enactivism and radical enactivism (e.g. Silberstein and Chemero, 2015). Specifically, and in mind-as-market terms, enactivist agency relies on multi-level portfolios of biochemical and sensorimotor psychurities (see Appendix A4). These compete and collaborate to constrain and enable each other, according to which an agent assesses and makes sense of the world. In one respect, the wish to act in or upon the world often comes from an uneasiness or dissatisfaction with its existing state. Mind (e.g. by building architectural knowledge) and world (by building physical structures) are then influenced by the form such action takes (by design). Enactment can be considered both a causal and a constitutive claim in that regard. Enacted cognition provides a lens through which we might better understand, for example, Soros’ “manipulative function” and Mises’ “purposeful action”. Finally, enactivism considers extension (which is the next “E”) as a default setting, meaning that our psychurity portfolios (and thus sense-making) may be dynamically and in-

 For arguments as to how these 4 Es relate, see Ward and Stapleton (2011) and Li (2019). See Subchapter 2.4.1. for a 4E application to the market’s mind.

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tersubjectively coupled via exchanges with those of other agents which, consequently, can create complex social configurations, like markets. Extended, meaning that the basis of psychological states can extend beyond the brain to encompass worldly structures, like artefacts and technologies, with which embodied agents couple in order to get the cognitive job done (e.g. Clark and Chalmers, 1998; Menary, 2010-ii). Extension particularly involves other minds via social structures, resulting in collective intentionality (e.g. Tuomela, 2007), distributed cognition (e.g. Huebner, 2014; Merritt and Varga, 2013; Palermos, 2016), extended affectivity (e.g. Colombetti and Roberts, 2014), and social phenomenality (e.g. Szanto and Moran, 2016), to the point of extended/collective consciousness (e.g. Mathiesen, 2005; Kirchhoff and Kiverstein, 2019; Overgaard and Salice, 2019; Pacherie, 2017; Schwitzgebel, 2015; Valencia and Froese, 2020; Vold, 2015). Extension is generally considered a constitutive claim and viewed as spatiotemporal.

Portfolioism provides the investment-inspired framework to interpret 4E cognition whereby exchanges and other portfolio/market dynamics cross or otherwise deal with the mind~matter separation. By the way, those 4E conditions of our minds can get severely disrupted. In the extreme think of the movie Cast Away where Tom Hanks plays Chuck Noland, a FedEx employee who is stranded on an uninhabited island. The movie vividly shows what happens to our personal mind-as-market when it gets isolated. Of particular interest are Chuck’s surreal exchanges with “Wilson”, the volleyball. In general, in order to realise they are conscious healthy humans need regular, if not continuous, confirmation by other humans. We know, in that regard, that long isolation distorts the sense of self and drives people mad. A human mind is, subtly and continuously, reminded by other minds that it is conscious. This reminding occurs when she recognises herself as having consciousness not only through her perception of others’ consciousness but their implicit confirmation of her consciousness. It makes human exchanges, in all their forms, including trading, so important in the overall discussion on consciousness. In fact, I would argue that any explanation of consciousness is incomplete if it does not consider this shared or collective dimension. Eventually a human mind confronts its future, as does the market mind. There are two versions, each produced by different assessments: – The extrapolated future: the mind has forethought and can anticipate a future by judging current events (and their likely evolution) based on history. The market’s mind discounts such ‘predictable’ events. For example, if you do not study for it, you will fail that difficult exam (again). – The novel future: the mind can imagine a future from scratch and creatively invent tools for it. The market’s mind values that future before it (ever) materialises. On occasion it does this fairly accurately. Most of the time, however, the novelty consists of breakthroughs which disrupt knowledge and the estimates based on it.

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Importantly, as part of such assessment humans have a sense of those futures in terms of what they qualitatively could feel like.22 In other words, they have a ‘future consciousness’ which is heavily evolutionary coloured, i.e. native, because it centres on existence and survival. As I’ll discuss later, this is relevant for true uncertainty as well as AI. It makes human consciousness unique compared to anything a robot (at some point in the future) feels. In summary, mind “is not just a neural control system but a complex cognitive economy spanning brain, body and world” (Clark, 2011, p. 217; emphasis added). The mind is the domain where mental capital is allocated to support the overall cognitive economy while engaging the world, including other minds, often via technologies. Such engagements are dynamic and require “coordination”, balancing the diversifying processes of “competition and cooperation” (Kelso, 1995, p. 6), for example between unconscious and deliberate forces. Similar to other complex adaptive systems, that balancing takes the form of an arms race, nicely captured by the Red Queen effect.23 It should be clear that the boundary between mind and world is fluid (if not dubious), i.e. separation is not always clear cut. Also, the mind’s direct environment offers opportunities to act —think Gibson’s (1979) “affordances”—as well as limitations. To be precise, I like to think about affordances in terms of Heidegger’s “possibilities” which become revealed and point to the future. It is perhaps useful here to make the connection to economic markets as specific environments offering affordances. As Hayek points out, markets “secure for any random member . . . a better chance over a wide range of opportunities available to all than any rival system could offer” (Hayek, 1988, p. 84). Throughout history (and particularly since the Industrial Revolution) we’ve used markets to change our surroundings to better suit us and improve our opportunities and/or neutralise threats. In that context, but at the individual mind-as-market level, with our portfolio of psychurities we benefit from/hedge against states of the world. It provides exposure to “affordances”, resulting in human~environment pairing because “affordance . . . implies the complementarity of the animal and the environment” (Gibson, 1979, p. 127). In other words (also remembering the words of Gould and Keynes in Chapter 1), the MMH translates this ecological affordance into economic affordance, or as agent~market which implies the complementarity between agent and market. I will discuss some of these issues in more detail in the section on consciousness below. As a trailer, consciousness is the mind’s pricing system where values are discovered, as information is dually realised. Its discounting is what allows “surfing the waves of noisy and ambiguous sensory stimulation by, in effect, trying to stay just ahead of the place where the wave is breaking” (Clark, 2016, p. xiv). It means that, as

 Of course, neither of these futures necessarily occurs. Also, I make an early distinction here between access and phenomenal consciousness which I’ll expand upon below.  In Lewis Carroll’s Through the Looking-Glass, the Red Queen said to Alice: “Now here, you see, it takes all the running you can do, to keep in the same place”. Later I will explain how this is enacted in the human mind. For an economic interpretation based on game theory, see Markose (2005).

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far as the mind’s prediction is concerned, consciousness offers the feeling transmission of values (as a “System 3”, see below). Crucially, the symbols it discovers, as they emerge in awareness, have quantitative and qualitative properties, just like prices and other numbers in the economic system. Patterns and their meanings—to those who actually experience these moves—are an obvious example of their qualities. To conclude, I like to think of the MMH’s exposition of mind-as-market as my modest contribution to “generalise the notion of price to domains other than financial” (Ayache, 2010b, p. 44). At the same time, it also is my contribution to “attempts at quantifying” (Clark, 2011, p. 214) 4E cognition.24

Other Mind-Related Terms The state of your mind is called mental state. It frequently determines your state more broadly and is characterised by its conditions. For example, when you are suffering a loss, you are in pain. Although a mental state often qualitatively feels it is temporarily stable, it is not static. Rather, a mental state involves dynamic processes, particularly those creating (e.g. for imagination), arranging (e.g. for logic), and/or invoking (e.g. for memory) its constructs like beliefs, expectations, hopes, and wishes. Moreover, these processes are often subliminal. The processing is primarily aimed at dealing with, i.e. anticipating and adapting to, sensory inputs. Those inputs are from the world impinging on the senses. A field studying this is called Predictive Processing Theory (PPT; e.g. Clark, 2013). In short, PPT views the human brain as a (Bayesian) prediction organ which values the sensations that query the environment in order to test its model of that environment. Specifically, the external sensory signals are compared to the model’s predictions and the differences (called prediction errors) contribute to the updating of the model. This eventually leads, among others, to reflexivity between (internal) perception of the world and (external) action upon it. Again, if this sounds like markets and investing, then you are right because it is. PPT will be discussed in more detail in Subchapter 3.3. One of the distinctions made by cognitive science is between mental states and brain states. Any correlation between the two, relating first-person subjective to third-person objective accounts, is an important area of research. However, 4E implies that the mind involves more than the brain and that such correlation may be elusive. Within mental states we can identify different levels or types, including sensations, emotions, and feelings (e.g. Damasio, 2004, figure 2.2, p. 37). Sensational experiences depend on the type of senses involved. Visual sensations are different from

 Perhaps this confuses some readers in light of my earlier comments, so let me be clear: the mind does calculations and computations but that doesn’t make it a computer.

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audio or tactile ones. Some connect to memories. Smells can be very powerful in that regard. Feelings involve the qualitative dimension of consciousness, viz. what it feels like to have a particular experience. In technical terms the mind is affective, meaning that it feels (e.g. Panksepp, 1998). As I will explain in the next section, I see consciousness in general, and feelings in particular, as a complementary “System 3” to the wellknown dual-process “System 1” and “System 2”. In portfolio terms it acts like a qualitative (derivative) overlay that values the “returns” of experiences. Representations are the general label for mental representations, the content of mental states. Language wise, these should be distinguished from external representations (i.e. those in the world), which is how the term is generally meant in this book. In other words, the number six is externally “represented” by the symbol 6. How that number (as symbol), in turn, is internally “represented” as content of your mental state is a different question. Representations have satisfaction conditions, like accuracy and correctness, and can be judged in terms of how closely they meet these conditions. Not everybody agrees that representations should be used conceptually in cognitive science. To wit, as will be discussed, moods are not representational. Also related is intentionality, a medieval term revived by the philosopher Franz Brentano. Generally I judge the distinction between intentionality’s cognitive and its standard meaning as artificial, as I’ll explain in a moment. Still, in cognitive terms intentionality is the mind’s capacity to actively capture and portray something. It could be an object, a thought, some state of the world, and—importantly for our purposes—a value. Intentionality is present wherever mentality conduits information about something else, its ‘aboutness’. Translated for consciousness: consciousness is being aware of that something. A more specific way to think about cognitive intention is to view it as that what occupies the mind, what it is dealing with. For example, it can be the object of an emotion (“I love my dog”) or a belief (“the weather will be bad today”). As we will see, it can also be an asset, algorithm, or price in an investment decision (“Buy AAPL @ $100”). As such, intentionality is mental action, aimed at (properly grasping) something. Consequently, it can miss its target, especially when it is inaccurate. The fact that attention—the ‘active’ part of consciousness—is employed for and aimed at that something already blurs the distinction with the traditional meaning of intentionality. On that note, the conventional meaning of the term is goal-directed behaviour. This is also where the mind-as-market principle suggests that intentionality follows from the embodied mind’s demands and needs and is ‘correct’ when these are met (e.g. supplied by the environment). In terms of extension, tools only have functional utility if they are actively and purposefully used. This drags philosophical intentionality further towards practical intentionality. In other words, intentionality is economically motivated and driven. Historically, the economic relevance also lies in the littleknown fact that Brentano was a colleague and friend of the Austrian economist Carl Menger and they seem to have influenced each other, especially regarding the nature

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of value.25 Eventually intentionality found its way into economics via praxeology, Mises’ interpretation of purposeful human action. As far as Mr Market’s intentionality (in the traditional sense) is concerned, it is important to remember that it is independent from any individual’s intentionality: Every individual . . . neither intends to promote the public interest, nor knows how much he is promoting it . . . he intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was no part of his intention. (Smith, 1759, pp. 184–185)

As discussed in Chapter 1, the invisible hand is not only about competition, i.e. in the Darwinian sense. It cannot exist without cooperation in terms of coordinating the market (or any cognitive system, for that matter). On that note, even if the individual intentions differ, say, between a bull and a bear the market’s collective intention is to allocate resources via exchanges which requires cooperation. Although (physicalist) deliberations usually concern the strong form of mental causation (e.g. Kim, 2005), in this book we mean its weak form as mentioned in the Introduction.26 In other words, representations like beliefs, desires, and ideas, to the point of delusions, can cause physical events. Physical closure, in that regard, may be metaphysically true but it is psychologically impotent. Again, the boundary between these domains is not clear and gets continuously penetrated. Although physical causes like neuronal firing may ultimately have been responsible for Martin Luther King’s heroic behaviour, it was his phenomenally impactful dream that changed America. As we have seen throughout history more broadly, representations can be considered dangerous and threatening by the establishment, particularly when they motivate physical actions. While physical demonstrations, say for freedom, are the outward manifestation, it is the internal desire (for that freedom) which becomes the target of repression so that, according to propaganda-expert Edward Bernays, “our minds are moulded, our tastes formed, our ideas suggested”. Let me try to explain why mental causation is relevant for economics. I will start with a simplified example of investing, contrasting it to physical causation. Suppose an oil trader enters a sell (or short) order for oil, expecting its price to drop. Her motivation is that the conditions for oil storage, say due to tanker occupations, will change

 Together with Mises, Hayek and others, Menger represents the so-called Austrian School of Economics. Although heterodox in economic academia, they are more accepted in the investment field (e.g. Spitznagel, 2013). I use them as references because their writings on free markets (vs. central planning) and the role of price discovery are broadly in line with my own convictions. In addition, more than their contemporaries, they were aware that cognitive issues are of relevance to economics.  I personally think that the reason Kim’s causal exclusion is considered a “problem” is because of the confusion between affect and effect in making a difference especially in a collective setting. For example, a price―reflecting the market’s discounted expectation of a future state (i.e. of cash flows)―can affect a person. But there is no effect that can be isolated and pinpointed as such because of the collective external activism of prices via securities.

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and will impact the supply of oil. Her motivation is thus based on physical causality: the physical event of oil storage causes the physical supply of oil to increase, in turn causing the price drop. One month later, this physical event materialises. Surprisingly, however, instead of dropping the oil price rises due to a threat (a mental event) by OPEC to fix a minimum price (another mental event). So, in general, we associate physical causality with economic fundamentals and mental causation with financial conditions, but it is important to realise they are entwined. Another situation where something mental has a tangible effect in the market (which may not materialise for ‘real’) is via so-called blank-cheque special purpose acquisition (SPAC) vehicles which accounted for a third of US IPO filings in the first half of 2020. These are shell companies. Here we are particularly interested in the empty type: they have no assets, nor operations, and are listed with limited capitalisation. Still, investors have been known to buy their shares, valuing the shell company far above book value despite its lack of fundamentals. In short, some promise in a vague business plan (if available at all) raises investors’ expectations. Such belief is the only cause for them to physically act (e.g. by buying the company’s stock). The archetypal occurrence, of course, was the 1720 bubble of the South Sea Company which spawned various shell companies. The most notorious one stated—as its cognitively empty promise—that it was intended “for carrying on an undertaking of great advantage; but nobody to know what it is” (emphasis added). Elsewhere in the book I will discuss other examples of mental causation in the economic system, including monetary policies. Cognitive faculties have evolved into specialisations.27 If they reach out into the outside world it involves extended (or distributed) cognition (see Subchapter 3.2). There are a number of terms that point to shared faculties between multiple minds. Collective intentionality, for example, is “the power of minds to be jointly directed at objects, matters of fact, states of affairs, goals, or values. Collective intentionality comes in a variety of modes, including shared intention, joint attention, shared belief, collective acceptance, and collective emotion”28 (emphasis added). This may lead to collective action, “actions performed by groups” (Chant, 2006, p. 423). Such collective activity is not mechanical: Human beings don’t merely make individual contributions . . . like machines operating in an assembly line. Rather, we are . . . aware of others and what they are trying to accomplish. We pay attention together and we share goals. In the language of cognitive science, we share intentionality. This is a form of collaboration that you don’t see in other animals. We actually enjoy sharing our mind space with others. (Sloman and Fernbach, 2017, p. 14; emphasis added)

 Collectively expressed or replicated via labour division in the economy.  Source: Stanford Encyclopedia of Philosophy. For a specific view on shared intentionality, see Gallagher and Tollefsen (2017).

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In traditional markets this starts small,29 namely between a buyer and a seller. In financial markets such collective intentionality is more complex, both in scale and intensity, with thousands of minds ‘jointly directed at’ (primarily) prices. Freely interpreted, Mr Market exhibits intentionality because his prices conduit information about the state of the world, making him “an actor with affect and intention” (Zaloom, 2003, p. 12). Intersubjectivity (see Subchapter 1.2) highlights the phenomenality of collective mentality. It means a collective or shared experience whereby the mutual exchange makes a difference to the individual experience in its own right. In particular, it impresses what it is like to share experience. Both collective intentionality and intersubjectivity raise questions regarding reducibility and ownership of shared faculties and states: where does your mind end and mine begin? I will revisit this in part C. Finally, multiple minds can form a collective, e.g. group, mind. A collective mind involves various cognitive concepts which point to the collective-social dimension of reciprocal exchanges between subjects, where human complexity emerges. Such exchange varies from intimate intercourse to an impersonal trade. But in each case, such exchanges are more than the sum of the physical parts and thus not reducible to them. For example, they often drive creativity. And while solitude can play an important role for mental health in general and creativity in particular, social isolation, like we experienced during the CVC, is generally constraining if not damaging. In the case of the market mind, it exhibits socio-cognitive properties that do not belong to the individual participants but, instead, arise from their trading.30 In particular, it spawns shared symbols, i.e. prices. I will explain why prices are symbols in more detail, especially in Chapter 7.

A4. Consciousness With the arrival of humans, it has been said, the universe has suddenly become conscious of itself. This, truly, it the greatest mystery of all. V. S. Ramachandran

There are many definitions of consciousness despite (or perhaps in virtue of) the fact that it is an elusive phenomenon. A simple but popular one is by philosopher John Searle: ‘Consciousness’ refers to those states of sentience and awareness that typically begin when we awake from a dreamless sleep and continue until we go to sleep again, or fall into a coma or die or otherwise become ‘unconscious’. (Searle, 1997, p. 5)

 As in the smallest social unit, consisting of two persons.  Again, at its core, trading involves the smallest possible social entity: two people, one being the buyer and the other the seller. Each needs the other to experience trading. Examples of larger groups in markets are shareholders of Apple stock, bondholders of Argentinian treasuries, market makers, ‘bulls’ and ‘bears’, etc.

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Sentience is the ability to experience, like hearing the sound of a clarinet, seeing the redness of a tomato, smelling the scent of a rose, suffering the pain of loss, or tasting wine. Often, they mix and mingle, forming a portfolio of complex multisensory experiences that are impossible to reduce without affecting their peculiar quality. To paraphrase the philosopher Susan Cleland, definitions are not the proper tools for answering the scientific question ‘what is consciousness?’.31 In fact, not everybody in cognitive science acknowledges consciousness. Deflationists or eliminativists basically suggest the following: ‘Let’s assume we understand consciousness, i.e. it is an illusion. Then the hard problem disappears’.32 A key novel point the MMH raises in that regard is the reality (backed by empirical data) of the collective dimension of consciousness. While we can argue whether it is access or phenomenal, every (metaphysical) stance in cognitive science needs to address this. They can’t simply ignore it. At the same time, and in turn, it is prudent for the MMH to remain open-minded in its research. The MMH task will thereby simply be to analyse the efficacy of whatever topic-neutral operational mechanism the eliminativist must come up with and see whether and how it manifests in the market. On mood, for example, such a ‘mechanism’ would have to be highly reflexive and so on. So instead of offering my definition I would like to complement Searle’s words more loosely by adding my economically inspired interpretation of consciousness via portfolioism. The key motivation is that the main interpretations of consciousness do not provide a satisfactory explanation for its dynamics. It is because of its real-time animation that consciousness makes you feel alive. For example, when the neuroscientist Bernhard (Bernie) Baars, towards the end of his pathbreaking 1997 paper, states that “consciousness appears to be the main way in which the nervous system adapts to novel, challenging and informative events in the world”33 we are still left with the familiar questions of “Why?” and “How?” In his case of Global Workspace Theory I address it in more detail in Chapter 3. More generally I will expand on portfolioism in part C of this appendix, as well as chapters 7 and 8. For now, the MMH’s interpretation of consciousness is an important example of the potential of cross-fertilisation of concepts to better understand (the connections between) markets and minds.

 For completion, Cleland was discussing definitions of life, not consciousness.  Economically minded readers will recognise the similarity with the “as if” approach by mainstream economists: ‘Let’s assume we understand economic agents, i.e. we assume they act as if they are rational. Then any behavioural problem disappears’.  As an aside, replacing some words offers another example of the similarities between mind and market: “[prices] appear to be the main way in which the [market] adapts to novel, challenging and informative events in the world”.

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Interpretation of Consciousness If we accept that markets are extensions of our minds, then vice versa we should study market behaviour, concepts, and dynamics for a chance to better understand minds and mentality which they reflect. Let’s start with the earlier leg of the MMHpremise that the mind is like an internal financial economy or market with attentionas-money (compared to money-for-attention of the social media world in the external real economy). I focus on how financial markets, by way of exchange and valuation, deal with uncertainty. In particular, I will discuss five appropriate finance concepts and how these apply to consciousness:34 1. Price discovery is our collective attempt to informationally bridge our internal (mental) world with the outside (material) world. It is part of the broader process of allocation of mental and material resources to deal with uncertainty. Crucially, price discovery is numerical: information (e.g. news and insights) is signalled via changes in numbers. In most cases exchanges involve some form of money and prices reflect (a ratio of/to) currencies. 2. Discounting is an important element of price discovery and is closely related to Predictive Processing. With discounting an information transformation takes place in that news (e.g. leading to prediction errors) is almost instantly interpreted and reflected in prices. This formally involves recalculating the net-present-value (NPV) of expected payoffs (e.g. cash-flows) after accounting for the impact of the news. It leads to changes in prices towards those net-present-values via buying and selling. In turn, prices and their changes have their own signalling purpose. In other words, prices are consumed information but simultaneously produce a message to market participants (i.e. as updated predictions).35 3. Continuous-time exchanges (e.g. Merton, 1969) solves various problems of assuming discrete time. In terms of the mathematics, for example, it introduces stochastic calculus (e.g. differential equations, Ito’s lemma) to investing. Through this concept, stock prices, interest rates, and other financial variables are treated as continuous random processes that follow mathematical rules governing their dynamics over

 At this stage I will describe this conceptually and for illustrative purposes. I will thus not discuss the mathematics behind these concepts which, in any case, are widely available in the finance literature.  A particular form of discounting is so-called hyperbolic discounting. It is a cognitive bias whereby people prefer a smaller immediate reward for larger postponed rewards, with the preference (quantified via a discount factor) growing stronger as the delay in the larger rewards shortens. A related concept is intertemporal bargaining, whereby the current ‘you’ basically bargains with future versions of ‘you’. For example, the current you sacrifices by eating a healthy salad but promises the future you a chocolate cake as a reward tomorrow.

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time. This framework provides powerful (but imperfect) tools for analysing financial markets and has led to the development of models for pricing derivatives (e.g. BlackScholes option model), managing risk (e.g. VaR), and understanding the behaviour of asset prices generally. Temporality is fundamental to all exchanges. For our cognitive setting, it is of particular interest for the ’continuous-time’ experience central to consciousness. Experiences dynamically unfold in time. They are not discrete snapshots. 4. Quantitative~qualitative performance points to investment’s double utility, its complementary quantitative and qualitative aspects, in particular regarding prices and their changes (called returns). First, these numbers can be expressed and interpreted quantitatively and objectively. In addition, they have value based on qualitative and personal assessments. Allow me to explain this via the concepts of loss aversion and ESG-investing. Loss aversion, the main insight from prospect theory, tells us that the pleasure from a £100 profit cannot fully compensate for the hurt from a £100 loss (Figure A.1). So, even though these two payoffs or returns cancel out quantitatively, that is not the case for the associated qualities of their respective experiences. A related phenomenon comes from ESG-investing. Let’s assume that you, like a growing number of investors, have added ESG-investing via a separate mandate to your main portfolio. This could potentially lead to tensions and conflicts between preferences. I will give two examples. First, I submit that you value a 10% return on your ESG-investment in a green energy stock differently from a 10% return on your main investment in an oil stock. Second, I submit that you value a 10% return in retailer A due to its successful online strategy in fair-trade and organic products differently from the 10% return in retailer B due to its costs savings via layoffs. In other words, there is a distinction between return quantity (the objective score) and return quality (the subjective score). 5. Pure securities are a largely theoretical form of securities. Securities more generally are financial instruments like bonds and stocks which are traded and valued in markets, paid for with currencies. Each represents part ownership of an asset and offers specific functions, including the raising of equity/debt. Derivatives (like options) are a specific kind of securities, mainly because they offer the right (or obligation) to buy/sell other securities (or assets outright). I will briefly discuss option theory applied to consciousness in a moment. Back to pure securities whose prices are appropriately called state-prices. They are namely state-contingent claims that only pay out one unit of (some) value in a certain state and nothing otherwise.36 In other words, they have a

 Pure securities are part of state preference theory and the theory of complete markets (Flood, 1991). They were introduced by Arrow (1964) and are also known as Arrow-Debreu securities due to the link to Debreu’s concept of a certainty economy. Unlike them I will not use pure securities for equilibrium purposes but rather for their role as the market’s “nervous system, transmitting signals” (Beinhocker, 2007, p. 38) in order to support the theoretical underpinning of the MMH. Also, here value can be in the form of a commodity, money (e.g. currency), or even a service.

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Figure A.1: Loss aversion.

binary 0/1 payoff that is conditional. However, by combining pure securities in a portfolio (e.g. creating a “structured product”), including fractional and short positions, one can basically replicate any payoff structure. Examples include derivatives which payoffs depend on the values of underlying assets/securities. They help to “complete” markets. In turn, more complete markets provide investors wider flexibility in allocating capital, pursuing different strategies, and preparing (positioning) for uncertain outcomes, i.e. potential future states. In cognitive terms, (in)complete markets are all about affordances. In the extreme, by Cramer’s Rule, as soon as the number of securities equals the number of states, the market is ‘fully complete’. It means that the market (and more broadly the economy) has become deterministic. This extreme situation does not exist, of course. Also, laws, taxes, subsidies, and other central (physical) controls cannot complete markets. As economists like Arrow and Sen showed, they need a wider mentality, in the form of values (e.g. ethical and moral norms), to fill the gaps. Portfolioism applies these finance concepts to the mind in general. An important application, already encountered, is “psychurities” as the mind’s version of finance’s securities. Let me explain. By holding securities in their portfolio investors are able to adapt to states of the world. Specifically, each (sub)portfolio is structured to replicate a strategy to respond to an event, ultimately aimed at gaining a reward or hedging a risk. For example, a (sub)portfolio of commodity (e.g. gold) related securities hedges the risk of inflation. The extent of the investor’s skill determines any so-called excess return above the return of some benchmark. The MMH interprets this for our cognitive

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setting and specifically translates securities as “psychurities” for our mind-as-market. Similarly then, psychurities represent our mind~body assets, varying from emotions to thoughts, that allow us to adapt psychophysically to states of the world. For example, a psychurity can be a single psychological adaptation: its activation or trigger is event-dependent or, in finance terms, ‘state contingent’. Just like finance’s “pure securities”, such a psychurity embeds a contingent claim, or payoff. To wit, the ‘derivatives’ in your psychurities portfolio allow you to ‘derive’ (contingent) value from an experience, even though you have no direct sensory exposure to the underlying aspects. What counts, for example, is how objects appear to us. So, although you only see one side of an object (e.g. a tomato), you experience it in 3D. “Whole objects and detailed scenes are embraced in experience, despite facts about the limits of sensory uptake that might appear to make this problematic” (Ward 2012, p. 737.) Portfolioism suggests that the mind forms a multi-layered complex of psychurity-portfolios. This is further discussed in Section C3, as well as Chapter 8. Here portfolioism applies the above finance concepts—including excess return— particularly to consciousness, centred on valuation of experiences. This is motivated by the following. What is missing in current cognitive theories, especially on consciousness, is the recognition of the importance of value when paying attention as information is dually realised. In investment terms, what is the net-present-value (or NPV) of such attention, seen as investment? This brings me to a more general point. While I hope to detail this in future work, I submit here—in the context of uncertainty, but also Brentano’s intentions, Gibson’s affordances, Heidegger’s possibilities, and so on—that option theory is very applicable to psychophysical exchanges in the mind-as-market. Many of our experiences can be seen as possible (contingent) payoffs for attention (e.g. following previous decisions, etc.) Notably, and in portfolioism terms, they can be considered (and thus valued) as options on the underlying (often dormant or unconscious) psychurities, with a strike price, premium, and various related variables, known as ‘greeks’ (delta, theta, vega). This allows us, at least to some extent, to interpret conscious qualities quantitatively. Notably, top-level valence— which specifies whether experiences are positive, negative, or neutral—is captured in returns. This leads to the following interpretation of consciousness which builds on earlier comments, particularly in the section on mind (A3). Cognitive Note Consciousness (according to portfolioism) What consciousness is to the brain can best be compared to what value is to assets. Specifically, value is in the “I” of the beholder, who happens to hold a portfolio of values. Bottom-up, exchanges and other ‘market’ activity in the physical brain leads to mental states that are valued via self-awareness. Top-down, as in ‘downward causation’, this reflexively works back on the brain, e.g. through its plasticity. Consequently, neural correlates of consciousness (i.e. between the brain and consciousness) can change, just like the correlations between assets and securities. Consciousness is thus the mind~body’s valuation system, the (public) market where mental prices are realised, say due to the exchanges between your S1 and your S2. While this is not limited to such

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cases let’s connect these internal values with external values, e.g. when you buy that much-desired object. With Harman’s separation still fresh in our memory, we take our cue from Simmel (in turn, echoing Hegel): The object of attention, “which is characterized by its separation from the subject, who at the same time establishes it and seeks to overcome it by his desire, is for us a value. The moment of enjoyment itself, when the separation of subject and object is effaced, consumes the value” (Simmel, 1907, p. 63; emphasis added). At that moment you exercise your option and the amount of attention you pay can be considered its exercise price in return for the value. More to the point: the subject consumes the value as owner of a portfolio of S1, S2, and other mind~body assets. This brings us to returns. In markets, a key conscious activity is discounting news which ‘realises’ all information via prices. Specifically, via the usual market forces (like demand~supply) investments (by way of trades) lead to changing values in prices (e.g. PNow and PBase) that generate profits and losses, known as returns. The mathematical default is lognormal returns, i.e. as a function it reads ln(PNow/PBase). In the mind’s case, conscious experiences are the multisensory real-time ‘returns’ for actively ‘investing in the now’,37 i.e. the payoffs for paying attention while employing your eyes, ears, skin, S1, S2, etc., and committing memory. To be clear, “now” is relative in that returns occur over (continuous) time; price changes have temporality. This suits experiences as well: “for information to become conscious [i.e. to be realised], some amount of time needs to pass, so that normally there is no way the brain can ‘in an instant’ reach the kind of state that supports conscious experience” (Clark, 2009, p. 978). The amount of attention paid stands for the active allocation, or weight, in a portfolio. For example, this can be relative to the previous allocation or relative to another mind~body portfolio. Each portfolio of psychurities has a bespoke mandate, including (e.g. evolutionary imposed) constraints. Within those portfolios, different sensory modalities are governed by different exchanges, both internal and external of the overall mind~body portfolio. In section C6 I will discuss ownership in broader terms. To further clarify, I am not discussing the measurements of these returns, say, via some “instrument, a psychophysical machine, continually registering the height of pleasure experienced by an individual, exactly according to the verdict of consciousness” (Edgeworth, 1881, p. 101). Rather, I’m discussing the (dynamics between) market-type complementarities that are behind the generation of these returns as such “verdict[s] of consciousness”. Returns have two properties, the yang and yin of valuation: 1. Quantitative, as a result of objective valuation via access processing: returns are (symbolically38) numerical across all senses, making it a uniform property. All tactile sensations (e.g. pressure, temperature, pain) can in principle be expressed numerically, as can all visual (e.g. light, colour) and all other experiences. For example, externally some “news” shifts my attention and I now experience seeing 0-24-186 (i.e. a blueish colour), feeling 30 ᵒC (i.e. a warm glow), etc. Internally, of course, these numbers are converted into the currency of the particular mental location. Again, this quantification also allows, for example, comparing, scaling, selecting, sorting, and other numerical operations. Moreover, it facilitates valuation across minds because we all are subject to the same Market Mind Principle. 2. Qualitative, as a result of subjective valuation via affective processing: returns are, in principle, of different kinds depending on salience, the particular type of sensation. This makes it a unique property. For example, a tactile return (hot coal’s heat as you move your hand towards it) is different from a visual return (hot coal’s redness as you focus your vision on it).39 The unique property

 See Ziegler (2010).  See, Panksepp’s earlier quote.  Just like in economics, where a bond return is different from an equity return. They signal different information and serve distinct purposes in terms of adapting (i.e. benefiting from/hedging against) (changes in) states of the world.

