Think Bigger: How to Innovate 9780231198844, 9780231552837, 0231198841

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Iyengar inspires the creative problem solver in all of us. — Michael Bloomberg

THINK

BIGGER HOW TO INNOVATE SHEENA IYENGAR

T HINK BIGGER

ALSO BY SHEENA IYENGAR

The Art of Choosing

Columbia University Press Publishers Since 1893 New York Chichester, West Sussex cup.columbia.edu Copyright © 2023 Sheena Iyengar All rights reserved Library of Congress Cataloging-in-Publication Data Names: Iyengar, Sheena, author. Title: Think bigger : how to innovate / Sheena Iyengar. Description: New York : Columbia University Press, [2023] | Includes bibliographical references and index. Identifiers: LCCN 2022032389 (print) | LCCN 2022032390 (ebook) | ISBN 9780231198844 (hardback) | ISBN 9780231552837 (ebook) Subjects: LCSH: Problem solving. | Creative thinking. Classification: LCC BF449 .I94 2023 (print) | LCC BF449 (ebook) | DDC 153.4/3—dc23/eng/20220726 LC record available at https://lccn.loc.gov/2022032389 LC ebook record available at https://lccn.loc.gov/2022032390

Columbia University Press books are printed on permanent and durable acid-free paper. Printed in the United States of America Cover design: Noah Arlow Cover image: Shutterstock

This book is dedicated to Ishaan.

CONTENTS

Preface ix PA R T ON E 1   What Is Think Bigger? 3 2  The Creative Brain 35

PA R T T WO 3   Step 1: Choose the Problem 65 What problem do you want to solve? 4   Step 2: Break Down the Problem 90 What are the subproblems that make up your problem? 5  Step 3: Compare Wants 108 What are the motivations and preferences of relevant decision-makers? 6   Step 4: Search In and Out of the Box 130 What solutions have been tried to date? 7   Step 5: Choice Map 163 Imagine and reimagine new combinations of tactics. 8  Step 6: The Third Eye 191 Do others see what you see?

Acknowledgments 211 Bibliography 215 Index 229

PREFACE

W

hat do you do when you have a problem and there is no known solution? Think Bigger shows you, step-by-step, how to create meaningful choices for whatever complex problem you face. My earlier book, The Art of Choosing (2010), summarizes years of research on one key question: How do we get the most from choice? There I describe the various dilemmas we face for different kinds of choices and what we can do to become better at finding and picking the best from the bunch. But sometimes we face a bigger problem: there are no choices to pick from. We have to create new choices; not choose among those we already know. Growing up blind, I faced this bigger problem again and again. Could I learn to cook? Would I ever be able to travel the world on my own? Could I become a scientist? Could I perform on stage? Today, I know the answers to these questions is “yes,” and I know the “how” behind them. That knowledge comes from my personal struggles but also from a treasure trove of new research on problem-solving. The result is this book: a method for creating new choices to solve complex problems of all kinds. I call the method Think Bigger. I set about this task in a formal way some ten years ago, when I became director of the Entrepreneurship Center at Columbia

PREFACE

Business School. I noticed that our many courses on entrepreneurship taught students how to implement a new idea—but not how to get that idea in the first place. Not all new ideas are equal, just like not all choices are equal. I found that the field of innovation offered methods to get new ideas, but these dated from more than half a century ago. They failed to take into account the recent breakthrough in neuroscience called Learning+Memory. It lets us actually see how imagination works in the human mind. This book guides you through the Think Bigger method in detail. The first part provides the theory, and the second part explains the six steps that make up the nuts and bolts of the method. A companion volume, The Think Bigger Workbook, offers even more practical detail. An Appendix lists the many people who helped develop, test, and improve the Think Bigger method over the past few years at Columbia. I began to teach Think Bigger to my business students as a formal course. Their ideas for innovation were so intriguing that I thought practitioners might want to hear them—so I invited experts from various fields such as medicine, finance, and retail to listen to the ideas my students created. Again and again, these seasoned professionals used the same word to describe how my students were thinking about problems and solutions: empowering. That's when the lightbulb turned on. I realized that Think Bigger had value beyond the classroom. All kinds of people want new ways to think about generating solutions to the complex problems that they face. Whatever your politics or station in life, I think we can all agree that our world badly needs more innovation. There are many success stories of those who have learned to apply Think Bigger to innovation problems of all kinds, in all fields of human endeavor—even in their personal lives. In this book, I will show you how to deliberately form creative ideas—and most importantly, how anyone can be creative once they understand the roadmap to creative problem-solving. By the end, I hope to help release us from the outdated paradigms that have kept the concept of creativity reserved for the transcendent few and open it to the many.

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1 WHAT IS THINK BIGGER?

SHE IS A BIG IDEA

I live in Manhattan, a small island rich in its unique capacity to capture our imagination—the creative part of ourselves that we often lose sight of in our frenzy of activity. Of course, this is part and parcel of being a New Yorker. We are in it, the very thick of it, where so much seems possible if we can keep ourselves from spinning out of control. To live like this without losing your way, you have to develop resilience and find moments of quiet equanimity. For me, those grounding moments come in the early mornings, when it’s chilly even in summer. I like to go out on bike rides while the world is still in its waking hour—when the relative quiet of the streets is peaceful and the cold city air still bites. When I ride, it’s in tandem with a friend. With so many dizzying options to choose from, I prefer riding a familiar route along the Hudson River and down toward the tip of the groggy island, aiming to arrive at our usual destination just before sunrise. No matter how often we make this journey—a kind of pilgrimage— I find myself in awe of what awaits us. I’m blind, so the experience unfolds in my mind, guided by my nonvisual senses and the descriptions I’ve read and heard.

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Figure 1.1 The Statue of Liberty with the Manhattan skyline in the background.

As the air shifts and warms, the first rays of sunlight cast a pink glow before us. This evolves into other colors as the beams of light reach across the harbor, dappling the water, then brightening the edges of the buildings on the opposite shore. Despite the brilliant show of light, my attention is fixed on a tall figure with a firm, inscrutable face. To call her beautiful would be reductive. Her aura seems as ethereal and far-reaching as the sunlight that has caught up to my gaze and slowly illuminates her from base, to body, to crown. Ah, the crown! As the light reaches its seven points in halo, each bursts outward with a sharp glow. They look like white-hot ingots piercing through the heavy morning haze. Only, they don’t give off any residue but light. After a while, her glimmering crown ignites upward, finally reaching the raised right arm that bears her lighted torch—which marks the end of our morning journey. 4

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You might think that New Yorkers would grow weary of the Statue of Liberty (see figure 1.1), and I’m sure many do. But I continue to draw much strength and calm from Our Lady of the Harbor. When I visited her as a child on annual school trips, I was already losing my sight. Perhaps I never saw her with my own eyes. Or if I did, she was a giant blur—and even up close, an amalgam of smaller blurs that I could not make coherent. Still, I was impressed by just how big she was—151 feet tall, on a 154-foot pedestal, and weighing 204 tons. I remember feeling her immense height through my feet, as I trudged my way up the seemingly endless winding staircase, step by step, to the crown. Inside the sculpture, I wondered where she came from—and thankfully, we learned that too. A French sculptor, Frédéric Auguste Bartholdi, created her as a gift of thanks from his country to the United States for serving as a model of democracy to the world. It took nine years to build. I wondered if during the building, he ever thought that one day, close to five million people would visit her every year, including special delegations of dignitaries from around the globe? She has become, by far, the most famous sculpture in the history of the world. And for me, a child of immigrant parents, an outsized symbol of all things that hold promise. To this day, tears fill my eyes when I hear Emma Lazarus’s famous poem inscribed at the sculpture’s base, where Lady Liberty speaks these words: Give me your tired, your poor, your huddled masses yearning to breathe free . . . Over the years, the nature of my interest—and faith—in Lady Liberty has changed but not diminished. I’ve come to appreciate other aspects of her greatness and better understand the stuff she’s made of. For instance, we all know that she is one of the foremost icons of America. And we know that she is also an important global symbol of tolerance, freedom, and possibility. She is somehow both stern and compassionate, with a significance that can be both shared and personal, multiple and singular. So much has been said and written about her as an inspiration, but rarely do we talk about the inspiration of her own creation. How is such a remarkable object thought? How might a child marveling at the Statue of Liberty learn to create something both like and unlike 5

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her—inspired by similarly notable objects but also unique? In other words, how exactly do we get our best ideas? And once we have an idea, how do we know if we should pursue it? That’s what this book is about: the pieces that come together to create our “big ideas.” Modern science—in particular, neuroscience and cognitive science—is revealing to us how creative ideas develop in the human mind. We’re learning to reconstruct what happened in the mind of Bartholdi, the sculptor, and in the minds of other great innovators throughout history. Here I present this new knowledge to you as a six-step method called Think Bigger. Think Bigger will enable you to do what Bartholdi did: generate, identify, and cultivate your best ideas. It will enable you to do this in your own way, depending on your own circumstances, and in your own time. At every step, I draw on the relevant science and on many instructive examples to explain my rationale and show you how to put this knowledge into practice.

THE STORY OF AN IDEA

The idea of the Statue of Liberty begins with Bartholdi himself. He was born in 1834 at Colmar, France, near the German border. His father died when he was two, and his mother was left to raise him and his brother by herself. Upon seeing Frederic’s blossoming talent in art, she moved the family to Paris to give him a chance to make his living as an artist. In Paris, the industrious Bartholdi toiled as an apprentice at various trades, which included stints working under the painter Ary Scheffer and the architect Jean-François Soitoux. It didn’t take long for his hard work to pay off. In 1853, at just eighteen years old, Bartholdi’s collection of sculptures was featured in the Salon de Paris. Two years later, the Salon again called on Bartholdi, sending him to Egypt, along with a delegation of artists, to study the country’s ancient art. When Bartholdi arrived, what struck him most was the scale of these ancient works. He marveled at the massive statues 6

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that guarded the royal tombs and became instantly transfixed at the behemoths that loomed before him at seemingly every turn. In that moment of reverie, a dream formed in his head: he wanted to create his own colossus. His chance came in 1867, when the builders of the Suez Canal invited sculptors to design a lighthouse for the Canal’s entrance. They meant it to serve both as a working lighthouse and as a tourist attraction for this new gateway to Asia. Bartholdi proposed a giant woman in swirling robes holding a torch to guide the way to the world beyond the canal. He called her “Egypt Carrying the Light to Asia.” In the end, the builders rejected all the artistic submissions and put up an ordinary lighthouse instead. When he returned to Paris, Bartholdi found another use for his sketch. His dear friend, Edouard de Laboulaye, was a member of the French national assembly, the president of the French Anti-Slavery Society, and would later be a Life Senator. Laboulaye saw the American Revolution and Civil War as triumphs of democracy that could inspire other nations, especially his own. He proposed a statue, paid for by the French people, as a gift to America to embody that inspiration. And he asked Bartholdi to design it. From there, the two friends set out to raise the required money. The cost for this massive undertaking was $250,000—around $5.5 million today. They traveled across France, urging everyone to donate what they could. By the end, nearly 160,000 French citizens did—poor, rich, farmers, maids, business owners, and other artists alike. For Laboulaye, this democratic source of funds was fundamental to the whole idea and fully embodied the spirit of the future project. In 1871, Laboulaye and Bartholdi went to America to choose and prepare the site. Bedloe’s Island was quickly determined to be the perfect place for Bartholdi to realize his vision, as it was the central focus of the landscape upon entering New York Harbor. Bartholdi declared that the island would be the “gateway to America.” It was the perfect place to display Liberty to the world. Once the site was established, the two friends carried on raising money across the United States. As an attraction, Bartholdi took 7

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Lady Liberty’s arm on the fundraising tour, where spectators could climb a ladder up the arm, to the torch, for the price of fifty cents. They eventually set the arm up in Madison Square Garden in Manhattan, where it stood for six years. All told, some 250,000 people made the climb. On October 28, 1886, twenty years after her inception, Bartholdi unveiled the Statue of Liberty to the world, and to history. Our iconic symbol of freedom was born. Thus marks the end of the story as the history books tell it. And it’s truly an inspirational story. We love such stories, where we can imagine ourselves as the hero like Bartholdi, who sets out on a quest, overcomes countless obstacles, and achieves a long soughtafter dream in the end. It makes us wonder if one day we too could do something great. Now, that’s all well and good. But let me ask you a different question—and I want you to really think about this before answering— how would you start? See, these heroic stories have two key elements: creative genius and ceaseless effort. You can imagine the ceaseless effort part: you work hard to make your dreams come true. But what about the first part: the creative idea? Can you pick any dream, and then just work hard to make it come true? Of course not. You must choose your dream carefully. But how? What makes for a good idea? And how exactly do you get one? Unfortunately, the traditional tales of heroic achievement skip that part. The process of generating the idea itself has remained a little black box, opaque and impossible to open. That is, except if you’re a creative genius. Then the box just opens, like magic. For the rest of us, that’s no help at all. If we’re not one of those lucky few, there’s nothing we can do. At least that’s what we’ve always been told. Well, I am here to show you that what we’ve always been told is wrong. There is no magic key. Anyone can open that black box of creativity—you just need to understand a few simple things about the mind, the process, and the people who have helped Think Bigger come into being. If you want to understand how this innovation on 8

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innovation can help set you on a path to unlocking your creative potential and generate big ideas, I invite you to join me. Over the next eight chapters, I’ll show you how.

OPENING THE BLACK BOX

I want you to imagine a fantasy animal. No, not a dragon, phoenix, or unicorn—it should be something entirely new. Pick up a piece of paper and pen, and then draw your creation. Now, look at your drawing. What do you see? Does it have eyes? Does it have arms, wings, or legs? What about a tail? Having done this exercise thousands of times, with all kinds of people from schoolchildren to Fortune 500 executives, I can predict that your creature has at least one familiar element. Even when we try to imagine the unthinkable, we don’t produce something radically alien. Whenever we create, we consciously and unconsciously draw upon what we already know. The elements are not new. The combination is new. From the figures we draw to the sentences we speak to the solutions we create to solve our everyday problems, we’re constantly innovating as humans. We learn from our experiences and our observations of the world around us, break it up into pieces, and use that knowledge to generate new ideas. Good, good, and good! That’s exactly what Bartholdi did when he imagined his colossus. He never told us how he got his idea, and it’s very possible that he himself wasn’t even conscious of what he was doing. But modern science tells us how the mind creates new ideas, and that lets us see the elements that Bartholdi brought together to make his new combination. So let’s answer the question “How did Bartholdi get his idea?” Remember Bartholdi’s first inspiration: the colossal tomb sculptures of ancient Egypt (see figure 1.2). Then the call for a Suez Canal lighthouse led him to draw a colossus in that form, with a torch as the light (see figure 1.3). As we see below, already he is close to his idea for Lady Liberty. 9

Figure 1.2 The greater temple of the Abu Simbel in Egypt shows statues of Ramesses II, the third pharaoh of the Nineteenth Dynasty of Egypt, who is known for his successful military campaigns and monuments. The temple is located on the Nile’s western bank, south of Cairo. Wikimedia Commons.

Figure 1.3 Bartholdi’s “Egypt Carrying the Light to Asia,” watercolor (1869). Wikimedia Commons.

WHAT IS THINK BIGGER?