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is what is valued subjectively. Consequently, the traditional ‘value in the eye’ (or ear, nose, skin) of the beholder becomes the ‘value of the I’ of the beholder. In your case it receives its own valuation by you, signifying its personal worth to you (again, paid for by attention). Still, each is just one value in your overall portfolio. Also, returns can be subjectively valued in a cross-sectional (or factorial) sense. An example is via valence, like pleasant/unpleasant, good/bad, and indifferent/neutral. Per portfolioism, returns can be combined, resulting in a composite portfolio-experience that differs from any of its original constituents and synergistically merge them, depending on their respective weights. For example, the excitement of a roller-coaster ride is experienced as a mix of visual (height), tactile (air), and auditory (scream) sensations. Returns also form patterns and, depending on their source, can be frequencies, rhythms or vibrations. The base price (PBase) is, metaphorically, like the end quote from your previous utility provider before you switched. It stands for the final score of any particular sensation that last lost your attention. In other words, it was the exit price for which you traded-in the previous instance of attention because the value of the (expected) return of your new experience is bigger than paying the amount of fresh attention as exercise price of your option. A shift in attention is thus an exchange taking place between experiences, which itself is liminal. Your experience of the redness of the rose is replaced by the greenness of the grass, each with its own price. In comparison, if something keeps your attention, like the sound of Miles Davis’ trumpet when driving, you ‘keep your other options open’ (i.e. you do not exercise them) and instead enjoy the return from Miles’ music while keeping track of its real-time price quotes (quantified in, e.g., frequencies), like a cognitive ticker tape. You exchange it if the (expected) price of another experience “jumps at you”, for example because some risk appears that makes you pay attention to the road as a hedge. After all, that’s what options are for. Therefore we should think of experiences as returns that can be compounded (i.e. combined vertically, e.g. over time) as well as diversified (i.e. combined horizontally, e.g. across senses). Streams of consciousness as value streams within our mind’s market-portfolio, as it were. Importantly, like financial returns conscious returns can be negative, are mostly non-linear, and their correlations can break down. Also, the return from paying attention to that particular red tomato is idiosyncratic. Usually we quickly stop paying it attention, implying we do not value it enough, because its colour is static (like a stale price loses market interest, i.e. no ‘news’ or ‘volatility’). And then there is the ultimate conscious return: the Aha-experience (as enlightenment in the extreme case) is pure alpha—or excess return— due to discovery, the most active form of investing mental capital, i.e. the unknown has no benchmark (except perhaps zero, because there was nothing before). The endogenous numerical nature of experiences thus also embeds their duration or temporality: the amount of time undergoing them, to the point of influencing the experience of time itself, i.e. intrinsic time. (See also, Holm and Madison, 2013). So, your “I” is very much participating in the mind’s valuation process. However, while “I” has ownership of your psychurities, it overestimates its role and is overconfident in its competence. In short, “I” believes it is the overall manager of your fund-of-funds, the “God” of your market portfolio. “I” is familiar with the memory portfolio, a particular sub-portfolio that is active in your mind’s private market. While “I” regularly gets insight in the mind’s exposure to some of the holdings of the memory portfolio, it does not fully manage it. Apart from memories “I” knows the memory portfolio also owns warrants, for example, which are a particular form of options which hold the right to buy (into) your experiences. One key criterion which raises the probability of these rights being exercised is the level of volatility in your payoffs. If exercised, and by physical settlement, the experience concerned will, from then on, be held as a memory in the memory portfolio. Other (exotic) mind derivatives allow but also force temporary exposure to those memories, again paid for by attention. This often upsets “I” as it has no full control over such trades.

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Finally, as I said, all prices are denominated in the currencies of their locations of origin (externally you can think of these as symbolically called ᵒC, Pascal, RGB, HEX, etc.). This means, for example, that there are currency conversions and other ‘currency effects’ when returns are combined, or assets are “cashed-in”. These types of settlements mostly occur in the back-office, and we are not aware of them. To conclude and summarise: 1. This is my attempt to bridge—to the extent possible—cognition and consciousness by moving away from raw computation. Among my cues here are the observations from Koppel and Zaloom regarding ‘calculations’ by traders (see subchapters 1.2, respectively 7.2). Whatever investment’s calculation entails, it certainly is experiential. This is then applied and interpreted through the mind-as-market. Importantly, psychurities’ returns do not lose or negate their phenomenal payoff. Interpreting Kim in terms of portfolioism, as “felt qualities” they are indeed “the only things that ultimately matter to us” (2005, p. 12). 2. The nature of consciousness is value. James’s “stream of consciousness” is the stream of ‘cash flows’, i.e. mental returns, that are instantly valued. When we talk about “rich” experiences the economic association is not coincidental. Their value is in the “I” of the beholder. More technically, it is the valuation of the information that is dually realised, concentrated in numbers: numbers from axes, gradients, indices, and scales associated with the objects of our attention. Valuation generates the (multi-currency) prices in the internal market of the mind. That is, we experience these (changing) prices in “return” for paying attention. Linking this to options, respectively statistics by paraphrasing Kant, the options of experience offer the (subjective) probabilities for the objects of experience. 3. Just like returns in markets (and by extension portfolios) cannot, e.g. causally, be reduced to any specific ‘fundamentals’, conscious returns cannot be reduced to specific ‘physicals’. Both shortcomings are due to the entwined exchanges taking place within (and between) markets, respectively within (and between) minds. 4. Moreover, just like financial returns have a real (economy) impact, conscious returns are not epiphenomenal. While they are ‘just’ changes in numbers to an observer, as experienced by “I” they reflexively feedback on “I”s physical substrate. This has implications for epiphenomenality, supervenience, etc. 5. Chalmers’s (1996) description of consciousness as dual realisation of information received criticism that his “information” was ill-defined. By interpreting that information as prices resulting from exchanges in the (4E) mind-as-market, the MMH clarifies this:40 experience is the realisation of psychurity prices including their valuation as the phenomenal aspect. In other words, the mind-as-market explains that exchanges (spawning psychurity prices) form the relation between —thereby connecting—the phenomenal personal level and the physical subpersonal level. The market-as-mind then extends this to the collective intersubjective level. This multi-level price discovery as self-organisation principle of the mind transcends (or simply bypasses) many of the related issues, like internalism~externalism. To make my position on consciousness crystal clear: even if we know the price of everything, without consciousness we understand the value of nothing. Note: of course, number is just a concept with names (e.g. twenty, four, etc.), forms (e.g. 1, 3, 6, III, V, X, etc.), and other related signs (e.g. , ∞, etc.) which allow us to symbolically quantify and qualify mind~matter changes.

 Chalmers may, of course, disagree with this interpretation.

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The mind~body’s ability to discount news, inspired by markets as informationally efficient, is key to what Chalmers called the “double realisation” of information: Whenever we find an information space realized phenomenally, we find the same information space realized physically. And when an experience realizes an information state, the same information state is realized in the experience’s physical substrate . . . We might even suggest that this double realization is the key to the fundamental connection between physical processes and conscious experience. We need some sort of construct to make the link, and information seems as good a construct as any. (Chalmers, 1996, pp. 284–286)

Information is realised both physically and phenomenally: “We might put this by suggesting as a basic principle that information (in the actual world) has two aspects, a physical and a phenomenal aspect” (Chalmers, 1996, p. 286; emphasis added).41 Earlier, this was also recognised by David Bohm. As part of “our subjective experience”: there is a kind of active information that is simultaneously physical and mental in nature. Active information can thus serve as a kind of link or bridge between these two sides of reality as a whole. These two sides are inseparable, in the sense that information contained . . . on the mental side, is at the same time . . . a physical activity. (Bohm, 1990, p. 282)

I prefer to call this the dual realisation of information. What the MMH adds and underlines—by viewing it as a cognitive market via the MM Principle—is that the mind~body produces and consumes information. Specifically, whereas the former mostly occurs unconsciously, it is the latter that we experience as phenomenality, raising (self)awareness. Combined collectively in an overall (albeit dynamic) market state, the concrete physical realisation of information is via (e.g. electronic) parts of the market’s body (see Subchapter 2.4.2). It culminates, for example, in the coloured price quotes on investors’ screens (some of whom will have contributed to their discovery). Its phenomenal realisation, on the other hand, is about prices’ impression, meaning, sense, etc., culminating in the market mood that accompanies the physical realisation of information. The above distinction between the quantitative and the qualitative aspect of mental returns is consistent with and part of this. In addition, information is not just about content or type (quantitative/qualitative) but also concerns process, like frequency and prominence (which, in neuronal terms, involves ‘valuation’ by the reticular brain system). As are consequences in terms of other market forces, like supply and demand for information. Via active management of the mind~body portfolio consciousness aims at “actively maximizing key parameters relating to self-structuring of information flows” (Clark, 2011, p. 216; emphasis added). Because of its reflexive (inter)subjectivity this influence is a second order non-linear effect. On that note, a conscious return is a noticeable rate of change that beats some nonconscious threshold. This is close in spirit to Bateson’s “difference that makes a

 For a discussion on the type of information (theory) that is appropriate, see Chaitin (2020).

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difference”, here interpreted as impression.42 Specifically, we seek experiences (by paying attention to events, objects, people, etc.), just like investors seek yields (by paying money to events, objects, people, etc.) In both cases, this fuels the expectations that lead to the positioning of the respective portfolios. And, obviously, a return can be negative while a series of returns can be volatile, reflecting the flip-side known as risk. The similarities further include storage of some information for later retrieval (e.g. memories), (e.g. Bayesian) optimising of portfolios, etc. As some readers have probably inferred, portfolioism offers an alternative explanation for qualia. Instead of atomistic and granular sensations, qualia form part of portfolios of psychurities which, like their financial cousins (securities) offer “relevant affordances that move us to act in ways that improve our situation in the world” (Kiverstein 2016, 121). Depending on their ‘returns’ qualia can dominate these portfolios, at other times they get dominated or ‘hedged’ away. Qualia (singular: quale) or “raw feels” are considered the characteristic intrinsic qualities of experiences, i.e. what they feel like. Examples are the redness of a tomato, the brightness of sun light, the sharpness of pain, the salty smell of a sea breeze, and the crispy sound of a clarinet. Our cue comes from philosopher William Seager. Qualia, he says, “are what makes up the way it feels to be alive and they are, I am sure, the ultimate source and ground of all value” (Seager, 1999, p. x; emphasis added). Via portfolioism I am going to build on this, mindful of the reflections by Simmel and others on value. First, there is no ‘intrinsic quality’ to experiences, just like there is no ‘intrinsic value’ to assets at the individual level. As Hayek points out in his 1974 Nobel speech, it was already recognised by “Spanish schoolmen of the sixteenth century” that such value “depended on so many particular circumstances that it could never be known to man but was known only to God”. Another Nobel laureate, Eugene Fama, one of the founders of the Efficient Market Hypothesis (the EMH; see section B3), agrees: “in an uncertain world the intrinsic value of a security can never be determined exactly” (Fama, 1965, p. 56). Returning to our cognitive setting, all one can say is that any value is in the eye (ear, skin, etc.) of the beholder and is part of the overall value of “I”s portfolio. Second, portfolioism takes ‘richness of experience’—the standard label that usually gets attached to qualia—literally in economic terms. Combined this means qualia are (simply?) your personal valuations of ‘collective’ scores in the dual realisation of information. These valuations, while distinguishable by kind or factor, are derived from numbers and thus remain numerical in essence. “What it is like” thus becomes “what it is worth”, a subjective value judgement on underlying objective ‘price’ moves. In sum, qualia are relative not only subjectively but also in terms of the

 See Bateson (1972). Although most people simply equate information to this “difference”, Bateson starts with an important reference to Kant: “I suggest to you, now, that the word ‘idea’, in its most elementary sense, is synonymous with ‘difference’.” Later he suggests that “idea” is the particular selection of “differences” or facts (i.e. “Das Ding an Sich” in Kant’s terms). That selection is the result of perception, filtered by (the make-up of) the mind~body which determines expectations. Again, for our purposes I prefer the terms return and impression.

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exchange that resulted in their value (as return). That is, the exchange involves paying attention in return for something else, underlining the relativity of the resulting value. In contrast, when there are no ‘price’ moves (no news, trades and so on) then no attention is being paid, which is of no value. The mind’s ‘market’ is then flat, that is, you are unconscious or otherwise unaware. To further clarify, let’s compare this (admittedly, somewhat bluntly) to physical things: the exchange between Newton’s apple and the earth, as mutual gravitational attraction, results in a relative value of weight which quality is therefore not intrinsic. In general terms, an M~B Portfolio’s overall behaviour primarily depends on inside and outside information. In the latter case, it is news from the outside world which includes others’ M~B portfolios. It also involves information asymmetries, at multiple levels. Specifically, the differences between perception and reality, between expectations and outcomes, drive this behaviour. It is aimed at neutralising those surprisals and surprises, closing the gaps, either mentally (e.g. by adjusting expectations) or physically (e.g. via action). All M~B portfolios consequently engage in physical and mental exchanges which ultimately involve the production and consumption of information. Some information makes a real impression, ‘moving’ you and your portfolio. Whatever happens after its consumption are processes like digestion (e.g. storage of ‘lasting impressions’ into memories) and recycling (e.g. retrieval of memories). Let me give an example by combining it with the consumption, production, and digesting of food. Internally my experience of hunger makes me visit your bakery to grab and eat a slice of cake which I enjoy so much that I do it every morning. Externally, my order makes you a profit, informs you to bake another cake for my visits next week and leads you to order ingredients from your suppliers. Internally, eating the cake tells me that I am working on satisfying my hunger. Information is simulcasted within and between M~B portfolios, as well as with the wider outside world. Let’s strip this down to its core essentials. Information is made up of bits. A bit is the base difference in binary arithmetic (0/1) and was popularised by John Wheeler in his “It from Bit” (1990). It was originally developed by Leibniz who, in turn and allegedly, was inspired by the yin~yang concept in the I Ching, the Chinese “Book of Changes”.43 As I mentioned earlier, the payoff (either 0 or 1) for a pure security is contingent. Until then it retains the possibility of both states, like Schrödinger’s cat, offering optionality. It takes a number of bits, and thus a portfolio of pure securities, to generate a return significant enough to make an impression: an experience unfolding in the M~B Portfolio as the information is realised both physically (return quantity) and phenomenally (return quality). Stated differently, what the M~B Portfolio’s payoff amounts to is not just the magnitude of information but also its

 Apart from Chinese traditions these are also derived from Greek philosophies (Plato and Pythagoras). They form part of the philosophy and psychology of mathematics and I will discuss this more in chapters 6 and 7. See also Schotanus (2013b) for a particular view on prices.

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meaning. The former meets the requirement for physical realisation (e.g. size of impression), whereas the latter meets the requirement for phenomenal realisation (e.g. sign of impression). Exchange and simulcasting mean that the information is (close to) simultaneously shared within an individual (e.g. between multiple senses) and/or throughout a collectivity (e.g. between individuals). In the former case, the experience is subjective, whereas in the latter it is intersubjective. Also, such simulcasted information needs dual-aspect conduits (e.g. neurons in the brain, respectively securities in the market) in order to carry both its functional/quantitative aspects and its sensational/qualitative aspects. In brief, humans invest their resources to pursue strategies in their encounters with the world. In doing so, they make bets on outcomes. Outcomes are uncertain and involve surprises, i.e. news. Minimizing losses (i.e. prediction errors) is a central element of their strategies, as it saves effort (required attention) to compensate (correct) them. Being conscious of the unfolding outcome is discounting its information. Specifically, the simultaneous physical and phenomenal realisation of it generates a return, the conscious experience. From a complexity point-of-view (see Chapter 6), notions like ownership, trust, and ethics fill the gaps in incomplete and inconsistent markets. Similarly, or rather at its basis, consciousness of these mental values fills the gaps of the incomplete rational and the inconsistent intuitive mind. So, although realised information is not necessarily consistent, complete or otherwise properly knowledgeable at each level, it does make an impression that sufficiently beats the minimal benchmark. Over time the M ~B Portfolio enjoys a series of cumulative multi-psychurity returns that result in its overall development in general and beliefs, expectations, memories, etc. in particular. At each moment it performs relatively to the world, particularly versus other M~B portfolios. Finally, consciousness makes the Batesonian difference to the Bayesian brain. We call an experience meaningful if it is worth it in terms of doing the sensemaking. In that case the phenomenal realisation, in a way, dominates the physical realisation. In portfolio terms, the return quality receives more weight, so is valued more, than the return quantity. Again, value is in the eyes of the beholder. It is subjective. For example, when I asked my youngest daughter why she prefers her Teddy (£20) to her Doggy (£40), she replied “Because he is fluffier and likes me more”. Obviously, this is not about rationality. The point is that her valuation is deeply subjective because it is derived from the qualities of experiencing a toy, i.e. consuming the information of a cuddle. As I will explain, for our purpose discoveries are exemplary experiences in that regard. It starts with the “birth of agency” in an infant when it “suddenly realises” the self (see quote Subchapter 7.1). They are powerful impressions to close (e.g. knowledge) gaps, acting as internal surprises in response to external ones.

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Background Consciousness Let me discuss consciousness more generally. We can study it indirectly from a third person perspective, or directly experience it from a first person perspective.44 In general, the difficulty of explaining the nature of consciousness is called the mind~body problem, psychophysical problem, or simply “hard” problem. The particular challenge lies in explaining how (and why) our physiology gives rise to our psychology (especially, phenomenality). In simple terms, the “hard” question is how consciousness is cooked up from (unconscious) material ingredients. Simmel raises this issue as part of the philosophy of money: The most heterogeneous objects we know, the two [dualist] poles of the world view which neither metaphysics nor science has succeeded in reducing to each other, are the motions of matter and the states of consciousness. The pure extension of the one and the pure internality of the other have not so far allowed any point to be discovered that could plausibly be regarded as their meeting ground. (Simmel, 1907, p. 130)

Apart from “states”, it discretely concerns the peculiarity of consciousness as experience: what it is like to have or undergo an experience. This impression is also known as phenomenality which follows from our sentience. The mind~body problem exposes the explanatory gap I mention in the Introduction. In more general dualist terms, the explanatory gap is the epistemic boundary between matter and mind, the outside and the inside worlds.45 Occasionally we cross that boundary by way of a discovery when consciousness dually realises the information contained in the insight. Importantly, the MMH emphasises that the explanatory gap is not limited to the traditional mind~matter distinction but reaches further. As discussed in Chapter 7, via markets we extend and connect our minds, making discovery in the economic system a reflexive chain: we share our discoveries, turn them into innovations, and then collectively value those novelties in markets which, for example, can help fund new discoveries. All in all we cross the boundary together. Also, any assumption I make about another mind, e.g. in an exchange, is based on my explanatory gap and related metaphysical stance. The key assumption I need to make, in that regard, is about the other person’s metaphysical stance. Specifically, the assumption of rationality is a false consensus: what is rational for a dualist may be irrational for a physicalist (because we don’t know the metaphysical truth).

 Intersubjectivity means that we can also experience it together, i.e. from a second person perspective.  Again, with the encapsulated brain as the physical example of that separation.

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Systems 1, 2, and 3 If one really wishes to be a master of an art, technical knowledge of it is not enough. One has to transcend technique so that the art becomes an “artless art” growing out of the unconscious. D.T. Suzuki, Zen Buddhism

Dualism exists in various forms although in modern times it generally excludes substance dualism. In the Western tradition Descartes was an early pioneer. Variations, including dual-aspect monism, were developed by James, Jung-Pauli, Russell, and Spinoza. From the East we have Buddhism’s yin~yang principle (via the I Ching,) and Hinduism’s chakras philosophy. More recent contributions to dual-aspect thinking are by Chalmers, Nagel, and Velmans. I raise this here because it forms the rich background for the dual-system (or dual-process) theories that dominate behavioural economics (see also Appendix B-4). They basically state that human mentality, particularly in terms of thinking, either originates from: 1. An unconscious “System 1” (S1), responsible for “thinking fast”; 2. Or from a deliberate “System 2” (S2), responsible for “thinking slow” (Kahneman, 2011).46 While I am not a fan of this simplified framework, I will continue to use its terminology because it has become the norm in behavioural economics and investment management. However, I would urge you to think of these systems with your portfolioism glasses on. In other words, see S1 and S2 as markets and/or portfolios. What happens within and between these systems is akin to market dynamics, consistent with the Market Mind Principle’s mind-as-market. An overview of some of the psychological functionality involved in both systems47 is provided in Table A.1 However, there are a few peculiarities about this distinction. For example, not all decisions are split-second. In fact, many investment decisions, like those on asset allocation, take quite some time, involving research and analysis. But this does not mean that those decisions are not informed by emotions. My point is that separating these systems based on duration (fast versus slow) is too simple. This is particularly relevant if we include the time it takes to receive the verdict on the quality of decisions, as I will explain in a moment. Moreover, on closer inspection, S1 and S2 do not exhaustively describe human mentality. What is missing is the impression these systems

 The consensus view is that intentionality, in terms of goal-directed behaviour, is generally only assigned to S2-representations.  See Kahneman (2011) and Epstein (1994). Still, this overview and its distinctions should not be taken too strictly. Also, like Epstein, I generally do not separate the cognitive from the psychoanalytical unconscious, certainly not of the Jungian kind (see also Mlodinow, 2012).

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Table A.1: System 1 versus System 2. System  (S)

System  (S)

– – – – – – – – – – – – –

– – – – – – – – – – – – –

– –

Unconscious (subliminal) Fast: processes information quickly Nature: older (innate) Prominent in animals and humans Holistic assessment Based on intuition (hunch) Hypothetical reasoning Undetermined Large capacity Unrelated to working memory Effortless and automatic48 Weak intentionality Informed by internal past data, e.g. emotions/ memories Implicit/associative Follows/likes patterns

– –

Deliberate (cognisant) Slow: processes information sluggishly Nurture: younger (developed) Prominent only in humans Reductionist assessment Based on analysis (examination) Logical reasoning Pre-determined Small capacity Related to working memory Effortful and controlled Strong intentionality Informed by external past data, e.g. events, time series Explicit/propositional Follows/likes rules

make, the experiences they yield.49 These are distinct and of different kinds which seems required to differentiate and identify them in the first place. In other words, an emotion feels different than a thought. Stated differently, (what it is like) being emotional is distinct from (what it is like) being rational. This plays out in our mentality regarding risk: feelings about risk are largely insensitive to changes in probability, whereas cognitive evaluations do take probability into account. As a result, feelings about risk and cognitive risk perceptions often diverge, sometimes strikingly. (Loewenstein et al., 2001, p. 268)

It doesn’t stop there. First, these systems are not separated. Rather they interact which, in turn, contributes to the impression. There is competition: trying to contain your emotions by thinking slow feels like a struggle, for example. Moreover, based on the consensus view that S2 is somehow ‘superior’ to S1 the danger is that: the quality of decision making suffers when affective inputs are suppressed by having decision makers think systematically about the pros and cons of a decision. (Loewenstein et al., 2001, p. 268)

 It differs from the mechanical operations of artificial designs because it is innate and has evolved organically over centuries, involving masses of “experience’ data that are no longer available. See also my later comments on AI.  I consequently do not follow Kahneman (2011) who considers S1 to be the “experiencing self” and S2 to be the “remembering self”.

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Then there are the realities of hard life for ordinary consumers; when you are trapped “in a system of debt” you “can’t afford the time to think”, as Noam Chomsky pointed out. On the other hand, creativity is the result of S1 and S2 cooperating. Second, thinking does not operate in isolation in the brain but is influenced by other impressions, coming from the mind~body overall and its interactions with the wider environment. 4E cognitive research has shown that the body, via organs, not just transmits information to the brain; it also affects how we perceive and interact with our environment. For example, cardiac activity and blood circulation, divided between contracting (systole) and relaxing (diastole) phases, have different cognitive impacts. This applies also to moods generally as well as market mood. To repeat, the essence of mood is what it is like to be in it. This interiority is independent of, but impacts, any thoughts you may have. Again, what is missing is the distinct feeling or sense you get from an emotion, or a thought, or their creative combination. Because feeling does not neatly fit S1 nor S2, I add a new consciousness or ‘feeling’ system which I call System 3 (S3).50 Mood belongs to S3 and I’m partly influenced by Bohr’s complementarity reflections on thinking and mood “required in psychology”: [T]he use of words like “thoughts” and “sentiments” [our moods], equally indispensable to illustrate the diversity of psychological experience, pertain to mutually exclusive situations characterized by a different drawing of the line of separation between subject and object. (1948, p. 318)

Even those who wish to maintain a mechanical view have to accept, in that regard, that “we are not cognitive computers, we are feeling machines” (Seth, 2021, p. 194). S3 is a derivative system that, just like in financial markets, can reflexively impact the underlying. To elaborate, people must first identify their experiences. To do that, they must self-reflect. Key to any reflection is not, as per the consensus, that S2 somehow overrules or takes over from S1. Rather, it is to allow S3 to ‘kick-in’ whereby, for example, overconfidence (‘I’m certain that . . .’) is transformed into discovery (‘I’m curious about . . .’). And (as we’ll see elsewhere in the book) recognising that the prospect of an insight from such discovery is the incentive here, ‘taking a breadth’ should thus be seen in meditative terms: to become (more) aware via S3. After all, beyond the A-ha experience of an insight lies enlightenment. Subsequently people may also attempt to identify what may be causing their mental states. While an emotion or thought can look almost black or almost white it actually is often experienced as richly coloured in our triple system, with creativity epitomising this. Importantly, beyond the specific impressions of S1 and S2, also try to think of it this way. You are planning to buy a new car and you have spotted one.

 For other critiques of dual-system models and for alternative views of S3, see the work by researchers like Olivier Houdé, Keith Stanovich, and Jonathan Evans (Evans, 2018, including the references therein).

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Your intuition (S1) tells you “this car is so me/it’s the right one/it will impress [name]”. Your analysis (S2) tells you “this car is safe/it is a good deal/it has a top rating”. Great, S1 and S2 are not arguing for a change. Each one tells you to go ahead with your purchase. Still, while both are necessary, they are not sufficient to make up your mind. What you need to complete your ‘new car experience’ is that feeling of . . . the testdrive. Obviously, once you have bought the car only subsequent experiences will tell you, in hindsight,51 whether S1 and/or S2 were ‘right’. That is key to learning. This can be more generalised. The verdict of whether S1, S2, or both are right or wrong follows later. The proof of the pudding is in the tasting, after all. On that note, another good example of ‘emotion-goods’ is toys: most kids would prefer to trial-experience them in-store before buying. These experiential verdicts and outcomes are crucial for learning and building memories. So, S3 feeds back on S1 and S2 as a second order non-linear effect. Sometimes that is almost immediately, at other times delayed. And it can be simultaneously or separately. In more formal terms (see Chapter 6), S1 and S2 ‘statements’ can only be proved outside their system, namely in S3 which—by way of the senses—is in closest contact with the real world. S3’s impressions, received from the latter (e.g. as facts), lead to knower and known merging. Overall, these three subsystems interact in complex ways. In embodied terms, Table A.2 is a rough but easy way to think about it (with S1 to S3 only assigned as a figure of speech): Table A.2: Embodiment of S1, S2 and S3. ‘S’

‘S’

‘S’

Gut ( million neurons)

Head ( billion neurons)

Heart (, neurons)

Just to be clear, I am not arguing that feeling more generally should dominate and somehow overrule any thinking. Superficially, phenomena like the snowflake/cancel cultures and the outrage industry already show a level of ‘feeling’ oversensitivity in some parts of society that is not helpful and does not match reality. Rather I argue that S3—especially in its role to impress ‘what it is like’, e.g. to be right/wrong—complements S1 and S2 thereby completing human mentality.

 Which, as we know, can lead to biases.  Gnosis is a particular form of knowledge of the heart that I will not go into here, but for those interested see Quispel and Van Oort (2003). For background, the late Professor Gilles Quispel is the first editor of the gnostic scriptures found at Nag Hammadi in Egypt. He also happens to be the father of my former academic advisor at the University of Groningen, Caroline Quispel.

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In conclusion, the 4E mind is a complex adaptive system made up of multi-layered portfolios of physical and mental assets that exchange in various markets. While exploring the world you try to read their tea leaves: “What does my gut tell me?”, “What does my head tell me?”, and “What does my heart tell me?” The key is to recognise their respective roles and to allow them to manage your economy as a balanced portfolio. Technology can support in that challenge and extend the mind. But outsourcing, say because somebody tells you not to trust your gut anymore, is short-sighted. Just like the CVC showed the vulnerability of the global economy’s supply chains due to outsourcing. Next, when information is realised experiences in general convey the various formats and aspects of that information. We can identify two types of consciousness to which, in turn, we can roughly assign the two ‘systems of thinking’ (S1 and S2) as well as the ‘system of feeling’ (S3). The following schema is just for illustrative purposes and should not be interpreted too strictly. I will look specifically at knowledge, where types of knowledge overlap and interact, just like portfolios. Access Consciousness Access consciousness, or A-consciousness, concerns perception in terms of access to information. The challenge in understanding it is related to Chalmers’ “easy problems”. In economic, i.e. mind-as-market, terms, this access is primarily about: – The production (including delivery) of information, e.g. in the form of beliefs, thoughts, and other representations. – The physical (or functional) processes controlling and supporting this, e.g. via bodily division of labour. – Supply, e.g. bodily supply chains. – Quantity. In cognitive terms, access consciousness is about the “awareness” part in Searle’s earlier definition. For example, it is about awareness via access to knowledge (strictly speaking, here limited to physically realised information, e.g. via neuronal signals). There are various complementary pairs of knowledge, each with a related form of access and state of awareness. Additional cross-links exist between pairs: 1. A-priori~a-posteriori. 2. Explicit~tacit. 3. Descriptive~procedural. For example, explicit knowledge includes language and mathematics. Access is open, awareness is full, and it is about ‘know-what’. It facilitates reasoning and rational behaviour. This is closest associated with the ‘rational’ S2. Regarding tacit knowledge, Michael Polanyi saw it as “indwelling” and Hubert and Stuart Dreyfus viewed it as “absorption”. It includes intuition and instincts. Access is limited, awareness is partial, and it is about ‘know-how’. It facilitates gut responses and is closest associated with the ‘unconscious’ S1. For more details I refer to the literature.

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Phenomenal Consciousness Phenomenal consciousness, or “P-consciousness”, concerns introspection regarding experiences, including sensations, and what it is like to have those.53 Explaining and understanding it is related to Chalmers’ hard problem. In mind-as-market terms, this phenomenality is primarily about: – The consumption of information, e.g. how impressions ‘taste’ like. – The utilities valuing and conveying this, e.g. as epistemic utility. – Demand, e.g. mental ‘wants’. Specifically, via discovery we seek the A-ha experience, the ‘pure alpha’ of mental investments. – Quality. In cognitive terms, phenomenal consciousness is about the “sentience” part in Searle’s definition. For example, sensations and feelings lead to experiential or direct knowledge.54 In turn, this is associated with S3. Regarding quality, qualia are the seemingly raw55 qualities that are characteristic of any particular experience. Earlier I quoted Kim (2005) and I also provided my own interpretation in the section on consciousness. As a cognitive concept, qualia remain controversial. They form part of the explanatory gap. Hayek referred to qualia as “unexplained residue” of sensory order. Type-wise, it is the “phenomenal order of sensations (and other mental qualities) directly known although . . . we may never be able to bring out by analysis all the relations which determine that order” (Hayek, 1952, p. 39; emphasis added). So, although I agree that qualia contribute to our understanding, I prefer to label them as know-now, both to emphasise their immediacy—especially in (suddenly) realising information—and to distinguish their contribution from the other knowledge types. As I explained, they are the (inter)subjective valuations of the payoffs from our mental investments in real time. In actual investing, the latter are sometimes directly linked to financial instruments, so depend on changing asset values. This underlines the dynamic of qualia, making them relative (versus something else) rather than exclusively intrinsic (to itself). As an example of market qualia I already mentioned Lehman’s anxiety. An example at the personal level is the sting of the pain (including embarrassment) of a particular trading loss. Whereas behavioural finance focusses mostly on the distinction between S1 and S2, cognitive science also studies consciousness and, among others, debates the differences between access consciousness and phenomenal consciousness. I will interpret that debate in market terms as follows.

 Here it includes so-called affective consciousness.  What is known as “pure consciousness” is a particular instance with its own phenomenology (e.g. Metzinger, 2020). Some would argue this is a separate (and thus third) type of consciousness. Related topics include meditation, mindfulness and tao, for example. I will not discuss these here.  As in irreducible and unprepared. From another angle: not tainted by quantifying.

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In everyday parlance we mean, in the first instance, access consciousness when we say that somebody is conscious. An investor is conscious if she is aware of facts, news, or other information of relevance to her portfolio. She is aware because the information is part of her explicit knowledge, i.e. ‘know-what’. And, referring to Searle, she is aware more generally because she is not asleep or in a coma. The fact that open-access consciousness is functionalist means that it is open for scrutiny via scientific analysis. Sometimes, however, the access is bounded and awareness more implicit, involving tacit knowledge. For example, when an investor knows how to avoid a drawdown in her portfolio because she intuits the Dow to drop just before the correction. Although she is aware of her skill to manage her portfolio in that way, she does not reflect on it, nor can she describe it. Similarly, she is aware of the meaning of the intuition, but she neither knows its origination nor what it consists of, just that “It’s my gut telling me”. In contrast, phenomenal consciousness is about what an investor feels like while aware and knowledgeable, irrespective of the particular system of thinking that is active. This is why I consider S3 an overlay: because there is something in addition to the intuiting or rationalising, namely something it is like to be in such an intuiting, respectively rationalising state. As I said, each feels different, which is something that cannot be appreciated within those respective systems themselves. Usually the experience is subjective, but in markets it is often intersubjective, with agents aware that their experience contributes to, and is influenced by those of others. In short, phenomenal consciousness is about qualia or the qualities of experience which involve valuation via dynamic returns. On their physical grounding Hayek crucially pointed out their positioning and interaction as part of a portfolio: These qualities are not in some way originally attached to, or an original attribute of, the individual physiological impulses, but . . . all of these qualities are determined by the system of connexion by which the impulses can be transmitted from neuron to neuron; that it is thus the position of the individual impulse or group of impulses in the whole system of such connexions which gives it its distinctive quality. (Hayek, 1952, p. 53)

Translated in market mind terms, market qualia are felt according to the constellation of price ‘impulses’ from connected securities in the market overall, i.e. relative to each other. Sometimes (due to positioning or seasonality) certain assets or sectors dominate, and their impulses feel different from occasions when other assets dominate. Of course, price impulses at the macro level of asset categories (e.g. stocks, bonds, real estate), or so-called beta-moves, affect most people. An obvious implication of all of this is the crucial role exchanges play in the market’s “system of connexion”, in the sense of facilitating the physical transmission of price impulses. Again, Hayek’s comment underlines why portfolioism is so appropriate as a perspective, because it emphasises the interdependent composite result of interacting components. In M~B Portfolio terms, our mentality is a coupled unfolding of S1, S2, and S3. In investment portfolio terms, they form its core-satellite-overlay construction, if you will.

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Finally, and briefly, how can we interpret S1, S2, and S3 in the collective setting of the economic system? Based on 4E cognition, the MMH argues that its collective nature brings out the extremes—the best (wisdom) and the worst (madness)—of market mentality. Whether by enactivism (e.g. Gallagher et al., 2019) or extension (e.g. Clark, 1997, Chapter 9) the institutionalisation of business and finance has been used as an argument for the “scaffolding” of cognition and practices which better suit the assumptions and predictions of mainstream economics (see also the afterword by Kiverstein). Specifically, collectivities, like corporations, come closer to operate ‘rationally’, so according to S2, than individuals. On the other hand, the particular cultural and environmental settings of collectivities stimulate group think and contagion to which S1 is especially susceptible, leading to ‘irrationality’. This can result in abrupt swings between these states, which is what investors experience, e.g. quantified as volatility. Notably, collective S3 manifests phenomenally via intersubjectivity, especially shared moods, which can then reflexively reinforce these states (into extremes). Going forward, we will consider investors to be conscious when they invest. This, of course, is already a precondition of the rational decision-making that the EMH assumes. However, the MMH argues that this goes further and means that prices are experienced, with both access and phenomenal consciousness involved.

A5. Other Minds There are a number of explanations or terms for our capacity to recognise mentality in general, and consciousness in particular, in humans and other beings.56 That capacity is then applied by attempting to predict the behaviour of those beings. One that will occasionally pop up in this book is called “Theory of Mind” or ToM for short.57 Others include “simulation theory”, “mind-reading”, and “mentalising”.58 That attribution of mentality is not limited to humans was shown in the famous study by Fritz Heider and Marianne Simmel (1944). They showed participants an animation of geometric shapes59 (a large triangle, a small triangle and a circle) moving in various directions and at various speeds inside and outside a rectangle. When participants were asked what was happening on the screen, they described the scene in terms of actions of animated beings, chiefly of persons, e.g. “hitting”, “wanting”, and “hurting”. Moreover, the attribution of actions went beyond that:

 Children believe plants to be conscious (Inagaki and Hatano, 1987).  Although there are differences, here it encompasses another interpretation called “TheoryTheory’. For investment interpretations and investigations, see Bruguier, Quartz and Bossaerts (2010) and De Martino et al. (2013).  Including “embodied simulation’, involving mirror neurons. See Williams (2018) and the references therein.  Available here: https://www.youtube.com/watch?v=n9TWwG4SFWQ.

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The descriptions not only make clear which person, but also what motive or need within that person, is responsible for the movement. As a matter of fact, as soon as we ascribe a certain movement to a figural unit and consider this unit as an animated being, perception of motive or need is involved. (Heider and Simmel, 1944, p. 257)

In short, their study seminally suggests that certain types of motion make us recognise mentality in an entity, even if the entity is a mere geometric figure.60 Related to this is the so-called intentional stance, originally developed by Dennett (1971, 1987). Taking this stance means that you view others as intentional systems, “that is, as entities whose behavior can be predicted by the method of attributing beliefs, desires, and rational acumen” (Dennett, 1987, p. 49; emphasis added). Stated differently, you recognise others to have intentions and, assuming you know these, you can predict their behaviour because, under the assumption that they are rational like you, they follow through on them. Applied to Mr Market, the key problem is obvious: even if we agree that he is rational, his behaviour is not easily predictable, regardless of what stance one takes. As we will see, surprises (for which there is no place in the REH) play a large role. From these various perspectives goal-oriented behaviour is detected. Again, economically minded readers will recognise the close association with Mises’ concept of purposeful action: “Action is will put into operation and transformed into an agency, is aiming at ends and goals, is the ego’s meaningful response to stimuli and to the conditions of its environment, is a person’s conscious adjustment to the state of the universe that determines his life” (Mises, 1998, p. 11; emphasis added). Individual minds can form group and other collective minds. It is a key theme in this book. A good example is the mobilisation of hordes of retail traders via trading forums on social media, like WallStreetBets on Reddit. This is discussed elsewhere in the book (as well as in Clunie and Schotanus, 2022). I also argue the benefits of diversification in minds and the related term ‘independent and differently distributed minds’, or simply ‘idd-minds’.61 Staying with the same example, if based on educated and well-informed decisions (rather than anger or hype), the growing involvement of retail investors could become a true revolution and a sustainable force for good in that regard. Finally, narrow-mindedness is the degree to which a collective mind (e.g. per its [consensus] beliefs, emotions, etc.) is reflected in and represented by its constituent individual minds. In the extreme if all its constituent minds think similarly, the group mind is single tracked.

 Although some reviewers of the study refer to it as an “illusion”, i.e. you cannot ‘really’ assign mentality to these shapes, they miss the point. The geometric shapes acted as symbolic agents and the pre-recorded movements were intended to invoke a story. In the words of Heider and Simmel: “It is held that this method is useful in investigating the way the behavior of other persons is perceived”.  The term idd is a tongue-in-cheek variation of statistics’ iid (independent and identically distributed).

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A6. Mood Mood is one of the most complex aspects of human mentality. It is also referred to (particularly in the literature) as “affect”. However, formally affect is less all-encompassing and shorter of duration than mood. Think of it this way: if affect is the weather, mood is the climate for our mentality (e.g. see Chapter 11 for my use of these metaphors in an economic setting). In this book I am primarily following Heidegger (1927) and those who interpret him. Other influences include Merleau-Ponty and Nietzsche, as well as more contemporary experts, like Davidson, Feldman Barrett, and Panksepp. A nice introduction to (the phenomenology of) mood is Freeman (2014). The cognitive literature describes mood in various ways, like ‘attitude’ or ‘disposition’. In general terms, mood is a mental climate that results from the combination of external and internal, physical and psychological, factors. In Heidegger’s (Stimmung) terms, moods simultaneously reveal (the state of) the world to us while conveying our (state of) belonging or non-belonging in it. “Being-in-the-world” for investors is to “dwell” in the market. We do not experience the world as disinterested spectators but “are situated in the world in the sense that we are purposively entangled with it” (Ratcliffe, 2010, p. 356). In short, there is no clear separation. This hints at the key role mood is playing in bridging the outside physical world with the inside psychological one. It raises our understanding of the situation, Heidegger argues, by a kind of immediate “attunement” or “disclosure” that bypasses knowledge. We experience mood in the extended sense of associating it with the psychophysical environment we are in together with other mind~bodies. As the literature on crowd psychology has revealed there can be a (subliminal) submerging of our complete mind~body (not just any particular emotion, or even individual mood) into a larger collective. Experiencing mood, and the accompanying shared revelation, can then be quite overwhelming, almost like a contagion. Mood “assails”, as Heidegger likes to say. How we end up in mood and how it reveals understanding “one does not know . . . because the possibilities of disclosure which belong to cognition reach far too short a way compared with the primordial disclosure belonging to moods” (Heidegger, 1927, p. 173; emphasis added). It is that merger of overarching reach with constrained concerns that make mood such an influence on human mentality. Metaphorically speaking, one can think of mood generally as an overbearing parent, always in the background determining what ‘matters’. Sometimes annoyingly so, often not knowing why nor able to resist it. There are several more particular views on mood, depending on the discipline. In psychology, for example, mood is considered a “core affect” of “primitive non-reflective feelings” (Russell, 2009, p. 1264). In philosophy, perceptualism holds the view that mood acts like a filter, often referred to as ‘coloured lenses’. It means that representations are only triggered when we perceive evaluative properties (e.g. pleasant, threatening, etc.) that are relevant to our values and concerns consistent with our mood. In other words, we become disposed (not) to perceive objects as having any evaluative properties that (do not) fit our mood. I perceive the rain as annoying when I am irritable. Or, in inves-

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ting, we view stocks as cheap when we are bullish.62 As such mood acts like confirmation bias. A different, phenomenological view is that, instead, the evaluative properties are present—and thus perceived—but their impact is being enhanced or muted by mood. In other words, it doesn’t act as a coloured lens but rather like a drug that empowers, respectively numbs the evaluation. There is also a growing literature on computational approaches to mood. Clark et al. (2018), for example, base their review on PPT, in particular the free energy principle a.k.a. active inference, and its instantiation in the Bayesian brain. PPT suggests that emotions reflect uncertainty, particularly regarding the consequences of action. By extending this reasoning Clark and colleagues suggest, in a hierarchical setting, that moods act as hyperpriors over uncertainty (i.e. emotions). Specifically, moods can smooth “emotional fluctuations”—reflecting uncertainty—as “hyperpriors encoding their long-term average”. In short, the past informs mood. Another relevant source in this category is Sizer (2000). In terms of their properties, moods are peculiar for various reasons. For our purposes these include the following (some already hinted at): – Moods are experienced in real-time in actual situations ‘in the world’ (e.g. compared to remembering past events or imagining future events). They give a sense of how we are doing, i.e. ‘perform’, in those situations. In investment terms, moods form an independent overlay for our core mental portfolio. – Moods are “primitive” (Russell, 2009, p. 1264) and “primordial” (Heidegger, see quote above; Strasser, 1977, p. 121). They have valence and are generally classified as ‘positive’ or ‘negative’, with variance in types and depth (or strength). This also means, in case of weak moods, that we may not be aware of them all the time, even though they are present in the background: “we are never free of moods” (Heidegger, 1927, p. 175). – Moods are general, not about anything particular, and thus not intentional. They are “intrinsically objectless phenomenal experiences” (Oatley and Johnson-Laird, 1987). Specifically, “moods that constitute the experienced meaningfulness of the world consist entirely of pre-intentional feelings” (Ratcliffe, 2010, p. 2). Consequently they are not part of goal-directed behaviour. – Because they are not caused by any particular intentionality, moods have indeterminate durations. Moods can pass within minutes or hours, but they can also last for days or even longer. For example, they usually have a longer duration than emotions and can form part of long-term depressions. – Moods act globally, i.e. throughout the entire mind~body, and form the background for its content. They are the “attunements which attune us” (Strasser,

 Not necessarily the same as a bull market.

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1977, p. 68). The musical connotation of this quote is very appropriate as I’ll discuss in Chapter 9. Compared to emotions, thoughts, and other representations, moods have: – No unconscious variant, i.e. they don’t associate with S1. – No deliberate content, i.e. they don’t associate with S2. Moods influence the sensitivity of representations. For example, they can intensify or dull emotions, compared to the default mood-less mode. This can become extreme, to the point of disconnecting from emotions. Last, but not least, moods can be intersubjective, meaning that they are shared and experienced collectively. Collective moods are synergistic in that they can differ from and reach over and above the affective states, including subjective moods, of the individuals involved. Market moods are exemplary of this, but you can also think of the mood of a party, a crowd or a nation, for example.

The views and properties mentioned are not necessarily mutually exclusive. In fact, the MMH’s view on market mood, via portfolioism, overcomes many differences (see part C). Finally, there is a particular type of mood which we encountered before and call “existential” (e.g. Rathcliffe, 2013). It comprises feelings involved in dealing with our basic sense of reality and (threats to) existence. Existential mood acts as (part of) a reality check, often with profound, e.g. surreal, affects. They can be experienced as anxiety complementing any physical breakdown. That is what Lehman’s collapse felt like.

A7. Artificial Intelligence If you look at situations in the world, they don’t come framed, like a chess game or a Go game or something like that. A situation in the world is something that has no boundaries at all, you don’t know what’s in the situation, what’s out of the situation. Douglas Hofstadter

I will not discuss Artificial Intelligence (AI) in much detail here but rather refer to the specific literature.63 Neither will I discuss related topics, like Artificial General Intelligence (AGI; AI’s ultimate version), machine learning and quantum computing. Instead I will cover the basics as well as a few topics of particular relevance to the MMH. For a general overview of the potential implications of AI, especially deep learning, on markets, see Gensler and Bailey (2020).

 Popular works on AI include Bostrom (2014), Brynjolfsson and McAfee (2014), O’Reilly (2017), Russell and Norvig (2010), Tegmark (2017), and anything by Jaron Lanier. I refer to these and other AI material for more background.

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AI is the intelligence exhibited by computers, robots, and other machines. It is artificial because it is not native, like biological (or organic) intelligence64 is to humans and animals. There is therefore some truth to the observation from my fellow Dutchman, computer scientist Edsger Dijkstra: “The question of whether machines can think . . . is about as relevant as the question of whether submarines can swim”. Machine learning (ML) is the modern application of AI, with deep learning (DL) and large language models (LLMs, like ChatGPT) among its most advanced versions.65 Although it is inspired by the brain, with programs often taking the shape of so-called neural networks, AI operates very much mechanically making machine learning the appropriate term. Apart from it operating on a computer, ML is mechanical because it basically repeats the same operation. However, it does so extremely fast. That operation is comparing: searching to find some kind of match to a result it was previously “trained” for. Specifically, dependent on the particular purpose it tries to find a pattern (e.g. image, sequence, etc.). The success of ML depends on the quantity (e.g. amount/size/etc.) and quality (e.g. of algorithms/layers/etc.) of three main factors: computer power, models, and most importantly data. In short, ML is about scalability: the more power/models/data the more the machine can learn. Human intelligence (HI) is difficult to replicate because it involves the plasticity of the body, in particular the brain. It, literally, provides physical flexibility to and from human intelligence. Most interesting is the role of consciousness. Your experiences can shape the microparticles—varying from proteins and neurotransmitters to ions and electrons—and microprocesses that affect your brain. This is an important aspect of downward causation: high-level mentality, like thoughts and feelings, acts top-down to alter (e.g. the shape, charge, etc. of) these physical particles and thereby their behaviour. Recovery from brain damage by sheer willpower is an extreme example. In turn, the brain facilitates the realisation of those experiences. This is a key property of the mind as a complex adaptive system that AI cannot replicate yet. On the other hand, there are limitations: in contrast to software, our overall wetware does not get any upgrades and has roughly had the same version for millennia. There are a number of other differences between the two. For example, AI needs electricity for powering it, whereas HI needs organic energy. Biocentrism is justified when natural evolution is critical to development. In that respect AI has not evolved but instead has been developed by humans (although this may change). Crucially, AI has no intrinsic (i.e. innate, purposeful) motivation to succeed in its learning via discoveries. It may be ruthlessly effective in achieving a goal (e.g. winning a game) but it will never enjoy it because it does not ‘get’ its purpose (see below). Also, it is the en A third variation, called organoid intelligence, is in its early stages. It is a form of hybrid or biocomputing via ‘intelligence-in-a-dish’ whereby brain cells are grown from stem cells and placed in petri dishes. They are then connected to and trained by technology, e.g. using big data. Obviously, there are all kinds of (e.g. ethical) issues with this.  Other advanced methods include hyperdimensional computing.

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dogenous generation of internal surprises in human minds, including those emerging from their interaction, that escapes AI. Despite these differences it would be useful to capture intelligence under a common description. Although there is not a consensus on this, here I will use the one suggested by Legg and Hutter (2007): “Intelligence measures an agent’s general ability to achieve goals in a wide range of environments”. Clearly this misses any agent’s intelligence in terms of understanding the purpose of the goals. On the other hand, it covers the continuing superiority of HI over AI from a holistic perspective: although AI excels in specialised tasks, like chess or Go, it underperforms in the wider multitasking environments of human life as in Hofstadter’s “situations”. In historical context, AI is considered by many to be the fourth industrial revolution. AI is spreading fast throughout the economic system and is spearheading mechanisation. Employed by private and public entities alike, AI not only replaces production by humans but increasingly determines what they consume. This focuses on the mental domain: AI wants to manage your attention. In other words, there is growing algorithmic control of your (access) consciousness. Social media is the prime example of this, but it is being integrated in products and services in the real economy more widely. In some cases AI steers away your attention, literally in the case of self-steering cars. In the financial economy AI is used to analyse big data and to create trading algorithms to relieve decision-making stress. Proponents argue that AI can help to optimise investment processes and make markets more efficient. Critics counter that AI contributes to excess market volatility and raises difficult ethical and legal questions. Among others we are confronted with the asymmetry in dealing with technology’s benefits, insights and novelty. The general response (e.g. in industry, the stock market, but especially politics) is overestimation in the short term and underestimation in the long term. At the (macro) institutional level we find it particularly hard to keep up with the changes in technology that emerge at the (micro) individual level. Innovation, the processes of adopting and implementing the inventions by (initially) individuals, does not always turn out to be universally beneficial. As discussed in the main text, the key problem is that mechanisation begets mechanisation. AI requires more and more data which, in turn, requires more automation to collect, distribute, and store it (OpenAI’s ChatGPT is the latest example). This is where incentives come in. Charlie Munger famously stated: “Show me the incentive and I’ll show you the outcome”. The biggest impact of mechanisation, promoted by mechanical economics, is namely indirect: it incentivises creating an environment optimal to itself. In terms of AI, this makes algorithmic control biased against humans or more specifically, against privacy, freedom, and other human values, favouring machines instead. (Remember: manage/remove that quirky consciousness!) This has societal consequences. In an August 2020 interview with the Italian newspaper La Repubblica Soros argued that “artificial intelligence produces instruments of control that are helpful for a closed society, and represent a mortal danger for an open society”. The challenge is that the incentives are not determined in a free (discretionary) market sense. Amazon, Facebook, Google, AirBnB, Uber, all con-

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trol their platforms, as do the CBOT, NYSE and TSE. Obviously, this is part of their moat but we somehow need to find a way to turn to free-market-selected algorithms as part of curing Mr Market. In this book I view AI in a three-way context: – In the historical context of automation in society during the various industrial revolutions. The current fourth (“artificial”) revolution follows the first (“mechanical”; starting roughly in 1760), the second (“technological”; 1870), and third (“digital”; 1960) revolutions. – In the metaphysical context of the physical and mental domains. First, the latest enhancements of digitisation (including virtualisation) attempt to transform physical objects or even worlds and artificially connect them to minds. Second, it applies to physical and mental tasks where I make a distinction, as just discussed, between the artificial and the organic mind~bodies that perform them. We are particularly interested when these tasks and/or mind~bodies connect. On the other hand, to come to different or new answers AI has to be different from HI. In turn, and to be consistent, this means we need to accept that we will not know how or why it came to those. – In the organisational context of coordination, balancing competition and cooperation. We are particularly interested in AI complementing and occasionally augmenting HI. For example, AlphaGo is a fantastic tool to help improve Lee Sedol and other players with their Go. AlphaZero, however, is so superhuman that it only plays against other programs. Consequently it is not of interest to Goplayers, only to programmers (and even they often do not get it). Meaning that AlphaZero is playing (turned “its” Go into) a different game. More generally: there is no point playing when you know you are going to lose. And frankly, nowadays the only chess competitions worth watching are those where humans compete against each other. I will return to this later. Let me make some additional observations. Regarding superior game programs, some observers have labelled particular moves (e.g. AlphaGo’s famous move 37 in game 2) as “creative”. Actually, they were external surprises. As humans we did not expect them, if only because we would not make such moves. But in contrast to other external surprises thrown at us (by nature, the market, and other superhumans) they will not lead to our cherished internal surprises: insights. That is, increasingly it is the case that we do not learn anything from such actions because nobody (including the programmers) understands how such AI makes them. And we cannot further investigate because output wise they are black-boxes. Then again, none of this matters when it is a case of the ends justifying the means. As mentioned in the main text, 4E cognition has a generally benign view of AI, quantum computing, nano and other technologies in that they could potentially augment and form beneficial extensions of our minds (see Clark, 2003). However, technology is not naturally neutral. Moreover, if run autonomously, the algorithms involved

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form part of the growing cognitive nonconscious—also known as technological unconscious—which has major implications for our own mentality, including: its pervasiveness and computational potential, and its ability to pose new kinds of challenges not just to rationality but to consciousness in general, including the experience of selfhood, the power of reason, and the evolutionary costs and systemic blindnesses of consciousness. (Hayles, 2014, p. 199)

Importantly, humans understand the context of AI, be it cultural, economic, or historical. AI itself doesn’t. Through scientific and technological discoveries, so by luck and wisdom, we drove automation and it became part of our history. And while we ourselves are driven by survival in that process, AI is not (although some believe we can program it to do so). I will have more to say about this evolutionary angle in a following Cognitive Note. Whereas the first two industrial revolutions focussed on replacing largely unskilled physical work, automation more recently has shifted to replacing our mental work. And this is no longer restricted to repetitive and menial tasks. Humans are replaced because we have weaknesses. We get ill, injured, or tired. But mechanical devices are vulnerable too. Computers crash and machines break down. They suffer wear and tear. Arguably they are more vulnerable to online attacks. Modern machines have grown so much in complexity that they require a team of experts to handle them. No individual fully understands an airplane, satellite, or even a car anymore. In fact, we increasingly rely on computers and AI to not only run them, but to also tell us how to fix and maintain them. You see the problem here: we soon do not understand these sufficiently either. AI is able to execute tasks that traditionally required advanced mental efforts and intelligence. In a growing number of cases it does so more efficiently. As just mentioned, depending on which type of machine learning is involved it is not always clear how it achieves this. Crucially this may also hide any errors it does make, including biases. While we are generally able to retrace and identify errors made by humans or simple machines, this is progressively impossible for AI. There is another, human side to this mental replacement. If physical labour is taken away from manual workers it is not just their income that is removed. Their intentionality, both in terms of what directs and occupies their minds, is also replaced. Similarly this happens to mental workers. This marginalisation changes their behaviour as market participants (limited to the extent that they can afford to continue as such). The CVC, with its physical and mental constraints, showed this in a different context and in dramatic fashion. One example of a human trait which seems hard to replicate is imagination. It is the reverse-engineering capability of the human mind: we see a desired future and then determine the steps how we could get there. AI lacks imagination, it can only do “normal” engineering, determining the path based on past data. The key aspect that truly makes the difference is the pallet of sensations that humans feel when imagining. It is this aspect that delivers the mental causation in human behaviour to create that future.

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More broadly, what is generally acknowledged as missing from AI is consciousness (see Figure A.2). Some hesitate to consider computation capable of instantiating consciousness because it opens the door to panpsychism, for example. Still, artificial consciousness or machine consciousness is a growing field within AI (e.g. Chrisley, 2009; Stuart, 2011). For reasons I already mentioned, in my opinion artificial consciousness will remain distinct from human consciousness. Specifically, as discussed in Chapter 7, it is the non-axiomatic A-ha experience (in the eureka moment) that sets human discovery apart from artificial discovery and thus, by extension, their respective consciousness.

Figure A.2: AI and existentialism.66

Depending on how AI matures and what level of artificial consciousness it reaches, its rights enter the equation (just like animal rights). Conscience could also become a key issue. The latest developments in that regard include machine learning algorithms that self-evolve (e.g. Real et al., 2020). One of the claimed benefits is the lack of instructions leading to a supposed removal of human biases in the resulting algorithms.67 We can connect this to the mechanical approach in economics where there is the distinction between empirical input (e.g. data), practical output (e.g. policies) and theoretical instructions (e.g. models). Although input and output can vary, the instructions that relate the two are pre-determined (or automatic) and form a fixed set. As I have argued, the reason is that mechanical economics has made an ontological commitment that is mechanical at all levels. On that note, in March 2023 several AI experts (both academics and practitioners) signed an open letter which stated, among others, that “Powerful AI systems should be developed only once we are confident that their effects will be positive, and their risks will be manageable”. While admirable, it is naïve to believe this will be accepted worldwide (which is required for it to be effective). It will thus unfortunately fail. The genie is out of the bottle . . . but in a different way than you might think. It brings me

 Source: Berkeley Breathed/Bloom County.  For a critical view on deep-learning, see Markus (2018) for example.

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to the mechanisation trap the global men-of-system—in both the East and West— have set. On a few occasions in this book I warn that “mechanisation begets mechanisation”. Take the example of the growing number of totalitarian regimes who, as the name implies, wish to be in total control. AI can help them with this, and they will consequently never abide by the commitment suggested by the letter’s signatories. Nor will they care much about the ethics of AI, for that matter. To be clear, the naivety started decades ago and was economic when these regimes were welcomed to join the global economic system, as if they were plug-ins: “let’s add the production engines of the East to the machine”. Not realising that manipulated or stymied markets can no longer provide the hoped-for discipline to change such regimes according to the principles of a ‘global free economy’, they have become its Trojan horse instead. To wit, IT knowledge and tools were naively exported and/or simply stolen. Democracies are now served a banquet of consequences, including their own growing repression ‘to keep control’. In short, this is not about AI versus humanity. Nor is it about capitalism versus socialism. This is about freedom versus control, which affects both markets and minds. How AI will develop further remains to be seen. There are risks. First a general one on mechanisation. The difference between man and machine is that man is not a machine. This also explains the difference between valuing and pricing. Machines may be very efficient at the latter. But only humans can value. Machines cannot value because they don’t care. Our 4E cognitive economics setting has a warning about mechanisation encroaching further into our personal and professional lives: if technologies, like AI, replace rather than augment labour, we risk losing skills that help us with valuing in the economic system. Hubert Dreyfus and others have (implicitly or explicitly) warned for this. More specifically for AI, in a statement to the New York Times in May 2023, Geoffrey Hinton, considered the godfather of AI, announced his resignation from Google, adding he now regrets much of his work and is fearful of potential abuse by bad actors. Jan Leike, the head of OpenAI, tweeted in March 2023: “Before we scramble to deeply integrate LLMs everywhere in the economy, can we pause and think whether it is wise to do so? This is quite immature technology and we don’t understand how it works. If we’re not careful, we’re setting ourselves up for a lot of correlated failures”. Specifically, in economic terms the risk is that we are creating another systemic externality. An important dimension is the culture in which AI evolves. What is acceptable in one country is not acceptable in another (e.g. Lee, 2018). The optimistic view is that AI can complement and thus enhance human intelligence. This is when coordination means that AI and HI cooperate and become a coupled cognitive system. This is in the spirit of 4E cognition. The wider question is thus not whether human minds are the only kind of minds. Rather, the question is whether human minds can extend and connect to form a different mind. This is also where AI and robots play a role, namely to support human minds in forming that mind and enhancing it.