Bartholdi then switches hands for the torch and bends the other arm to hold a key object. We find those elements in La Verité, a painting by Jules Lefebvre from the time Bartholdi made his Liberty design (see figure 1.4). Now, what of the crown, with the seven points that form a halo around Lady Liberty’s head? That Bartholdi finds in his pocket, on the back of a five franc silver coin (see figure 1.5). It’s the seal of the French Second Republic, which overthrew the last French king in 1848. The figure is a version of the Roman goddess Libertas. Last but not least, the face—what can we make of that inscrutable, regal visage? Well, it’s the very face Bartholdi’s eyes gazed upon when he first came into the world. Many commentators have noticed the uncanny resemblance between the face of Bartholdi’s mother (see figure 1.6) and that of Lady Liberty—and how he stayed close to his mother throughout his life. When asked if his

Figure 1.4 La Vérité by Jules Lefebvre, oil on canvas (1870). Wikimedia Commons.

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Figure 1.5 Obverse (left) side of the great seal, adopted in 1848; 174 years ago. The headdress of the Liberty featured on the obverse side is similar to that of the Statue of Liberty (Liberty Enlightening the World), which would be offered by the French people to the U.S. people forty years later. Wikimedia Commons.

Figure 1.6 A portrait of Charlotte Bartholdi. Courtesy of Granger Academic.

WHAT IS THINK BIGGER?

mother’s face was the inspiration for Lady Liberty’s, Bartholdi did not deny it. Now we can answer how Bartholdi got the idea for the Statue of Liberty. She is the size and form of the colossal statues guarding the Egyptian tombs. She has the role and siting of the Suez lighthouse. She has the posture of La Verité. She has the crown, name, and symbolism of Libertas. And she has the face of his mother. See figure 1.7. We might want to believe that artistic endeavors are different from other everyday acts of creation. Painting a masterpiece is not at all like drawing up your grocery list for the week or solving a mathematical equation. Artists are greater than us—they must have some magical ability to think of ideas unbounded by the past or present. Everything they create is completely new. Right? Well, your favorite masterpiece might feel entirely new. It might even give you a new perspective on life. But there remains an elusive, undeniably familiar feeling in each artistic creation we admire. Consider the work of the most famous artist of the twentieth century, Pablo Picasso. Known today as one of the most prolific artists ever, Picasso is estimated to have produced fifty thousand pieces of art. His distinct style of using bold, distorted figures also helped make modern art the main event rather than a sideshow. Where did he get this distinctive style? The popular answer is simple: Picasso was a genius. It came out of his head like magic. But in reality, like Bartholdi, Picasso put together previous elements. Take a look at these two self-portraits (see figures 1.8 and 1.9). Notice the difference: the painting on the right, from 1907, looks like it was made by an entirely different artist than the one on the left. The one on the right is the style that made Picasso famous. The one on the left is not. In those six intervening years, what caused this change? Well, starting in the mid-nineteenth century, artists had a problem. They made their living by painting portraits and landscapes realistic enough that the rich, and not-so-rich, bought them to hang in their homes. The camera was invented around 1825, and over the next decades, photographs became better, cheaper, and faster—and 13

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Figure 1.7a-f A comparison showing the progression of Bartholdi’s inspirations for Lady Liberty, compared to the statue itself.

WHAT IS THINK BIGGER?

Figure 1.9 Picasso’s Autoportrait Expressionist painting dated from his “African Period” (1907). Oil on canvas. © 2022 Estate of Pablo Picasso / Artists Rights Society (ARS), New York

Figure 1.8 Picasso’s Autoportrait Expressionist painting from his “Blue Period” (1901). Oil on canvas. © 2022 Estate of Pablo Picasso / Artists Rights Society (ARS), New York.

people began to buy them instead of paintings. Toward the end of the century, a new style solved the problem: Impressionism. At first glance, an Impressionist painting looks like a photograph. But as you get closer, you begin to see the scene dissolve into separate brushstrokes meant to give a particular impression that the painter wants to convey. Stylistically, it was something the camera could not do. Look back at those two portraits. Picasso’s self-portrait in 1901 is not exactly Impressionism, but the 1907 self-portrait has such great distortion that you would have to stand very far away to think it’s a photograph. 15

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Picasso came of age as a painter when Impressionism was already a mainstream style. A few painters broke away in small ways—like Georges Seurat, who broke the separate brushstrokes into even smaller “points,” and Vincent Van Gogh, who swirled the separate brushstrokes into hypnotic waves of color. But it was Henri Matisse who first broke completely with the whole idea of small units of paint like brushstrokes and points. He used big patches of color— he called them “volumes”—in scenes that showed recognizable figures that were very distorted in color and shape. Technically, the breakthrough was to use semiabstract volumes of color. Matisse’s first great painting in this new style was The Joy of Life. In the spring of 1906, it appeared in an independent Paris exhibition. It drew big crowds and became the talk of the Paris art world. Picasso had never met Matisse, but they both knew Gertrude Stein. She became famous for her modernist writing and as the host of a salon in her Paris apartment that drew many modern painters— and also writers such as Ernest Hemingway, F. Scott Fitzgerald, and Ezra Pound. Picasso went to see The Joy of Life and then asked Stein to introduce him to Matisse. She took Matisse to visit Picasso’s studio. The two painters met a second time at Stein’s, and that’s when Picasso found his style. During this fateful meeting, Matisse brought along an African sculpture. It was a Vili mask from Congo. Paris art shops had just started importing art from France’s African colonies and those in the avant-garde were always on the lookout for such cultural influences. When Picasso later asked Matisse to dinner, he brought along the sculpture. There before him in the Paris café were the two inspirations that Picasso would bring together to make his own new style. That night he went to his studio and started painting. And that painting is still one of the most famous paintings of modern art: Les Desmoiselles d’Avignon. In it we can clearly see Picasso’s two inspirations (see figure 1.10). Picasso never admitted his debt to Matisse. He reveled in the mystique of the singular creative genius. Matisse, on the other 16

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Figure 1.10 (a) Henri Matisse’s The Joy of Life. © 2022 Succession H. Matisse / Artists Rights Society (ARS), New York. (b) Henri Matisse bought this sculpted figurine created by the Vili people of the Congo—it had a huge impact on him and on his friend Pablo Picasso (Credit: Archives Matisse, Paris). (c) Pablo Picasso’s 1907 painting, Les Demoiselles d’Avignon, the first Cubist painting of the legendary art movement. © 2022 Estate of Pablo Picasso / Artists Rights Society (ARS), New York.

PART ONE

hand, proudly cited his sources. For The Joy of Life, he especially drew from The Bathers by Cézanne and Persian miniatures from medieval Iran (see figure 1.11). Now that we understand how these three great artists (Bartholdi, Picasso, and Matisse) got their ideas, it might seem that all they did was take what they saw and combine it in new ways. Could it really be that simple? First, let me be clear: this in no way takes away from their talent or achievements. It just explains how they did what they did, without the magical thinking that has always been attached to the “singular creative genius.” Like all successful innovators, they A

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Figure 1.11 (a) Paul Cézanne’s 1905 painting titled The Bathers. Philadelphia Museum of Art: Purchased with the W. P. Wilstach Fund, 1937, W1937-1-1. Wikimedia Commons. (b) Adam honored by angels on a Persian miniature portrait. Wikimedia Commons. (c) Henri Matisse’s The Joy of Life. © 2022 Succession H. Matisse / Artists Rights Society (ARS), New York.

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were essentially “strategic copiers.” By this, I mean they learned from examples of success, extracted the parts that worked well, imagined new ways of using those pieces, and combined them to create something new and meaningful. Innovation is nothing more, and nothing less, than a new combination of old ideas. Yet we know from personal experience that all ideas are not equal. Often, people go through a draining cycle of generating idea after idea only to find that the best idea is something banal. It’s why we can admire the genius behind artistic masterpieces. Even as we break down their big ideas and lay out the individual elements to see how they combined them, the whole of their creations are more meaningful than the mere sum of the individual parts. This is the signature of every successful innovation— whether it’s your grandma’s famous apple pie, the Apple phone in your pocket, or a great work of art. The French scientist and mathematician Henri Poincaré explained how to generate good ideas in his 1913 book, The Foundations of Science: “Invention consists in avoiding the constructing of useless combinations and in constructing the useful combinations which are in infinite minority. . . . To invent is to discern, to choose.” We’re all capable of generating an infinite number of creative combinations—let’s call them “choices.” Creating a new choice that’s valuable calls for great discernment. You must pick apart the choices you’ve identified and the routes you could take to make your idea real, and that’s no easy task. Of the multitudes of pieces you could combine, and the infinite ways you could combine those pieces, it’s the creator’s discernment that decides which of the myriad combinations to keep. The common definition of an innovation is “something new and useful.” By definition, every combination is new. That’s the easy part. The hard part is to identify a high-quality combination that’s useful as well. So, how do we create the most useful combinations (which, as Poincaré notes, are in the infinite minority)? That’s the question this book will answer. We can now refine our definition of innovation: a novel, useful combination of old ideas that come together to solve a complex problem. 19

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This definition echoes an older statement by the economist Joseph Schumpeter, known today as the founder of entrepreneurial studies and the source of the idea of “creative destruction.” For Schumpeter, the role of innovation is “to produce means to combine the things and forces within our reach.” In Think Bigger, I focus on innovations that respond to a stated problem. It might seem that some innovations come out of the blue, but the reality is that even those innovators saw how their innovation solved a particular problem. If it did not solve a problem, it wasn’t a “useful combination,” in Poincaré’s terms. So the keen innovator would pass it by. You only take action on innovations that solve a problem. In Think Bigger, you first identify and define a problem you want to solve. This is true even for artistic innovations. Bartholdi’s problem was how to symbolize freedom and democracy through sculpture, and Picasso’s was how to find a unique style beyond Impressionism that the public likes. If you talk to artists as they work, they will tell you how they solve a series of problems to make their creation. Or in the case of Picasso, they just might not tell you all of the ways in which they go about it. Above all, Think Bigger provides a way for a single individual— you—to get a better idea. You can do each step as a group as well but always in the same sequence: first each person alone, then put together a team result. We will see that most other innovation methods rely on the team, rather than the individual, for the actual idea. That is, they skip over the question of how creative ideas form in the human mind and simply say that putting ideas together from many people makes the idea creative. As we see from Lady Liberty—and all other real examples of innovation—that’s not how it works in the real world. Yes, Bartholdi needed other people at each stage, both as sources for inspiration and to help implement them. But the most important creative steps happened in his own mind. Many hands make light work, but they don’t make the light work. That is, a team is made for work, not for thinking. 20

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So, if a team does Think Bigger together, each person will have better ideas, and the sum total will be better too. If a team does not follow Think Bigger, each person will have fewer creative ideas, and the sum total will be less creative. Throughout the method, there will be moments where I unravel the process of innovation like I did for Picasso, Matisse, and Bartholdi, as if it was a conscious method on the innovator’s part. In reality, if you asked Bartholdi how he got his idea, he might not be able to answer. Those few moments of inspiration are fleeting, and he spent much more mental effort on implementing his idea than on pondering how he got it in the first place. Picasso, on the other hand, was a wily competitor who knew exactly what he was doing. In Think Bigger, we stay conscious of each mental step because that’s the only way to repeat the steps for other ideas in the future. We unlock the black box and make the problem-solving exercise accessible for all and repeatable. Think Bigger empowers anyone, anywhere, to solve a problem—whether it’s personal, professional, or universal. Being deliberative allows us to speed up the process of searching for and finding a solution rather than just waiting for an idea to spontaneously arise. By providing more people with the Think Bigger tools, I believe we will also have a better chance at helping us—individually and collectively—create the solutions for the greatest problems we face in the world today. Think Bigger offers you a set of tools and skills to solve any kind of complex problem, and then solve the next one too. Consider a birdhouse. If I give you a complete set of tools, instructions, and pieces to build a birdhouse, then guide you through the process, what you build might not be the greatest birdhouse ever. It will have flaws in the structure and nicks in the wood. But you won’t only have that birdhouse; you will know how to build another birdhouse—one possibly better than the last. Think Bigger teaches you how to innovate. And like any other skill, you get better the more you do it. The first time you use this method, you won’t have a perfect result. A novice only becomes a master through practice. 21

PART ONE

THINK BIGGER IN ACTION

Before I walk through the six steps of Thinking Bigger, I want to show you two well-known innovations that have become an integral part of modern life. Just as we did when we told the story of how Bartholdi and Picasso made their masterpieces, we will once again take two innovations and deconstruct them to better understand the thought process that created these products. Before, I showed you the pieces that the innovators combined. Now I will explain the series of steps that brought the pieces together. These steps match our Think Bigger method. Let’s begin with a problem that everyone faced in hot seasons or hot climes in the days before air conditioning. Imagine it’s a hot summer’s day in 1840 Philadelphia. You find yourself sweltering as the sun steadily beats down on your head. You contemplate what might cool you down and ponder a treat that’s cold, sweet, icy, and creamy. Thinking back to an article you read, you imagine tasting the ice cream George Washington once paid two hundred dollars for in the summer of 1790. Then you remember reading about the creamed strawberry ice delicacies that Dolley Madison made for James Madison’s second inaugural banquet at the White House. What about that advertisement you saw for Joseph Corre’s Parlor advertising ice cream at the price of eleven pence per glass? Too bad that was 3 percent of your yearly income as a housekeeper! Today, we take for granted the ice cream truck that zooms down the street, blasting a repetitive tune that tempts us to buy a cone for a pittance compared to our yearly income. We stock our freezers with tubs of ice cream from the grocery store for birthday celebrations or to prepare for the summer. And don’t forget that ice cream is the best antidote for a broken heart. Ice cream has become a household staple—even for vegans. It’s affordable for many of us. But it wasn’t always that way. If you lived in the 1840s, the high price of ice, the intense labor, and the time it took to produce made ice cream nearly impossible to enjoy unless you were wealthy. So how did ice cream become so ubiquitous? And who do we have to thank for making ice cream accessible for everyone, everywhere? 22

WHAT IS THINK BIGGER?

That would be Nancy Johnson. She was in her fifties, a volunteer for the American Missionary Association, the wife of a professor, chemist, and physicist, and the mother of two. Johnson saw that making ice cream was actually very timeconsuming, physical work, and it was also very expensive. So she set out to find a way to make the process less labor-intensive and less expensive by reducing the necessary products—like ice—while preserving the final product for longer. It seemed like a waste to spend nearly half the day making ice cream only to have it melt in an hour. Johnson found several problems that needed to be solved. Let’s present her main problem as a question: How can I make ice cream more accessible for everyone? In order to make this broad question more solvable, we break the main question into four smaller questions: 1. How do I make the outer container smaller so it uses less ice? 2. How do I make the ice cream colder faster and preserve it? 3. How do I create a method of mixing the cream that is less labor-intensive? 4. How do I make ice cream smoother and creamier?

How did Johnson solve the first subproblem? Ice was expensive: $2.13 a pound, or $68 in today’s money. In those days, you kept ice in large containers like bathtubs and took the ice out as you needed it. Butter churns used tall wooden buckets, but that would take too much ice. Johnson used a simple wooden bucket instead (see figure 1.12). That held the ice and the rock salt to slow the melting. Of course, wooden buckets weren’t new. They were invented about four hundred years before Johnson’s time and were in common use during the nineteenth century, and they were cheap and easy to handle. And they certainly helped solve the first part of her problem: use less ice. How did she solve next the second subproblem? Well, since freezers did not yet exist, this was going to be tricky. She started by searching for the ways other foods and beverages were kept cold. That led her to pewter. By the Middle Ages, long before Johnson’s 23

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Figure 1.12 Wooden water pail, typically used for wells. Wikimedia Commons.

time, certain inns used pewter for mugs to keep beer and ale cold (see figure 1.13). More recently, pewter bathtubs kept water warm. Before Johnson, when you made ice cream by hand, you used a ceramic bowl that you kept carrying back to the tub of ice to make it cold again. She replaced the ceramic with pewter and set it in her wooden bucket with a layer of ice packed around it. This kept the mixture cold and cut lots of time. And pewter was cheap. It was a simple mixture of scrap metal, such as tin, copper, bismuth, antimony, and even leftover silver. So Johnson replaced the bathtub of ice with a wooden bucket that held a single layer of ice and replaced the ceramic bowl that went inside with a pewter one. Then cover it with a pewter lid, and your ice cream stays cold for hours. Now for Johnson’s third subproblem. Stirring a mixture of cream, sugar, and other flavorings for hours on end was a grueling task. It led to stiff arms, injured backs, and pulled shoulders. Pausing often to rest just made the production time even longer. Was there a simpler way to continuously mix the ingredients without using so much arm power? 24

WHAT IS THINK BIGGER?