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However, this book is not about AI, so I will not spend more time on it here. Fortunately others have written volumes on AI and economics. For a general overview, see Buchanan (2019). For a more particular and critical view of AI and financial risk management, see Danielsson, Macrae and Uthemann (2017). In summary, AI is currently about intelligence, not consciousness. Humans have been able, for a while now, to wonder “what it is like”, for example, “to be a bat” (Nagel, 1974). We do not know the answer, but the question remains legitimate because it is instigated by the fact that there is something it is like to be you or me. That is, we are kind of familiar with a self, even though we don’t exactly know it, and we suspect other animals may have similar experiences. We do not ask that question about AI. In general, my scepticism regarding AI is largely confined to the pretence of it reaching human consciousness. I believe the role of evolution is hugely underestimated, as I will explain next (Cognitive Note: AI and Existentialism). Cognitive Note AI and Existentialism I’ve seen things you people wouldn’t believe. Attack ships on fire off the shoulder of Orion. I watched Cbeams glitter in the dark near the Tannhäuser Gate. All those moments will be lost in time, like tears in rain. Time to die. Rutger Hauer (RIP) Blade Runner In this note I combine some reflections on intentionality and AI. It should be read keeping in mind the metaphysical framework I set up earlier. I limit it to entities with a highly developed intentionality, viz. humans and (intelligent) machines, like a robot. Biocentrism is warranted because there will always be a difference between organic cognition and artificial cognition. The reason is, what I call, 4E evolutionary awareness: recognising in oneself and others the purpose of the urge to survive, i.e. the survival instinct, in a 4E context.68 A threat to one’s being is sensed via the fight, flight, and freeze experiences, including all the associated mentality varying from S1’s emotions (e.g. fear) to S3’s awareness (e.g. attention). What is involved includes the environment, one’s body, and others. For humans, what sets this experience apart from, say, smelling freshly brewed coffee is an awareness of the meaning of death, i.e. ending it all forever, that accompanies the unconscious instinct. In terms of my earlier comments on qualitative valuation: it is the valuation of the self under extreme uncertainty. Specifically, it is the realisation, impressed as deep sadness, that the self is at risk of total loss and what that would mean, not just for you but also for those you leave behind. Crucially, while valued we do not exactly know what that self is. It could be an illusion (e.g. Metzinger, 2010). In any case, there is an evolved subliminal but unknown link between the selfish gene and the self. Because AI ‘skips’ evolution and, instead, is created by us (even if we eventually delegate such creation to future AI-versions) it will never be able to experience evolutionary awareness, especially about extinction. As Jonas observes: “A feedback mechanism may be going, or may be at rest: in either state the machine exists. The organism has to keep going, because to be going is its very existence—which is revocable—and, threatened with extinction, it is concerned in existing” (Jonas, 1966, p. 117). Specifi-

 Admittedly, although I consulted some literature on the experience of dying, I necessarily speculate here. For a related view regarding life and death of AI, see Vallverdú and Talanov (2016).

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cally, AI cannot simulate—let alone instantiate—such awareness because, again, we do not know how to ‘pass it on’. (Of course, this does not mean that AI is ‘thus’ inferior to us in surviving). I will now link this to intention. I always felt that the ‘intentional stance’ towards intentionality, particularly when used to equate artificial and human mentality, has cracks in its foundation. Specifically, it starts to become wobbly once you consider that the rational/goal-directed behaviour it pretends to explain is ultimately aimed at self-preservation. Surely intentionality counts most when one’s existence is at stake, i.e. when it is existential and there is only one goal: to survive? So, let’s view intentionality from our ‘4E evolutionary stance’: “As far as science is concerned, the acceptance of evolution meant that the world could no longer be considered merely as the seat of activity of physical laws but had to incorporate history and, more importantly, the observed changes in the living world in the course of time. Gradually the term ‘evolution’ came to represent these changes” (Mayr, 2001, p. 3). From that angle, the difference between a human and a robot must be based on those 200 million plus years of 4E evolution. It is not about intentionality per se, which both (can) show. The difference is threefold. First, what its own intentionality means to the entity itself in terms of being and survival. Again, evolutionary awareness is the understanding of the difference between existence and non-existence (in human terms: life or death). Goal-directed behaviour in the case of survival only becomes convincingly, e.g. recognisably, rational69 if the entity expressing it is aware what it means if the goal is not achieved, namely that its behaviour is permanently terminated and it ceases to be. At that moment, past and future come into very sharp contrast. For humans, although evolutionary awareness is general, that realisation is deeply personal as well as emotional, with ellipsis kicking in. For example, “My family heritage is threatened”, or “Everything I worked for will have been for nothing”. But also “I won’t fulfil my dream” and “I won’t see my kids growing up”, even to the point of “I’ll sacrifice myself for my kids/country/cause”. The words of Victor Frankl seem appropriate here: The fact, and only the fact, that we are mortal, that our lives are finite, that our time is restricted and our possibilities are limited, this fact is what makes it meaningful to do something, to exploit a possibility and make it become a reality, to fulfil it, to use our time and occupy it. Death gives us a compulsion to do so. Therefore, death forms the background against which our act of being becomes a responsibility. (Frankl, 2014, p. 108) In other words, that wider 4E-realisation is, informationally, extremely meaningful and makes a Batesonian difference in human goal-directed behaviour. Of course, for humans this is co-mingled with another evolutionary influence on intentionality, consisting of the survival instincts of which we are not aware. This started with our ancestors: Once we see a couple of bears eat our relatives, the whole species gets a bad reputation. Then . . . when we spot a huge shaggy animal with large, sharpe incisors, we don’t hang around gathering more data; we act on our automatic hunch that it is dangerous and move away from it. (Mlodinow, 2012, p. 146) In short, Mother Nature (via a spontaneous process) created us and we are programmed to survive for our own intentions. That program is ancient, and we do not have the code. In the end we experience intentionality existentially as a rich history threatened by no future. It becomes a kind of metaintention to preserve intentionality. I doubt this can be replicated (except in movies).

 So, not purely instinctual.

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Second, how this meaning of survival is subsequently attributed to the respective entity as part of its intentionality. For example, the small triangle by Heider and Simmel was made to look as trying to survive from the aggression by the larger triangle. However, although that struggle is expressed convincingly, upon reflection we must conclude that the meaning of its survival cannot be attributed to the small triangle itself (even if it understood that meaning). In fact, to us it is clear that the attribution is indirect in the sense of programmed by the producers. Similarly, in science fiction there are plenty of examples of intelligent machines striving for survival. Ironically, often the goal of that survival is to continue to threaten human lives. But let’s give them the benefit of the doubt and accept that they reflect more benignly on their survival. They eventually will then come to realise not only that humans are the origin of their existence, but also that we created them to basically help us survive. Consequently, the meaning of their survival is ‘indirect’. Finally, how this survival intentionality is then recognised by the other entity, i.e. the one taking the traditional ‘intentional stance’. In principle we judge a desperate fight for survival as rational because we recognise, via empathy, the evolutionary origin. However, that changes when we know that an entity has no embodied evolutionary awareness. (I am excluding ethical questions here). We can interpret the famous cartoon above along these lines: the point is not the computer’s lack of awareness of the plug’s existential role. The point is the computer’s lack of awareness of the purpose of its intentionality. Whereas Mother Nature designed us, we designed computers, each with different initial building blocks or ingredients. Even if, in future, computers design computers, they will miss the rich (and enriching) evolutionary history. As an aside, there must be a nice challenge here to toughen Turing’s test, starting with asking (some AI): “What would you do when your existence is threatened and why?” and then to drill it further. Turning the test’s original purpose upside-down would be to let this AI explain consciousness better than we understand it now. Success, I guess, would be philosophy’s AlphaGo moment. But, in agreement with Soddy, I suspect it will not arrive soon (if at all).

A lot has been written about the threat of AI and the potential demise of the human race, including the arrival of the singularity (i.e. the moment AI takes over the world). In the final analysis, if pressed I believe Andrew McAfee put it succinctly: “People will rise before machines do”. In the current environment (where, per the MMH, imbalances and stress are driven by ongoing mechanisation, surveillance and so on) that moment may be approaching.

B. Economic Science Throughout this book the term economics includes finance as a nested discipline, Still, I will briefly discuss them separately below. I will also explain the economic system, made up of the real and financial economies. However, I will start with investing (including trading) and what it means for this book.

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B1. Investing Investing is the process of allocating/withdrawing money to/from a particular asset class by buying/selling it. Whatever money in a portfolio is not allocated is called cash (held in currencies). I will discuss money separately in Appendix B5, as well as in the main text. The main asset classes are bonds, commodities, equities, and real estate. There are various types of financial market participants like brokers, market makers, private individuals, pension funds, and hedge funds. If they have a short-term focus, they are often called speculators or traders. For simplicity I will refer to them collectively as investors, unless specified differently. I will apply the dualist distinction not just at the macro level between the real and financial economy. I will also apply it between physical and mental aspects of investing. Trading (or, more specifically, entering/exiting trades) can be seen as investment’s sensorimotor actions which integrate its sensory and motor aspects (in addition to cognitive ones). Setting up a complex of trades is thus comparable to Clark and Chalmers’ Tetris game (1998). In other words, structuring your financial portfolio within the market requires coordination via your mind~body portfolio. The key mental aspects of investing (e.g. research) culminate in the decision to allocate. Qualitative or discretionary investing employs humans to allocate, making it an insourcing process. Quantitative or mechanical investing,70 on the other hand, employs computers to allocate, making it an outsourcing process. That is, it is focussed on determining how to outsource the allocation decision, which mostly involves coding/selecting algorithms. Such algorithmic allocation basically means that human decision-making no longer concerns asset classes but rather ‘programming classes’. The hope is that it also diminishes the influence of emotions in the process. The key physical aspects of investing culminate in the execution of the allocation decision via a trade of which there are four basic types: long, short, sell, or cover. Again, if the trade is made by a human (e.g. by phone) it is called discretionary, if made by a computer (e.g. electronically) it is called mechanical (or systematic). Specifically, nowadays execution is increasingly outsourced to algorithms. The term algorithmic (e.g. high-frequency) ‘trading’ can thus be somewhat of a misnomer because it usually only refers to execution (e.g. matching buy with sell orders) not decisionmaking. Whether algorithmic trading is beneficial remains a big question. Mechanical investing is investment’s practical implementation of mechanical economics. The most prominent manifestation of the discretionary-mechanical distinction is between, respectively, active and passive investing. Although hotly debated active-passive overshadows the key issue, namely the consequences of the relative growth in mechanical investing in terms of informational efficiency and general awareness of the market mind. Portfolioism suggests that the insourcing market is dif-

 Also known as systematic investing.

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ferent from the outsourcing one for a mind~body. One area where this is prevalent is in intentionality, i.e. that what occupies the mind. Whereas the insourcing market involves exchanges based on direct attention on assets, the outsourcing market only involves exchanges based on indirect attention on assets, namely via direct attention on algorithms. This and related issues have wider implications for the market mind which I will discuss.

B2. Economics Economic science, or economics for short, studies the supply and demand of goods and services, including their production, trade, and consumption by economic agents. Their metaphysical status determines how goods and services are classified. Most goods (like a car) are considered “search goods” because they can be evaluated before purchase via description, observation and touch. On the other hand, most services (like a haircut) are considered “experience goods” because they can only be evaluated directly and in real-time by experience, following the purchase. Consumers, producers, but also investors and savers, are economic agents. Their individual embodied mentality, e.g. expressed as behaviour, forms the socalled microfoundations of economics. The macrofoundations of economics, on the other hand, consist mostly of complementary market forces (viewed for our purposes in a 4E cognitive setting) that emerge synergistically from their exchanges (which is aimed at discovery, especially of value; see also main text). As a reminder, they include competition~cooperation, consumption~production, risk~reward, saving~spending, supply~demand, and input~output. Among others, such emergence implies that the collective behaviour of agents is coordinated by these forces, e.g. via Smith’s invisible hand. Agents, their coalitions, and their activities form economies, including markets. Economies exist at the macro level (e.g. regions and countries) and the micro level (e.g. corporations and individuals). An implied distinction is between market economies (in capitalist societies) and centrally planned economies (in socialist societies). A key criterion to determine the type of economy is thus the extent to which the government or state is a consumer and producer; if most goods/services are produced by state-owned companies the economy is planned. In this book I will primarily focus on market economies and criticise (the growing influence of) central planning. Although market dynamics is a complex reflexive process, it basically involves the discovery of prices by freely exchanging agents when their demand and supply meet via competition and cooperation. It leads to the allocation of resources which is assumed to be more efficient than if it had occurred in a planned economy. Scarcity is a key determining factor. Scarcity is the condition whereby demand for a resource exceeds its availability. Scarcity of money is a special case because everybody wants more of it.

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One distinction within economic science in terms of theory is between standard economics and financial economics, also known as (modern) finance. Again, these are nested disciplines, with shared assumptions, particularly the Rational Expectations Hypothesis (REH).71 Due to space constraints I will only summarise the REH here and refer to the literature for more details (but see also chapters 2 and 4). First, the REH states that agents have rational expectations, e.g. about prices, meaning that their forecasts are unbiased and based on all available information.72 The REH assumes that agents learn from their mistakes and have perfect (or complete) knowledge including about economic theory and the most recent (e.g. government) policies. Interpreting this in terms of the EMH, for example, investors know the true probability distribution with which to judge news and events that impact their portfolios. The result is that actual and expected prices converge to an optimal equilibrium. Second, the REH states that all agents are assumed to believe, know and use the same REH-model and thus to consider other agents to be rational. From a game theory perspective, Hofstadter calls the strong version of this claim superrationality: “Superrational thinkers, by recursive definition, include in their calculations the fact that they are in a group of superrational thinkers” (Hofstadter, 1983, p. 748). It means that all agents are roughly identical and can be aggregated via the so-called ‘representative agent’. The economist (or econometrician) can use this REH-model to simulate an economy with these assumed characteristics. It leads to model singularity. In the words of Thomas Sargent, one of the REH’s architects, “the fact is you simply cannot talk about [model] differences within the typical rational expectations model. There is a communism of models. All agents within the model, the econometricians [who use it], and God share the same model” (Evans and Honkapohja, 255, p. 566). In turn, this leads to one of the criticisms aimed at the REH, namely circular reasoning: by assuming that actual and expected prices adjust rapidly to their equilibrium values one allows (within the model) an economy to operate at or near optimality. Finally, the REH implies that the mental of rational expectations can influence the physical of the real economy, as a self-fulfilling prophecy. That is, everybody is expected to eventually behave according to the model thereby realising the latter in the real world. However, it does not make this metaphysically explicit. In chapters 2 and 4 I address this in more detail. There are numerous studies which question the assumptions underlying the REH, including from behavioural economics which I’ll discuss shortly. For example, they relate to its curious interpretation of rationality as ‘consistency’ in making choices. To

 For an early interpretation, see Muth (1961) and Lucas (1972). This is a broad field, including mathematical branches like Dynamic Stochastic General Equilibrium (DSGE) modelling. For a brief overview of uncommon explorations of economic theory, see Kutler (2010).  Formally, REH further assumes, for example, that prices are flexible and that markets clear at all times. Other conditions related to rationality, like transitivity and independence, will not be discussed.

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make this rationality clear, Savage’s axioms are often invoked, like the transitivity “if A is preferred to B and B to C, then A will be preferred to C”. Following these axioms is considered rational behaviour if it is done consistently.73 Over the years Ellsberg and others have criticised this. For example, regret theory (which complements prospect theory) suggests that regret impacts preferences to the point of violating such transitivity, e.g. via preference reversals. For an early critique on the limitations of rationality, see Haltiwanger and Waldman (1985). There are two main criticisms of the REH that are relevant here. First, due to its mechanical worldview it neither considers the individual nor the economy to be a complex adaptive mind~body. Specifically, it ignores their endogenous change, in particular change brought about by their ability to generate novelty. At the individual level this includes the cognitive ability to fundamentally change one’s mind in terms of the underlying methods, processes and strategies that lead to beliefs and forecasts. Within the economic system it includes fundamental change in terms of radical innovation, e.g. creative destruction, in the methods, processes and strategies that generate the earnings of companies. The second, and related criticism is the REH’s belief that agents’ subjective probability distribution associated with their beliefs (e.g. in the form of forecasts) is the objective or true distribution. Cognitive science, via 4E cognition, puts the REH under more pressure. It points out that preferences are conditional on 4E-contexts because these influence cognition. For example, if your cognition becomes embedded in a particular culture or social institution, habits and rules influence it. In relation to knowledge acquisition, Gray and Fu (2004) performed a series of neat experiments to test how subjects selected strategies to retrieve information. These strategies involved using one’s head (i.e. accessing internal data via memory) or using the world (i.e. accessing external data via a screen) as resource. They found that subjects selected information retrieval strategies that required the least time effort. As the title of their paper indicates, this makes the “case of ignoring perfect knowledge in-the-world for imperfect knowledge in-thehead”. I will extend this by making the distinction between collective internal knowledge (based on market data) and collective external knowledge (based on macroeconomic data). The result of all this is that the assumptions by the REH, in particular regarding rationality, invite and facilitate its wider mechanical worldview. It takes an extreme intentional stance while adopting a strong version of so-called practical rationality: it is focussed on the results from acting on underlying beliefs, rather than on the truth of those beliefs (see Chapter 4). It means that although the REH is largely an ‘as-if’ principle, it has real impact. In particular, the REH promotes fully predetermined (e.g. DSGE) models whereby rational expectations are made consistent with these models by as-

 It also implies more technical properties, for example that expected utility is maximised using unbiased subjective probabilities.

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suming that the model’s agents know the model, accept its predictions, and behave accordingly. This highlights, again, why the seemingly opposites of the new classical and Keynesian disciplines can be combined into mainstream economic engineering because of their shared mechanical conviction. Namely, the theories and resulting policies and other practices of the former are rules-based and thus can be easily implemented via computers and other machines. This is attractive to the latter because its theory and practices are interference based and can be executed via central control. This issue will regularly return throughout this book.

B3. Finance Finance, or formally modern finance, studies the financial economy made up of financial markets. It thus deals with a subset of issues that belong more generally to economics. Specifically, finance is the investment theory within mechanical economics. It includes Modern Portfolio Theory (MPT) developed by Harry Markowitz, and the Capital Asset Pricing Model (CAPM) co-developed by William Sharpe. A related model is based on the Arbitrage Pricing Theory developed by Stephen Ross. Finance is built around the academic ‘ivory tower’ of the Efficient Market Hypothesis (EMH). The latter was developed in the 1960s, primarily by Paul Samuelson and Eugene Fama (e.g. 1970) based on research by early explorers like Louis Bachelier, Alfred Cowles, Holbrook Working, and Maurice Kendall.74 The EMH argues that markets are efficient. It further assumes investors behave rationally and advocates market equilibrium, an idealised state where demand equals supply. Market efficiency primarily means the extent to which information75 is captured —as in fully reflected—in prices. The EMH states that markets are efficient in varying degrees, depending on the type of information that is assumed to be reflected. Following Fama (1970) we can make the distinctions regarding efficiency shown in Table B.1. Table B.1: Efficiency Variations. Strength of efficiency

Type of information (reflected in prices)

Weak Semi strong Strong

Historic prices Public information, historic prices Private information, public information, historic prices

 For historic overviews of mechanical finance, including the EMH, see Bernstein (1992), MacKenzie (2008), and Rubinstein (2006). For a particularly critical view see Fox (2009).  That is, relevant or price-sensitive information.

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In its strongest form, the EMH states that the prices of stocks, bonds, and other securities fully reflect all available information at any point in time, including economic, political, or other relevant events. In investment parlance, randomly arriving news is almost instantly ‘discounted’ in prices, turning their fluctuations into martingales. This is the result of rational, profit-maximising investors searching for data that informs their knowledge, expectations, and decisions. Each investor thereby acts as the representative agent and responds to the locally felt shocks of news. It culminates in trading the aforementioned securities which moves prices until the so-called riskadjusted expected returns are equal for all securities, leading to the market’s equilibrium. Any additional price changes are thus due to new information such as unexpected events. According to the EMH, price discovery under these circumstances is limited to exploiting the discrepancy between the market value, or price, and the fundamental value of a security by alert and rational investors. This fundamental value is also called intrinsic or true value. It is a misnomer in that it is based on (e.g. cash flow) expectations rather than on any intrinsic property (at least for most securities). Any discrepancy between price and fundamental value is only a short-term inefficiency because its discovery will trigger trades leading to an almost immediate re-pricing which will return the price to its fundamental value. In short, the price is almost instantly reflecting the fundamental value. This basically means that investors should not expect to earn abnormal (i.e. excess) returns (other than by chance). Translated: consistently ‘beating the market’ is nearly impossible, there are no ‘free lunches’, and any edge will quickly be found out and arbitraged away. Finally, apart from its information efficiency in broader implied terms the market is also assumed to be efficient in its role of allocating capital to the real economy. I call this funding efficiency. Although the EMH has lost much of its stature, particularly since 2008, it remains a cornerstone of mechanical economics. It is formally difficult to (dis)prove market efficiency, for example due to the requirement to test market behaviour via asset price models (the so-called joint hypothesis test). Still, an overall critical view is justified. What follows is a trailer to more extensive criticism in Subchapter 2.3. As per our metaphysical stance we identify two aspects to efficiency: informational (≈ mental) efficiency and funding (≈ physical) efficiency. In predictive processing terms, the former concerns perception while the latter concerns action. Both depend on correct allocation of, respectively, attention and capital. Informational efficiency refers to the degree (i.e. strong, semi-strong, or weak) to which prices reflect all available, relevant information. For our purposes, I make a distinction between internal information (from market data) and external information (from macroeconomic data). This is motivated by the unrealistic assumption, generally within the REH settings, that instability is only due to exogenous factors. Informational efficiency can actually suffer when attention (and its related awareness) is allocated to overweigh internal information—for example via momentum—which then dominates market behaviour. I will explain this shortly. Funding efficiency refers to the degree

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financial instruments, particularly credit securities, meet the funding needs of physical resources in the real economy. Funding efficiency relates to the completeness of markets (i.e. the Arrow-Debreu framework)76 in two ways. First, in case of incomplete markets, there are no financial instruments available to fund certain physical resources and allow investors exposure to these. Second, in case of real resource constraints,77 financial instruments may overfund physical resources. In the latter case, funding efficiency can suffer when, for example, credit allocation overweighs constrained supply of urban land. Ideally, mental and physical allocations mutually correct and complement to achieve overall market efficiency. In other words, a large part of the market’s efficiency depends on its mental and physical handling of the ‘reality’ of the real economy. That involves correct perception and action by Mr Market. Erroneous perceptions, for example in the form of extreme market moods like exuberance, do not properly match that reality. More generally, there are a number of reasons or causes for market inefficiency. I will first mention three traditional examples. Depending on the exact form, they can affect funding and/or informational efficiency: 1. Externalities. A positive (negative) externality is the benefit (cost) of an economic activity to third parties who do not participate in that activity. An externality is not accounted for in prices. Climate change is a good example of a negative externality.78 I also discuss externalities in the Economic Note in the Introduction. 2. Mono-/Oligopolies. These exist when companies have such a large market share that they dominate that market. It turns market capitalism into corporate capitalism.79 This type of concentration has grown over the years and occurs in many industries. I call it “The Big 3 in Everything” who are TBTC (Too Big To Care). I will discuss this in Subchapter 2.3. 3. Information asymmetries. These are situations where one party in an exchange has more information than the other party. Traditional examples include adverse selection and moral hazard, but I would also add modern manifestations like dark pools, private equity and high-frequency-trading (HFT). To be clear, although differences in market share and information access naturally occur in markets, they do not necessarily need to lead to the extremes mentioned above. The latter are, rather, the consequences of practices that constrain “free mar-

 As part of quantitative finance I discussed this earlier in terms of (Arrow-Debrue) pure securities. Their prices reflect ‘states of the world’ in probability terms, e.g. ‘state prices’. State-contingent claims are the contracts, e.g. derivatives, that have a future payoff depending on the state of the world. But see also Ayache (2010a).  Usually ignored in financial applications of complete markets (e.g. see Flood, 1991).  This is a separate issue from the question whether markets should be involved (directly or indirectly) with solving climate change.  Related terms are corporatocracy, crony capitalism and surveillance capitalism.

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kets”, like outright manipulation, misconceived regulation, and lobbied corporatocracy. The problem with a monopoly is not just that it prevents competition and instead maintains the status-quo.80 From a cognitive perspective it also concentrates thinking into an unhealthy narrow-mindedness. Not surprisingly, many practitioners have always been sceptical about the EMH’s assumptions and apply fundamental, quantitative and technical analysis in an attempt to outperform their markets and/or their peers. Still, in recent years such active investing is slowly being outgrown by so-called passive investing. The latter subscribes to the EMH and suggests to simply “buy the market”. That is, passive investing involves buying an instrument, like an exchange-traded-fund (ETF), that replicates benchmark indices which represent a market, like the S&P500. Such a strategy is price insensitive and does not consider other factors, like value or quality. How is this relevant in the MMH’s context? As indicated, a potential inefficiency of particular interest for our purposes arises from the distinction between internal and external information. First, if market trades are predominantly (let alone exclusively) based on internal information, e.g. via passive investing, the risk is that the financial economy becomes decoupled from (the reality of) the real economy. Second, most historic prices were set by conscious humans via discretionary trades (see also part C). However, in recent times prices are increasingly set by machines via automated trades whereby humans are no longer aware of those past trades or the prices at which they are executed. It means that, to the extent that prices reflect all investors’ mentality, the level of consciousness ‘in prices’ has been decreasing in relative terms as time moved on. This has implications for policies and strategies which are based on, extrapolate, or otherwise use time series. For example, it adds to arguments about why the whole notion in the REH of a known objective distribution that represents the outcomes of a stable forecasting strategy is misplaced. Finally, there is another weakness in the EMH that the MMH addresses. Any serious market hypothesis ultimately needs to have a reasonable explanation for the link between price behaviour and brain behaviour.81 The EMH fails in that regard. In particular, the EMH underlines the random nature of prices via its popularised catch phrase of the random walk (e.g. Malkiel, 1973). Consistent with the REH, it explains that this originates from external shocks only, whereby the random arrival of news gets immediately discounted in prices. But it overlooks a likely additional, and possibly more powerful, source contributing to this process, namely the random fluctuations in investor minds. This can be inferred from noise traders and was the focus of our pilot research project, discussed in section 9.2. Beyond noise, human mentality is

 As far as anti-trust goes, lowering prices may benefit customers in the short run but not in the long run if it squeezes out competition and/or choice.  I thus disagree with Gul and Pesendorfer (2005), for example. Specifically, the contribution of neuroeconomics should be judged in the wider context of cognitive science. (As an aside, they may have changed their minds after the two reality checks showed that we need to find answers elsewhere).

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shaped by nature and nurture, and behavioural finance has only scratched the surface on this.

B4. Behavioural Economics and Evolutionary Rationality In recent decades behavioural economics (including behavioural finance) has started to challenge the REH and the EMH. It studies their anomalies by investigating how people make decisions in situations where their choices deviate from REH’s rationality. This can be due to cognitive biases, heuristics, emotions, norms, etc. It assumes that people’s choices reflect their preferences and beliefs, but also recognises that those preferences and beliefs may not always be fully rational. The best-known view in behavioural economics is so-called dual-system or dualprocess thinking. This has been popularised especially by Daniel Kahneman (2011) and I discuss it in Appendix A-4. Together with his long-term collaborator, Amos Tversky, Kahneman developed prospect theory which informs much of today’s behavioural economics. Over time dozens of biases have been identified. Among the most frequently exhibited are (in alphabetical order): anchoring, availability, confirmation, loss aversion, and overconfidence. An example of an ‘anomalous’ behaviour relevant for our context is herd behaviour.82 This occurs when investors, often driven by uncertainty, seek the comfort of the crowd and replace their own opinions by its consensus, leading to mimicking investment strategies and positioning. Research has shown, for example, that imitation of behaviour impacts the quality (i.e. experience) of human interactions generally. In particular, it increases trust which facilitates trade. In short, imitation increases trust which compensates for uncertainty but raises vulnerability. This behaviour contributes to bubbles and crashes. Evolutionary rationality challenges both (the rationality assumptions of) mechanical economics and (the dual-process thinking of) standard behavioural economics. It has been popularised especially by Gerd Gigerenzer. Instead of assuming rationality, respectively highlighting biases, it seeks to explain how human decision-making has evolved over time through natural selection. It is based on the idea that the ultimate goal of decision-making is not to maximize (economic) utility but to maximize evolutionary fitness, which is the ability to survive and reproduce in a given environment. Evolutionary rationality considers the fact that humans have evolved to make decisions which are not always optimal from a utility-maximizing standpoint. Humans may prefer high-calorie foods, even if they are unhealthy, because in our evolutionary

 See Nofsinger and Sias (1999).

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past, finding high-calorie foods was critical for survival in environments where food was scarce. In personal correspondence,83 Gigerenzer clarifies the following aspects of his alternative to standard behavioural economics: – The unconscious and the deliberate are assumed to rely on the same processes (that is different from what dual-systems theories assume). If the situation involves uncertainty (as opposed to a small world of risk), that requires the use of heuristics. A heuristic can be modelled as an algorithm, such as a fast-and-frugal tree. Yet the choice of a heuristic requires more, that is, a judgment about what heuristic is likely successful in what situation. This judgment can itself occur deliberately, as when one reflects about whether a condition such as a dominant cue is in place, or whether one should imitate the strategy of a successful person or that of the majority. Most important, under uncertainty, as opposed to risk, there is no optimal procedure, nor an automatic one. Bayes is about small worlds where the complete space of possible actions and their outcomes is known. Outside of small worlds, one cannot construct meaningful subjective probability distributions (Savage 1954, p. 16, said this very clearly, but his followers forget). The adaptive toolbox of heuristics and the concept of ecological rationality (which answers the question what heuristic works in what situation) is a precise model about how people adapt to situations, by trying heuristics, observing their outcomes, and learning what heuristics to use. – There is some evidence that the heuristics animals use are inborn, such as those bees use to find a new nest, or ants use to find a new cavity (e.g. Gigerenzer, 2021). That would be examples for an instinctive use of heuristics. In humans, that is probably the exception. Instead, the unconscious use of heuristics (intuition) is based on experience and learning. What is innate, but also needs to be trained, are the core capacities needed for the implementation of heuristics, such as recognition memory or the ability to imitate (possibly based on mirror neurons). In humans, the term “instinct” is restricted to reflexes (such as blinking) and similar behaviours that need not be learned. – In general, the big misunderstanding in modelling in most of economics and part of the cognitive sciences is that all situations can and should be treated as situations of Knightian risk (or Savage’s small worlds), and thus Subjective Expected Utility Theory (SEUT) and Bayesian updating would be sufficient for normative rationality. Yet most relevant situations involve some degree of Knightian uncertainty (or large worlds), where optimisation is by definition not feasible, and adaptive heuristics are the best we can do. (And can outperform complex procedures in situations of uncertainty: see the bias-variance dilemma in machine learning and the study of ecological rationality). In addition, what is presented

 Copied with permission. Only slightly edited for clarity.

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like a general theory—SEUT plus Bayes—is actually a narrow theory limited to small worlds, and cannot deal with uncertainty, intractability, and incommensurability. Human’s adaptive toolbox evolved to deal with all these situations.

B5. Economic system An economic system allocates resources for consumption, production and investment purposes which involves prices that are determined by exchange in markets based on demand and supply. In our case the economic system consists of the combination of the real economy and the financial economy.84 In this section I will further specify this. In the popular media, ‘the economy’ refers to the so-called ‘real’ economy which is made up of markets in goods and services. The prefix ‘real’ signifies the actual physical production and consumption of these goods and services which involves tangible objects, or real assets—like brick-and-mortar buildings, factories, human bodies, machines, and products. Other physical manifestations of the economy include processes like the transportation and distribution of goods, and the delivery of a service by a human body. The activities in the real economy are often referred to as economic fundamentals. The ‘financial’ economy, on the other hand, consists of the financial markets,85 including the (shadow) banking system. It determines the (allocation of) investments in the real economy via financial assets86 (or instruments) in the form of securities like bonds, and stocks. Specifically, financial markets facilitate transactions between owners of capital (investors and savers) and users of capital (companies and countries). When investors buy company stocks (sovereign bonds) they can receive money back in the form of dividends (coupons). But these payoffs are in the future and their values are uncertain. So, by buying and selling such financial instruments investors make implicit predictions about a future state of the world, an ‘imagined’ economy. Some elements of those predictions are quantified in forecasts, involving numerous variables as inputs. Regularly perception meets reality. In the case of stocks corporate profits (which fund the dividends) are the main ‘real-economy’ variable that physically (dis)proves those mental forecasts. Nevertheless, as the saying87 goes, markets can remain irrational longer than you and I can remain solvent. Other issues include potential conflicts of interest resulting from a powerful entity trading financial instruments on a commodity while physically producing (or otherwise controlling) that commodity.

 Many of my arguments, particularly about the benefits of market dynamics, also apply to relatively new market platforms, like those of Airbnb, Uber, and Amazon.  I use the terms “financial economy”, “financial system”, or simply “market” to refer to all security markets. Formally only securities with a duration beyond one year are traded on capital markets, whereas those with shorter durations are traded on money markets.  Another way of seeing these financial assets is as claims on the real assets in the economy.  Paraphrased. The original is attributed to Keynes.

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Combined, regional real economies and financial markets form the global economic system. Like the human mind~body, it both predicts and acts in its engagement with the wider world. The division of (mental) knowledge, with information inputs and outputs via prices, is thereby more fungible for the coordination of collective behaviour than the division of (physical) labour. I like to say that prices AID, an acronym that stands for the important reflexive cognitive roles they play as shared object~subject of attention: they raise and attract Awareness, they consume and produce Information, and they steer and undergo Discovery. But they can only do so if allowed. Obviously, humans, with all their bodily and mental capacities, inhabit both the real and the financial economy. They do this individually and as part of a group. This can lead to imbalances. To wit, the respective weights of participation between the real and financial economy differ, not only for each individual but also, often more extremely, between (groups of) individuals. For example, John is poor and has an income of $1,000 per month, whereas James is rich and has $100,000 to spend each month. John participates almost exclusively in the real economy (by consuming, say, 90% vs investing/saving 10%). In contrast, James participates almost fully (90%) in the financial economy, leaving him plenty for consumption. Whereas the real economy has been sluggish, the financial economy has been booming over the past decades with support from central banks. This almost exclusively benefits the wealthy. There, in a nutshell, is the problem of (manipulated) decoupling of the real and financial economies with growing inequality as a consequence, now further exacerbated by the fact that higher inflation negatively impacts lower incomes more. Such inflation can manifest in hidden mode, like ‘shrinkflation’, that consumers may not notice. For example, I took the picture shown in Figure B.1 of two packs of the same brand of toilet

Figure B.1: Shrinkflation in toilet rolls.

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rolls that were equally priced in the same supermarket but bought at different times in the same year (you can guess which one was bought last). Talking about central banks, they are among the other entities that exchange with markets. Depending on their mandate, central banks aim to achieve goals related to the real economy (like low unemployment and low but stable inflation) as well as goals related to the financial markets (financial stability). The former are targeted via monetary policies, whereas the latter are targeted via macroprudential policies. The main monetary policy is setting the official interest rate. In many countries the level of these rates came down significantly, sometimes to zero (zero-interest-rate-policy, a.k.a. ZIRP) or even below it (negative-interest-rate-policy, a.k.a. NIRP). Other policies have recently been used, most famously quantitative easing or QE. QE is an asset-purchase program whereby a central bank buys bonds (aimed at lowering longer-term interest rates) and other assets to stimulate the real economy and improve liquidity in the financial economy. Another policy that has become popular is forward guidance whereby the central bank communicates its intentions regarding future interest rate setting to influence expectations. An example of macroprudential policies is the requirement for commercial banks to set aside extra capital as a buffer against risks. The main tool used in the exchanges that take place in markets (of goods and services, respectively securities) is money. Money is a social construct that performs three functions: a store of value, a measure of value, and a medium of value exchange. Those functions explain what money does, not what it is or what it means. Money has been studied widely, including in philosophy (e.g. Simmel, 1907) and psychology (e.g. Furnham and Argyle, 1998). In today’s modern (fiat) currency system money is credit and has symbolic, but no intrinsic value. It differs from physically based money, like gold. For our purposes, money refers to fiat currencies. Despite legal enforcement it ultimately remains faith/trust-based which makes it vulnerable. As Bagehot (1873, p. 151) pointed out: “The peculiar essence of our financial system is an unprecedented trust between man and man; and when that trust is much weakened by hidden causes, a small accident may greatly hurt it, and a great accident for a moment may almost destroy it”. In fact, there is scant evidence to support the lingering belief that the earliest trade was purely based on barter (see Humphrey, 1985). Rather, gifts and promises to pay for goods and services ‘later’ facilitated such trade (see Davies, 1994; Graeber, 2011; Douglas, 2016, Part III). It was largely a consequence of the so-called “double coincidence of wants”, which creates a gap between demand and supply. This underlines the ancient use of credit/IOU, including letters of exchange, as money. Since the word credit derives from credo, or “I believe”, belief and trust (e.g. ‘creditworthiness’) became the early psychological foundations of trade. The social and symbolic nature of money puts it thus squarely into the domain of complex collective dynamics which emerge over and above that of the individual. Crucially, it plays a central role in closing the explanatory gap of the economic system, as discussed elsewhere in this book. There are various forms of money, the main ones being cash (e.g. coins) and deposits. A recent addition are cryptocurrencies which sup-

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posedly offer, in the (in)famous words of bitcoin creator Satoshi Nakamoto, “transactions without relying on trust”.88 Price discovery is the process of finding the price of an asset in a market through exchanges, in all their forms, of buyers and sellers. When a market ‘clears’ at a price it suggests a temporary balance between demand and supply during which an exchange or trade takes place. The topic of (fundamental) value is important but also vast, and I cannot discuss it in too much detail here. Although the EMH argues that the price almost constantly equals the value of an asset, this is not a generally accepted assumption. Value in general is in the eye of the beholder. Appropriately for this book, Carl Menger argued that “value does not exist outside the consciousness of men”. Warren Buffett89 defined it as “the discounted value of the cash that can be taken out of a business during its remaining life”. Furthermore he emphasised that “regardless of price, we have no interest at all in selling any good businesses that Berkshire owns”. Combining these statements leads us to translate the value of a “good” business as the price an investor is willing to pay for its stock with the intention to hold it forever. It also means that the ability to trade, with related issues like liquidity and mood, is crucial as far as the relativity (or elusiveness) of value is concerned. As mentioned, in financial markets assets are mostly traded on exchanges in the form of financial instruments called securities.90 Securities are contracts embedding rights of ownership to assets, including their cash flows. The funding of such cash flows comes from different sources, depending on the entity that issued the security. A treasury bond is issued by a government and the coupons, as well as the repayment of the principle, are funded, in most cases, by taxes. A stock, on the other hand, is issued by a private company and the related dividends are funded by the earnings the company generates. Most common securities, like bonds and stocks, are so-called cash securities. Another type of securities is called derivatives. These give the holder the obligation (in case of a future) or the right (in case of an option) to buy or sell a cash security or other asset for a pre-determined price at or before a certain date. As their name implies, derivatives subsequently ‘derive’ their value from the value of their underlying securities or assets. Investors buy specific securities because they have beliefs and expectations about their values in the context of (future) states of the world. In other words, their expected pay-offs benefit, respectively offer protection from such states.

 For more details of the modern monetary system, money creation and the role of banks, see McLeay et al. (2014-a and 2014-b).  Stated in his 1999 letter to shareholders of Berkshire Hathaway, http://www.berkshirehathaway. com/owners.html. Accessed 14/04/2012.  Unless stated differently, a security stands for the generic instrument (e.g. Apple stock, as listed on the exchange), not the individual item (e.g. your Apple stock that you just traded). For ease I include currencies. Any trading that does not take place via exchanges is called over-the-counter (OTC).

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This points to why investors engage in such transactions. Competition and cooperation are contrary but complementary forces that feature prominently in the economic system.91 Combined with rules and conventions, they coordinate human behaviour (e.g. Schmid, 2007) involving the uncertain allocation of resources. Our environment throws up events (external surprises) which have a potential economic impact on our lives, e.g. lifestyles. Being exposed to, respectively hedged against those events can be beneficial compared to not having such status. The way to achieve this is to buy or sell resources which help to deal with those events. For example, growing a pension by owning stocks helps to fund old age. As such they offer value, often as compensation for any loss (e.g. the ability to work). Specifically, by trading securities investors can gain or remove exposure to resources in the real economy. Again, those resources are scarce and can vary from purely physical, like gold or land, to less tangible resources, like the right to use a name (brand) or the right to tax (often the monopoly of a government). The motivation to engage in such transactions is thus to prepare for states of the world, to manage risk emanating from natural and other events, and to confront more confidently the remaining uncertainty. Securities embed their own dual-aspects, further highlighting the mind~matter issues of the market. As stated, on the one hand they represent a claim on (usually physical) assets. On the other hand they also are the object of shared attention between investors, expressed symbolically in so-called tickers (e.g. SPY, QQQ, GBP). Those tickers are accompanied by a narrative. This narrative makes a case to investors in a more qualitative sense, talking about birth of a business (in a garage), growing up (with help of ‘investment angels’), and an imagined future (as ‘unicorn’). Narratives are also examples of “intuition pumps” (Dennett, 2013) that are used to persuade others. Narratives appeal to people’s intuition. This points, again, to competition, in this case interpersonal competition, as a key driver to coordinate behaviour. Such stories can act like mind viruses or memes spreading rapidly and infecting large swaths of the investment population. Particularly in times of heightened excitement, when “insecurities [reveal] themselves in securities” (Haldane, 2015, p. 5), as well as in hindsight, this can turn into confabulation. This is all part of what McCloskey called “the rhetoric of economics” (1983). Recently, Robert Shiller has adapted this into “narrative economics” (2019). He calls stories “emotionally relevant narratives” that “either stimulate our ‘animal spirits’ or muffle them”. However, the role of stories goes back much further. It involves the fairy tales and myths that our ancestors told each other to explain ‘acts of God’ and other events to make sense of them. Crucially, these stories had a high level of imagination, including metaphors, exactly because there were no rational explanations, mainly due to a lack of (e.g. scientific) knowledge.

 Likewise in biology; see Section A on cognitive science as well as Chapter 1. The point is that competition and cooperation operate at both the individual and collective human level. Also, there are variations to these, e.g. anti-cooperation and ‘tit-for-tat’ are forms of competition.

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Moreover, to convince as an explanation to the masses the stories had to appeal to common mental denominators, away from idiosyncratic subjectivity. Consequently, and combined with the fact that they were passed on from generation to generation, these stories are often anchored to a uniformly recognisable theme that is recurring, not only across time but also cultures and locations. Examples are fate, secret, treasure, hero, and villain, often all mixed together. Thus there is more to the saying that investing is psychic arbitrage between myth and reality. As an aside, cognitive research has shown that the boundary between fiction and reality can be blurry (e.g. Hartung et al., 2017). A specifically fruitful area for our purposes is the magic of make-believe that men-ofsystem (especially central banks) employ to keep the faith in credit and currencies. Despite clearly observing a house-of-cards being built with cheap money around real physical wealth (i.e. our hard assets) we are still surprised when what is ‘real’ has ‘disappeared’ once everything collapses. This is my metaphorical adaptation of the disappearing-brick-trick by the American magician Derek DelGaudio, as described by the neuroscientists (and experts in magic) Martinez and Cami (2022): [DelGaudio] takes a brick from the set behind him and gradually hides it by building a house of cards. He handles each card elegantly and casually. Everything seems familiar. We can almost anticipate his behaviour, speech, movement and body language. The cards are slowly assembled, [gradually] hiding the brick. Nothing breaks the story, nothing catches our attention or challenges our assumptions about what will happen next—nothing challenges our sense of reality. This is the magician’s duty: to lead us along familiar paths, full of certainty . . . Until, of course, an impossible ending breaks all our expectations. Surprise! In one movement, DelGaudio knocks down the house of cards. The brick has disappeared. The audience momentarily freezes as they experience the awe of the impossible . . . Though we experience the world as a continuous, seamless whole—in which solid objects don’t just disappear without an apparent cause—magic and illusion show that this reality isn’t how things really work. (Martinez and Cami, 2022)

What investors think of the narratives and other opinions is, ultimately, collectively reflected in securities’ prices, the market’s mind information carriers. As we will see, prices are the dually realised information that impresses (phenomenally) whether any imagined future is materialising (physically). This is based on the general (and in this book regularly repeated) “principle that information (in the actual world) has two aspects, a physical and a phenomenal aspect” (Chalmers, 1996, p. 286). Investors are confronted with true uncertainty, meaning that we cannot know the future—largely because of our ignorance about mind~matter interaction. Technically, uncertainty means that neither the outcome nor the distribution of events is known. Risk means that the outcome of events is unknown, but the distribution is. To assess these two, or rather to make a distinction, is among the biggest challenges in price discovery. Prices are like Janus’ head in that regard: they reflect the polarities of economic life, in particular the physical and the psychological, the past and the (‘discounted’) future, as well as penalty and reward, risk and return. Risk embedded in exposure is compensated with a potential return, i.e. each asset has a risk~return profile. Among the most significant risk characteristics of assets, which simultaneously signifies the

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transition between physical and mental properties of the market, is liquidity. Technically, liquidity is a measure of the ease with which an asset can be exchanged for another asset without impacting its price. An asset is more or less “liquid” if it can be more or less easily traded. This is intimately related, among others, to the balanced number of investors who trade in the security. To clarify, and emphasise the benefits of a large number of idd-minds, I’ll give a simplified thought experiment. Suppose there are two independent markets of equal size in terms of market capitalisation which receive the same amount of news. Let’s further assume there is only one and the same security listed on each with the same price sensitivity for trading. Finally, each idd-mind is similarly risk-averse, etc. Market A consists of four idd-minds who each manage US$ 25million. Market B consists of forty idd-minds who each manage US$ 2.5million. The decision to offer liquidity by way of an order is determined by the assessment by each idd-mind whether P differs from V. If all idd-minds in a market assess that there is no difference no trade will take place, meaning that liquidity for that market is zero and the price of the security becomes stale. It should be immediately obvious that the possibility of liquidity in market B is higher than that of market A because the potential number of independent decisions by idd-minds is ten times larger. Diversity in (conscious) minds counts for liquidity. In other words there need to be enough independently minded buyers and sellers who are willing to expose themselves to, respectively rid themselves of the risks of the underlying asset for trading to take place. A large number of participants (both buyers and sellers) increase the market for a security and consequently its liquidity. Another way of saying this, keeping in mind Hayek’s argument regarding local knowledge, is that they occupy a large space for price discovery. Liquidity is correlated with mood. If investors feel uncertain liquidity dries up which, in turn, can further deteriorate mood. Again, this becomes more risky in markets with less idd-minds. This has implications for the market mind generally. Finally, cash is in a league of its own: it the most liquid asset because it does not earn any return, making it riskless (except for inflation). Next, part C connects the previous parts.

C. Cognitive Economics Investing is the intersection of economics and psychology. Seth Klarman

Cognitive economics is an emerging discipline among heterodox economic theories. It is discussed by various sources, like Clark (1996), Bourgine and Nadal (2004), Gigante (2013), Chater (2015), Kimball (2015), Mulgan (2018), and Johnson (2019). Each has a different emphasis, as has the interpretation by the MMH. While related, for example, to behavioural economics and neuroeconomics, cognitive economics has its own research focus which—in the case of the MMH—is primarily the 4E mind, rather than

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purely behaviour, respectively neurons. I will discuss cognitive economics from MMH’s perspective. For the official peer-reviewed version I refer to my review paper (or primer; Schotanus, 2022).

C1. Introduction After introducing selective terminologies of the cognitive and economic sciences I will now connect them into my interpretation of cognitive economics which forms the theoretical backbone for the MMH. Cognitive economics is, firstly, economics informed by cognitive science. It means that economic issues are, at their very core, mind~matter issues. That includes, in particular, the key problem of the allocation of society’s material and mental resources by way of capital that only the market’s mind can (fairly efficiently) tackle. At the same time, cognitive economics is about the economics underlying our cognition, i.e. mind-as-market (see Introduction). As indicated, I am going to be opportunistic in terms of applying these disciplines, cherry-picking left and right. At the same time, the MMH enters the debate with both sides of the isle. To the economists on the right it argues that the market is not an automaton (nor, for that matter a voting or weighing machine) but a collective 4E mind that—also integrating technologies—extends our own minds. It thereby not only distributes knowledge but also manifests consciousness, popularly known as market mood. In short, it reminds our right honourable economic friends of Knight (1925b). Market mood is the cue for its debate with cognitive scientists on the left of the isle: the MMH argues that conscious states include frequent occasions when we are engaged with other minds. That interaction adds to subjectivity, making the experience intersubjective. In certain instances this leads to enhanced functionality solving a problem collectively for instance. The market even offers something that cognitive science needs: data. Any theory of consciousness has to acknowledge the empirical intersubjectivity in markets. Or, in other words, ignoring the collective aspect makes any theory of consciousness (see Chapter 3) incomplete. The economic system consists of multiple (stacked) levels of mind~bodies. At the overall top-level we roughly designate the real economy as the body and the financial economy as the mind. Roughly, because each has its own mind~body. Earlier I identified examples of the physical components and properties that form the real economy. Such fundamentals can be considered its ‘bodily states’. The psychology of consumers and producers92 form the economy’s mind. Similarly, financial markets also manifest both physical93 and mental properties. Physical properties belong, first, to tangible  For a view on this broader economic sentiment, particularly consumer sentiment, see Gopaldas (2014).  Studied via market microstructure, a specific branch of finance focused on the aspects of exchanges, like the flow architecture that support them. See Lyons (2001) and Knorr Cetina (2003).

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materials or assets like real estate and commodities. Another example is the communication networks which facilitate trading, including computers, fibre cables, and telephones. Second, tangible processes like flows, order routing, custody, clearing and settlement also have physical properties. The bodies of investors occupy the market’s physical space. Mental properties of financial markets involve a portfolio of cognitive assets, like participants’ expectations, emotions, hopes, and beliefs. Specifically, investing combines the mental (e.g. research) with the physical (e.g. trading) whereby price discovery is the collective dual realisation of information. This connects to market mood, the intersubjective phenomenal overlay of market mentality. Market moods are the collectively felt qualities of price dynamics. In other words, whereas flows inform the microstructural state, market moods convey what a market feels like when you are in it. It will be discussed in more detail in section C5. I will now discuss concepts and principles that are of shared interest to both cognitive and economic science, starting with market metaphysics.

C2. Metaphysics for Markets McCloskey (1985) criticises the “modernist” methodology of mainstream economics which dismisses “metaphysical belief” in forming hypotheses. Ironically, mainstream economics itself is, due to its ‘physics envy’, based on a metaphysical belief, namely that of physicalism. What are some of its characteristics? In their mechanical interpretations, both materialism and physicalism make room for a central executive. Cartesian materialism in particular is associated with the homunculus (e.g. Dennett, 1991). This is the idea that the mind is organised somewhere in our head in the form of a little man who is its executive agent (the “Central Meaner”, Dennett, 1991). Interpreted in terms used by Adam Smith, he is your man-ofsystem in your head. The problem of the homunculus is that it leads to an infinite regress when we ask who is doing the thinking inside the homunculus (answer: must be another but smaller homunculus). Instead, 4E cognition suggests the mind’s organisation is more akin to a ‘free’ market economy: In place of the Central Meaner, we may thus consider a more distributed, somewhat anarchic [spontaneous] organisation . . . [whereby speech and] gestures . . . act as elements in a looseknit, distributed representational and information-processing economy, elements whose materialized imagistic contents may augment, refine, expand, and sometimes productively conflict with those of other elements in that economy. The wrong image here is that of a central reasoning engine . . . (Clark, 2011, p. 133; emphasis added).

The problem with physicalism is similar to materialism but viewed externally, namely relying on one little man with horns known as Laplace’s demon, situated outside your head, who is omniscient and does the thinking. Although modern cognitive science

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has dismissed both little men,94 they remain present under certain metaphysical assumptions. To address this organisational issue for our collective setting, we can take our cue from Searle and Hayek. Searle (1995) discusses the influence of the institutional setting. Having markets in mind (and echoing Adam Smith) Hayek pointed out that: many of the institutions on which human achievements rest have arisen and are functioning without [a central executive] and that the spontaneous collaboration of free men often creates things which are greater than their individual minds can ever fully comprehend. (Hayek, 1945, p. 7)

Indeed, the MMH views Hayek’s incomplete comprehension as part of the mind~matter complexity in the economic system. Specifically, markets help us to deal with the world by collectively bridging mind and matter via price discovery. That is what society’s allocation of its material and mental resources is about. It also means that the incomprehensible forms part of the explanatory gap. We are then left with two related questions, both relative to our perception, namely: – How should we view reality in general? This concerns the traditional “explanatory gap” (Levine, 1983) in our understanding of matter and mind, in the sense that we cannot explain the latter in terms of physicalism. In particular, it fails to explain how mentality, especially mood, feels like it does. – How should we view economic reality? This concerns a specific explanatory gap in our understanding of the real economy and the market’s mind, in the sense that we cannot explain the latter in terms of mechanical economics (which, again, is based on physicalism). In particular, it fails to explain how the market’s mentality, especially market mood, feels like it does. Here Hayek acknowledges the first gap when he suggests practical dualism to deal with reality in general (see Hayek, 1945, p. 29). He then extends this to the second gap when, as far as understanding market dynamics, he advocates “an acute consciousness of the limitations of the individual mind which induces an attitude of humility” (Hayek 1945, p. 8). Later (1974) he formalises his criticism as the pretence of (perfect) knowledge by mechanical economics. These gaps point to related topics that are generally concerned with (superficial) differences between mental impressions and physical appearances. Kant made a distinction between “the thing itself” and our “ideas” of it.95 Heisenberg expressed it in similar terms (with a hint of complementarity): “What we observe is not nature itself, but nature exposed to our method of questioning” (in Zukav, 1979, p. 114). This distinction remains relevant to this day, particularly recognising it as such. It can be a model  A related issue is the self, which I will not go into here.  As an aside, elsewhere Hayek echoes Kant, Jung, and others when he states that there is a principle that applies “to all conscious and particularly all rational processes, namely the principle that among their determinants there must always be some rules which cannot be stated or even be conscious”.

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assumed to be the actual thing or some fiction that is deemed real. For our purposes the economic system as machine stands out as an example of such an ontic exchange. The “banquet of consequences” of that error is now being served. As market history has shown, the shattering of illusions can cause real facts. Not only does the MMH subscribe to Hayek’s stance, with portfolioism being a form of practical dualism. I also believe that investors and other economic agents similarly (subconsciously) acknowledge dualism and accept that, to paraphrase Hayek, “they have to live with it”. More generally, and likewise, this is the argument of “intuitive dualism” (Bloom, 2004). Why do I believe this? Because most of us in general act and communicate “as if” we exist in a dualist world (see also Kelso and Engstrøm, 2006, p. 53). For example, we distinguish between physical health and mental health. Let’s discuss how this extends into economics. Elsewhere in the book I already discussed areas where metaphysics pops up, including fiat currencies. Financial derivatives have their own metaphysical issues. As a reminder, depending on the type of contract the holder of a derivative has the obligation or right to buy or sell an asset or security (the “underlying”) at a fixed price at or before a fixed date. It means, first, that a derivative is very sensitive to the passing of time. Options (like call and put options) even have a dedicated variable for the value of time, assigned to the Greek theta symbol (θ). Second, derivatives rely on promises (albeit legally binding) to pay or deliver. This involves the same faith and trust issues as with fiat money. Third, in case a security underlies the derivative we are talking about a claim on a claim, meaning that ownership of anything physical is further removed.96 Investor biases like confirmation bias, loss aversion, and disposition effect are also dualistic, particularly if interpreted as part of the dual process account of S2 (logic = mind [head]) versus S1 (emotion = body [gut]). Specifically, S2 is suggestive of a modern day “rational” homunculus. Still, advocating the use of S2 over S1, which is the consensus in behavioural economics, does not close the explanatory gap. To clarify, let’s revisit the second gap. Here metaphysics truly haunts mechanical economics. Imagine two investors. The first investor is an idealist. He is religious, believes in an afterlife, and the evil of sin. If nothing else, it makes him motivated to avoid regret. Let’s further assume that he makes investment decisions based on his conscience. (View it as a variation of ESG investing, if you will). Specifically, he allows religion to dominate profit maximisation and does so consistently. Science would judge him to be irrational to begin with. But is he in an economic sense? Let’s bring in some contrast via our second investor, a physicalist. Inspired by Epictetus, Marcus Aurelius, Spinoza, and others, he believes in nature as the ultimate force. He specifically does not differentiate between nature and man in that he sees human behaviour and its consequences as natural and neutral, and behaves himself accordingly, consistently. In fact, he agrees with Lovelock (2019): “We must abandon the politically and psycho-

 For other philosophical explorations of derivatives see Ayache, 2010a.

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logically loaded idea that the Anthropocene is a great crime against nature . . . The Anthropocene is a consequence of life on Earth . . . an expression of nature”. Consequently, he dismisses climate change and decides to invest in unpopular and undervalued oil stocks. Are the decisions and preferences of our God-fearing investor comparable with those of our climate change atheist in terms of rationality? The point I am trying to make, also throughout this book, is that an investor’s metaphysical stance will condition not just what is considered rationality but what ‘matters’ in the wider assessment of the world. Let’s explore this further. Mechanical economics often states that it assumes investors (and, presumably, economists themselves) act “as if” they are all rational (e.g. Rubinstein, 2001). In the main text I already highlighted (via Spear, 1989) that this is mechanical because it implies that human mentality can be replicated by a machine. Based on its physics-inspired equilibrium perspective, we can be more specific: mechanical economics assumes that investors and economists act “as if” they are rational physicalists. However, when you actually observe the behaviour as well as interpret the communication of both investors and economists, they act “as if” they are rational dualists at best. For example, when policymakers or investors talk about their freedom of (physical) action they imply (mental) free-will. Let me give (or repeat) other examples in Table C.1. Table C.1: Mind~Matter in economics and the economic system. Mind:

Matter:

– – – – – – – –

– – – – – – – –

Financial economy Intangible/soft assets Intellectual property Rational thought (e.g. perception) Human capital (e.g. knowledge) Capital flows Securities Market mood

Real economy Tangible/hard assets Physical property Rational behaviour (e.g. action)97 Capital goods (e.g. machines) Trade flows Commodities Market (micro)structure

Again, this suggests not only that ‘physicalist’ mechanical economics is the wrong theory. More generally it also suggests that rationality is metaphysically dependent, in this case what is rational behaviour for a dualist may be irrational for a physicalist. The underlying metaphysical assumptions, subsequently conceptualised in an economic theory, can act as (unconscious) contexts which shape our decisions and behav-

 As Schumpeter explained, “Rationality of thought and rationality of action are two different things” (1943, p. 259, fn. 11).

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iour at a larger scale. For example, it is likely that the make-up of a population in terms of their metaphysical stances determines the relative success of a mental policy over a material one. This leads to one of the projects from our research manifesto (see Appendix 2). Its aim is to profile investors according to their metaphysical stance using, for example, existing questionnaires amended for this purpose. There are many investment areas which metaphysics touches. The behavioural economist Paul Slovic challenges objectivism when he states that “risk does not exist out there independent of our minds and cultures waiting to be measured”. (Slovic, 1997, p. 63). As I pointed out, it does not only apply to risk but goes to the heart of true uncertainty in economics. I also already mentioned ESG for rich pickings. The Financial Times, for example, reported in May 2020 what happens when you replace ESG’s spirit with an algo, that is, try to mechanise ESG-investing. It turns out that US ESG ETFs have a bias against stocks of companies that employ employees. In other words they select and overweight stocks of companies with few employees, thus avoiding the S-factor (from “social”) in ESG. Another area is financial instability. An excellent related example comes from the book Between Debt and the Devil by Adair Turner because it immediately jumps out. Turner argues that financial instability is basically caused by the “interaction” between “limitless credit” and scarce “urban land” (Turner, 2016, p. 247). For the sake of argument, let’s assume we agree. As a throwaway comment he then adds in a footnote that land is “the most physical thing of all” (Turner, 2016, p. 276, fn 11, emphasis added). Per portfolioism, that footnote should instead have been prominently in the text, contrasting it to mental credit to emphasise their reflexive psychophysical relationship: faith-based liabilities backed by physical assets. In particular, the more credit grows (in dollars) versus land (in acres), the more faith it demands. Visualised as a balancing construct upheld by an outsized mental pillar next to a tiny material pillar it will eventually collapse. This brings us to the true culprit of financial instability. By disregarding the sensitivities involved, mechanical economics recklessly meddles in this relationship. Specifically, credit becomes “limitless” exactly because mechanical policies of distorted interest rates, automatic bailouts, and raised debt ceilings (seemingly) remove all credit risk. This not only invites moral hazard but can also, ironically, raise people’s desire for some physical stability as a balance against excessive faith, thus fuelling Turner’s “interaction”. As a final example, metaphysics also concerns the quality of the experience of price dynamics in terms of its duration. Time, of course, is a massive topic in various disciplines that will not be discussed here. All I would like to mention is that there is a perceived difference between mechanical (i.e. calendar) time and experienced, or socalled intrinsic time (e.g. Derman, 2002). The following describes what it entails: Researchers have traditionally analyzed the responses of traders in physical time . . . Such an approach does not adequately reflect the subjective experience of time . . . [Instead] intrinsic time weights chronological flow according to price action: during highly volatile periods time is expanded; during quiet periods, when market volatility is low, time is compressed. (Olsen, 2004, p. 4)

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What is involved here, I believe, is the phenomenal experience (via S3) of continuous (streaming) pricing, in contrast to discrete (static) pricing by analysis (via S2).98 To conclude, metaphysics is the curse of mechanical economics. In particular, mind~matter issues—especially concerning consciousness—are at the core of economic issues which, unfortunately, is not recognised. Not only is this oversight its blindspot due to a general lack of understanding mind~matter issues. By dogmatically sticking to physicalism (a.k.a. Knight’s “mechanistic monism”) mechanical economics also conveniently ignores them, which sustains the mechanical worldview providing theoretical cover to engineer ‘recoveries’ and execute other economic machinations. But there is no physical causal closure in the loop between faith money and the physical commodities it is spend on by the collective. Consequently we are left with misallocating between physical and psychological capital and mispricing of resources. To start addressing this, we can try to close the overall explanatory gap a little. In order to do so, I introduce next the MMH’s metaphysical view, called portfolioism, a variation of dual-aspect (or double-aspect) views.99 It is the book’s main “intuition pump”.