Figure 1.13 Pewter mug dating from 1219, used to keep ales cool. Courtesy of Sotheby’s.

To remedy this, Johnson added a hand crank—an invention that went back to first-century China. From there, it spread to the Roman Empire and on to the rest of Europe. The Eastern Mediterranean even implemented hand cranks to grind spices and coffee (see figure 1.14). In this application, the hand crank dramatically cut the time and effort it took to stir the ice cream. Now for the last subproblem: lumps and crystals. One of the most frustrating parts about making ice cream by hand was that after all that effort and expense, the cream often separated and formed big icy lumps or smaller icy crystals. A butter churn would force a wooden disc with holes in it down through the barrel (see figure 1.15). The holes prevented the lumps and crystals, but Johnson needed to scrape the colder ice cream off the sides or else it would freeze. So she fixed spatulas (see figure 1.16) onto the crank to scrape them through the mixture. Spatulas for greasy food also had holes to let the grease drip out—like the butter churn disc. All in all, Nancy Johnson combined four simple things to solve the overall problem: the wooden bucket, pewter bowl, hand crank, 25

Figure 1.14 An antique herb/spice grinder featuring a metal hand crank and a drawer to the base that collects the processed herb or spice. Wikimedia Commons.

Figure 1.15 Plunger butter churn. Wikimedia Commons.

Figure 1.16 Wooden spatula with holes, used for cooking. Illustration by Emmaline Ellsworth.

WHAT IS THINK BIGGER?

and her “dasher” paddles. In 1843, she filed U.S. patent number US3254A (see figure 1.17). The Library of Congress identifies her simple invention as a “disruptive technology” that made it possible for everyone to make high-quality ice cream without electricity. Johnson then sold her patent to William Young, a wholesaler of kitchen equipment who mass-marketed the device as the Johnson Patent Ice-Cream Freezer. Manufacturing ice cream soon became a nationwide industry when a Pennsylvania milk dealer, Jacob Fussell, opened the world’s first wholesale ice-cream factory in 1851. Steam power later automated the churning process, and mechanical refrigeration aided ice cream’s storage and transport. By the 1870s, electric power and motors, packing machines, and new freezing methods sped up ice-cream production tenfold. Each iteration of the ice-cream maker used Johnson’s device as the base mechanism. Notice the structure of this innovation process. It starts with defining the problem in a specific and concrete way. Then you break

Figure 1.17 Nancy Johnson’s final patented product from the U.S. Patent Office, 1843.

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it down into essential parts. Next, you search for solutions that already exist to identify ways the different parts of the problem can be solved. You then combine the pieces in a new way that makes them all work together in harmony. Let me give you one more familiar example that shows the basics of the Think Bigger method. Once again, it starts with a problem to solve. In 1899, when Henry Ford founded his own car company, a motor vehicle cost from $850 to $2,000—well beyond the average person’s means. Ford saw a problem worth solving: How could he make the car affordable for the average person? Like Johnson, Ford broke his problem down: 1. How do I reduce the cost of labor? 2. How do I reduce production time? 3. How do I reduce the cost of materials?

Let’s start with labor. The previous century’s Industrial Revolution brought in the assembly line, where products are lined up on the factory floor and specialized workers move along the line to put in standard parts. In 1906, Oldsmobile was the first to apply that concept to automobile production. Ford imitated that process, but he wasn’t satisfied. He wanted to use fewer workers to make more cars—or the same number of workers to produce cars faster. Note how this blends into the second subproblem: to reduce production time. The answer to these pieces of the puzzle came from outside the auto industry. William “Pa” Klann, Ford’s chief engineer, visited the Swift slaughterhouse in the Chicago stockyards with Ford’s problem breakdown in mind. He saw how the animal moved on an overhead line from station to station, where workers stood still and took off different parts in the butchering process. It was a moving disassembly line. Do it the other way—for cars—and you have a moving assembly line. When Ford reconfigured his factory from a stationary assembly line to a moving line, the result was dramatic. The time it took to build a car fell from 12.5 hours to 90 minutes. 28

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That left the third subproblem: reducing the cost of materials. Ford noted that paint was one of the most expensive items he used. And the available resin-based oil paint took more than a month to dry. In the 1920s, chemists developed a new kind of paint—nitrocellulose black lacquer—that dried in less than a week and cost half as much as oil paint. Once applied and rubbed down, the black lacquer paint gave a unique shine to the cars, similar to the gloss on Japanese art and woodwork, hence the process being dubbed, “japanning.” By 1927, Ford began japanning all his cars—a change that inspired his famous quote, “The customer can get the Model T painted in any color he wants, so long as it’s black.” We can now see how Ford made the car more affordable. He broke the problem down into parts and found previous solutions for each subproblem: the Oldsmobile assembly line, the moving tracks in a slaughterhouse, and japanning. It was a new combination of previous elements. Before his innovation, in 1908, Ford sold 6,389 Model Ts at $850 each. Starting in 1915, he sold 472,350 at $350 each. In 1925, the figures were two million sold at a price of $250 per car. By that time, with incremental improvements, each car took only thirtythree minutes to build. Other industries adopted Ford’s assembly line, drastically reducing costs and production times for countless products across the globe. But note how Ford innovated: every ingredient in his equation already existed. Ford identified useful existing solutions for each of his subproblems by searching within—and outside—his own industry. By combining these solutions, Ford created a big idea. Notice how Ford searched far and wide. He learned a new tactic to reduce his cost of labor from an entirely different industry; meatpacking. By searching among existing methods, he found one that reduced the cost of materials to build cars. This element is relatively low tech—a moving chain. Too often, people think that innovation equals new and more complicated technology. Even when there is a new technology, it typically solves only one narrow problem. It’s up to other innovators to make new combinations to apply that technology to new problems. For example, before Henry 29

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Ford, Karl Benz found a new use for the internal combustion engine that Etienne Lenoir invented: the motorcar. Figuring out how to use a new technology to solve new problems calls for creative combination—not more technology. You have probably been told at some time to “think outside the box.” But has anyone ever told you how to do it? Successful innovators, like Johnson and Ford, looked for solutions to pieces of their puzzle in two places: within their own industry and then beyond it. That’s thinking “inside” and “outside” the box. You need both. Think Bigger recreates what Johnson and Ford did in six clear steps—and after learning these steps, you will understand how to most effectively take what you know, search for what you don’t know, and implement the findings into something actionable. The result is a new and exciting way to solve your biggest problems.

THE THINK BIGGER ROAD MAP

Now that I’ve walked you through the essential characteristics of innovation, you’re ready for the Think Bigger Road Map: your guide to our six steps. I will lay out the steps in sequence, but keep in mind that innovation is never completely linear. You will likely go back and forth between each step. In every step forward, you will also look back. Everything stays “in draft,” subject to revision, until you find your solution. Figure 1.18 is our road map. For now, don’t worry about the details within each part. Just note the progression from step to step. Step 1: Choose the Problem

The start of Think Bigger is choosing the right problem and understanding it well. This takes time and good judgment. The problem must be hard enough that no one has figured it out before but not so ambitious that the solution remains a fantasy. For example, no one has invented a pill that cures every disease on earth and costs only one dollar. Don’t be the first to try. There are multiple ways 30

Figure 1.18 The Think Bigger Road Map.

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to define any problem. Your task is to choose from among them the one for which you can generate meaningful solutions. You must choose a worthwhile problem to solve, and that is no easy feat. Some problems are too big to solve with the current state of human knowledge, some are too small to make it worth the effort, and some don’t provoke in you enough desire to persist in finding a solution. Step 1 of Think Bigger helps you solve this very first problem: how to choose the right problem to solve. Step 2: Break It Down

Any major problem is made up of multiple, smaller problems. To crack the big problem, identify and solve the smaller problems. Make a long list of subproblems and then pare it down. You end up with five to seven key subproblems, because that’s about as much complexity as the human mind can handle at one time. Step 3: Compare Wants

You now have your problem and its breakdown. Before you start the search for the elements of a solution, you need to step back and understand the big picture. In this step, you will identify three groups and what they want from a solution. These groups are you, the target of your solution, and third parties who matter for putting the solution into action. You list the wants from all three, compare them, and then use that analysis to help select from among the multiple solutions you create. Your “Big Picture” Score will serve as your selection criteria. Step 4: Search In and Out of the Box

Each industry, branch of science, or area of expertise has its own ideas and methods that narrow its thinking. It’s common to hear that complex problems need multidisciplinary solutions. But when they try to work together, their ideas and methods clash. Think Bigger solves the problem. Ford didn’t need an expert at meat 32

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processing to join his team: he took just one element, the moving line, as part of his own solution. Think Bigger doesn’t try to merge disciplines or negotiate across them. It’s non-disciplinary rather than interdisciplinary. Ask yourself if anyone, anywhere, at any time, has solved one of your subproblems? If yes, how? Make a list of these solutions. Like Ford and Johnson, you collect what works from multiple and disparate sources and even eras—recall that butter churning and japanning were both very old crafts. Step 5: Choice Map

Innovators tend to highlight the one solution they put into action. But the reality is that they tried out different combinations, at least in their minds, before arriving at the best one. They tend to forget those previous permutations. Think Bigger brings them to the fore. You keep moving and turning the pieces around until—eureka!— the whole emerges. In this step, you will lay out all the pieces of the puzzle, combine and recombine, until they click into place. I will give you techniques to create and use multiple combinations that are both useful and novel, and then use your Big Picture Score to pick out the one that best fits the multiple wants you need to balance. Step 6: The Third Eye

You now have an idea that feels like a flash of insight. But what is it, exactly? How does it differ from what’s already out there? How will others see it? In this final step, you take what you have been working on primarily by yourself—in your own bubble—and go outside to find out what others “see.” What you'll find is they don’t see it with their two eyes but with their third one. The third eye is a real phenomenon of working memory where an image forms in their mind. You’re not asking for their feedback or judgment about the quality of your idea. Rather, you want to know what they see in your idea to help you see it better yourself. In so doing, you further develop your idea and determine if it’s something you truly want to pursue. 33

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THE INNOVATOR WITHIN

At this point you might ask yourself, “Can I do it?” That is, do you have the mental ability to follow in the footsteps of Bartholdi, Picasso, Johnson, and Ford? Before you read this chapter, perhaps your answer was “no.” But now I hope you see that the answer is a resounding “yes.” Each step of Think Bigger is completely within your grasp. Altogether, the six steps lead you to a big idea. There is no guarantee, of course, that Think Bigger always works. You can’t solve every problem in the world. But Think Bigger shows you how to try. Once you see the process broken down—and understand how even the greatest innovators came up with their new ideas—I’m certain you will feel confident that you can do it too.

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2 THE CREATIVE BRAIN

THE APPLE OF INSIGHT

You might know this story: In the summer of 1665, Isaac Newton was a student at Cambridge University. When the Great Plague of London spread to Cambridge, the town emptied out to the countryside and Newton fled to his family’s farm seventy miles north in Grantham. On the farm, he whiled away the time under a gnarled apple tree. That is, until one fateful day, when a ripe apple dropped from the branches above and hit him on the head. Eureka! The apple fell to earth—not sideways, not up—because the earth pulled it down. And if the earth pulled this apple, the earth must pull on everything else, including distant objects like the sun and moon. Objects pull on each other! That’s how the planets, the moon, Earth, and the sun stay in orbit! In this moment, in his mind’s eye, Newton saw the law of gravity writ large. The insight came to him all at once, like magic, and science has never been the same since. That is one version of the story. Here’s another. Isaac Newton attended Grantham School before enrolling at Cambridge at the age of nineteen. In both places, he studied the

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latest mathematics and the works of the scientists who came before him, especially Archimedes, Aristotle, Galileo, Descartes, and Kepler. In his fifth year at Cambridge, the plague drove him out. And during that period of retreat at his family farm, he made his first big breakthrough. The Biographia Britannica of 1760 quotes Newton himself on his method of discovery: In the beginning of the year 1665 I found the method of approximating Series and the Rule for reducing any dignity of any Binomial into such a series. The same year in May I found the method of tangents of Gregory and Slusius. . . . And the [following] year I began to think of gravity extending to the orb of the Moon, and having found out how to estimate the force with which [a] globe revolving within a sphere presses the surface of the sphere, from Kepler’s Rule of the periodical times of the Planets being in a sesquialterate proportion of their distances from the centers of their orbs I deduced that the forces which keep the Planets in their Orbs must [be] reciprocally as the squares of their distances from the centers about which they revolve: and thereby compared the force requisite to keep the Moon in her orb with the force of gravity at the surface of the earth, and found them answer pretty nearly. All this was in the two plague years of 1665 and 1666, for in those days I was in the prime of my age for invention, and minded Mathematicks and Philosophy more than at any time since.

You might not understand all the references and reasoning behind this passage—I know I don’t. But the method is clear. Here we see a scientist piecing together a solution to the problem of gravity, element by element. In this brief passage alone, he cites two contemporaries: Gregory and Slusius (plus Kepler of the preceding generation). Elsewhere, he credits other scientists too. In a letter to Edmund Halley, namesake of the famous comet, he writes, “Bullialdus wrote that all force respecting the Sun as its center & depending on matter must be reciprocally in a duplicate ratio of the distance from the center.” Piece by piece, Newton assembled his universal law of gravitation: every particle in existence attracts every other particle with a 36

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force inversely proportional to the square of the distance between their centers and directly proportional to the product of their masses. He published this law in Philosophiæ Naturalis Principia Mathematica in 1687—more than twenty years after the plague. In that great work, and in other letters and writing, Newton cites the many distinguished scientists he drew from. In a letter to Robert Hooke, he makes this general statement about his method: “If I have seen further it is by standing on ye shoulders of Giants.” Upon examination, we find that this quote isn’t even entirely original: five hundred years earlier, John of Salisbury wrote, “Bernard of Chartres used to say that we are like dwarves perched on the shoulders of giants.” But what about the apple? The sole source of the apple story is a memoir of Newton’s life by William Stukeley, published in 1752, twenty-five years after Newton died. Stukeley was a much younger friend of Newton who admired him greatly. After dinner, the weather being warm, we went into the garden, & drank tea under the shade of some appletrees, only he, & myself. amidst other discourse, he told me, he was just in the same situation, as when formerly, the notion of gravitation came into his mind. “why should that apple always descend perpendicularly to the ground,” thought he to himself: occasion’d by the fall of an apple, as he sat in a contemplative mood: “why should it not go sideways, or upwards? but constantly to the earth’s centre? assuredly, the reason is, that the earth draws it. there must be a drawing power in matter. & the sum of the drawing power in the matter of the earth must be in the earths center, not in any side of the earth. therefore dos this apple fall perpendicularly, or toward the center. if matter thus draws matter; it must be in proportion of its quantity. therefore the apple draws the earth, as well as the earth draws the apple.