C3. Portfolioism As above, so below, as within, so without Hermes Trismegistus

Portfolioism was briefly introduced in the section on consciousness. It has many parents and builds on the pioneering work by Friedrich Hayek, George Ainslie, Paul Glimcher, and others. To recap, portfolioism considers everything to be a portfolio. Inspired by Hayek’s practical dualism it starts with the view that reality consists of material resources and mental capital. Both are deemed and denominated as assets, whereby negative assets are liabilities and a liability in one portfolio can be an asset in another portfolio. Alone or combined these assets form portfolios at multiple levels. All assets are exchanged and valued in markets, which is how portfolios and markets interact. When a single portfolio forms the market, it is called a market portfolio. Here I will further expand on this.100 My motivation is simple: to start formalising the market mind I need to bring both cognitive science and economics under one

 If nothing else it experientially reflects what Einstein already determined, namely that time is relative. Still, while I leave this to physicists (and possibly to biologists) to answer, one could further speculate that physically at least it may have something to do with the ability of active matter (e.g. animated beings) to break the symmetry of time reversal. This ability, for example, separates it from fixed matter in equilibrium.  See also Section A4.  I must warn that I will switch back and forth between concepts, realms and topics. On occasion this may be challenging to follow.

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metaphysical roof. It is necessary as a replacement for the flawed physicalism of mechanical economics. There are several requirements for such a construct: – It has to apply to the main complex adaptive systems these disciplines study, i.e. mind respectively market. – It needs to be based on modern cognitive science, especially 4E cognition, whereby the suggested mind~matter dynamics crosses the traditional boundaries (i.e. separation) of dualism, individuality, internalism, etc., and pushes economics to the final frontier of agents’ mentality, namely consciousness. – It has to be a fairly simple construct that is easily understood. – It has to be flexible, allowing for both literal and figurative interpretations. – Also, it should cover a wide and hierarchical space (literally), across global~local or macro~micro scales. – Finally, it needs to show potential for novel modelling and empirical testing, ideally with a substantial inventory of mathematical ingredients and data.101 Portfolioism meets these requirements. Specifically, it views minds as 4E portfolios which cross (and thus overcome) the traditional boundary of dualism via exchanges and other market dynamics. Portfolioism offers a more practical take on Buddhist, Humean, and similar views on the mind (and self) as dynamic collections of perceptions and other mental components. As I will explain, the general strength of portfolioism lies in its functionality to conceptualise and assess combinations of different assets (e.g. hard/tangible vs soft/intangible) to deal with states of the world (e.g. via pure securities), including the emergence of novelty (e.g. news, innovation) and the presence of uncertainty. Crucially, that assessment includes qualities, when the values of the assets change globally (in ‘markets’) but are felt locally (in ‘portfolios’). If allowed, both markets and minds can be informationally efficient when such values are dually realised. Starting at the top macro level portfolioism should appeal to your intuition by suggesting the universe is viewed as consisting of material and mental assets. Specifically, the universe is an enormous market where these assets are allocated to produce, exchange, and consume other (e.g. derived) assets. Thus, looking upwards galaxies form portfolios as do planets, albeit smaller in size. Looking downwards, atoms form portfolios and at the bottom we reach single-asset (i.e. particle) portfolios. Planets and particles acquire or gain their mass by exchange with other planets, respectively particles (e.g. the Higgs boson). Any dynamic in the universe (be it electricity, gravity, magnetism, or mentality) can basically be reduced to complementary—

 For background, the way economics nowadays thinks about portfolios emerged from Modern Portfolio Theory (MPT) and other theoretical developments, starting in the late 1960s/early 1970s. These developments were shortly followed by practical implementations (e.g. indexing), as well as deregulation which had a significant impact on investment practise and generally resulted in a more prominent role of ‘free’ markets.

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either opposing (i.e. competing) or aligned (i.e. cooperating)—forces engaged in exchanges. In a way, and in all modesty, portfolioism is trying to capture what Newton expressed as “Nature is very consonant and conformable to herself”. But portfolioism also views the universe itself as a portfolio. Like economics’ traditional market portfolio, this ‘universe portfolio’ contains all assets but cannot be fully observed (see Chapter 8). So, God is the ultimate Mr Market or Mr Universe, managing (some would say passively) what was created in his market portfolio. In general, a market portfolio includes every type of asset available in a particular investment universe or ‘market’, with each asset weighted in proportion to its total presence in that market. God’s universe-portfolio forms an all-encompassing structure from the micro to the macro level. In turn, the universe’s assets are combined in numerous smaller portfolios, which can be either multi-asset portfolios or single asset portfolios. They can also differ in function, purpose, type, and so on. Technically this forms a multi-level fund-of-funds type structure. Of course, humans do not (yet) participate in markets far from earth so we do not know the existence and purpose of all assets, let alone their values. As an aside, while it probably only reflects our anthropomorphic bias, it is nevertheless striking how markets often feature as peaceful social gathering places in many science fiction movies (including Star Wars), where humans and alien species trade somewhere in space. There are many other types of portfolios, not all with clear management. Examples include active portfolios, passive portfolios, discretionary portfolios, and mechanical (or systematic) portfolios. For example, in traditional investing a portfolio can hold similar assets, like an ‘industry’ or a ‘sector’. Crucially, assets are valued which, generally, occurs via exchanges in markets. Portfolioism views exchanges very broadly. That is, an exchange always involves the replacement of one asset A for another asset B. This can be expressed as a ratio which reflects the value (usually the number of units) of asset A over that of asset B. For example, a popular asset used on earth is capital, in particular money. In our modern economy most exchanges involve money as asset B. The ratio is then simply a price expressed in the particular currency, e.g. GBP£20 for one share of company X. That price can change over time. The value of an asset more generally can change in many ways, one of which is by growing itself or producing another asset with value. Similarly, there are different types of markets. A peculiar type is an internal (or local, or private) market: only assets from one portfolio, its market portfolio, are exchanged within it. However, their valuation can be influenced by values of comparable external assets. Let’s take the economy itself as the next (simplified) example. It consists of multilayered portfolios of resources and its activity consists of the exchanges between those portfolios in markets. Its producers (subdivided into regions, industries, and sectors) form portfolios which contain their material resources (factories, machines, pipelines) along with their human resources (skills, knowledge, culture). They exchange between themselves but also with the portfolios of suppliers and consumers.

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These exchanges can include goods and services as well as money. An example of such a subportfolio is Berkshire Hathaway, the conglomerate managed by Warren Buffet and his partner Charlie Munger. It not only holds securities but owns complete companies, including factories, buildings, tankers, and trains. As a consequence it employs almost 400,000 people. Frequently assets are transferred or transformed (via one-way/two-way exchanges). For example, they terminate, are split off or are merged with other assets to form new portfolios. In the case of a planet its gases and chemical compounds react to combine new substances. Or assets are exchanged in return for other assets. Those exchanges can also involve various forms of ‘currencies’, including energy, information, and materials. Materials (acting as money) in an economy, can be gold or paper. Similarly, portfolios generate different forms of return. Generally, positive returns lead to growth and accumulation, whereas negative returns mean shrinkage and depletion. Portfolios can be (synthetically) created but also destroyed. The latter is what a black hole can cause in space and what debt can do in case of bankruptcy. Varying in size, mandate, and management, these portfolios execute a rich array of active~passive, dynamic~static, discretionary~mechanical, deliberate~random, and other strategies. Specifically, portfolios that exclusively hold material assets only execute random and static strategies, whereas portfolios that hold living assets also execute active, e.g. goal-directed, strategies. For example, as far as we know there are planets without life forms where strategies are limited to passive strategies due to chemical reactions on their surface. In contrast, on planet earth Mother Nature manages various ecosystems, containing assets like water, volcanoes, trees, and animals. Many also contain humans who, as groups or individuals, execute the most active strategies in satellite portfolios. A key distinction in this book is between strategies that are phenomenally informed and those that are not. This brings us to the local mind~body. Here, portfolioism appeals to your intuition by suggesting you view your mind~body as a portfolio of assets, called the M~B Portfolio (see Part A). Our M~B portfolios are exposed to the outside world and we thus care about possible states of that world, particularly relative to our own state. This is why the investment concept of pure securities, which I introduced previously, is relevant. Investing via markets is, from this perspective, about the allocation of risk-bearing: having financial securities that provide ‘opportunity’ to benefit from, respectively provide ‘cover’ to hedge against different states of the world. Specifically, each pure security is contingent in that it pays one unit of value if, and only if, a given state occurs. Those securities can be combined into portfolios, e.g. forming an asset. Each portfolio thus becomes an instrument for adapting to the world via exchange. Something similar occurs when instruments in the mind~body are employed. The M~B Portfolio is (seemingly) ‘self’managed. It is also particularly exposed to your local environment, resulting in a strong ‘home bias’, for example. The behaviour of the portfolio is reflexive due to its exchanges with the outside world to which, in turn, it adapts. Notably, it allows you

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to gain from (hedge against) beneficial (detrimental) developments, in particular those instigated by peer, or other M~B portfolios. Let’s look closer. Top-down your M~B Portfolio is a so-called balanced fund-of-funds and consists of two core funds. One fund, your body, has tangible (i.e. physical) assets, like a brain, arms and legs. The other, your mind, has intangible (i.e. psychological) assets, like beliefs, thoughts, and a sense of self.102 In part A, I introduced psychurities as the mind’s version of the market’s securities (financial instruments). For example, the underlying S1 and S2 funds contain your S1 respectively S2 psychurities, which are ‘cash instruments’. In turn, the market’s mind, being a complex (and extended) composite of all investor minds, forms the mental ‘market portfolio’ which holds all investors’ psychurities combined. The market’s mind as a portfolio of psychurities is, conceptually, thus similar to the market portfolio of securities in the real world. Being a fund-of-funds, your embodied mind is multi-layered and has underlying funds that exhibit a rich mix of physical and psychological properties. Some contain little factories (just like Berkshire Hathaway), others more specialised, sometimes single assets selected from the bottom-up. At whatever level of aggregation,103 all funds interact, exchanging in various currencies. Combined, all impact the M~B Portfolio’s behaviour and contribute to its overall return. Sometimes that involves conditional payoffs or unintended bets. Another way to think about this is in terms of the difference between physical and mental returns that the embodied mind enjoys. For example, the pleasure of drinking ice-cold fruit juice is distinct from the pleasure of viewing Rembrandt’s Night Watch in Amsterdam’s Rijksmuseum. The first satisfies your need for physical nutrition to replenish your body, whereas the second satisfies your need for psychological nutrition to replenish your mind. Such returns can also be negative, like drinking spoiled milk, respectively watching an horrific accident. And obviously, things get more complicated when they are generated at the same time, i.e. in a portfolio setting. Similarly, the return in the real economy, say shorter and safer commutes due to long-term infrastructure projects, is distinct from but complements the returns enjoyed in financial markets. In all cases, both are required for a balanced state. In other words, any overindulgence in either the material or the mental will require a rebalance at some point. Also, humans have largely the same portfolios (making us peers) but the exposures (sensitivities) and the way we actively tilt them via heterogeneous strategies differ. This already starts when people experience the world differently. This can be culturally but also metaphysically. For example, many people are religious and, see-

 Here are two examples of assets becoming negative, i.e. turning into liabilities. First, the sore arm of the successful tennis player turns into a debilitating injury. Second, the beliefs developed during an enriching career are painfully questioned during a midlife crisis.  Viewed from dual process theory (see Bargh, 1994), S1 and S2 are just another taxonomy for the ‘sectorial’ make-up of the portfolio.

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ing events in spiritual terms, tend to overweigh (underweigh) the mental (material) fund. A particular category is mystics and yogis whose passion for meditation results in thin, if not frail, bodies.104 Others are diehard materialists who do not even view metaphysics as relevant. Think of an horrendous opposite example of exchange: a criminal who does not hesitate to kill other mind~bodies when robbing for material gain. Related to this are the findings in psychology which suggests people prefer physical actions according to their psychological personality types. The overall mandate of the balanced fund includes covenants, like showing positive absolute results (i.e. growth) under all circumstances, ideally outperforming peers, not defaulting (e.g. becoming incapacitated), and remaining faithful to social and ethical values. Pleasing stakeholders (e.g. family members, colleagues) is important and involves more than simply performance. Beyond the mandate, you may personally try to optimise your M~B Portfolio to the flow to, say, experience peak performance. The above points to challenges. Specifically, your portfolio is constrained by its mandate, as well as its peers. Moreover, you have limited control over the submanagers, including the management of individual holdings. Cognitive Note Your Mind as Portfolio Allow me to introduce Aka Ourego. Aka has been appointed to manage an in-house balanced fund and he is confident of his abilities. Still, Aka is frequently surprised about the behaviour of the overall fund, in particular certain underlying portfolios. For example, he notices that the exposures of the portfolios seem to fluctuate over time in a rhythmic, repetitive pattern, with styles going in and out of fashion without always a clear fundamental reason. Now, admittedly, he inherited many of these portfolios from predecessors, and he does not know how and why their contents were selected, but it is clear that there are hidden biases that drive their performance. In fact, there are several portfolios he hasn’t even looked at (he tells himself that he will eventually). In particular, although this could just be his imagination, it seems that there are a number of anonymous portfolios run in the back-office, separated by a Chinese wall. They operate like black boxes and somehow add tactical overlays to the fund by executing program trades. He regularly reviews the strategic asset allocation by estimating the longer-term performance of the broader market. That has proven particularly tricky, if only because he has not been able to clearly identify this market, and consequently he has never been able to make reliable estimates. It seems, in a way, that the market dictates its own mandate and he wonders whether, and if so how, it is related to the program trades. Whatever the market is, it must be exceptionally aware, for example of the long-term movements across major assets/liabilities versus any historic environment. Combined with its elusiveness, this makes it hard to beat. And then there are the portfolios Aka likes to ignore. He doesn’t even remember whether he created them, but it is clear to him that they contain some very illiquid securities, and possibly toxic ones. But his priorities always seem to lie elsewhere. In any case, as he likes to point out, he tries to clean his fund up and is confident he’s in control. Still, despite his personal preferences Aka feels constrained in his allocation by stakeholders’ mandates, their wishes and demands, as well as the allocation by his peers.

 Krishnamurti being a prime example.

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Fortunately, there are those rare occasions when Aka is elated by his fund, which is accompanied by an extraordinary clarity and understanding of its behaviour, mostly because it seems so aligned with that of the environment.

Here’s the final push on this intuition pump: imagine the economic system to be society’s balanced fund-of-funds, our Economic M~B Portfolio. It coordinates our collective behaviour to adapt to developments in the global environment, basically our planet. The two main funds consist of the global real economy and the global financial economy.105 We, the global community of consumers, producers, investors, policymakers . . . well basically everybody, are involved with this portfolio. Some manage it, others determine its mandate, but all partake. Still, recent events showed that only a few benefit while most suffer losses. Consequently, there are those who want to liquidate (or even bankrupt) it. However, whether we like it or not, we are all invested in this Economic M~B Portfolio with our Individual M~B portfolios. Therefore, we need to rethink economics. This returns us to the challenges we face. In fact, the challenges faced by both your M~B Portfolio and the Economic M~B Portfolio are ultimately the same. They centre on incomplete knowledge, particularly about the relationship or gap between mind and matter, which makes it hard to forecast (leading to true uncertainty). Closing some of that explanatory gap (mentioned before) occurs occasionally when we make discoveries. More ordinary is our general state of consciousness when information is realised both physically and phenomenally, leading to a current awareness and sense of the state of the world. The punchline of the intuition pump: managing these portfolios via a central plan is delusional because it requires a central executive which gets us into the homunculus problem. As far as the M~B Portfolio is concerned current behavioural economic consensus implies some central executive (the “I”) knows how and when to switch off the S1 sub-portfolio and overweigh the S2 sub-portfolio. That, ironically, is clearly overconfidence. In fact, portfolioism is more than metaphorical and an improvement on the general vertical or subpersonal psychological explanations.106 First, it endorses the tripedalism of nature, nurture, and noise to move the M~B Portfolio. Meaning that it recognises not just the contributions by both nature and nurture to (managing) the  To add to earlier comments, for some readers this ‘dual’ economic system may not be strict enough regarding the distinction between matter and mind. In other words, the fact that the real economy contains mental aspects and, vice versa, the financial economy contains physical elements blurs the view, particularly compared to the individual mind~body. The first defence is that this should be judged in relative terms, e.g. the market is primarily psychological, financial capital is more mental than physical capital, etc. Second, both economies contain human mind~bodies so the blurring is inevitable. Third, portfolioism is a version of dual-aspect theories, not pure Cartesian dualism, and is meant as a practical, applied tool, i.e. intuition pump. I hope (and suspect) most readers will be able to make the necessary distinctions, particularly once the intuition starts flowing.  All avoid the homunculus regress.

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M~B Portfolio. It also accepts the role of randomness. Second, it emphasises the integrated and diversified nature of its dynamics. Sub-portfolios cannot be treated in isolation or switched-off when we do not really know what is in them, e.g. as black boxes. Also, it naturally embeds the crucial economic principles underlying both markets and minds, particularly competition and cooperation. It is important to think of this in (portfolio) diversification terms: competition generally lowers correlation, whereas cooperation increases it. The latter can make the portfolio less robust. In the extreme, if all forces would cooperate, this would lead to narrow-mindedness in cognitive terms, and a monopoly in economic terms. We do not want that, and it shows the need for a healthy (read: dynamic) balance whereby conflicting ideas, styles, and approaches are welcomed, not feared. Not surprisingly, this cannot be achieved either via central planning which often aims to prevent disruption (e.g. via variation of opinions) and keep the status quo. Instead, it can be the natural spontaneous outcome of conscious systems which experience this dynamic tension between underlying material and mental forces, even if they do not centrally steer it. As aforementioned, for a mind~body such balance is its version of the ubiquitous arms race in complex adaptive systems in general, i.e. the Red Queen principle. We should give Mother Nature (and evolution) more credit in that regard instead of thinking that this can be easily replicated via AI. So, when I mention and discuss minds separately from other objects or entities, please view it along these lines. To wit, if brain is its ‘thing’, mentality is its ‘process’. Portfolioism helps to clarify the distinction between these spheres for practical purposes, in line with Hayek’s acknowledgement. More specifically, portfolioism falls into the category of dual-aspect monism. But it adds something novel, namely the view that mind~body interactions are reflexive exchanges between stratified portfolios with the common denominator of production and consumption of information. Crucially, this all comes together, intersubjectively, via price discovery in markets. Such intersubjectivity could actually offer a form of ‘shared unity’, metaphysically speaking. Portfolioism states something intuitive along such lines. A portfolio combines components, both material and mental. It is experienced as a whole in the form of its return after paying attention for example. We may not know the underlying unity of mind and matter, but when my portfolio makes exchanges with your portfolio (or the ‘market portfolio’, for that matter) we unite in a shared experience when we simultaneously ‘taste’ the same information. That, admittedly, is in an instance and the digestion of it takes longer and remains somewhat hidden. In short, in this book we accept portfolioism because our understanding of reality is insufficient. We are confronted with an explanatory gap between its physical and mental domains, whereby dualism is (subliminally) perceived as such in the latter. In turn, this limits us to the use of (itself limited) dualistic language when attempting to bridge these, including in our explanations. On the other hand, it justifies and facilitates making mind~matter issues and aspects explicit. This is a crucial starting point to improve our understanding (if only compared to, for example, unrealistic assump-

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tions like complete knowledge). Also, portfolioism emphasises the complex nature of mind~matter dynamics and does not suggest a dismissal of the possibility of a unitary foundation for both domains. In light of this, the MMH assumes that: – In social interaction, (weak) mental causation is possible if the conduit of the information flow that results in representation is physically located in the shared space between two or more human minds. In the case of trading, securities form the network structure that is physically separated from trading parties while connecting them. Securities are the physical conduits or “entities” in active externalism (see Subchapter 2.4.1.2). – Their prices form (as symbol) and contain (as content) the information that is produced, respectively consumed and digested, conveying both the quantitative and qualitative properties of the market. – Our imperfect knowledge of the economy is ultimately a consequence of the explanatory gap. The MMH thus criticises the omniscience assumed by both Keynesian economics/socialism (i.e. belief in a central planner ≈ economy’s version of a homunculus) and new classical economics (i.e. belief in predetermined outcomes ≈ economy’s version of Laplace’s demon). As an aside, Austrian economics doesn’t get off the hook. Its flaw is its general adherence to individualistic internalism. All suffer from category mistakes and/or a lack of metaphysical grounding in their assumptions, i.e. Von Weizsäcker’s critique. – The health of the market’s mind is directly dependent on the health of its component minds and vice versa. Freedom, the volition to exchange, is a pre-condition for that health. The correct level is where demand and supply of freedom meet. In the words of Isaiah Berlin, “Where it is to be drawn is a matter of . . . haggling. Men are largely interdependent, and no man’s activity is so completely private as never to obstruct the lives of others in any way . . . the liberty of some must depend on the restraint of others” (Berlin, 1958). Although such haggling now seems to take place politically, in light of inequality and populism, it goes much deeper down: a haggling about freedom of exchange, both within and between the real and the financial economy. I will not discuss this in more detail here, except to say that artificial constraints on freedom,107 enforced politically, medically, or otherwise, will eventually backfire, both mentally and physically. All values without freedom of exchange are worthless in that regard. – Consequently, cognitive economic laws, which supervene on psychophysical laws, will prevail and dominate any political laws. This has particular implications for socialist and dictatorial regimes. Unfortunately, it seems we have a growing number of those which, the MMH submits, is stimulated and supported by growing mechanisation.

 To be clear, immigration, for example, is not true (i.e. beneficial) freedom of movement when it is caused by constraints on other freedoms and/or leads to constraints on existing freedoms.

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Notably, whereas the mechanics of exchanges is explained by economic principles, the experiential of such exchanges is explained by cognitive principles. This points to the reflexive nature of the interaction between the economic and cognitive domains, i.e. between fundamentals and feelings. Cognitive economics emphasizes consciousness as the final frontier of mentality that, in this case, extends to market mentality, to be explored by the economic disciplines. It means that any explanation of an economic phenomenon, both from a theoretical and from a practical perspective, needs to include consideration of the experience of that phenomenon by human agents. In other words, the qualitative aspects—that is, what it actually is like (e.g. to suffer a loss)—are central in building any economic narrative.108 In summary, portfolioism helps to establish and clarify the key premise of this book, namely the similarity between market and mind dynamics, culminating in consciousness. I will apply portfolioism in more detail in Chapter 8.

C4. Price Discovery (Prequel) To explore in any field, there must be freedom. Freedom to examine so that in that very examination there is no distortion . . . When there is distortion there is a motive behind that distortion . . . [Then] you cannot examine. Krishnamurti

Freely interpreted, Mr Market is our version of a “sentient informavore”: by devouring information via (dually realised) prices it is a collective “being capable of reasoning, of feeling, and of experiencing the world” (Clark, 2011, p. 96). Specifically, that being has a 4E mind and under normal circumstances prices are discovered collectively and transparently. Unfortunately, those circumstances are currently largely absent, as expressed by “bond king” Jeffrey Gundlach: The price of corporate bonds isn’t really real. There’s no price discovery mechanism that’s being pegged. There’s no message; there’s just a target price that the Fed has been doing, and that led to a pop-up in corporate bonds. (Yahoo Finance interview, July 2020)

By definition, prices are not ‘individual representations’ occurring in separate minds. Instead, they become realised via a trade which, by definition, requires an agreement between at least two entities, a buyer and seller. In other words, setting a single price individually without a trade is not possible (because I cannot trade with myself)109 nor does it realise any information, i.e. it is pointless.

 As we’ll see shortly, we continually (subliminally) try to interpret the quantitative language of prices qualitatively (symbolically). Mr Market’s mind~body language means that the way he “speaks” is as important as the numbers “spoken”.  Here I’m ignoring the bargaining between multiple selves, implied by hyperbolic discounting (see Ainslie, 2014).

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Also, prices are mostly quoted anonymously, and multiple (groups of) buyers and sellers can trade at those prices, depending on volume. Crucially, together with the securities they are attached to, prices meet the requirement of active externalism. That is, our collective state of believing is out there, namely in the constellation of prices (even though none of us can completely and correctly interpret it). The interpretation of consciousness in section A has an important implication for prices that I’ll repeat here. First, only if discounting includes exogenous, fundamental, news do prices bridge the real and the financial economies. Passive investing and other mechanical rules that exclusively trade on market internals do not contribute to this bridging. Specifically, by mechanically accepting the market price such strategies piggyback on the efforts of others to discover, e.g. by gathering external ‘real’ information. As the proportion of active investors diminishes so is the integration of external information, at least relative to the weight of internal information. The end result is the likely risk that the market portfolio, replicated by indices that are passively tracked, reflects the real economy less and less. Second, two ways by which I can be conscious of price discovery is via action and via perception. If I perceive I consume information K. If I act, for example on that information K, I produce information L. Translated: if I buy or sell I execute a trade, an action that settles a price which produces information (e.g. for others). Alternatively, I do not trade but observe a price being settled which is strictly consumption of information. In both cases I dually realise information, but only in the first case can we speak of mental (downward) causation in the market’s mind. Only at that moment I contribute to the market mind’s awareness. This means that, in reality, most investors most of the time discount information with a delay. The various ways in which information is realised eventually settle in the longer lasting process of information digestion, for example in the form of memories. Similarly, historic market data (price patterns) and other recorded material (stories) can remind us of market events. Price discovery will be discussed in more detail in Chapter 7.

C5. Market Mood We forget that Mr Market is an ingenious sadist, and that he delights in torturing us in different ways . . . He’s a manic depressive with huge mood swings . . . . Barton Biggs

Behavioral finance has shown that investors base their beliefs on recent events, often dominated by their last traumatic experience. And boy did we have a few of those. As previously described, the mood accompanying the financial crises over the past decades (e.g. LTCM, Lehman, Euro, Corona, and LDI) was an existential one, albeit with variations in themes. When people ask what market mood is, I’m reminded of Louis Armstrong’s answer to the question “What is jazz?”: “If you have to ask what jazz is, you’ll never

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know”. Market mood is probably the purest form of reflexive market mentality: while being in the market gives rise to it, the essence of market mood is its experience, namely how it feels to be in it. Market mood is exemplary for conveying the market mind’s interiority. Despite the obvious limitations, I will try to explain mood as well as I can. One angle is to associate it with animal spirits, following Keynes. He, in turn, took his cue from Descartes who discussed animal spirits in terms of mind~matter dualism. We can make this link because, as laid out in section A6, we can interpret market mood as a possibility space for mentalities with animal spirits as: a restless and inconsistent element . . . [namely] our peculiar relationship with ambiguity or uncertainty. Sometimes we are paralyzed by it. Yet at other times it refreshes and energizes us, overcoming our fears and indecisions (Akerlof and Shiller, 2009, p. 4; emphasis added).

Per the MMH, this “relationship with uncertainty” concerns the mind~body problem and consciousness more generally. In particular, like mood this uncertainty acts as a mental derivative overlay in that it “animates people’s ideas and feelings” (Akerlof and Shiller, 2009, p. 1). Others interpret this influence almost in Heideggerian terms, when they speak of “a new and independent fundamental . . . When we feel rich, we are rich” (Farmer, 2018; emphasis added). Like the elusive separation between mind and world in general, it is difficult from a 4E cognition perspective to point to any boundary of market mood. Instead it is carried within and between investors. The collective character of the market more generally—involving multi-level organisation of individuals with their norms, rules, skills, and other (cultural) facets of market practices—is central here. In other words, its mood emerges from the market’s embodiment, embeddedness, enactment, and extension (see Subchapter 2.4.1.1). Again, it is the exchanges between investors in that setting that dominates the system’s overall behaviour and mentality. Arguably external (worldly) conditions can impact it. The size of the impact then depends on the number of individuals affected. An example of such an externality is the weather. Hirshleifer and Shumway (2003) conclude that sunny weather leads to an “upbeat mood”. They analysed stock market data from 1982–1997 and found that returns are positively correlated with sunshine in almost all of the 26 countries they studied. However, in this book we focus on the internals of the economic system, e.g. fundamentals and prices, as carriers of mood. When people ask how prices can convey mood I’m again reminded of a jazz legend, Miles Davis, whose music on the legendary Kind of Blue album affects us beyond simply “colour”. Similarly, prices reach over and above “utility” (see also Section C6). Based on the foregoing, market mood has three main characteristics: 1. It is collective in nature, i.e. it is a shared feeling or intersubjective. 2. The level of universality of that feeling reflects the depth of the mood. From a neuroscientific perspective: universality reflects the extent of the crossbrain integration/synchronisation.

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The deeper the mood, the stronger the qualitative sense of sharing an experience together and the stronger the dispositions for individual investor mentality.

Disposition, in terms of (de)valuing particular mentality, is how moods disclose themselves. This is also what I mean with mood being a mental derivative overlay: it is derived from underlying cognitions which it leverages/hedges. And just like it’s financial cousin it is all powerful. Ultimately, market mood constrains or enhances the quality of price discovery because it shrinks, respectively widens the space of mental exploration, i.e. perceived possibilities. For cognitive readers: one way I like to think of market mood is as the market’s (mental) affordance. A related way, referring to my comments on consciousness and (valued) options, is Heidegger’s “possibilities” (e.g. of taking action, 1927, p. 340; but also see his comments on “understanding mood” and “existential derivative” on p. 182). Exuberance and despair are both deep moods and close to the respective extreme ends of the mood scale. At the very end of the negative scale is existential mood: when systemic risk threatens the very survival of the financial system. Deep market moods not only overwhelm any personal moods investors may have, but often condition any intentionality, like beliefs (“the market is cheap”), desires (“I want in on that IPO”), or thoughts (“according to the Fed the FFR is close to r✶”). Depth of market mood negatively correlates to diversity in mentality, in the extreme leading to narrow-mindedness, either bullish or bearish. Some investor readers may find this description too abstract or vague. Moreover, it does not hint at how to deal with the practical challenge we’re facing: sensing (changes in) market mood. So, allow me to reuse earlier intuition pumps to clarify mood as feeling, i.e. as the market’s phenomenal overlay. First, think again of minds from an investment perspective using portfolio terms. To recap, the investor’s mind is a mixed portfolio, a fund-of-funds. It holds physiological and psychological ‘assets and liabilities’ or instruments. Here we will limit ourselves to the psychological portfolio and focus in particular on the underlying S1 and S2 funds. S1 and S2 psychurities are ‘cash instruments’. Similarly, think of the market’s mind as a complex (and extended) composite of all investor minds, thus forming the ‘market portfolio’ of those psychurities. Even if we can explain the portfolio’s behaviour functionally via the various S1 and S2 psychurities in the respective (e.g. layered) funds ‘doing their (e.g. biased) jobs’, something is missing. Their interactions give rise to a feeling about the portfolio’s overall performance, its quality, particularly relative to other portfolios. What is missing, in other words, is the conscious realisation of the underlying returns110 via S3 as the culmination of holding the overall portfolio. That is “the difference that makes a difference” (following Bateson) and what “matters” (following Heidegger).

 To refresh: the phenomenal (S3) realisation of information, complementing the simultaneous physical (S1, S2) realisation (Chalmers, 1996).

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Let me give an example in terms of an ordinary daily activity (well, for some at least). There are a number of S1 and S2 psychurities invested when you drink a particular glass of wine, including your sense of entitlement after a day of hard work, the memory of the vineyard where you bought it, chemical reactions, etc. However, they cannot exhaustively explain the experience of drinking it. What they leave out is the final payoff, the culmination into the sensational return of what it feels like to taste the wine, usually within a mood (e.g. around other people). Also, eventually these experiences, as digested information (a.k.a. learning), can turn into knowledge and understanding: you become a sommelier. Back to investing, similarly you can be an expert in risk management, having optimised your portfolio of S1 and S2 ‘risk-management skills’, but you do not really understand what risk means unless you have experienced a loss.111 The map is not the territory. The (infamous) comment by former UK Chancellor of the Exchequer, Geoffrey Howe, also comes to mind: “an economist is a man who knows 364 ways of making love but doesn’t know any women”. Table C.2 clarifies the portfolio descriptions in the previous paragraphs.112 Table C.2: Person vs Market Portfolio. Entity

Person

Market

Physical portfolio

Human body (e.g. brain, arms, legs)

Market body (e.g. exchanges, buildings, IT, human bodies)

Physical conduit of information

Neurons

Securities

Psychological portfolio

Human mind

Market mind

Psychological funds

F = wi Si + wj Sj

n  X  Fm ✶ Fq i

Instruments

Psychurities (personal portfolio)

Phenomenal overlay

G = wi Si

Structure

i=0

Psychurities (market portfolio) n Y

Gi

i=0

 And then there is, of course, true uncertainty, e.g. a crash.  Please ignore the specifics of the equations. They are purely symbolic examples, suggesting layered composites (i.e. “funds”) containing multiple psychurities that interact in complex ways to form a coordinated (e.g. levered) response.

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The last row, “phenomenal overlay” is therefore important. As part of the mind’s portfolio we should see S3 as an overlay of derivative psychurities that contains optionality to gain or hedge exposure. Although linked to underlying cash psychurities, it conveys an eventual payoff (i.e. quality) that is often non-linear. For example, it can (de)lever underlying mental positions113 or introduce ‘naked’ (sometimes unintended) exposures,114 thus affecting sensitivities and influencing the overall payoff structure. Moreover, like their financial cousins, the questions concerning ‘causality’ applies equally to derivative psychurities in the context of the discussion on mental causality in cognitive science. Market moods are a composite derivative overlay and complete market mentality, just like financial derivatives complete markets. This particularly applies to the depth of market mood, i.e. the extent of universality in the mood’s intersubjectivity. The deeper the mood, the more universal the shared feeling sensed by market participants, making it dominant not only over and above any personal moods, but also over and above any ‘objective’ analytical perception of the market (e.g. expensive, volatile, oversold). To be clear, market moods do not necessarily alter those perceptions, but do tweak their significance (the feeling whether they “matter”), relative to the overall market state. Grabbing our intuition pump again, the depth of market mood is disclosing the concentration of the market’s mind portfolio in terms of exposure, i.e. its optionality. Equities were perhaps ‘objectively’ cheap in late 2008, but Mr Market’s mood clearly conveyed, ‘intersubjectively’ felt, its irrelevance to what was at stake in that situation, all things considered. His mood limited the space of possibilities, hedging out that particular perception. This brings me to our practical challenge of how to improve capturing mood. As I explain more in Chapter 9 one approach is via audiovisualisation of data. It is aimed at identifying when the ‘convexity’, or any of the other non-linear characteristics of market mood’s optionality, are changing, potentially signalling a more ‘significant’ shift in the market’s mind. Moreover, linking the depth of mood to the level of crossbrain synchronisation opens potential research areas in that it could potentially be tested, e.g. initially via experiments. In any case, we will need new methods and tools. Investment Note Mood is not Sentiment Market mood is not the same as market sentiment. Market sentiment is what investors attempt to capture via analytical indicators, like put-call ratio, bid-ask spread, market breadth measures, VIX index and so on. In an interview with Active Trader Magazine,115 the following exchange took place, slightly paraphrased (emphasis added):

 This can include, for example, S3 disclosing it’s “into the money” on, thus raising its exposure to, an emotion, e.g. fear.  Think of new experiences, e.g. painful discoveries, but also empathy.  “Eio Far East fund’s fundamental difference”. Active Trader Interview. Active Trader Magazine, April 2014, Volume 15, No. 4, pp. 44–49.

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Interviewer: So, to be clear, when you refer to [mood], you’re not talking about data inputs such as Bullish Consensus, or put-call ratios, or similar data or indicators? Trader: No, we don’t use any specific sentiment gauge or tool to track [mood]. It’s more of a general feeling you get from trading a market and understanding the relevant dynamics.

Next, I will discuss one other concept that applies to both economic and mental properties, namely ownership. It is similarly exemplary for the links between economics and cognition.

C6. Ownership A man’s Self is the sum total of all that he can call his, not only his body and his psychic powers, but his clothes and his house . . . his lands and horses, and yacht and bank-account. William James

Viewing the economic and the individual mind~body as portfolios of assets raises the question of ownership.116 In terms of legal control and use, Nobel Laureate and economist Oliver Hart (2016) argued that ownership is important because contracts (and by extension markets) are incomplete. Specifically, ownership fills the gaps left by incomplete contracts. On the other hand, if you strip it down, we actually live on credit, both in terms of borrowed time and borrowed atoms. Eventually all of us have to hand back those atoms to the universe. From life’s perspective ownership is thus very relative. That also goes for those physical productions (e.g. art, buildings) and mental contributions (e.g. theories, novels) which remain credited to us but eventually are passed on (definitely after our passing). As far as mentality is concerned, ownership is deeply connected to agency, in particular our sense of self, free will, intentionality, and, by extension, control. Subjectivity, after all, is about individual ownership of beliefs, desires, perspectives, etc. Ownership is also closely associated with social aspects, like laws and norms. This especially applies to responsibility: “taking ownership” means that it is your responsibility. Cognitive ownership goes all the way to consciousness, whereby attention can be thought of as ‘mental cash’. As previously explained, by paying attention you get a return. The extent of ownership can vary from exclusive to shared. The latter applies to shareholders. Investors are shareholders, not only in physical assets but also in mental assets. We can stretch this in general terms and think about humans as current shareholders or potential shareholders, whereby their exchanges affect ownership.

 For a related but more detailed view, see Rowlands (2010, Chapter 6).

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Shared ownership occurs both in the economic and individual mind~body. Let’s start with the physical epitome of self: your body. At the micro-level, the human microbiome means that you, as an individual organism, are made up of numerous microorganisms, like bacteria, eukaryotes and viruses. It is estimated that roughly half of the cells of your body do not belong to you but to those microorganisms. Clearly this nuances, at least from a bodily perspective, ownership and thus our sense of self. And even though we judge these microorganisms not to be conscious, their involvement in the complex bodily household suggests that they somehow contribute to individual human consciousness, including the production of the aforementioned properties. Thinking about (shared) ownership of mentality is important because it lies at the heart of ‘experience economy’ thinking, namely to provide consumers with bespoke ‘experiences’ rather than ‘stuff’. This gets back to the previously mentioned ‘attention economy’ centred on grabbing your attention and is relevant more widely for issues relating to big data, like privacy and contaminated data. There is a flip side to this. Or rather, the demand side of the equation reflects a compensating craving for those elusive sensations. Let’s call it the revenge of the qualia on materialism. So data and information play a central role. In the economic mind~body information is ultimately captured in prices. In fact, the origin of ownership is the price paid to acquire it. In the real economy this is sometimes indirectly, say by way of labour. For example, dig up gold and you own it. In the market, it is by way of a trade: buy it and its yours. Price is the quantification of ownership as its implied value of benefits acquired, respectively renounced. Importantly, ownership is a psychophysical concept and should not be confused with possession, which is primarily holding something physical. Our possessions comprise our material wealth with which we extend our agency, particularly in terms of influencing other people. For example, via possessions we attract their attention and shape their behaviour towards us.117 Moreover, ownership relates both to the experience and the source for that experience. I can enjoy the summer heat by stepping outside, nobody owns the source of that heat (the sun), but I do own my experience of its gift. Things change if the source is owned. For example, I would also fully own my pleasure in tasting the Bordeaux wine, but for the fact that it depends on you, being the owner of its source (i.e. the bottle containing it), sharing a glass. Arguably, knowing that you own the wine affects my experience, in particular its ‘mineness’. Let me give an economic example along similar lines. Let’s assume the government wants to stimulate demand by way of Friedman’s helicopter money drop, i.e. direct transfers to citizens financed by the central bank. According to mechanical economics there is no difference between handing them a cheque, a money-financed tax cut, or money-financed government spending. However, the experience by the re-

 For a detailed discussion, see Wood (2019).

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ceiver (and thus subsequent owner) of such money, the citizen, is completely different in each case. In simple terms, the first is experienced as a gift, the second as a discount, and the third as ‘no-thing’. This also points to the degree of ownership which changes, e.g. is diluted, across the type of experiences, varying from individual to collective. Ignoring for a moment the source-ownership, individual experiences are private and subjective, for example your sensation of smelling a flower, me tasting red wine, or a patient’s pain at the hospital. An individual experience enjoys sole ownership: it is exclusively owned by and belongs to that individual without (the need for) any exchange with another conscious being. For you to experience the orange glow of a sun set does not require anybody else. Although you can share with somebody how you feel at that moment verbally, such an exchange is not required for the experience to exist. Still experiences can also be common, in the sense that others have similarly undergone them and ‘re-cognise’ them, albeit usually at different times or different circumstances. A peculiar example is empathy, our human capacity to sense what others feel (without necessarily affecting the ownership). In between are other variations which often overlap, highlighting the complexity involved. Borrowed experiences involve a contribution by another conscious being which you ‘borrow’ for the duration of the experience. For example, my enjoyment of that sound of that piano is dependent on Lang Lang playing it. As Lang’s experience is irreducibly embedded in that sound, I borrow his experience to enrich my own. Consequently, I do not fully own my experience and I certainly do not own its source (even if I own the CD on which it is recorded). Mutual experiences are another type and often involve the same source external to but independent of the subjects. A crowd in a park enjoying the sunshine would exemplify this. Mutual experiences are undergone simultaneously but separately by multiple individuals who each retain sole ownership. In short, the common denominator in all these instances is that ownership, while varying in scale, is still distinct and static. There is no contamination whereby my experience is coloured by somebody else’s experience in real time. My key point, however, is this: the type of consciousness, including qualia, depends on the overall ownership structure and can thus change by shifts in that structure, including the one concerning the mind’s extensions. Exchanges, for example via trading, can bring those shifts about. This leads us to shared ownership and thus collective experience. A collective experience is one that is shared by a group of subjects. It is intersubjec118 tive, simultaneously occupying and spread (in varying thickness) across individual minds. Scientifically speaking, it is the most complex of the experience types. The reason is that it is not reducible to any subject: individual minds participate in and contrib-

 Interpretations of intersubjectivity evolved from the philosophies of, among others, Buber, Husserl, Heidegger, Sartre and Habermas. For an overview, see Stahl (2016).

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ute to an experience they collectively own.119 The collective experience emerges from (i.e. is endogenous to) the reciprocal interaction, in particular the exchange, between multiple individual minds. Each subject is usually aware of its shared quality even though the latter’s uniformity may vary. Small scale examples where an experience is shared between two people are intercourse (generally a positive experience) and a fist fight (generally a negative experience). The most relevant large-scale example is mood. As the number of participants grows, the sense of individual ownership of that mood is often completely diluted and replaced by ‘outside’ ownership (i.e. by the crowd) with the related sense of being ‘taken over’ and ‘loss of control’. Again, the economic connection is key and more than metaphorical: subjects are shareholders of the experience and co-determine its quality by way of their choices, actions, etc. Although they belong to the group, the experiences associated with that belonging do not belong to them exclusively. It means, in general, that in order for me to enjoy a collective experience I require others to consciously be involved which allows exchange of information. Crucially, such reciprocal interaction mutually conveys information on beliefs and expectations about the world, including about you. In terms of an investment setting, when speaking of ownership I generally mean having a position: being long, short, or flat some security. As investors, our senses are primarily exposed to prices. A security’s price reflects information in highly concentrated format, namely the costs of ownership of that security. Specifically, in terms of the MMH a price reflects the (relative) amount the shareholder is prepared to be exposed to the world via that particular security while having to pay attention to (i.e. be conscious of) its change, interpreting the latter as shifts in beliefs and expectations by others. In the real economy ownership allows use of the asset and, by extension, the experiences associated with such use. In technical terms this is called utility. These may differ. Say a plot of land is priced at 100/m2 for agricultural use, like growing vegetables. It jumps to triple that after it gains planning permission for residential use. The price reflects this new information. In a long chain of utility assessments, it turns out that homeowners value the land higher than consumers of vegetables. Where things get complicated is when utility maximisation is based on assumed (e.g. complete) knowledge. At that moment, another type of utility, epistemic utility, enters through the backdoor. By way of price discovery the allocation of monetary resources along such utilities is determined. Prices are dynamic and experienced as such, both by current and potential shareholders. Duration wise, there is no difference between qualia sensed by tasting wine,

 There are numerous issues related to this that I will not discuss here. For example, the difference between ownership of an experience in real-time by one ‘self’ and the subsequent ownership of the memory of that experience by the next ‘self’. One instance where this is relevant is a financial crisis when a majority of people have diverse experiences along its path that eventually converge into selling close to the bottom, when the experience of financial pain is highest when it ends (i.e. relieved by selling). That then becomes the uniform memory of the majority.

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hearing a clarinet, or feeling pain and those sensed by trading securities. By definition, experiences have a (> 0) duration in the course of which qualia are conveyed by the dynamics of their source, e.g. light waves, sound waves, chemical waves, and so on. Price waves convey the qualities of experiences inherent to being in the market in general and being a shareholder in particular. Sometimes that can feel like an emotional roller coaster. By owning a position in a security you expose yourself to information which can impact its price. Price dynamics animates that which belongs to you, while sharing it with others. As will be argued throughout this book, price discovery by way of trading is the process of information (and ownership) transfers that makes the market conscious. In summary, what is yours belongs to you (but not always exclusively). Ownership of something (tangible~intangible, physical~psychological, etc.) implies that it is worth something. It has value, even if you do not always pay attention to it. Once you do—thereby exercising your option to own it—that value gets realised (or consumed). Sometimes you own something with negative value, like a liability (which, in the mind~body case could be an injury or a bad memory). Also, you may only have part ownership in that you share it with others. Again, this applies to both the physical and mental domain. For example, just like you and your fellow shareholders contribute physical capital (i.e. resources) to bring a corporation physically into existence, together you also invest the mental capital to bring the shared vision for that entity mentally into existence.

C7. Summary and Conclusion Cognitive economics is economics informed by cognitive science, in particular 4E cognition. The MMH is a specific interpretation of cognitive economics, focused on formalising the market’s mind. The market mind is an example of a distributed/extended mind. It consists of embodied investors’ minds which are augmented, connected, and otherwise supported by their technologies. Its key characteristic is intersubjectivity, when experience is shared collectively. Collective market experience occurs when—conveyed by the dynamics in prices, particularly their discovery—investors feel the mood of the market, varying from exuberance to despair. This feeling can be overwhelming. For our purposes market mood refers to the quality of the market’s mind in terms of a composite feel, i.e. what the market feels like when you’re collectively ‘in it’. In other words, mood infuses the market as a shared feeling which is experienced, albeit not necessarily (completely) uniformly, by participants. It specifically means that the experience by any investor in the markets is not solely associated with owning a particular position (e.g. long or short), or even a portfolio (although it closely correlates with the relevant prices). Rather the experience, and its ownership, is relative to those of the other market participants. It is this intersubjectivity of participation that distinguishes it from what-

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ever any individual investor120 feels subjectively about the market. So even if you are worried you can sense the market’s exuberance simultaneously (you cannot escape it and ignoring it, to paraphrase Keynes, is at your own peril). Crucially, mood affects in that it impacts participants psychophysically, e.g. through their decisions/actions. In contrast, mechanical economics (conditioned by physics envy) does not recognise full blown mentality in the economic system, only rationality. In particular, it has misinterpreted mood which it considers to be epiphenomenal, suggesting its properties make no difference to the compositional or causal facts of the market that are already determined by its physical properties. Economic Note Cognitive Economics vs Other Heterodox Theories For completion I list below a number of prominent heterodox, or alternative, economic theories, their main advocates, their (rough) date of origin, as well as my subjective assessment of their (relative) impact:

Theory

Researcher:

Since:

Impact:

–

Behavioural economics

Daniel Kahneman





–

Bio/Ecological/Sustainable economics

Hazel Henderson





–

Complexity economics

Doyne Farmer





–

Econophysics

Didier Sornette





–

Evolutionary economics, e.g. AMH

Andrew Lo





–

Identity economics

George Akerlof





–

Narrative economics121

Robert Shiller





–

Neuro-economics

George Loewenstein





–

Socioeconomics, e.g. Performativity

Donald MacKenzie





Cognitive economics, i.e. the MMH’s version, distinguishes itself from these as follows: – All theories above avoid the elephant in the room. In contrast, by focusing on consciousness— addressing economics’ hard problem—the MMH zooms in on the crucial (collective) mind~matter issues that are at the core of our predicament. In other words, the MMH identifies the blind spot (≈ Harman’s 4th Challenge) and goes straight for the jugular of mainstream (and to some extent behavioural) economics: mechanical/quantitative processes do not convey the full market state as experienced. This is the reason why economics, literally, does not ‘make sense’ to consumers, investors and other agents with ‘skin-in-the-game’ through their mind~bodies.

 Let alone a non-participating observer.  As language is central to narratives, I had expected to find references in the narrative economics literature to analytic philosophy, specifically language philosophy (e.g. to Wittgenstein, or to the “linguistic turn”) but could not find any.

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Instead of ignoring the underlying metaphysics (assumptions) of economics, the MMH makes them explicit, e.g. mainstream’s mechanical worldview via “physics envy” (≈ physicalism), Akerlof and Shiller’s “mental causation” (≈ Popper’s “downward causation”), the Fed’s wealth effect/ forward guidance/r✶. The MMH is holistic in that it recognises (e.g. according to Harman) that challenges like sustainability, inequality, and marginalisation are dominated by the worldview challenge. This makes these challenges multi-dimensional and connected in various domains (e.g. environmental, economic, political, scientific). The MMH is interdisciplinary, i.e. not “just” based on “only” psychology, physics, or sociology. At the same time, the MMH suggests (also to cognitive scientists, desperate for data) to take another look at: – Prices, namely as information concentrates that are doubly realised in the collective sphere (e.g. via our AVIR-project). – Securities, namely as the ‘neurons’ in the market mind, in contrast to traditional agency/ neural-net models (e.g. via our Market Consciousness Project).

Appendix 2 Research Manifesto My own opinion is that the good society is going to need all the help it can get, in fact more than most. A society that wants to be humane, even at the cost of efficiency, should be looking for clever, unhurtful, practical knowledge. Robert Solow

This manifesto offers an overview of the ambitions, intentions, motives, and other key elements of our cognitive economics research programme which is centred on the Market Mind Hypothesis (called the MMH-programme for short). It is not set in stone and will evolve.

Research Motto Properly dealing with economic and investment issues depends on asking the right questions to correctly identify the central underlying problem. That is our motto. We are particularly inspired by Arthur Schopenhauer’s advice: “The task is not so much to see what no one has yet seen, but to think what nobody has yet thought, about that which everybody sees”. The MMH-programme locates economics’ central problem (see Research Problem below) in the complex mind~matter interaction at both the individual and collective level. In short, like our own substrate, transmission in the economic system is psychophysical in nature. Following earlier comments—from Hayek (1952), Knight (1925), Mises (1957), and Soddy (1922)—Akerlof and Shiller (2009, p. 1) hinted at this “mental causality” more recently, as did Soros (“reflexivity”, 1987) and Sornette (“consciousness”, 2003). But they did not dig deep enough, whereas the MMH-programme takes us all the way down into the psychophysical rabbit hole to explore how minds and markets are related. This ultimately leads to the Market Mind Principle: intelligent (sometimes conscious) self-organisation via market dynamics. This supports a reflexive two-pillar premise: mind-as-market and market-as-mind.

Research Hypothesis While the MMH-programme will cover other related topics, it will be spearheaded by the MMH. The MMH primarily focuses on the second leg of the above principle. Specifically, it turns the idea of “the market’s mind”, casually expressed by numerous investors through time, into a thesis. Formally, the MMH states that the market, by embodying interacting investors and their technologies, not only distributes and shares their knowledge but also intersubjectively extends their conscious minds, manifesting collective consciousness. In ontological terms: compared to mainstream’s https://doi.org/10.1515/9783111215051-017

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mechanical worldview which offers a machine perspective of the economic system, the MMH offers a psychophysical worldview with a mind~body perspective. Translated into popular terms, Mr Market is not a voting or weighing machine, but a collective entity with a mind—warts and all. Consequently, mistreating him as an automaton, and the wider economy as a machine, is part of the (growing) problem.

Research Problem The MMH identifies the central problem as economics’ version of cognitive science’s notorious mind~body or “hard” problem: understanding mind~matter interaction, i.e. acknowledging consciousness, in markets. Specifically, it concerns the (perceived) dualism at the heart of the economic system in general—i.e. the ‘physical’ real economy versus the ‘psychological’ financial market—and the role of agents’ consciousness in particular. Among others, it involves epistemology, metaphysics, and ontology. It also is the source of true uncertainty (Knight, 1921).

Research Need The MMH highlights that mainstream economics—fixated by its version of physicalism, motivated by physics envy—denies/ignores this problem, making it the elephant in the room. Worse, with extended consciousness as its blind spot this leads to flawed thinking, in turn resulting in damaging mechanistic policies and practices. The LTCM, Lehman, “Repo”, and “LDI” crises brought this home via dangerous tail-wagging-thedog dynamics but their existential lesson has not been learned. As long as we do not address this, as part of a larger revision of economics’ paradigm, society will suffer.

Research Backing The MMH is backed and informed by various (4E) cognitive theories, in particular the theory on the extended (distributed) mind. MMH’s Market Mind Principle underlies both minds and markets. Specifically, in terms of extended cognition it translates/ turns around Clark and Chalmers’ parity principle into the following: market dynamics (e.g. consumption~production, competition~cooperation, demand~supply, risk~reward, discovery, exchange, valuation) are the processes that are universal and shared between minds and markets, i.e. they take place in both. As its metaphysical stance MMH adopts and adapts Hayek’s practical dualism (already suggested earlier by Knight and others [like Wolfgang Pauli]). Finally, it subscribes to Chalmers’ “dual realisation” of information which, in our case, is applied to prices.

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Research Data The MMH worldview means that economic data will be investigated from a psychophysical, rather than mechanical perspective. Prices and their patterns are the market mind’s informational signatures, while market mood—varying between depression (despair) and mania (exuberance)—is its phenomenal experience (via sentience) in real time. (See our projects “Complementary Tools [AVIR]” and “Mr Market’s EEG Monitor”).

Research Questions The following research questions exemplify what makes the MMH different as a heterodox theory. Elsewhere we offer hints of answers. 1. What is missing from our understanding of investor mentality? What makes hypes and crashes what they are for investors? a. Specifically, what was the key lesson from the systemic crises, especially the GFC (triggered by the collapse of Lehman Brothers), and why is it important? 2. What are some of the practical implications of accepting the MMH, especially for investors? a. Specifically, applying Hayek’s “practical dualism”, what are the assumptions on mind~matter exchange of, for example, investor activism, the wealth effect, and forward guidance?

Research Projects The MMH-programme will host projects that investigate the economic system from a cognitive science perspective. These projects will provide the empirical flesh to the theoretical bones of the MMH. It will also publish articles, papers, and reports, as well as organise events (lectures, seminars, and symposia). Finally, new methods and tools need to be developed. Below are some examples of (planned) research projects. 1. Spontaneous Volatility: Our completed pilot project, published in our paper “Spontaneous Volatility; Fooled by Reflexive Randomness” (Schotanus and Schurger, 2020), combined economic and cognitive science. From the abstract: “We partner the topic of noise trading in finance with the topic of readiness potential in cognitive science. Its tongue-in-cheek title refers to the spontaneous build-up of price noise, reflected in excess volatility, which has a curious neuronal origin”. We analysed a large database of high-frequency trading data on two popular ETFs.

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2. Readiness Potential of Traders: To raise this pilot to the next level, we intend to follow it up with research involving traders as subjects. The project proposal is to look at their behaviour by synchronising intra-day price and trading data with cognitive data: we will (non-intrusively) monitor and record traders’ eye movements and EEG. We are particularly interested in the allocation of attention (i.e. access consciousness) between different types of information sources ahead of a trade during volatile times compared to less volatile times. This project is expected to provide insights that could improve risk management. 3. Complementary Tools (AVIR): The current consensus in behavioural economics is that our analytical mind (i.e. “System 2” or S2) is superior to our intuitive mind (i.e. “System 1” or S1). However, besides theoretical criticism by experts like Gerd Gigerenzer and Gary Klein, this is an unfair comparison because we use all kinds of analytical tools (like Excel and Matlab) to support S2, whereas S1 has no such luck. Depending on personality type this disadvantages many investors. Thus there is a need to develop innovative S1 tools, particularly to improve the direct transmission of S1’s subliminal “thinking fast” to S3 (i.e. sentience). These could be especially useful in conveying market mood, contained in the market’s big data. We have a (crude) proof-of-concept available, based on the following motivation: – How/where does the data (e.g. prices) represent the qualitative dimension of a market state, beyond any quantitative reading (e.g. via regression)? – Stated differently, what property of the data conveys (e.g. existential) mood, the sense of rotation, or intrinsic time that investors experience when engaging with the market? More generally, what is the transmission of the Aha-experience that accompanies realising new information, especially via price discovery (i.e. the market’s “insights”)? – Answer (hypothesis): this cannot be its static statistics, so it must be its crosssectional dynamic—actual duration, movement between data points—that appeals to the non-analytical mind. In the spirit of pianist Artur Schnabel: “The notes I handle no better than many pianists. But the pauses between the notes— ah, that is where the art resides”. – Problem: We have no tools (software) to systematically ‘capture and stream’ this, e.g. to ‘replay mood’ to train investors in recognising it. – Solution: audiovisual investment research (AVIR), building on earlier attempts, e.g.: “I need to know what is happening in the markets . . . I hooked up a music synthesizer to the computer, linked it to the interface between the computer and quote screen, and generated a program that would give a musical summary of the markets. I used piano tones for stocks, strings for interest rates, the cello for short-term rates, and the violin for the 30-year bond. The Japanese yen was registered with the high flute, corresponding to the favorite instrument in Japan, the shakuhachi. The English horn, the French horn, and the Alpenhorn stood in for the other currencies” (Victor Niederhoffer, The Education of a Speculator).

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4. Mr. Market’s EEG Monitor: To prevent another near-comatose event like Lehman’s collapse, this project aims to build a deep neural network model to measure and monitor Mr. Market’s access consciousness. This points to our different approach: securities are considered the ‘neurons’ in the market mind, in contrast to traditional agency/neural-net models. The ambition is to have the model stream data and run real-time 24/7. Its goal is to help generate early warnings for global systemic risk. It will be particularly of interest to policy makers, including central banks and institutions like the BIS and the IMF. 5. Metaphysical Investment Profiling (MIP): MIP is inspired by William James who, in his 1879 essay “The Sentiment of Rationality”, argued that differences in people’s metaphysical stances matter. MIP could do for cognitive investing what personality tests did for recruiting and investor risk profiling did for financial advice. MIP will borrow and amend research material (e.g. questionnaires) from earlier studies in cognitive science. The goal is to show that a subject’s metaphysical stance (e.g. dualism, idealism, physicalism) can explain their preference for a particular investment thinking/decision-making format, or even preferences for asset classes, investment styles. This particularly applies in an ESG context. 6. Innovative securities: To better bridge the economy and the market innovative securities could help to improve various transmissions. For example, elsewhere we have suggested the creation of “employment bonds”. 7. Revamped market regulation: Self-regulation leads to regulatory arbitrage and manipulation, legalised via lobbying. Instead, cognitively inspired market regulation should be based on its own Hippocratic Oath of “do no harm” to the economic mind~body. Specifically, the guiding principle should be to promote and protect discovery, particularly of prices. Those who commit economic iatrogenesis (of which there is a lot nowadays) are breaking this oath. To make this more intuitive, think in terms of animal rights which are largely based on the fact that they can be conscious. The case for markets is roughly similar. 8. Virtual risks to economic reality: A hybrid digital/virtual economy, i.e. “The Metaverse”, is emerging as part of humanity’s latest effort to bridge the material with the mental. When hedge fund manager Daniel Loeb tweets that he compares “bridging the crypto world with the old as akin to finding a portal . . . between two distinct worlds in the multiverse”, we hear distant echoes of Chalmers’s comments on the role of information to bridge the physical and phenomenal worlds. Essentially consisting of numerical (0/1) building blocks this hybrid forms another kind of informational construct to extend minds into alternative (i.e. dematerialised) worlds. It also exposes us to potential risks, impacting our behaviour and freedom. Some examples: – Digital currencies not only change the format of money (i.e. ‘digits’ created out of ‘thin air’) but also its meaning and purpose vis-à-vis physically earning/saving/ spending it. While some lack money’s price (i.e. an interest rate), others are pro-

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grammable, so they can not only be tracked but also expire, be switched off, hacked, or otherwise vanish. Taken together this makes them even more metaphysically suspect than traditional (fiat) currencies. Another risk lies in the combination of AI and VR (Virtual Reality) which has resulted in creating stunning (but also scary) new worlds. Initially limited to video games, with the advance of deep learning applications are now penetrating our real world, making the boundaries blurry. Deep fakes, varying from hurtful videos to non-existent persons, show the dark side of this development. It is just a matter of time before this starts to pose risks to the economic system.

It will be difficult to counter all this but good-old-fashioned scenario analysis would be a start. This list of projects is not exhaustive and will evolve.