What exactly struck Newton under the apple tree? It wasn’t an apple. And it wasn’t the existence of gravity. For centuries, scientists knew that objects attract each other from their centers. The 37

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idea of a “center of gravity” goes all the way back to Archimedes, born in 287 BC. Newton did not discover gravity: he discovered the precise mathematical formula that explained it. And he did so not under the apple tree but in methodically addressing the problem over the two decades after that incident, by standing on the shoulders of giants—especially Galileo and Kepler, who worked on exactly the same problem. If working out the formula for gravity wasn’t a new problem, what made Newton succeed? Well, he was doggedly committed to solving that one problem among the hundreds of other problems that science faced in the middle of the 1600s. For reasons we’ll explore much more deeply in later chapters, it’s important to understand that passion is a key element for effective, creative problem-solving. So yes, let’s remember Newton under the apple tree. Not for solving the problem of gravity, but for finding a worthwhile problem he very much wanted to solve. History is filled with special people who had these special moments of insight. Remember the story of the Buddha, who sat under the Bodhi tree and attained enlightenment? What about the story of Archimedes crying, “Eureka!” as he sat in his tub observing the occurrence of volume? And what about Steve Jobs, whose idea for the Apple I, the first personal computer, came to him as he sat in his garage with a typewriter wired to a television screen? Do you know the backstory of Dr. Martin Luther King Jr.’s iconic “I Have a Dream” speech? It came about because a woman shouted from the crowd, “Tell us about your dream!” Or maybe you know Joan of Arc heard voices that told her to lead the French army to defeat the English, who had already conquered half the country? We love these stories. They remind us of the magic that we, as humans, can be capable of. In one moment, we might see what nobody else does—and in that realization, we’re able to change what others see and do forever. Every time we hear these stories, we’re reminded of how powerful any one individual can be. And yet, we still find ourselves wondering if these flashes of insight are truly

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random and just out of our reach, meant only for special people in special places at special times. In Think Bigger, we offer a method that leads you through the same steps as Isaac Newton and all the other innovators through time. But can you really do it? Or is there something different about these people that makes them more creative than you?

SPECIAL PEOPLE

I want you to take two creativity tests. Here’s the first one: It’s easier for me to remember people by . . .     A. their name     B. their face When you listen to a new song you are more interested in the . . .     A. lyrics     B. melody or rhythm When you fold your hands, which thumb is on top?     A. right     B. left Cross your legs. Which leg is on top?     A. right     B. left

If you answered mostly As, you are a “creative type,” or more rightbrained. If you chose mostly Bs, you are an “analytical type,” or more left-brained. Here’s the second test. Look at each statement below and mark if they apply to you or not: • You’re better with faces than names. • People have described you as “perceptive.” • If someone’s mad at you, you can tell without them having to say a word.

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• When planning a party you think about the big picture rather than smaller details. • You’re not a big planner; you prefer to be spontaneous. • You’ve been called out for daydreaming. • You are easily distracted. • You’re daydreaming or getting distracted right now. • You admire a whole artwork first then focus on smaller details. • You’ve dabbled in art, just because you were curious. • If someone’s arguing, you’re more likely to believe them if they get emotional. • You tend to get emotional about things yourself. • You’re not afraid of taking risks. • You tend to trust your gut instinct over anything else. • You work best if there’s music or TV on in the background. • Procrastinating is a skill you’re extremely familiar with. • You’re more of a visual learner and tend to remember details if you can see them. • If you had the chance to live in a fantasy world instead of reality, you would. • You relate more to fictional characters than people in real life. • You tend to doodle whenever you’re taking notes. • You get restless easily. • You aren’t afraid of what others might think of you.

RESULTS: If you marked more than ten items from this list—congrats! You’re more right-brained and creative than left-brained and logical. Now let me ask, does this kind of test make sense to you? I certainly hope not. Unfortunately, these tests are extremely popular. They are all over the internet. When I look up the phrase, “Right Brain-Left Brain Quiz” on Google, I get nearly sixty million search results. On BuzzFeed alone, if you enter “Right Brain,” “Left Brain,” or “Creative Type,” hundreds of thousands of tests will populate on your screen, including the two quizzes you just did for me now. The idea that some people are creative and some are not is a very old one. This thinking took on a scientific angle in the 1860s, when 40

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the neurologists Paul Broca and Karl Wernicke noticed that people with damage to a particular area on the left side of the brain had speech and language problems. This led to the split-brain theory, which postulated that the left and right sides of the brain do different tasks. In 1981, Roger Sperry won the Nobel Prize for his work affirming the split brain, demonstrating that some brain diseases were best treated by severing the connection between the two sides. With this insight, Sperry performed further experiments to better understand the nature of the split mind. In tests, he showed his subjects two different objects: one was observed using their left eye only and one using their right eye only. When asked to explain what they saw, all participants drew what they saw with their left eye but described what they saw with their right eye. Sperry concluded that there are “two modes of thinking”: the verbal (left-side brain), which recognizes and analyzes words, and the nonverbal (right-side brain), which recognizes shapes, patterns, colors, and emotions. Sperry’s findings went on to inspire an array of tools claiming to help individuals become more right- or left-brained. For example, Betty Edwards wrote Drawing on the Right Side of the Brain, which uses drawing techniques to help you be more creative. Dr. Ken Gibson created a series of quizzes and exercises for people to sharpen their left brain and become more analytical. We love identifying ourselves as creative right-brain types or analytical left-brain types because we believe that typecasting ourselves gives us insight into our character. Knowing what “type” we are makes us feel as if we can better understand who we get along with, where we work best, and what kinds of jobs we’re more likely to be good at. But recent experiments are beginning to show us something else: there is no left brain or right brain—at least when it comes to thinking. Since Sperry’s Nobel Prize, the field of neuroscience has made huge leaps forward. One major breakthrough came from Seiji Ogawa, who figured out in the early 1990s how to use MRIs to show the brain at work. See, your left and right brain are exactly the same physically. Left and right, however, do matter for physical movements: your left brain controls your right hand and leg, and 41

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your right brain controls your left hand and leg. Except for your eyes: right controls right, and left controls left. That alone explains Sperry’s results about the right and left hand and the right and left eye. But more conclusive still are MRIs that show people thinking. There is no creative or analytical portion of the brain, nor is there any mental activity that’s solely creative or analytical. Whether you’re working on a math problem, painting, science experiment, or writing a song, you’re constantly using all of your brain. There is no mental difference between the left and right hemispheres. Here’s a more recent experiment that showed the whole brain at work. In 2006, neuroscientists tracked brain images of adults, children learning algebra, and mathematically advanced children as they solved three problems: a basic arithmetic problem, an algebra problem with three levels of difficulty, and a geometry problem. The images showed that as they worked, each person’s neural system lit up like a Christmas tree—on both the right and left sides of the brain (figure 2.1). As participants explained how they went

Figure 2.1 An fMRI image of the brain with both the left side and right side lit up at resting and active states, showing how the brain constantly uses both sides. Nielsen et al, “An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging,” PLOS One, August 14, 2013.

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about solving the math problems, they used just as much creativity as they did analysis. It’s impossible to disentangle the two when problem-solving. But some people do seem to be more creative than others. If the difference in their right brain doesn’t explain it, what does? Are people susceptible to depression, like Van Gogh or Sylvia Plath, more likely to be creative? Or are happy people, like Tom Hanks, more likely to be creative? In studies, only one associated personality traits seems to emerge across the spectrum of creative people: they’re curious. And that’s something you can control. The same is true for persistence, which helps you actually accomplish tasks, including creative ones. That too is within your control. In Think Bigger, that’s all you need to start: be curious and persistent. We give you all the other tools, at each step, to guide you in your creative task. With practice, these steps become a habit, and so you develop a creative mindset that will help you solve problems of all kinds into the future.

BRAINSTORMING

Think back to the last time you had a really creative idea. Where were you? What were you doing? If you’re anything like the thousands of people I’ve asked over the last decade—from high school students to senior executives at Fortune 500 companies—then odds are you didn’t say, “During a brainstorming session.” Over the years, only a handful of individuals have told me a brainstorming session is where they came up with their best ideas. Around the world, all kinds of people and organizations set out to solve creative problems by brainstorming. As a formal technique, brainstorming dates from 1938, when advertising giant BBDO promoted their top vice president, Alex Osborn, to save the company after it had lost a large number of clients during the Great Depression. To attract new clients, Osborn decided that he should bring his whole team together to come up with the best ideas for advertising campaigns. Brainstorming, or “thinking up” as Osborn originally 43

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called it, became their most-used method for ideation—and it took the world by storm: Osborn and BBDO pumped out advertising campaigns to encourage U.S. armament during WWII and for high-octane clients such as General Electric, Chrysler, American Tobacco, BF Goodrich, and DuPont. As the method gained traction, Osborn renamed it “brainstorming” because the act itself was a “brain-storm”—a sudden neurological explosion from individuals in a group setting. And so came the pervasive gathering of colleagues saying, “Let’s brainstorm a solution.” Whenever we need an idea fast, we brainstorm. Why did Osborn invent brainstorming? Here was his problem: in company-wide meetings, junior staff rarely spoke. Senior management dominated the conversation. His solution was to hold weekly “group-thinking” sessions that gave everyone an equal chance to speak. He ran the meeting and made sure to ask the junior staff for their thoughts. There are many variations on the basic theme of brainstorming. This is a list of rules from IDEO (https://www.ideo.com/), a famous creative company that offers a brainstorming service to clients: 1. 2. 3. 4. 5.

Go for quantity. Encourage wild ideas. Defer judgment. Build on the ideas of others. Stay focused on the topic.

These are the rules Osborn came up with in 1938. From banks to advisory firms, tech companies to manufacturers, public relations agencies to media companies, nonprofits and government agencies, brainstorming dominates creative thinking today. But let’s ask an obvious question: Is brainstorming really creative? It certainly solved Osborn’s original problem: how to get everyone to speak. And if you were to pick a problem and practice these rules in any social setting, it would certainly involve others in an interesting conversation. It can be fun to brainstorm. But, does it actually generate great ideas? 44

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Let’s analyze the five rules of brainstorming. First, brainstorming is a numbers game: Rule 1: The more oysters you crack open, the greater your chance of finding a pearl. Rules 2 and 3 serve the first rule, to make sure that all ideas see the light of day. As for Rule 4, it sounds promising. But if you take the first three rules seriously, you might have a hundred ideas to build on. If I say, “Let’s make our product glow red in moonlight and green in sunlight,” and you say, “Let’s make it transparent,” what do we do? Then someone else says, “Make it reflect the color of the sky.” And that’s only three ideas. When we mix in the dozens of other ideas, we have what I call “idea diarrhea.” Last but not least, Rule 5, which, in my opinion, is a straitjacket. You might have experienced this in your own work, where you realize you’re solving the wrong problem and you shift your focus to something else. That means finding the problem is part of the creative process—you don’t assume you have the right problem and then go on to brainstorm solutions. In fact, the evidence is unambiguous—brainstorming does not work! In a seminal study on brainstorming from 1987, social psychologists Michael Diehl and Wolfgang Stroebe collected ideas from participants gathered in groups of four in a traditional brainstorming session. They then took the ideas of four individuals who worked separately and collected their ideas into one list. Researchers went on to compare the output from both groups and found that participants who generated ideas alone produced significantly more than individuals who worked in traditional group sessions: Those who ideated alone produced twice as many unique ideas as those who worked in a brainstorming group. Increasingly, scientists have seen the creation of bias embedded in the group brainstorming process—and the outsized impact this has on creativity. Our biases are informed by group feedback. And we have come to understand just how stifling group dynamics can be on an individual’s creativity. Individuals tend to self-censor in a variety of ways: they omit data, anchor on whatever idea was presented first or most recently, choose what’s most convenient, and so on. This process tends to compound over time and create 45

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groupthink, which discourages creativity and individual responsibility. Consequently, academics and practitioners alike have become disenchanted with the act of brainstorming as a formal method of idea generation. As we proceed through Think Bigger, it will become abundantly clear why this process is far more creative than brainstorming. What brainstorming really does is draw from the direct experience of people in the room; in other words, information sharing and surfacing. If I tell you, “Quick, throw out an idea!” you will draw on what you already know. If the people in the room have lots of experience—and diverse experience—brainstorming is very efficient for solving ordinary problems. That’s because the sum total of the experience in the room probably has all the solutions you need. But note that Henry Ford did not ask his engineers to brainstorm. He asked them to search the world for ideas to use—that’s how Pa Klann found the moving meatpacking line. Think about it this way: if five people brainstorm as a group, they draw on the knowledge of only five people. We’ll call that “in-thebox” thinking. In Think Bigger, we ask you to draw on the knowledge of all humanity throughout recorded history, invest in hearing the ideas of others, and expand your knowledge beyond your comfort zone. We will refer to that as “out-of-the-box” thinking. Where brainstorming confines, Think Bigger expands. Which seems more creative to you? Brainstorming today goes by many different names. The most popular is Design Thinking. There we find three major steps: customer anthropology, brainstorming, and product prototyping. Think Bigger has nothing to say about the first and last aspects— customer anthropology and prototyping are fine. Think Bigger replaces the middle step: brainstorming. There are countless other methods like Design Thinking that embed brainstorming at their core—especially forms of research, analysis, and implementation. For all of them, Think Bigger has nothing to say about those other steps. But when it comes time to get your creative ideas, that’s when you need Think Bigger.

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CREATIVE SPACE

Does your physical environment play a role in your creativity? Was there something special about say, Newton’s apple tree? I wondered, so I looked it up. The tree still exists (figure 2.2)! This is the very tree that Newton sat under 350 years ago. Is there something special about this particular apple tree? It doesn’t seem very inspirational to me. It looks quite ordinary. And the surrounding lawn, buildings, and other trees look ordinary too. Now, take a look at the photos in figure 2.3. As you might guess, these are Google’s offices. Many companies around the world imitate this style to help their employees be more creative. Does it work? Alas, we have no evidence that it does. We might ask the Google guys, Larry Page and Sergey Brin, “Where did you get your idea?” For Google itself really is a great innovation. Initially, we know they got specific elements of their idea inside the dull graduate school cubicles at Stanford University, then in a humdrum garage where they set up their first office. As far as we can tell,

Figure 2.2 The tree at Sir Isaac Newton’s home in Grantham, England. Courtesy of the BBC.