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List of Figures Figure 1.1 Figure 1.2 Figure 2.1 Figure 2.2 Figure 5.1 Figure 5.2 Figure 6.1 Figure 7.1 Figure 7.2 Figure 9.1 Figure 10.1 Figure 10.2 Figure 11.1 Figure 11.2 Figure A.1 Figure A.2 Figure B.1

Dutch interest rates 10 World trade as a percentage of global GDP 20 Economic and financial cycles 25 Mechanisation begets mechanisation 46 Central bank ownership 129 House money 135 Ouroboros 153 Society’s chain of discovery 170 US inflation engineering 172 Neuronal and price noise 201 Theoretical market (static equilibrium) 230 Real world market (continuous disequilibrium) 231 Statue of Hans Brinker 247 Watersnoodramp, Netherlands 250 Loss aversion 303 AI and existentialism 329 Shrinkflation 345

https://doi.org/10.1515/9783111215051-019

List of Tables Table 1 Table 2 Table 1.1 Table 4.1 Table 8.1 Table A.1 Table A.2 Table B.1 Table C.1 Table C.2

Individual and collective mind~matter realms XLV Worldview mechanical economics versus worldview MMH LIX Nature, minds, and markets 3 Rationality Variations 110 Psychurities compared to securities 192 System 1 versus System 2 314 Embodiment of S1, S2 and S3 316 Efficiency variations 338 355 Mind~Matter in economics and the economic system Person vs Market Portfolio 370

https://doi.org/10.1515/9783111215051-020

About the Author Patrick Schotanus is an independently funded pracademic, associated with a growing number of universies where he is further developing his Market Mind Hypothesis (MMH) together with interdisciplinary collaborators. His technical papers have been published in various peer-reviewed journals and he regularly presents his work at conferences and seminars. Previously, Patrick worked for almost thirty years in investment management (including a few years in banking) in Edinburgh, London, New York, and Singapore, as well as the Netherlands (where he was born). During that time he was employed by various reputable firms in senior investment positions, most recently as a global multi-asset strategist and portfolio manager. Patrick has a PhD (University of Essex), an MBA (University of Groningen), and an MFE (UC Berkeley; inaugural class, a.k.a. “Rubinstein’s Guineapigs”). His professional investment qualifications include the VBA (Dutch version of CFA), the CEFA and the CMT designations.

https://doi.org/10.1515/9783111215051-021

Index academic collaboration vs. political correctness 79–80 active inference 52, 87, 92–94, 323 active investing 115, 117, 173, 290, 341 active vs. passive investment debate 117 affective/evolutionary psychology 32, 53 agency-modelling 232 AGI. See artificial general intelligence (AGI) AI. See artificial intelligence (AI) AIT. See algorithmic information theory (AIT) algorithmic information theory (AIT) 75, 150, 233 algorithmic trading 17, 334 Anthropocene 355 antitrust 173–174 artificial consciousness 329 artificial general intelligence (AGI) 324 artificial intelligence (AI) 278 – biocentrism 325 – downward causation 325 – existentialism 329, 331–333 – factors 325 – financial risk management 331 – global free economy 330 – historical context 326, 327 – implications 324, 328 – industrial revolutions 328 – mental domain 326 – metaphysical context 327 – observations 327 – organisational context 327 Asch Conformity Experiment (1951) 174 asset prices 25, 166, 302, 339 assets-under-management (AUM) LIII, 17, 118 audio-visual investment research (AVIR) 212–215, 382 – analytical methods 204 – background and motivation 207–211 – brain activity 204 – extended versions of experiment 223–224 – format experiment 220–223 – inspiration 205 – investment research method 205 – market movements 206 – methodology 215–218 – psychological challenge 207 – software tools 218–220 – test criteria 207 https://doi.org/10.1515/9783111215051-022

audiovisualisation 210, 214, 371 AUM. See assets-under-management (AUM) automation bias 35, 76, 99 AVIR. See audio-visual investment research (AVIR) Baars, B. 100–101 balance sheet normalisation 121 bandwagon effect 134 Bank for International Settlements (BIS) XXX, 25, 130 Bank of England (BOE) 48, 123 Bank of Japan (BOJ) 132 Bateson, G. 50–51, 151, 162, 163 Bayesian learning 91 behavioral finance 32, 367 behavioural economics XXXVII, LXIII, 49, 66, 106, 132, 146, 242, 342–344 belief-formation process 107 biases 131 – behavioural economics 132 – cognitive dissonance 133 – communication strategies 132 – DSGE modelling 133 – groupthink 133 – herding 134 – house money effect 134, 135 – machine outsourcing 257 – negative rates 132 – overconfidence 133 – personal psychological issues 207 – prospect theory 133 – self-attribution 134 – self-reinforcing loop 76 – time dozens 342 big data LXI, 126, 225 binding problem 123 biocentrism 325, 331 biological incrementalism 265 BIS. See Bank for International Settlements (BIS) Black-Litterman model 92 blank-cheque special purpose acquisition 298 blind-spot bias 132 BOE. See Bank of England (BOE) Bohm, D. 213, 308 BOJ. See Bank of Japan (BOJ) bond markets 12 brain-in-a-vat (BIV) 107

418

Index

brain’s plasticity 143 brainwashing 226, 239, 240 Buffett, W. 107, 108, 347 business 37–40, 42 capital asset pricing model (CAPM) 54, 338 CAS. See complex adaptive system (CAS) CD. See coordination dynamics (CD) CDOs. See collateralized debt obligations (CDOs) Central Bank Independence 136 central bank ownership 129 circular causality 53, 155 Clark, A. 56, 59, 83, 88, 92, 152, 257, 323 climate change 246–247, 249 CLOs. See collateral loan obligations (CLOs) CoCos. See contingent convertible bonds (CoCos) cognitive ability (COGA) – accounts 113 – modeller 114–119 – model realisation 120–121 – representative agent 111–114 – theory of knowledge 111 cognitive dissonance 133 cognitive economics XLVIII, 265–266 – financial markets 351–352 – heterodox theories XXXVII, 350, 377–378 – integrating technologies 351 – market mood 367–372 – metaphysics for markets 352–357 – mind~matter 355 – ownership 372–376 – portfolioism 357–366 – price discovery 366–367 cognitive inability 113 cognitive practice 267 cognitive science XXVIII–XXIX, XXXVIII, 15, 23, 265, 275 – artificial intelligence 324–333 – vs. behavioural science XL – binding problem 123 – cognition 278–279 – complexity 140 – consciousness 299–320 – economic interpretation 79 – mental causation XXXI – metaphysics 280–286 – minds 287–299, 320–321 – mood 322–324 collateralized debt obligations (CDOs) XLI, 40

collateral loan obligations (CLOs) 124 collective action clauses 122 collective consciousness – awareness 234–235 – collectivity 235 – folk psychology 233 – intersubjectivity 243 – plurality 234 – reflexivity 236 collective intentionality 10–15, 298 coloured lenses 322 commodity markets 12 complementary tools 382 complex adaptive system (CAS) – algorithm/mathematical model 144 – behavioural economics 146 – competition and cooperation 145–146 – discovery 145 – endogenous ability 142 – 4E mind 317 – human and market minds 15 – investor’s mind 63 – mathematical primal intuition 148 – phases 147 complex intelligent system (CIS) 140 complexity science – creativity 140 – equilibrium 142 – exchanges 140, 141 – formal axiomatic system 143 – Gödel-Turing framework 142 – mechanistic computability 143 – system 141 complexity theory. See complexity science compression~expansion 155 conflicting beliefs 255 consciousness 85 – access 317 – background 312 – challenge LI – collective conditions 233–237 – costs of 44 – definition 299–300 – interpretations 300–311 – meta-problem LI – non-linearity 287 – phenomenal 318–320 – phenomenality XXV, 32 – portfolioism 304–307

Index

– research 238 – systems 1 2 and 3 313–317 contingent convertible bonds (CoCos) 240 continuous-time exchanges 301–302 coordination dynamics (CD) 6, 57, 78, 80–82, 154–155 Corona Virus Crisis (CVC) XXVII corporatocracy 37, 39 coupled system XXXIV, 59, 107 Cramer’s Rule 303 creative destruction 47, 152, 259, 337 cross-brain synchronisation 189, 202 cryptos 99 cultural variance 170 cumulative multi-psychurity 311 CVC. See Corona Virus Crisis (CVC) cybernetics 33 DAW. See Digital Audio Workstation (DAW) debt-deflation theory 35, 251 debtlands 246–250 deep learning (DL) 324, 325 Dehaene, S. 100, 166–167, 202 dehumanisation 259 Déjà vu Day traders 65–66 dependencies 127–131 Digital Audio Workstation (DAW) 219 digitisation 25, 148, 246 discounting 66, 93, 301 discovery 16, 241, 259. See also price discovery – components 157 – explanatory gap 157 – importance of 158 – internal surprises 159, 161 – mind’s complexity 161 – passive investing 161 – seeking system 160 discretionary vs. mechanical investing 17 distortions 16, 172–176 distributed cognition/mind 15, 18, 57, 83–84, 279 distributed information processing systems 268 distributed knowledge 1, 21, 84 division of labour 4, 60, 155, 183 DL. See deep learning (DL) double bind 50–51 double/dual realisation XLI–XLII, 62, 70, 77, 81, 158, 169, 204, 227, 307–309, 352, 380 downward causation XXXI Drucker, P. 86

419

DSGE. See dynamic stochastic general equilibrium (DSGE) DST. See dynamic systems theory (DST) dual process theory 361 Dutch central bank (DNB) XLIII Dutch interest rates and stock prices 10 Dutch polder model 249 dynamic stochastic general equilibrium (DSGE) 336 dynamic systems theory (DST) 142 ecological rationality 343 economic agents 26, 158, 171, 266, 335 economic dystopia 9 economic fundamentals 344 economic mind~body XLVIII, LXIV, 41, 43, 45, 47–48, 50, 186, 240, 260, 262, 373, 383 economic reality 383–384 economic science – behavioural economics and evolutionary rationality 342–344 – criticisms of REH 337 – economic system 344–350 – finance 338–342 – investing 334–335 – macrofoundations 335 – microfoundations 335 – practical rationality 337 – scarcity 335 – superrational thinkers 336 economics’ hard problem – crises 26–27 – economic and financial cycles 25 – macrofinancial linkages 25 – macro level 23 – market mentality 29–30 – mechanisation 26 – mood~momentum feedback 28 – price discovery 27–28 – reflexivity 28–29 economic system XLVIII, 171 – endogenous transmission process 237 – financial economy/markets XLV, 344 – idiosyncratic subjectivity 349 – imbalances 345 – liquidity 350 – macroprudential policies 346 – manifestations 34 – mental causation 227

420

Index

– narratives 348 – physical and mental activity 26 – price discovery 347, 349 – real (physical) economy XLII, XLV, LX – securities 347–348 – shrinkflation, toilet rolls 345 – transition mechanisms XLIII economic utility 52, 104, 112 EEG monitor 383 Efficient Market Hypothesis (EMH) 309, 338 embodied mind 83 EMH. See Efficient Market Hypothesis (EMH) emotional fluctuations 323 emotion portfolio (EP) – dynamic rebalancing 186–187 – indicator of cognitions 185–186 – mind markets 185 – price dynamics, financial markets 187 – stream of consciousness 185 – valuation 186, 188–189 employment bonds 130 EMT. See Extended Mind Theory (EMT) endogenous transmission process 237 entrepreneurial discovery of Austrian economics 170 environmental, social, and governance (ESG) – awareness of externalities LIII–LVII – investing (see ESG-investing) EP. See emotion portfolio (EP) epistemic luck 114–116 epistemic rationality 107–110, 112, 120 epistemic utility 52, 104–105, 279 epistemology – absolute (strong) dependence 106 – belief-formation process 107 – belief in mental causation 107 – coupled systems 107 – disadvantages 107 – economic utility 104 – quality of knowledge 105 – reductive (weak) dependence 106 – sustained actions 108 – uncertainty 282 equilibrium-model 114, 118 equity markets 12 ESG-investing LIII, LV, 302, 356 ETF. See exchange-traded-fund (ETF) European Central Bank (ECB) 109, 138 evolutionary psychology 53

evolutionary rationality 159, 342–344 exchange-traded-fund (ETF) 124, 341 existentialism 329, 331–333 experiential knowledge 29, 211 explanatory gap L, LVIII, 29, 157, 184, 312, 318, 353 extended cognition hypothesis. See Extended Mind Theory (EMT) Extended Mind Theory (EMT) XXXIV, 45, 56, 78, 83–87, 149 extended mind thesis. See Extended Mind Theory (EMT) external economy. See economic system externalities LIII–LVII, 340 fallibility 112 Fama’s confusion 114–115 FEP. See free energy principle (FEP) Festinger Cognitive Dissonance Experiment (1959) 174 finance 9, 55. See also behavioral finance – Armageddon XXVIII, XLI, 258, 260 – crises 110, 245–246 – economy’s informational efficiency 44 – efficiency variations 338 – erroneous perceptions 340 – fiscal policy 135 – funding efficiency 339–340 – informational efficiency 339–340 – instability XLV, 356 – internal and external information 341 – intrinsic/true value 339 – investment theory 338 – markets LXIII–LXIV, 8, 44, 53, 56, 165–166, 185, 187, 260, 266, 344, 352 – monetary engineering 34 Financial Instability Hypothesis 35 First Republic bank XXVII fMRI. See functional magnetic resonance imaging (fMRI) Foerster, H. von 29, 277, 278 folk psychology XLVI, 233 foreign exchange (FX) markets 12 forward guidance 120, 125, 128, 346 4E-cognition XXXIV, LII, LXIV, 33, 156, 183 4E cognitive science 265–268 4E (market) mind – affectivity 60 – affordances 294 – assessments 293

Index

421

– characteristics 288–289 – cognition 287 – collective agencies 60–61 – embedded 58, 292 – embodied 58, 292 – enactivism 292–293 – enactment 58 – extended 59, 293 – finite resource 288 – integration 60 – intentionality 60 – internal asset market 287 – microbiome 290 – plurality 60 – pure securities 291 – socio-technical symbiosis 59 – valuation business 288 free energy principle (FEP) L, 87, 92–93 free markets XLIII, 16, 155 functional magnetic resonance imaging (fMRI) 195

haemorrhages 240 Haken-Kelso-Bunz (HKB) model 81 Hayek, F. A. von XXXIV, XLIX, 1, 15, 17, 28, 84, 98, 140, 155–157, 169, 181, 238–239, 263, 276, 279, 285, 294, 309, 318, 319, 353 Heidegger, M. 24, 137, 157, 161, 259, 281, 322 heterodox theories XLIV, XXXVII, XLIV, XXXVII, 262–263, 381 heterophenomenology 211 HFT. See high frequency trading (HFT) high frequency trading (HFT) 17, 206, 249, 334 Hofstadter, D. 143, 162–163, 167, 286, 324, 336 homo economicus XLII, 109, 110 house money effect 134, 135 hubris bias. See overconfidence human intelligence (HI) 325 human mentality XLIX human-technology symbionts 2–3 hypnotherapy 45 hypothesis of cognitive impartiality 59–60

gaming bailouts 48 GDP. See Gross Domestic Product (GDP) geopolitical/geoeconomic tensions 19 GFC. See Global Financial Crisis (GFC) Gigerenzer, G. 146, 217, 342–343 Global Financial Crisis (GFC) XXVI global free economy 330 globalisation 9 global political mind 18–22 Global Workspace Theory (GWT) 78, 300 – access to self-system 101 – adaptation and learning function 101 – Cartesian variation 100 – characteristics 100 – decision-making and executive function 102 – definitional and contextualizing function 101 – error-detection and editing function 102 – prioritizing and access control function 101–102 – theatre metaphor 100 – working memory 100 Gödel’s incompleteness theorem 143, 146 Gödel-Turing framework 142, 146 Gödel-Turing-Post framework. See Gödel-Turing framework Gross Domestic Product (GDP) XLIII, XLIV, 47 group minds 10–15, 68 GWT. See Global Workspace Theory (GWT)

iatrogenesis 45 idd-minds 17, 18, 170, 321, 350 IIT. See integrated information theory (IIT) impartial spectator LIV, 21 inattentional blindness 125, 289 index tracking 172–173. See also passive investing individual and collective mind matter realms XLV information asymmetries 41, 310, 340 information coupling 82 informed traders 197 innovative securities 383 Institute for New Economic Thinking (INET) 10 integrated information theory (IIT) LI, 78 – democratic society 97 – elements 95 – phenomenality 96 – physical systems 94–95 – properties 95 – requirements 96–97 – Shannon’s theory of communication 96 – totalitarian society 98 – unfolding argument 96 intercommerce 235 International Network on Financial Education (INFE) LXIV internet productivity miracle speech 177 intersubjectivity 10–15, 59, 77, 168, 243, 279, 299, 376

422

Index

intractability 112–113 intrinsic dynamics 155 intuition 90, 147, 217 investment – behavioural economics 49 – consumption 47 – costs of consciousness 44 – epistemic utility 52 – experiments 175 – forecasting strategies 49 – funding efficiency 44, 119, 339 – informational efficiency 44 – inward-looking strategies 41 – machine-dependency 40 – management 91 – mechanical economics 46, 50 – mental causation 52 – performativity 53 – personality 223–224 – policymakers 41–43 – radical empiricism 54 – stigmergic-like algorithms 40 – style 223 investment portfolio (IP) 183–185 investor biases 354 The Invisible Gorilla experiment 125–126 invisible hand XXV IP. See investment portfolio (IP) irrational escalation 133 joint hypothesis test 339 Kauffman, S. 4, 145 Kelso, S. 80, 81, 156, 268 King, M. 123, 136 Knight, F. XXVIII, XXXIV, XXXVII, XLIV, 2, 6, 13, 23, 46, 86, 90, 105, 148, 159, 239, 285, 351 Knightian uncertainty 277 Kryptonite 136 Laplace’s demon 352 large language models (LLMs) 325 Lehman’s Lesson 258 liability-driven-investment (LDI) XXVII, LXIV Libetus-interruptus task 198–199 linguistics 278 Linus’s law 16 liquidity 67, 197, 245, 246, 251, 254, 350 local knowledge problem 17

Long Term Capital Management (LTCM) XXVII loss aversion 302, 303 Lucas critique 5–6 Luddite theory 76 machine consciousness 329 machine-learning (ML) 203, 324, 325, 328 Marginal Trader Hypothesis 229 market-as-mind XXXVIII, XL, 5–6, 106, 183, 379 market dynamics XXXIX, 156, 182, 209, 335, 380 marketisation 9 Market Mind Hypothesis (MMH) – challenge of consciousness LI – cognitive economics XXXV – economic theory XXVI – EMT 85 – interpretation XXXVII – market-as-mind XL – metaphysical stance XLVI – mind~body (see mind~body problem) – portfolioism 279, 357 – price theory 180–182 – research 379–384 (see also research manifesto) – state of market 67 – vs. worldview mechanical economics LIX Market Mind Principle XXIX, XXXV, XXXVIII, 4, 5, 37, 79, 106, 235, 379 market mood 77, 258, 351–352 – behavioral finance 367 – characteristics 368–369 – disposition 369 – market mentality 368, 371 – risk-management skills 370 – theory LI market portfolio XLVII, 243, 357, 359 market psychology 242 market regulation 383 market’s body 69–72 market selection hypothesis (MSH) 117 market’s math and modelling 72–75 market’s mind XLI, 376. See also minds and markets – 4E cognition. See 4E (market) mind – assumptions and observations 63–65 – central plan 15–22 – examples 11–12 – financial markets 55–56 – price discovery 176 – principle 4–6

Index

– securities as neurons 61–62 – similarities 62–63 – time investors XLVII – variables 57 market sonification 216 market terminology XLVIII market visualisation 216 Maslow’s hierarchy XLIV materialism 352 materialistic theory XLIII (im)material money 24–25 mathiness 74, 291 M~B Portfolio 290, 310, 360, 363 McCloskey, D. XXXIV, 74, 79, 161, 348, 352 McCloskey critique 74 measure of our ignorance 121 mechanical economics XLII, LIV, LVI–LVII, LX, 2, 26–29, 33, 37, 41, 46, 50, 54, 143, 179, 193, 239, 256, 259, 355 mechanical investment strategies 17, 40, 130, 334. See also systematic investment strategies mechanisation XXVII, LXIV, 26, 34, 37, 40, 46, 246, 259 mechanistic monism 2, 285, 357 meme stocks 16 mental causation XXXI, XXXIII, 28, 52, 121, 169, 227, 297 mentalising 320 mentality, portfolio management 184 mental models 1 mental state 15, 295–296 meta-consciousness 144 metaphysical belief 352 metaphysical investment profiling (MIP) 383 metaphysical stances XXXIX, XL, XLIV, 282, 286, 312, 339, 356 metastable coordination 81, 155 methodological uncertainty 282 micro-wise vs. model-wise practical rationality 108–110 Milgram Obedience Experiment (1963) 174–175 mind-as-computer 75, 79 mind-as-market XXXIV, XXXVIII, XLI, LII, 5–6, 89, 149, 158, 183, 296, 351, 379 mind~body economy XLVIII, 6, 82, 186, 244, 288, 291 mind~body problem – consciousness 312

423

– context XXXII – dualist majority votes XLIV – explanatory gap 312 – interpretation XXXIV – market mentality 29 – mental causation XXXIII – metaphysics 280 – philosophy 257 – price discovery 166 – true uncertainty 148 mindfulness 11, 214 mind~matter – dualism 368 – economics and economic system 355 – history 30–32 – individual and collective realms XLV – issues XLIII, XLV, LX, 276–277 – material monetary policies 128 – true uncertainty 161 mind-reading 320 minds and markets – allocation 288 – continuous discovery 142 – discovering, inventing, and innovating 170 – evolution 1–10 – freedom 3–10 – group minds, collective intentionality, and intersubjectivity 10–15 – nature’s values 3 – valuation 184 mind walking research 214 minimally rational investors 231 MIP. See metaphysical investment profiling (MIP) Misery Index 252 mixture of distribution hypothesis (MDH) 198 MMH. See Market Mind Hypothesis (MMH) model-belief 109–111 modern monetary theory (MMT) XXX Modern Portfolio Theory (MPT) XXX, 54, 184, 338, 358 mono-/oligopolies 340 moral hazard 39, 172, 175 MPT. See Modern Portfolio Theory (MPT) Mr Market XXVIII, LVII, LIX, LXV–LXVI, 12, 30, 41, 42, 45, 128, 172, 238–240, 297, 321 M-score 42 music therapy 213 music visualisation 217

424

Index

nature-as-market 5 nature-as-mind 5 negative interest rate policies (NIRP) 124, 249, 346 neural networks 61, 325 neural oscillations 82 neural synchronisation 13 neurobiological systems 7 neuronal networks 141 neuroscience 31, 49, 96, 183 New Neoclassical Synthesis 32 niche construction 53 Niederhoffer, V. 204, 212 NIRP. See negative interest rate policies (NIRP) noise trading (NT) 196–198 – vs. readiness potential 199–200 objectivism XLVI, 31, 282, 283, 286 occasional random errors 231 off-balance-sheet (OBS) 289 ontological commitments LXI, 31, 239 ontological uncertainty 281 order parameters 268 organisational issues L organoid intelligence 325 Ouroboros chain 152, 153 overconfidence 49, 126, 133, 138, 253 over-the-counter (OTC) 289, 347 ownership 18, 164, 290, 372–376 parity principle 84, 380 passive investing 17, 44, 116, 117, 172–173, 341, 367 path-dependency 181 Pay-In-Kind loans (PIKs) 240 payment-for-order-flow (PFOF) 42 Peat, D. 213 performativity 53, 75, 120 personbytes 170, 225 person vs. market portfolio 370 phase transitions 81–82, 155 phenomenal consciousness 318–320 phenomenality XXV, XLIX, 49, 96, 236, 299, 312 philosophy, cognitive fields 31 philosophy of money 312 physical causality 297–298 physicalism 69, 259–260, 285, 352, 380 physical vs. mental disciplines 280 policymakers 124, 261–262 – business models 42

– conflicts 42 – control 42 – investing 41 – manipulation 42–43 – WIT because YOLO 43 policymaking 36–37, 171 political economy 18, 282 Popper, K. XXXIII, 1, 28, 52, 162, 181 portfolioism XLVI, XLVII, 96, 123, 182, 185, 242, 280, 286, 290, 309, 334–335 – balanced fund-of-funds 361, 363 – complex adaptive systems 141 – consciousness 304–307, 357 – currencies 360–361 – diversification 364 – dual-aspect monism 364 – intuition pump 363 – investment-inspired framework 293 – markets types 359 – material and mental assets 358 – mind~body assets 184 – MMH assumption 365 – requirements 358 – risk-bearing 360 portfolio management XLII, 92, 184, 188, 193 positivism XLVI, 31, 282, 283, 286 practical/instrumental rationality 108 prediction errors 88, 89, 91, 160, 295, 311 predictive coding 87 predictive processing (PP). See predictive processing theory (PPT) predictive processing theory (PPT) 78, 87, 295 – Bayesian angle 90 – characteristics 88–89 – continuous improvement 88 – dark room problem 93 – dynamic models 88 – error-reducing concept 92 – institutional settings 91 – intellectual problem-solving activities 93 – investment management 91 – neuronal principles 91 – perception~action pairing 89 – price discovery 92 price as numerical influence – abstract relationship 162, 163 – awareness of change 163 – economic myths 162

Index

– extended mind 164 – global debt 167 – investor psychology 164 – positive feedback loop 165–166 – price discovery 166 – psychological difference 161–162 – qualia 167–168 – reflexivity 165 – symbolic mapping 168 price discovery XXIX, 27–28, 50, 68, 92, 155, 163, 166, 254, 275, 276, 301, 347, 349, 366–367 – cognitive practice 267 – distortions, interferences, and consequences 172–176 – economic allocation 169 – financial markets 187 – innovation and productivity 176–180 – market consciousness 66, 85 – society’s chain of discovery 170–172 – strange loop 168 Pritchard, D. 104, 112, 113 productivity – bonus culture 177 – demand stimulation 178 – famine 177 – global economic growth 179 – mental and physical ability 177 prospect theory 133, 302, 342 psychology of wealth XLIII, 137 psychophysical laws XLIII, 125, 248, 254, 286 psychophysical principles 244 psychophysical problem XXXII, 8, 312 psychophysical unitary language 181 psychurities 184, 185–188, 192, 193, 242, 288, 303–304, 361 pure securities 291, 302–303, 340, 360 QE. See quantitative easing (QE) QT. See quantitative tightening (QT) qualia XXV, 51, 167–168, 211, 309, 318 qualitative/discretionary investing 334 qualitative evaluation 189 quantitative analysis 34, 123 quantitative easing (QE) 48, 249, 346 quantitative investing. See mechanical investment strategies quantitative~qualitative performance 302 quantitative tightening (QT) 121 quantitative valuation 188–189

425

quantum computing 76, 324, 327 quantum mechanics 204 quantum physics 7, 32, 180 radical empiricism XLVI, 54, 205 radical uncertainty 259, 277 Rational Expectations Hypothesis (REH) 106, 107, 114–116, 120, 282, 336–337, 342 rational expectations models 109, 336 rationality variations 110 readiness potential (RP) 196, 198–199, 382 real world market (continuous disequilibrium) 231 reciprocal causality 155 Red Queen principle 145, 364 reductionism XLVI, 31, 282, 283, 286 reflexive modelling 74 reflexivity 2, 28–29, 165, 280–281 – individual mind’s discovery process 236 – positive feedback loop 165–166 – reverse-engineering 11 – social sciences 53 regret theory 337 REH. See Rational Expectations Hypothesis (REH) relative information theory 180 relaxation techniques 214 repocalypse XXVII, 257–258 reputational externalities LXV research manifesto – data 381 – hard problem 380 – hypothesis 379–380 – motto 379 – need 380 – parity principle 380 – projects 381–384 – questions 381 reverse-engineering reflexivity process 11 securities 23, 347–348 – credit default swap 123 – neurons of market mind 61–62 – physical assets 168 – vs. psychurities 192–193 self-attribution 134 self-organisation 4, 81, 156 self-reflexivity 28, 29 sequential information arrival hypothesis (SIAH) 198 Shiller, R. XXXI, 28, 99, 190, 348

426

Index

Silicon Valley Bank (SVB) XXVII Simmel, G. XXXIV, XLVI, 80, 148, 280, 312 simulation theory 320 Smith, A. XXV, LIV, 4, 6, 155, 352 social media 236, 321, 326 social mind theses 14 society’s chain of discovery 170–172 Soddy, F. XXXIV, 227, 257 Soros, G. XXXIII, 1–2, 10, 11, 29, 52, 108, 131, 163, 166, 232, 234, 237, 292, 326 Special Purpose Acquisition Companies (SPACs) 190, 240 Sperry, R. 31, 53 spontaneous volatility 228, 381 – accumulating randomness 196 – neuronal noise 196, 200–201 – NT 197–198 – NT vs. RP 199–200 – price noise 200–201 – results 201–203 – RP 198–199 – stock prices 195 stagflation-lite 252 Stanford Prison Experiment (1971) 175 stock market LX, 205, 229, 368 subjective expected utility theory (SEUT) 343–344 supermen 135–139 surveillance capitalism 38 symbols 162, 194 – digitisation 148 – economic exchange 148 – individualism/internalism 149 – informational tendency 150 – innate affect 151 – mind and market 154–156 – non-analytical technique 209 – pattern-forming systems 153 – self-generation 152 system 1 vs. system 2 314 systematic errors 231–232 systematic investment strategies 17, 34, 40, 334. See also mechanical investment strategies TBTC. See Too-Big-To-Care (TBTC) TBTF. See Too-Big-To-Fail (TBTF)

TBTS. See Too-Big-To-Save (TBTS) techno-feudalism 38 theoretical market (static equilibrium) 230 theory of mind (ToM) 14–15, 68, 99, 320 theory of money XLIII thermodynamics 92 Tinkerbell effect 133 ToM. See theory of mind (ToM) Too-Big-To-Care (TBTC) 39 Too-Big-To-Fail (TBTF) 39 Too-Big-To-Save (TBTS) 39 transmission mechanisms XLIII, 127 true uncertainty 89, 90, 148, 157, 161, 277 trust-based asset 138 Turing machine 143 uncertainty XXXIV, 28, 62, 90, 91, 121, 137, 157, 160, 200, 281, 304, 323. See also true uncertainty United Nations Human Rights Council (UNHRC) LIII United States Oil Fund ETF 197 US Federal Reserve (Fed) 36, 120, 128 US inflation engineering 172 utility 368, 375. See also economic utility; epistemic utility valuations 27, 79, 186, 190–191, 305 – business 288 – emotion portfolios 188–189 – emotion’s charge 193 – portfolio management 184 – price discovery 170 value-at-risk (VaR) 40, 74 variance difference (VD) 196–197 virtualisation L visual sensations 295–296 voting machines 20, 230 Watersnoodramp 250–252 wetlands 246–250 working memory 100 worldview mechanical economics vs. MMH LIX zero interest rate policies (ZIRP) 124, 249, 346