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A

B

C D

Figure 2.3a-d Scenes from Google offices. Courtesy of Business Insider and Wikimedia Commons.

the unimpressive physical spaces they occupied in those formative moments had nothing to do with the quality of their idea. We find the same thing for Bill Gates and Paul Allen of Microsoft, Mark Zuckerberg of Facebook, and just about any innovator you can name—it’s often the garage. Even our favorite mystery writer, Agatha Christie, had her aha moment for Murder on the Orient Express in a place as unremarkable as the bathtub. There are 48

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countless examples of ordinary places where people came up with creative ideas. Google actually states that putting people in “nonordinary” settings stimulates the right side of the brain. But we now know that’s a myth. If you work in a room with red polka dots on the wall, it doesn’t open your mind to new possibilities. It puts red polka dots into your memory. Your next idea will anchor on the red polka dots. The most creative wall is blank. It allows your mind to wander freely, looking for connections. It’s lack of stimulation that you want, which lets your brain do its work without distraction. The best real-world test for this comes from Bell Laboratories. A mecca of twentieth-century innovation, the company had two New Jersey sites: Murray Hill and Holmdel. The Murray Hill site was an old, factory-like building from 1941 that placed function over beauty with its utilitarian space, narrow halls, plywood offices, and movable, clunky furnishings. It cost three million dollars to fit out. The Holmdel site looked like a spaceship with a futuristic facade made of 6,800 panes of glass, a reflecting pool, an atrium with 3,600 plants, and a water tower shaped like a transistor. It cost thirtyseven million dollars—more than twelve times Murray Hill. Which site was more creative? Frumpy old Murray Hill produced the transistor, the laser, the solar cell, and at least three Nobel Prizes. Sleek, shiny Holmdel gave us the push-button telephone, touch-tone dialing, the fax machine, voice mail, the cell phone, microwaves, and at least one Nobel Prize–winning scientist. Together these two headquarters produced the first communications satellite, digital cell networks, and fiber optic cable. They were both creative! And it had nothing to do with their respective designs. We can say two things about creative space: first, no distractions. You need a place to think on your own. Second, you need a way to run into others in a casual way, like around the coffee pot, water cooler, or break lounge. That’s it. Green plants might make you more cheerful, which is good—and dark, dingy spaces might make you feel low, which is bad. But creativity is not about what goes on around you. It’s about what goes on in your head. If you have ever 49

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been to a slaughterhouse, it’s a grisly, bloody scene. But there, Pa Klann had a great idea.

MIND WANDERING

Let’s go back to the question we asked earlier: when you think back to the last time you had a really creative idea, where were you and what were you doing? The most common responses to this question are standing in the shower, driving in the car, exercising, cleaning at home, or chopping vegetables for dinner. It seems many of the answers to our hard questions and tricky problems come to us miraculously, without any effort, when we aren’t even trying to work them out. All we have to do is let our mind wander—which is no trivial thing. We actually spend about four hours a day like that. That’s a quarter of our waking lives. Your mind also wanders at key moments during tasks that call for more attention. When you do a math problem, part of it’s easy enough and your mind doesn’t wander. It marches right along. Then you hit a snag. You pause. Hmm  .  .  . Your mind wanders. Ah! Got it. You see an answer to that problem-within-a-problem. Then your brain marches along again. Mind-wandering gives you the creative parts of your solution, even when you’re focused on your task. Mind-wandering is a part of being human—it’s what we do naturally and it has a variety of psychological benefits for us. But rather than thinking of mind-wandering and daydreaming as magicmakers, we must think of them as supplements to the real labor that’s done when we put our brains to work to come up with our best ideas. Agatha Christie did not just take a lot of baths to conjure up her detective stories—she worked hard at her desk, hour after hour, on the craft of writing and storytelling. This built a foundation from which the “magic” of mind-wandering could potentially be valuable and available. There is plenty of research that shows you’re more likely to have your most valuable aha moments while working. Through 50

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the lens of Learning+Memory, we see that our best ideas come to us when we’re on task. While it might feel good to have that feeling as you stand in the shower or sit by the beach, those moments are not as insightful as we first think. Despite feeling more important and creative at the time, when ideators look back at ideas that came as aha moments, they tend to view them as less creative and less important than those that didn’t. Why is this? For mindwandering to lead to an aha moment, you need to have enough on the shelves of your memory to make up the pieces of your new idea. It seems the aha moment serves us best when we use it as the spark that keeps us inspired when we inevitably hit roadblocks during ideation. As we look into the history of the aha moment and try to pin it down in a practical sense, we might find some benefit in knowing that it’s in our DNA. That’s right—chimpanzees can have aha moments too. Meet Sultan, a chimpanzee from sunny Tenerife in the Canary Islands. As part of a research experiment, German scientist Wolfgang Köhler locked Sultan in a big wire cage with a ripe banana on the ground outside, just beyond his reach. On the ground inside the cage was a short bamboo stick. On the ground outside the cage, closer than the banana, was a longer bamboo pole. Sultan stared at the banana. Then he picked up the short stick and poked it through the wall of the cage to pull the banana toward him. But the stick wasn’t long enough to reach. Next, Sultan ripped off a loose piece of wire from the cage. He straightened it out and stuck it through the cage wall. Once more, it was too short to reach the banana. Seemingly dejected, he plopped down on the ground. Stared at the banana. Looked around the cage. Then, he spotted the longer bamboo pole outside. He looked back and forth between the pole and the shorter stick and suddenly jumped into action. He quickly picked up the short stick and poked it through the cage wall to reach the longer pole. He pulled the pole toward him until he could reach it. Then he took the long pole and stuck it through the cage wall to reach the banana at long last. In victory, Sultan slid the banana back toward the cage until he could grab it. Success! 51

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The story of Sultan dates from 1914. It’s the first recorded instance of a scientist observing an aha moment as it happens—and depending on your perspective, it might appear that Isaac Newton and a Canary Island chimpanzee had essentially the same experience. Köhler certainly thought he saw something quite significant in Sultan, and he went on to conduct the experiment with many other chimpanzees. He notes that each time, after seemingly giving up, the chimp “gazes about him.” In the course of these tests, there are always some long pauses during which the animals scrutinize the whole visible area. Then comes the moment that Köhler calls Einsicht, or “insight” in English. It’s as if a lightbulb flashed in Sultan’s head. The solution came in an instant, and Sultan sprang into action. Köhler was one of the founders of Gestalt psychology, where “a thing cannot be understood by the study of its constituent parts but only by the study of it as a totality.” In Think Bigger, we look from multiple perspectives: a totality can be understood by knowing its constituent parts. Similarly, by looking at the big picture, we can understand different pieces from alternate perspectives. Sultan has to have the pieces of the puzzle in his mind. Otherwise, the aha moment will never come. I showed you earlier how modern neuroscience overturned the idea of a left-analytical and right-creative brain. The new model of the brain is called Learning+Memory. It fills out the picture of what actually happens in our minds as the pieces come together. Eric Kandel won the Nobel Prize in 2000 for his work on this model. He explains, “Memory is the glue that binds our mental life together. . . . We are who we are in large part because of what we learn and what we remember . . . The human memory system forms abstract internal representations that arise from previous exposure to similar images or experiences.” Neuroscience shows that all thinking is an act of memory in some form. That includes imagination, creativity, innovation, and other variations of “new” thoughts. That means the components of the thought are not new. Only the combination is new. Let’s do a test to see if we can do what Sultan did—put together the components we need to solve a problem. 52

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Tell me: Is this correct? 28 + 32 60 I imagine you said “yes.” Very good. I bet you got it fast. Now try this one: Is it correct? κ η’ + λ β’ ξ’ I’m guessing your brain just froze. You have no idea if it’s right or wrong. If you happen to be a scholar of ancient Greek, you’ll realize this equation as the same as the previous one: 28 + 32 = 60. The first answer came to you from your memory. There are eight symbols: six numbers, a plus sign, and the underline. They were already on the shelves of your brain. And you have done similar calculations countless times. The knowledge, symbols, and indicated procedure are all stored in your memory. They quickly come together and you see the answer. In the second equation, five of those symbols are probably not in your memory, so when you automatically search for them, you come up empty—except those of you who know ancient Greek. You might think the first equation is purely logical, just a mathematical formula. It’s not at all creative. But if that were so, you would get the second one right as well. No, there is always a creative task of recombining from memory to match the problem at hand. You had a creative solution to a logical problem. Pure logic is impossible. The content of a logical problem comes together through creative combination. Here’s another creative exercise. Take a few moments to come up with a totally new word that rhymes with “airplane.” Did you do it? Here’s what I came up with: stairpane      carmain      artain      tropain 53

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My guess is that your answers look similar in the following way: they are made up of letter clusters already familiar to you. Stair, pane, car, main, art, pain, plus the second half of mountain and the first half of tropical—these are already in my memory. Just like the math problem above, your mind pulls the discrete pieces from the shelves of your mind to combine them in a different way. The only difference between the math problem and the language problem is the content—numbers versus letters. The method is the same. Through Learning+Memory, we’re constantly retrieving memories and making connections. Even when we see something new, we recognize certain parts of it. So we “see” both with our eyes and our brain. We can only see a dog if we already know what a dog looks like. If not, we just see a blob of color and shape. This was one of the earliest discoveries that came from modern psychology. In the late nineteenth century, Hermann von Helmholtz showed that perception includes rapid guessing and hypothesis testing in the brain: “Is that a dog? Yes, it’s a dog!” This happens so fast you don’t even feel it. But if it’s dark out, or the dog is far away, or if it’s a strange kind of dog you have never seen, it takes longer, and you can actually experience the processing happen. Kandel describes what happens in our minds when we’re in a room and the lights go off. Our brain retains, as an act of memory, what we saw in the room. It’s an extreme case of fill-in-the-blanks. The distant dog is a medium example. But even when you see the obvious dog, you guess what else you know about dogs—for example, to judge whether this one is friendly or not. In mind-wandering, we do the same: we fill in the blanks with what our memory expects. In the words of psychologist Richard Gregory, “Our brains create much of what we see by adding what “ought” to be there. We only realize that the brain is guessing when it guesses wrongly.” We now see that creative combination for innovation is along the same continuum as everyday thinking. They are both acts of imagination based on memory. The human brain is the earth’s greatest warehouse. The Library of Alexandria of ancient Greece once held a copy of everything ever written in the Western world, 54

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or so the legend goes. Your little old brain holds a lot more than that—and it adds more every day. From the moment you’re born, your mind absorbs information, breaks it down, and stores it on shelves of memory. Later, when we need to think, our brains pull together memories from different shelves to form new thoughts. All thinking, logical and creative, comes from memory. When we look back on Sultan the chimp, we realize he was able to figure out how to retrieve the banana because of his memory. He had to see the pieces first—the long and short sticks—before he could use them for a solution. And this gives us a clue to the quality of our ideas. They’re only as good as the pieces we put together. If you’re stuck on a problem, you’re probably missing a piece of the puzzle. It’s not on the shelves of your brain. Go out into the world and find it. Now that we know how the brain works to make new combinations, we can see that creativity is within our grasp. It’s no longer a mystery. Anyone can learn how to be creative and how to apply creativity to any problem. Still, it’s important to understand that just because anyone can learn how to be creative, that doesn’t mean generating big ideas is easy. It’s not. Creative thinking is truly accessible to all, as long as we learn how to effectively structure the creative process and stick to the structure we put in place. It’s work. And with some effort, I’ll show you how to be creative while maximizing your chances at a big idea.

WORKING IN TEAMS

Are you more creative if you work alone or in a group? Decades of research shows that we’re more creative when we start the ideation process first by ourselves. After having thought about an idea alone, we can enter a group setting. In thinking individually first, and then sharing with a group, we can avoid falling into the collection of biases that lead to groupthink. Reflect on what we’ve learned so far about the way our brains form thoughts and ideas: our brains naturally collect the pieces from our memory shelves that already exist, leaving out the bits we need to search for. That’s why it’s important for you to first 55

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identify and pull together the nodes of information relevant to the problem that you personally have. Only then can you move back into a group setting, where the main point of the group work lies in what you gain from others. Your fellow group members will act as a reminder of the missing information nodes that exist in your brain that you may have forgotten—or give you access to information missing from your current inventory. Throughout the Think Bigger six-step method, at every step, I always want you to complete the step alone first. Then, if you’re working in a group, share the information you learned and your ideas with the whole team. If you’re working in a group setting, a general rule we follow in the Think Bigger method is to never work with more than five people per group. Teams larger than that are likely to lose individual performance as members get lost in the mix or silenced, which is what we want to avoid as we learn to Think Bigger. .

THE LINK BETWEEN FREEDOM AND CONSTRAINT

I can still vividly remember the first experiment I ever conducted. I was in the first months of my PhD program at Stanford University and decided to set up a room at one of the most famous and highly rated preschools in the country: the Bing Nursery School. In a small classroom, with a single window and a table at its center, I placed toys all around. I wanted to see how motivated the children I invited would be to spend the time required to build a full Lego set. At the time, many studies spoke on the importance of giving people choice to motivate them. So I placed a Lego set, with its bright blocks in primary colors, as the star of the show—dead center on the table, surrounded by the other toys. As these three- and four-year-olds entered the room, they would look at the Lego set on the table, smile, and examine the other toys around them. After a few minutes, they would sit down by the table, and rather than pick up any Lego bricks or other toys, they would stare out the window. I couldn’t understand why. Was there 56

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something wrong with the Lego set? Did they not want to play with any toys? These children simply waited for me to dismiss them from the room to go back to their regular classes. At first, I thought the toys I chose weren’t interesting enough. So I checked out many different toy stores in the area with the hope of finding something these children would like. But over and over, as the Bing Nursery School children came into my room, piled up with all kinds of toys and trinkets, they would sit quietly in their chairs and stare out the window. It was strange to me that in the condition in which the children were supposed to be the most responsive, with a multitude of options surrounding them, the opposite was happening. Despite all the options around them, they continued to simply look out the window. Upon observing this behavior, I decided to get rid of all the other toys and keep one primary game in the room: the Lego set. Now, when the children came into the room, they would go to the lone table at the center of the room, stare at the box of Lego sitting on its surface, and then begin to build with the blocks. Often, when their time was up, I would have to interrupt their keen focus to send them back to class. Suddenly, it seemed like they were intrinsically motivated—and not because they had a lot of choices to make. Rather, it was because they had only one. It’s important to remember that at the time, scientific consensus spoke of the importance of giving people choice to motivate them—and the prevailing wisdom was, the more, the merrier. But what I observed was the opposite. I wondered why. Fast forward a few years after my failed experiment at the Bing Nursery School to when I began my doctoral dissertation. As I was thinking through past experiments, I began to ask myself the question more seriously: “What was actually going on there? Is it possible that some part of what I was seeing was something scientists hadn’t yet thought about?” I wondered, are people motivated by unlimited choice? Or, do they need constraints? In particular, do they need limits? Thus, the Jam Study was born. Near Stanford University, there was an upscale grocery store offering people seemingly endless choices. The typical fare included 57

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hundreds of types of mustard, mayonnaises, and vinegars, nearly hundreds of seasonal fruits and vegetables—and how could I forget the countless varieties of olive oil? The list goes on. It felt like a wonderland of options. So I set up two tables just inside the entrance: one had six kinds of jam, and the other had twenty-four. Surely the table with more options would lead to higher sales. Right? Sixty percent of people who entered the store stopped at the table with twenty-four. Forty percent stopped at the table with six. So far, so good. But what happened next changed the trajectory of our collective understanding of choice. I noticed that of the people who encountered 24 jams, only three percent went on to buy a jar of jam—whereas, of the people who encountered six, 30 percent of them bought a jar of jam. In other words, the result was exactly the opposite of what I—and the consensus of my field to that point—predicted. Since the Jam Study was published in the year 2000, there have been over nine hundred follow-up studies that have gone on to show the negative consequences of offering people more and more choices. For instance, don’t give people too many choices for investment, or they won’t choose any—the same is true about health plan options. And the more you look for that perfect soulmate, the more options you see, and the worse they get. Even when you give people creative tasks, like writing an essay or creating a piece of art, the more choices they get, the worse they do. Is there an optimal number of choices then? A choice of six jams must be better than a choice of two. But twenty-four is too many. What about twelve? Or fifteen? As it turns out, the psychologist George Miller has done important research toward showing us the right number. He found that people were able to keep seven items in mind as they made a choice—plus or minus two. More than that and the result is cognitive overload, where people tend to get confused and make a bad choice—or as with the jams, no choice at all. Inventors, artists, and musicians, the people we deem most creative, have long known the value of putting constraints on choice. They work within forms and structures, many of which they break only to establish new boundaries. If choice is indeed something we 58

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make, as we make art and make music, then surely we can look to those creative disciplines for guidance. The great jazz musician Wynton Marsalis once said, “You need to have some restrictions in jazz. Anyone can improvise with no restrictions, but that’s not jazz. Jazz always has some restrictions. Otherwise, it might sound like noise.” And jazz is the “freest” of all musical forms! Thus, the Think Bigger method inherently balances these two competing and seemingly opposing forces. The desire for you to feel free is met but in a cognitively doable way. That’s why I give you the structure of a Choice Map and limit the materials within it. Without the constraints Think Bigger provides you with, your ideas end up as “noise.” The method offers a deliberate, tactical way of thinking that balances your need for freedom of thought and expression with guiding structure. Formally, we embody these limits in three specific tools that help you build your new idea. Let’s end this chapter with an overview of the three tools of Think Bigger.

THREE CREATIVE TOOLS

Typically, when we need an idea—and not just any idea, but a really good one—we collect as many as possible. We might brainstorm endlessly or use crowdsourcing to generate myriad ideas. The rule of thumb is that for every 10,000 ideas, there is at least one good one. With that logic, you ought to just keep collecting as many as you can. There is bound to be a unicorn somewhere in the mix. And, how do you pick the unicorn? We assume that it will be easy to spot because it’s self-evident and, when it’s not, everyone can simply vote for their favorite idea and we’ll just pick based on the consensus. Anyone or any organization who has tried this method knows that, at best, this approach yields mixed success. Think Bigger is the opposite. The three tools I introduce you to assume that you, the creator, are interested in quality over quantity. If you use the Think Bigger method, then every idea you generate will be, by definition, both useful and novel because those two criteria are embedded in the structure of the method. You will still 59

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have options to choose from—but rather than quantity, we go for quality. And in the Think Bigger method, we do not assume that the best of the pile will naturally rise to the top. Rather, we have a deliberate method to choose the best solution. And now, the three tools. The first tool is called the Choice Map. It serves as your personal library for that one problem, where you store all the elements as you build your idea. You will note from the road map in chapter 1 (see Figure 1.18) that the Choice Map is the tool you will use to generate multiple solutions to your chosen problem. You have your problem at the top of the Choice Map, you’ve broken the main problem up into a manageable set of subproblems—typically around five. For each subproblem, you then collect unique tactics that address each subproblem. Once you have a completed Choice Map, for example a 5 × 5 grid, you now have all the materials needed to create multiple solutions that solve your problem. For every solution you create, you pick one tactic per subproblem and combine them in different ways, thus ensuring that every solution is useful and nonredundant from the prior solutions. For example, take our standard 5 × 5 Choice Map—if you were to combine every possible set of five tactics, you have 3,125 potential new ideas. Thus, the Choice Map enables you to create multiple novel, useful solutions for your problem. The Choice Map gives you the opportunity to find an optimal solution. Steps 1, 2, and 4 guide you through the process of creating your Choice Map while Step 5 shows you how to Choice Map so you can generate myriad ideas. In Think Bigger, you will use Choice Mapping for idea generation. This is my alternative to brainstorming. The second tool, the Big Picture Score, is what you use for your selection criteria. The Big Picture Score considers all the different wants associated with the problem. How does this solution need to feel to the people who matter: the creators, the potential users, and—let’s not forget—potential allies or competitors. With the Big Picture Score in hand, you will be able to compare and contrast the various ideas you generated. You can then identify your potential unicorn(s). 60

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In Think Bigger, we think about what we want our solution to feel like before we start generating ideas because our “wants” give focus during both the ideation process and the selection process. Thus, in Step 3 I will guide you through how to create the “Big Picture.” The third tool is the Third Eye Test. You might think that once you “see” the new idea in your mind’s eye—the aha moment— you’re done. But what you see is a fantasy. A creation of your mind. But, will others see it too? In this step, we have three unique kinds of feedback that we collect in order to learn if our idea is working the way we want it to and for deciding if it is worth taking to the next step. This tool comes last, as the sixth and final step. It is the last step before implementation. Now we move to Part II and start our Six Step journey.

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3 STEP 1: CHOOSE THE PROBLEM

E

instein once said, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.” It is here Think Bigger begins. You might have picked up this book with a problem in mind. Or maybe you have many problems and you don’t know which to choose. The first step in Think Bigger aims to help you in either case: choose a problem you want to solve and can solve. In Think Bigger, you learn not to take your problems as selfevident. And like Einstein, you write and rewrite, frame and reframe your problem from myriad perspectives in order to discover the problem that is most meaningful and feasible to solve. It is the step we often spend the least amount of time on, even though it is the step we should spend the most amount of time on. If you find the right problem to solve, you set yourself up for success.

HOW DO YOU SAVE THE WORLD?

I write this book in the midst of the worst epidemic the world has seen since the Spanish flu of 1918, which killed an estimated fifty million people. As of July 2022, COVID-19 has killed an estimated

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6.4 million people worldwide. We are still in the thick of it, so the full measure of devastation is yet to be known. As a professor at a prestigious university brimming with smart and inquisitive minds, I often hear students say noble things like “I want to save the world.” COVID provided a stark occasion to do exactly that. But with such an enormous problem, with so many complications, where do you start? Thankfully, enough brave innovators stepped up to show us the way. In each case, they found a smaller problem to solve in their domain: either to help contain the disease or repair much of the damage it causes. One by one, innovation by innovation, they helped save the world. Let’s meet one of those innovators. Stacey Boland, a project engineer at NASA’s Jet Propulsion Laboratory (JPL), was working on a satellite mission to track different types of air pollution and correlate that data with human health on the ground. Then COVID19 grounded the world and her office shut down. She went home. From there, she stayed in touch with her team, and for weeks, they wondered what was going to happen. Finally, they decided enough was enough—instead of contemplating what could be, they decided to ask what they could do given the circumstances. They were engineers. They were used to solving complex problems. Was there something they could do? The team’s two leaders, David Van Buren and Roger Gibbs, held daily WebEx meetings asking the group, “Is there anything any of us could do to help?” They would read the headlines and make a list: • Can we solve the problem of the mask shortage? • Can we fix disrupted supply chains? • Can we create a contraption that stops people from touching their faces? • Can we create an alternative hand sanitizer to help the shortage?

These were all important problems and they all needed to be solved—but the JPL team knew that in order to be successful, they had to choose a problem in which they were experts and had access to the right resources. Time was of the essence. They decided the 66

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right thing to do was focus only on problems they knew they could solve. So, every day, the JPL team went on WebEx and revised their standing list. With no more information than you or me, they simply asked, given their expertise, what problem could they solve? Then came the news of the ventilator shortage. Due to a lack of ventilators and disrupted medical supply chains, COVID patients in the ICU who likely could survive if they had a ventilator were at risk of dying in large numbers. Van Buren realized the JPL team had incredible engineering talent and, despite not having medical expertise, they could do something about the ventilator shortage. They were space engineers, after all—they build machines for unmanned missions that have to work. Despite having no experience with human life support machines, Van Buren decided this was something they could help with and recruited a team of engineers who wanted to help—Stacey Boland among them. The team spoke twice daily so they could focus on their solvable and meaningful problem: How can we create a ventilator that will help alleviate the ventilator shortage? During and after these meetings, Stacey wondered which parts of ventilators were necessary in saving the lives of COVID patients. The ventilators being used by hospitals had a large number of functions and were thus very complicated machines. Out of all these functions, what did doctors need that applied just to COVID? Could they make a useful ventilator with fewer parts? Could they make it more portable? Could they simplify it so even the average person could operate a ventilator? By interviewing experts in the medical field, Stacey and her team realized that in most COVID cases, the functions in a fully equipped ventilator simply weren’t necessary. The team continued to examine their initial problem, in hopes they’d develop a question that was more specific: How do we make a ventilator that treats COVID patients, feels user-friendly to doctors, and avoids disrupted supply chains? Immediately, they began to build the blueprint for an easyto-use ventilator, with Stacey writing its manual to define how it needed to work before being designed—all remotely via WebEx. The manual was so easy to understand that it didn’t matter what 67

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language you spoke—the diagram made it clear which piece was meant for what purpose. There is a famous piece of NASA folklore from the Apollo 13 mission that all its engineers know well. In space, the main systems shut down and the crew had to improvise a solution with just the equipment on board the spacecraft. Stacey and the JPL team took up the same spirit: to use only parts they already knew how to get in the disrupted COVID supply chains. The JPL team’s ventilator, dubbed VITAL, used only parts that could be obtained through the team’s existing commercial and manufacturing channels. Working with NASA, under management by Caltech, they received license agreements to work with twentyeight global partners (out of the ninety-six proposals received) to ensure regular ventilators could be manufactured when and where they were needed in the world. The prototype for the VITAL was the size of a briefcase, weighed around ten pounds, and used only four hundred parts—compared with the 2,500–2,800 pieces that made up a regular ventilator. Upon sending the prototype to Mt. Sinai Hospital in New York City, a COVID hot spot during the start of the pandemic, they received a remarkable response from the medical staff: the VITAL ventilator not only performed well, but it both felt and looked like it belonged in a hospital. It could be used anywhere at any time due to its small, portable size. The instructions were clear and the device was simple in its build—with half the buttons and knobs of a regular ventilator. Where most ventilators only allow respiratory therapists to use them on patients, the VITAL ventilator was simple and functional enough that the FDA approved its emergency use for all trained healthcare professionals. Look at the two images depicting a regular ventilator compared with the JPL ventilator: In only thirty-seven days, Boland and her team went through all the steps of Think Bigger. The first key step was finding the right problem to solve. Note how they narrowed the problem in steps: from “COVID” to “ventilator,” and then to a “certain kind of ventilator.” They found a problem both ambitious enough to matter yet realistic enough to be within their grasp. That’s how you change the world. 68

Figure 3.1 NASA’s Jet Propulsion Team with their iteration of a ventilator for COVID-19 patients. NASA.

Figure 3.2 A standard ventilator. Wikimedia Commons.

PART TWO

PROBLEMS, PROBLEMS

Let’s hear from Einstein again: “One must develop an instinct for what one can just barely achieve through one’s greatest efforts.” This advice is simple but profound. You want to stretch yourself as far as you can but no further. Otherwise, you will fail. And you find that level of difficulty not by some analytical formula but by “instinct”—that is, you feel your way to it. The VITAL team did that. And that’s what we do in Step 1 of Think Bigger. Here I give you the tools to help you find a problem you care about and then define it in a way that helps you search for unusual solutions that can make your big idea a reality. As you embark on this journey, you will work on exercises that help you state and restate your problem the same way the Jet Lab team did—to arrive at a problem you find meaningful and solvable. You state your problem as the first element of your Choice Map (see table 3.1). To begin, your problem is just a draft. That means it can change, and probably will, as you work your way through the other five steps of Think Bigger. Writing down your problem is key. The Center for the Interdisciplinary Study of Language and Literacy tells us that writing forces us to focus, plan, and organize our thoughts. Writing is a creative act—you actually create your thoughts as you write them down. If you have more than one thought, write them all down. Then study what you wrote. You want to find the version of your problem that best reflects what you actually think.

table 3.1 Think Bigger Choice Map Main Problem: Subproblem 1

X

X

X

X

X

Subproblem 2

X

X

X

X

X

Subproblem 3

X

X

X

X

X

Subproblem 4

X

X

X

X

X

Subproblem 5

X

X

X

X

X

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If you have a problem you know you want to solve, I want you to take out a piece of paper and write it down in less than three sentences. Remember this is just a draft. In some cases, you might want to solve a problem that matters to you. In other instances, you might be stuck solving a problem for others. No matter the circumstance, take a few minutes to write the problem down and do your best to phrase it using words that spark meaning in you. The more you are able to articulate your problem using words that transcend their own meaning, the more equipped you will be to stay inspired in the next steps and the more likely you will be to create more meaningful solutions. It might also be possible that you have many problems spinning around in your head that you know are useful to solve—these problems might come up as you watch the news, during certain parts of your morning routine, or at work. The list of problems will grow very long as you look at each aspect of your life and you can’t solve them all. You have to choose a meaningful problem to solve for. To do so, complete the following thought exercises. Take out a piece of paper and write down your answers to the following prompts. For each prompt, try to jot down five to seven items. Work on this exercise throughout the week, during different times of the day. 1. Identify the problems you deal with every day that you wish you could solve. Don’t limit yourself to what’s possible—go beyond and write down everything you can think of. Then, every day, at the end of the day, reflect on these problems that were so annoying to you that you want to solve them the most. Which problems continue to come up? If you have a redundant or repetitive problem, that redundancy might become the reason to choose to solve it. 2. Think about the topics that interest you or the ones you would like to learn more about. Often, in our daily lives, we get so caught up with work and errands that we forget we have the potential to learn. Every day I want you to jot down the things that interest you most. These interests could reveal the problem you’re most passionate about, or 71

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they could become something you learn more about in the process of problem-solving. 3. Find the things you care about most in your daily life. When we notice what we care about—our meals, our pets, reading a good book at the end of the day—we automatically begin to look for ways to make those moments better. Make note of when you feel that deep sense of purpose because it’s within those moments we find what matters to us most.

Look through your full list of answers. If you see certain problems that arise more than once, zero in on them. If your identified problem, interest, or purpose seems too broad, consider the smaller problems within that problem. For instance, if your interest lies in recreation, maybe you don’t see enough opportunities for adults to partake in organized sports. If you focus on the arts, you might feel that more people should be exposed to classical music. If you’re professionally interested in managing an organization, maybe you notice your work team could use some help on a project. In your lists, you will begin to see your motivations surface. When you narrow them down enough, you will begin to see the formation of a problem that you can and want to solve. Once you have thought all this through and have identified a problem you believe you want to work on, write down a description of it in a few sentences. This is an important step in identifying your problem, since putting the words in your head onto a physical piece of paper helps you better understand the problem you want to solve for, and why. Writing it down will help you concretize your thinking and become more precise about the problem you’re interested in solving. In my Think Bigger course, my MBA and Engineering students have used Think Bigger to challenge themselves to address problems of all kinds. Some try to take on problems that lead to the creation of a product, like a skin cream made of all-natural products that preserves well or an unobtrusive type of scaffolding that blends in with surrounding buildings. Other students take on social causes, like making composting accessible to all neighborhoods in 72

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the city or closing the education gap among first-generation immigrants. Yet other students take on problems related to cutting-edge technology, like creating a better cybersecurity platform for small businesses or creating an app that connect students taking the same trains home so they can travel safely together in New York. You might ask at this point, “Why a problem? Can’t innovation just happen by itself, without a problem to solve? Stating a problem puts constraints on our thinking. Shouldn’t we think freely, without constraints? The wildest ideas are the most creative. Reach for the stars. Dream the impossible dream. Right?” Wrong. There is a long list of creations that inventors devised without trying to solve a problem. And they fail. Nobody wants them. The invention might work. But remember our definition of innovation: something new and useful. If nobody wants to use it, then it’s not an innovation. And “problem” simply means that someone wants something but has a hard time getting it. It does not have to be life-or-death, like a COVID ventilator. Nancy Johnson solved the problem of cheaper ice cream. I am glad she did. Your initial list of problems serves another function too: it reminds you that there are lots of problems worth solving. So if your problem statement changes through the steps of Think Bigger, it doesn’t mean your previous statement was wrong. You’re just changing direction to solve a different problem. Each change refines your own understanding of what’s possible and what you truly want to solve.

THE WRONG PROBLEM

Paul Nutt, a professor at Ohio State University, studied the business decisions of 358 companies over twenty years. He found that half of the decisions failed because they aimed to solve the wrong problem. A common mistake was imposing a solution, such as “How should we take advantage of this new technology.” You use technology to solve a problem—so you have to know what that problem is. 73

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This error is easy to make. So often, problems seem self-evident. Let’s not waste time on that. Time is of the essence—let’s speed ahead to solutions. Think of the last time you were placed in a large group—let’s say, in a conference room with six other colleagues—and told to solve a problem in your company. Did you and your colleagues ask one another if you were on the same page? It is more than likely you were quick to assume the colleague who you grab lunch with everyday identified the same problem you did, and the same could be said for the manager leading your group meeting—they believe everyone is thinking about the same problem they are. After all, you work for the same company, you’re colleagues who see each other every day, you know the pitfalls of your business, so you must all be on the same page. So, you jump straight into it—a quick meeting to solve this problem your boss wants you to solve will give you thirty minutes back in your day. You start spitting out solutions and it feels great! You’re getting somewhere! That is, until, your manager scratches their head, takes a step back, looks at the mess on the white board and says, “What is it we’re trying to do here?” There is far less consensus and far more complexity to the problem than you all originally thought. And, realistically, there is no way for everyone to be on the same page—you all bring with you a diversity of perspectives and opinions, so how could you arrive with the same, specific problem in your mind? This phenomenon was observed in a study by Thomas WedellWedellsborg, where 106 top executives reported that half the time, they only realized what the real problem was after wasting lots of time and energy trying to solve what they thought was the problem. Nutt and Wedell-Wedellsborg draw the same conclusion: people tend to neglect the step of problem definition and rush right into solutions. Usually to their detriment. This method of assuming everyone understands the same problem works out fine for simple problems, or personal ones. But for complex problems, failing to identify the right problem at the right level results in confusion, wasted effort, and bad outputs. If you

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want to find the right solutions, the way to begin in a group setting is to go around the room and ask each person to individually describe what he or she sees as the problem. That way, everyone can first understand the ways in which the problem is defined and framed. Right from the start, you begin to see the myriad complexities attached to your problem and can work to create one collective problem definition that everyone can solve for. One source of error in the process of problem definition is the human tendency to think we know more than we actually do. The psychologists Philip Fernbach and Steven Sloman refer to this as the “knowledge illusion effect,” in which we overestimate our expertise and underestimate the complexity of things. If you were to ask your friend how a toilet flushes, they might say, “Well, yes, all you do is push the handle down and the tank is drained to release the pressure and flush the water out.” Or if you were to ask a colleague if they understand how a microwave oven works, most will reply, “Of course.” But, if you were to ask them to draw a diagram and explain exactly how either of these examples works in their entirety, I can say with a high degree of certainty that none of them could do it. The knowledge illusion effect’s usefulness extends far beyond our understanding of simple objects. This concept can be directly applied to how we think about almost everything—snowflakes, microwave ovens, economic policies, and global warming. If you consider the presence of this effect in regard to a team setting, where many individuals suffering from it are setting out to solve a problem together, it’s a wonder any problems get solved at all. That’s why it’s so important to understand our biases before identifying a problem. If we don’t, the chance that we solve the correct problem at hand is reduced. In Think Bigger, we devote a full step to identifying a problem that’s large enough to matter but small enough to be solved. It should also be a problem that everyone involved understands and wants to solve. This step is crucial and takes time, effort, revision, and reflection to appropriately make a determination of the problem before you move on to solving it.

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STEP ANALYSIS

Once you pick out the problem from your longer list that you want to set out to solve, rewrite it as a question of “how.” For example, let’s say you want to grow your business by 10 percent this year. Rewrite that as “How do I grow my business?” That’s what you need to figure out, whether it’s 9 percent or 11 percent, this year or next. Once you get your solution, you will have an idea of what is possible and when. At that point you can say, “By implementing my solution, I see how to grow my business by 15 percent over nine months.” This kind of detail comes in your implementation plan. It’s not part of solving the problem of how to get there. Our phrasing is also open-ended enough to permit many possible answers. A common mistake is to embed a single answer in the question. For example, “How do I create a mobile application to reduce food waste?” This assumes that the answer is a mobile app. We rephrase that to say, “How do I reduce food waste?” Even among my Think Bigger students, an average of 51 percent jump to the idea that an app should be their solution—and while it very well might be part of their inevitable solution, the app doesn’t necessarily solve the problem they identified. A mobile app might not even end up part of any solution, so you shouldn’t anchor on a mobile app. Closed questions reduce your chances of being creative by suggesting there is a single “correct” solution to your problem. Open questions give you more choices for creative solutions. This is true even as you narrow your question. Remember that the VITAL team chose a narrow problem within the massive COVID problem: ventilators. But they did not say, “How do we adapt NASA technology to make a ventilator?” They left the solution open. And the solution did not use any NASA technology. It used simple parts they located through NASA’s vast network of suppliers. Once you’ve decided on your open-ended “how” question, we test whether it’s too broad or narrow. Think of an upside-down pyramid. Up top, at the widest level, you have a huge problem. At the 76

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bottom, at the narrowest level, you have a tiny problem. In between are different gradations. You move up or down the pyramid, making your problem wider or narrower, until you find the right level. We call this Step Analysis. Step up to widen your problem. Step down to narrow it. Step up and your solution makes a bigger impact, but it’s harder to solve. Step down and your solution makes a smaller impact, but it’s easier to solve. Your motivation works both ways: you want to make a bigger impact, but if the problem is impossible to solve, it saps your desire to work on it. Move up and down the steps until you find a level where your motivation is greatest. That’s the problem you want to solve. We call this your personal sweet spot. Take a look at the blank Step Analysis chart (see table 3.2). Put your draft problem question in the middle tier. Then restate your problem, up and down, until you arrive at your personal sweet spot. If you’re in a group, do this first as individuals. Then pool your charts to arrive at a single one for the group. You will find that people step up and down to very different problems. For example, let’s say your question is “How do we reduce world hunger?” One person might step down to “How do we reduce hunger in poor countries?” Someone else might step down to “How do we reduce hunger among poor people in rich countries?” You can see right away that these details matter. These two different questions send you on the hunt for very different solutions. Tables 3.3 and 3.4 provide two examples of step analysis. You can see how each step up or down represents a crucial decision in defining your problem.

table 3.2 Blank Step Analysis Chart Step Up: Step Up: Draft Problem: Step Down: Step Down:

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table 3.3 Step Analysis (Example 1) Step Up: Reduce all harm to the environment Step Up: Reduce all pollution Draft Problem: Reduce plastic pollution Step Down: Reduce single-use plastic bags Step Down: Reduce single-use plastic bags in my neighborhood table 3.4 Step Analysis (Example 2) Step Up: Replace all nonbiodegradable materials Step Up: Replace plastic with biodegradable materials Draft Problem: Reduce plastic pollution Step Down: Reduce plastic pollution in my country Step Down: Reduce plastic pollution in my city

In the process of stepping up or stepping down your problem, you might find that you want to frame the problem in a completely different way. That is an important lesson to learn because it gives you the opportunity to explore and examine the different ways you can address your problem, until you find one that is just right. If you skip this step and press on with your first problem statement, you’re in for some nasty surprises down the line. In a group, such surprises can lead to conflict or gridlock. So don’t be afraid to have a full and honest discussion up front, where each member of the group can talk about their Step Analysis and explain the rationale behind it. The group needs to find a sweet spot that keeps everyone motivated, interested, and eager to solve the problem. Once you arrive at your sweet spot, do a final check. Ask yourself these two questions: 1. Can I feasibly solve this problem? 2. Am I motivated to solve this problem?

If the answer is yes, you are ready to proceed. 78

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SMALL IS BEAUTIFUL

If you want to Think Bigger, first think small. This might sound backward, but it’s informed by my decades of personal experience in this space. In general, I find that people start too high on their Step Analysis. Sometimes they do start too low, out of fear they won’t be able to solve something very ambitious. But the key to scaling in Think Bigger is to first solve a problem. From there you will be able to see better how wide your impact can be. The VITAL team went from very high—the COVID crisis—to very low—a single-use ventilator simple enough to use that you don’t need a specialist to run it. Once they had their solution, it turned out to be so cheap and easy to both make and use that it took off as a global phenomenon. They could not have predicted the future potential of their innovation before they built the VITAL ventilator. Let’s take a really big problem: the elimination of segregation and racism in the United States. Dr. Martin Luther King once said, “I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character.” Here, he imagined a world where race no longer mattered. This was a lofty dream that King did not achieve. What he did achieve was leading a movement that forced Congress to pass the Civil Rights Act of 1964, which made racial discrimination illegal. Now, that is a remarkable achievement. How did he do it? Well, Dr. King started small: the Montgomery bus boycott. Inspired by Gandhi’s campaign of non-violent disobedience in India, the Black residents of the city—most famously Rosa Parks—boycotted the segregated bus system and went to jail for it. It worked, and that inspired the Southern Christian Leadership Council to do it again and again across the south. That led to the Student Non-Violent Coordinating committee where thousands of college and then high school students got arrested for sitting in at white lunch counters and other segregated venues. The result was a mass movement. And that’s how a quarter of a million people were there on the day of Dr. King’s great speech—the largest gathering in the history of the United States up to that time. 79

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Now, in contrast, consider a seemingly mundane problem: How do I make watching movies at home more convenient? Reed Hastings set out to answer that question. He faced this issue directly when he had to pay forty dollars for returning a movie late to Blockbuster (the movie was Apollo 13). On the drive to Blockbuster he passed a gym and realized that nobody pays a late fee for gyms. Gyms require a monthly fee. And with this fee, you’re allowed to go as many times as you want. Why not do the same for videos? Hastings had just sold his software company, so he had time and money to work on the problem. Online commerce was coming— Amazon was already big—so he wondered if he could cut costs by skipping physical stores. Why not use the mail instead? Keep in mind the technology of the day. This was just when the DVD appeared to rival videotape. So, Hastings bought a DVD and mailed it to himself. It came through fully intact. Like Henry Ford, Hastings drew on a new technology, not simply because it was there but because he could see the piece of his problem it solved—delivering movies safely to people’s homes. And so was born the Netflix empire, which eventually commanded an $82.5 million IPO and at last count, was valuated at more than $84.82 billion. Netflix is a top contender to Hollywood. As of 2022, eight movies created by Netflix Studios have received Oscar nominations. The important thing here is not in Netflix’s success— even their success is not consistent. The important thing to note is the methodical way in which Hastings approached problem-solving in structured steps. In the end, Netflix’s success did not come from its income or subscriber base, but rather, from Hastings’ ability to revolutionize its domain (video rentals), break into something new (streaming), and dominate the market (for a time). Only after solving the smaller late-fee problem did he move on to solve a bigger problem: how to make watching movies at home cheaper and easier for everyone? Hastings would never even have dreamed of that bigger problem if he had not solved the smaller one first. The message here is not to lower your sights. Dr. King never gave up his dream of a nation where race didn’t matter. But he found a way to make progress toward that goal by solving smaller problems first. 80

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THE PASSION TEST You have to be burning with an idea, or a problem, or a wrong that you want to right. If you’re not passionate enough from the start, you’ll never stick it out. — ST E V E J O B S

It is the soul’s duty to be loyal to its own desires. It must abandon itself to its master passion. — R E B E CC A W E ST

Think Bigger helps you fulfill a passion you might not have even known you had. We don’t think very much about our passions. We’re busy with the myriad tasks of life and work. The truth is, you have many possible passions. Not just one. And not just the first one you think of. If you asked Stacey Boland before COVID hit, “Would you like to build a ventilator?” she would probably tell you she is busy on her satellite project. But if she took the time to think about it, and she stepped back from her current endeavors, the answer might be “yes”—even before COVID. She was an engineer, after all. And it was a very interesting and worthy puzzle. It’s impossible to foresee all the twists and turns your life will take. So you can’t know for sure what you will be passionate about at some future moment in time. In response to this basic human truth, I’ve created my own Passion Test for this first step of Think Bigger. It helps you answer the question, “Is this a problem that I am willing to dedicate significant time to thinking about in the short-term?” Once you have the problem you want to solve, practice describing it in three to five minutes. Say it out loud again and again until it’s fixed in your memory. Then take it on the road: tell it to twentyfive people. These might be friends, family, coworkers, or even strangers you meet for some other reason. Describe your problem. Then ask if they have ever thought about it—and who might want most to see it solved. After describing it to twenty-five different people, you will know what you feel about the problem. I call this Idea-Working. Did it 81

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excite you each time? Or did it become tedious to repeat it? Perhaps after this process, you might feel even more excited to solve it. People might even say things that lead you to change the problem statement. That’s fine. Is the final statement still something you very much want to solve? You might want to go back to Step Analysis, up or down, to find a problem that motivates you more or seems more feasible to solve. Do this Passion Test—if even a shorter version—whenever you modify your problem statement in the rest of the Think Bigger steps. If you discover after doing the Passion Test that you’re bored, then move on. You should find a problem to pursue that's more tailored to your wants and interests. Here is another tip, this time from Stacey Boland herself. She reported that the JPL team repeated their problem out loud every day, like a mantra. It kept them motivated and focused and united them as a team. Each person felt privileged to be in on the action— to have the chance to make an impact on the wider world. This Passion Test is an adaptation of a passion exercise that was created by my PhD student, Carl Blaine Horton, who is also a coauthor on the Think Bigger Workbook. We wanted to find a way to help the students taking the Think Bigger course learn whether the problem they were about to spend an entire semester solving was something they could be passionate about. We knew from our own experience that simply asking the question, “Do you feel passionate about this problem?” was not enough. Passions ebb and flow. Instead, what we decided to do is create what our students have labeled “one of the most fun days at Columbia Business School.” We call it “The Innovation Marketplace.” On this day, approximately one hundred MBA and Engineering students come into the classroom with a problem they are interested in finding a solution for. Over the course of two hours, they get into groups of three— no more and no less—and pitch their idea for less than a minute. They explain the problem they want to solve and why it feels important to them. In limiting the number of people they present to per rotating group, over the course of two hours, they present their problem anywhere from twenty times at minimum to forty times at

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maximum. Imagine pitching your problem repeatedly in a two-hour period—you get bored or energized. Each session starts the same. We have them fill out a survey that asks several questions: What is the problem you want to solve? Write it down. On a scale of 1-7, one being not at all passionate and seven being very passionate, how passionate do you feel about your problem?

Then, they are off to the races! As my students pitch their ideas to one another, the room hums like a beehive in the middle of Spring. When the pitching session comes to an end, we have them fill out a follow-up survey and ask: On a scale of 1-7, one being not at all passionate and seven being very passionate, how passionate do you feel about your problem? Throughout the session, how often did you revise your pitch? (In Think Bigger, this question means that they either stepped up or stepped down their problem.) What is the problem you want to solve? Write down your new problem definition.

By having students write down the problem they came in with and then writing the problem they leave the room with, they are more likely to see how much their problem changed over the course of the two hours. On top of that, we use Natural Language Processing to analyze their responses to record linguistic changes made to the problem so we can better record their revisions. You can view the linguistic analysis in figure 3.3. You might think that if you’re truly passionate about something, you’ll stick to your guns. The content of your idea shouldn’t be shifting, right? Actually, I believe it is the opposite. If you are actively thinking about the problem, then you should be open to different ways of framing and reframing it. It is only through framing and reframing that you learn what the problem of interest is

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Figure 3.3 Passion Test data comparison.

and whether you are motivated to spend the mental and emotional energy it takes to make something more meaningful. Now, think back to the structure of the Marketplace I explained. It seems like a basic Idea-Working session, right? Well, here is a little twist I like to throw in that raises the stakes: every single person in the room must choose three people to “invest” in. Using paper printed in three different colors to represent different monetary amounts, we create our own currency. A pink slip means you receive a $300 investment, an orange slip means you receive $500, and a green slip means you receive a $1,000 investment. Students are not allowed to invest in their own problem, they can only invest in the problems they hear. At the end of the class, we look at the numbers and see who wins—or, who received the most investments. I have been doing this for many years now and it is always my students’ favorite day. They rave about how much they learned, how exciting it was, and how it helped them meet new people. I even get the self-proclaimed introverts to say they had an amazing time! And, over the course of several years, I have accumulated some interesting insights. Here is what I learned:

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1. Nearly 20 percent of my students report that they had a decrease in passion for their idea after pitching it to their fellow MBAs and Engineering peers for two hours nonstop. 2. 35 percent of my students change the way they pitch their problem over the course of Idea-Working. 3. Students who reported an increase in passion after the passion exercise and revised their pitch received four times the amount of peer investments than the average participant. While the average participant received around $1,150 in peer investment, those who reported “more passion” and “revised their pitch” received $4,882.

I have often thought that a variation of the Innovation Marketplace would be an ingenious tool for any CEO to improve corporate innovation. Every company has problems, problems, problems. But, which one should you solve for? Which ones would get the greatest commitment from within your organization? Every year, it is a standard practice to have the Partners and Executive level members gather for an annual retreat. Imagine if the retreat were to start with the simple but profound exercise to help the future of the company. The CEO asks, “Can you write down a problem confronting our organization that, if we were to solve it, would make a significant difference for the bottom line? Tell me what the problem is and why it matters.” Like the students in the Marketplace, have individuals pitch their problems in rotating groups of three. They too must invest in the problems that they believe have a meaningful impact on the future of the organization. Imagine what leadership could learn about the problems in their organizations, at all levels, from such a simple exercise! What are the problems that bubble up to the top? What are the problems that are phrased similarly? What are the problems worth solving? What problems would make a significant difference to a company’s bottom-line? The Innovation Marketplace is both revealing and exciting. It surfaces the problems we find meaningful and tests us repeatedly, until we find a problem that is worth solving.

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A CHANCE TO SAVE THE WORLD?

We all know the stories of heroism that emerged during the pandemic, whether they were the stories of frontline workers, grocery store clerks, or the scientists who developed the vaccines. The heroic tale of the COVID-19 vaccines and their efficacy rates can be told by the stories of the scientists who discovered them—Katalin Karikó of BioNTech, Dr. Drew Weissman at the University of Pennsylvania, Dr. Philip Dormitzer of Pfizer, and Hamilton Bennett of Moderna. But they all start with one piece of the puzzle. The story begins in 2018, when Pfizer started working with a small German biotechnology company called BioNTech, founded by Uğur Şahin and Özlem Türeci. What made BioNTech unique was its founder’s ability to recognize the value of Katalin Karikó and Drew Weissman’s breakthrough research on the use of mRNA technology. Despite being used for experiments in cancer treatment and Zika virus protection, mRNA technology had yet to reach its full potential. The partnership with Pfizer enabled the small team of scientists to start developing an mRNA-based flu vaccine, rather than the less effective antigen-based vaccine, to respond to the adaptive ability of the flu virus. It seemed like a far-off dream, until December 2019 when China reported the outbreak of a mysterious respiratory illness in Wuhan. The world watched as SARS coronavirus-2, dubbed COVID-19, began to spread across the globe—at first, in a slow, steady pace, then all at once. By January 2020, the China Centers for Disease Control (CDC) posted the sequence of COVID-19 in the GISAID database, a virus sequence sharing platform. Only two months later, COVID-19 was declared a pandemic as the disease was found to be highly transmissible between asymptomatic and symptomatic individuals alike. Time was of the essence, and it became clear that creating a traditional vaccine, with viral antigens, would take too long. Health officials announced that a vaccine would need to be developed rapidly, fast-tracked in a huge clinical trial, authorized for quick-use, massively scaled up, and distributed around the world. 86

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Uğur and Özlem realized this could be BioNTech’s once-in-alifetime chance to save the world. The two founders contacted Dr. Philip Dormitzer, then the vice president and chief scientific officer for viral vaccines at Pfizer, suggesting they shift the BioNTech-Pfizer collaboration from working on a vaccine for the flu to a vaccine for COVID-19. With Dr. Dormitzer’s approval, the scientists at BioNTech used the viral sequence of COVID-19 to test the real power of mRNA technology and create a new vaccine. The scientists at BioNTech and Pfizer, whose original problem draft was, “How do we make an mRNA vaccine that works in the lab?” (as had been done with Zika virus) scrambled to identify the new problem they were confronted with. They now had to ask, “How can we save the world with a full-scale, industrial vaccine response that delivers billions of doses of an mRNA vaccine proven to be safe and effective in a diverse group of people?” It might have been a long shot, but Pfizer and BioNTech knew mRNA was the technology they needed to find a quick solution. Dr. Dormitzer told me on a phone interview, “We weren’t even sure if the mRNA technology would work to its full potential. We were expecting a 60 percent efficacy rate, similar to that of the flu vaccine. It was a gamble but well worth the risk. We needed staying power from our scientists and trial participants to ensure certainty.” He concluded, “When vaccines fail, it may not be because they don’t work—it’s often because they are halted, shut down, or funding is cut. We couldn’t risk failure at this time.” The scientists at Pfizer and BioNTech found that they could use mRNA instructions from SARS-CoV-2 to build a unique protein-based antigen—not viral—for the vaccine. Pfizer’s CEO Albert Bourla gave Dr. Dormitzer and the team at Pfizer unlimited resources to ensure the vaccine was produced as quickly and efficiently as possible. Not long after Bourla announced his support, the Food and Drug Administration, the World Health Organization, the Center for Disease Control, and the United States government came together to initiate Operation Warp Speed with the incentive—and $18 billion U.S. dollars of funding—to find a solution fast. 87

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Though Pfizer funded the vaccine development solely with money from its investors, the collaboration organized and encouraged by the U.S. government with Operation Warp Speed was substantive. Pfizer’s and BioNTech’s story came to represent more than just finding a simple solution; it came to represent a rare moment in time where people across industries—governments, large organizations, and countries—could unite for the good of the world. About halfway through 2020, scientists at Pfizer realized they could begin testing the vaccine prototypes. As they proceeded, their initial problem had to be broken down into smaller problems that could be delegated out to different team members. These subproblems included: • How do we choose the right antigen and type of RNA for the vaccine? • How do we test the safety and efficacy of this vaccine in record time? • How do we scale up the manufacturing of a type of vaccine that had only been produced in pilot batches for small clinical trials previously to a level that could immunize the world population? • How do we distribute a vaccine that must remain between −90 °C and −60 °C (or −130 °F and −76 °F) for most of its shelf life?

To ensure the needs for the vaccine were being met, Dormitzer gathered leadership across all departments to meet every morning and night, daily, to repeat the problem they were solving and its subsequent pieces. It was in these meetings, day in and day out, that Dr. Dormitzer said the most ideas came about. And it was because of those meetings, where everyone shared their ideas and feedback, that the vaccine was able to be made in 248 days with a 95 percent efficacy rate—much more than the originally anticipated 60 percent efficacy rate. Pfizer’s mRNA vaccine will go down in history as one of the greatest innovations of the twenty-first century. By any definition, it is a success. You might say that their success was inevitable. After all, they had unlimited resources and human capital dedicated to the creation of the vaccine. But let’s not forget how many problems that we are willing to commit resources to, that remain without 88

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an applicable solution. In the end, the Pfizer-BioNTech COVID-19 mRNA vaccine reminds us that when we define a problem in a way all parties involved in the solution can understand, we are more likely to create solutions that work. No matter who you are, or what problem you are solving— whether it’s big or small—the first step to solving your problem is defining it so the purpose is clear to you and others. It is only as you keep asking this question, repeatedly through the process, that you discover the particular parts of how to think about and talk about the problem you are solving.

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T

ake a look at your main problem in the Choice Map and then your subproblems. Examine them closely. You may discover that the real problem you want to solve is not the one at the top of your Choice Map but one of its subproblems. Too often, we start off the process by picking a problem that’s too big or too vague. It’s only as you break the problem down that you start to see the specific problem that you want to solve.

RULES OF THE GAME

In 1891, James Naismith was a physical education instructor at Springfield College, Massachusetts. He was thirty-one years old. For three months of the year, his students played outside: football, baseball, lacrosse, rugby, and soccer. But in the long, dark winter, they grew bored, restless, and rowdy cooped up in the gym. Naismith’s boss, Luther Gulick, gave Naismith the task of solving the problem with something new for the students to do. Together, they broke down the problem, knowing that they needed the activity to solve four subproblems: 1. It has to fit in an indoor room, not on a vast field outdoors.

STEP 2: BREAK DOWN THE PROBLEM

2. It needs the speed, effort, skill, and complexity of a field sport, to keep the students in shape physically and mentally and prevent boredom. 3. It can’t be rough: the players will fall down on a hard floor, not soft earth. 4. It has to be a team activity that involves lots of students at once in the confined space.

Naismith then drew various elements from existing field sports to make a new team game. Lacrosse and soccer both use a round ball the team passes to each other in order to put the ball in a goal— but you can’t touch the ball with your hands. Rugby and football have passing too, and you use your hands. In soccer, you can’t push or hit opponents—if you do, their team gets a free shot. Naismith had most of the puzzle: a team game where players pass the ball with their hands, can’t push or hit each other, get a free shot when they do, and put the ball through a goal. But what goal? In rugby and football, the goal is a line. In a small room, that’s too easy. Soccer and lacrosse have a big net. That’s better, but the goal is still too big. Should he just shrink the net? The last piece of the puzzle came from an informal game Naismith played as a child: duck on a rock. Each team puts a big stone—the duck—up on a boulder. The two teams throw stones at each other’s duck to knock it off the boulder. In the gymnasium, Naismith nailed up a peach basket to serve as the duck and used a soccer ball for the throwing stone (figure 4.1). Thus was born the game of basketball. In this chapter, you will take this approach to your own newly defined problem. You will break it down into subproblems, just as Naismith did, within the overarching goal of solving the larger problem. You will learn why it’s important to break a problem down, what happens when you don’t, and how this is more than mere analysis—it’s a creative process in and of itself. One final note. We can’t be sure whether Naismith knew of the history of other ball sports or if he borrowed from them. But the ancient “Mesoamerican ballgame” offers a striking similarity to modern basketball: a vertical hoop set high on a wall that you throw 91

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Figure 4.1 James Naismith with a basket and ball. Wikimedia Commons.

a ball through. Perhaps Naismith knew about it. Perhaps he didn’t. Or perhaps this Mesoamerican sport managed to influence other games that in turn, centuries later, influenced Naismith. Either way, the point remains the same—the elements of creative breakthroughs don’t change.

BUILD YOUR CHOICE MAP

Remember George Miller, who found that people could only handle five to nine items at once in their minds? Naismith came in just under that, at four subproblems. I try to avoid problem breakdowns that go beyond five. This is not a hard and fast rule. But in my experience, it works. With too many subproblems, it’s just too overwhelming to understand them all at once and see an idea that solves them. 92

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table 4.1 Think Bigger Choice Map Main Problem: Subproblem 1

X

X

X

X

X

Subproblem 2

X

X

X

X

X

Subproblem 3

X

X

X

X

X

Subproblem 4

X

X

X

X

X

Subproblem 5

X

X

X

X

X

Now that you have your draft problem at the top of the map (see table 4.1), it’s time to fill in the subproblems. Remember that the Choice Map is not a form to fill in, like a job or loan application. It’s a map of an unknown and shifting terrain within your mind. As you make discoveries and take new turns, you record your changing path. Your choices appear on the map and direct your journey toward further choices, until you reach your destination. What is a subproblem? To put it simply, it’s a piece of the larger puzzle. When you solve each piece, they come together to solve the main problem. Later in Think Bigger, for each subproblem, you will fill in the cells of that row with examples where someone has solved it to some degree, sometime, in some domain. Think of Bartholdi and the Egyptian tomb sculptures, or Newton and Kepler’s rule. The cells fill with the elements that make up your eventual solution. The subproblems guide your search for those elements. It may be tempting to hurry through the problem breakdown or to assume that the subproblems are obvious. As with defining the overall problem, that’s a big mistake. The more thoughtful and deliberate you are at this point, the better your results will be. Don’t rush. Take the time to think: more, deeper . . . and bigger.

BREAKDOWN VS. ANALYSIS

Breaking down a problem is not a new idea. You will find lots of ways to do it. Barbara Minto, a McKinsey consultant, who cited 93

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Aristotle as her inspiration, came up with the popular MECE concept: break a problem into pieces that are mutually exclusive (ME) and comprehensively exhaustive (CE). Engineers use root cause analysis to find the symptoms of a problem. Porter’s five forces help you break down a company’s strategy to find its competitive advantage. SWOT breaks down a situation into four categories: strengths, weaknesses, opportunities, and threats. Marketers break a problem down into the four Ps: product, price, place, and promotion. There are countless versions of problem breakdown to choose from. Why do we choose subproblems? Because the other methods come at an earlier stage. They are really about problem “finding.” They analyze the situation to help you identify a problem to solve. In Think Bigger, you already identified the problem. Now you break it down. It’s fine to use all those other methods before you do Step 1 of Think Bigger. But once you identify your problem, those other methods don’t set up your search for examples. Look at the Choice Map: How would you use those methods to fill it in? To show you what I mean, let’s examine root cause analysis in action. This example comes from a standard Wikipedia article: A machine stops because it overloaded and the fuse blew. We ask why it overloaded. The answer: a bearing wasn’t oiled enough. Why? The answer: the automatic lubricator pumps too little oil. Why? The answer: the pump has a worn shaft. Why? The answer: metal scrap gets into the pump and erodes the shaft. Root cause: metal scrap. Fixing this will prevent the whole sequence of problems. Compare this with an incomplete cause analysis that leads to the replacement of the fuse, the bearing, or the oil pump, eventually resulting in a recurrence of the problem.

That’s nice and clear, right? But the Wikipedia example continues: The real root cause could actually be a design issue if there is no filter to prevent the metal scrap getting into the system. Or if it has a filter that was blocked due to lack of routine inspection, then the real root cause is a maintenance issue. 94

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As it turns out, the metal scrap is not the root cause after all! We have to go back even further to see how the scrap got into the pump. The Wikipedia example goes on: If the pump has no filter, or there is a lack of expert maintenance, again we ask, why? And so on. We go back and back and back, through a series of deeper and deeper causes. Ultimately, we might get to the actual design of the pump or labor issues that affect the scheduling, staffing, and training of maintenance staff. Is there any end? How far back do we go? The writer of this example stopped at the oil pump, which presents a problem that can be solved by improving filter maintenance or, if there is no filter, by adding one. Great! These are legitimate solutions. But they were not a result of the root cause analysis. The solution did not come from the analysis but from the experience of the engineer who knew that the filter problem was easy enough to address, so there was no need to go beyond it. Root cause analysis assumes the root cause of your problem is smaller, or more solvable, than it actually is—which is why it works best when assessing simple, self-contained systems like machines. In reality, you can’t always tackle “root problems”; they often have multiple causes that are too large or unsolvable. Root cause analysis is fine for the technical, mechanical problems that have a manageable root to trace back to. Root cause analysis can’t tackle problems like “I don’t make enough money as a waiter.” The root cause might be “I am not working enough shifts,” which in turn might be caused by not having enough extra time to work another shift. An alternate cause for this problem might be that customers aren’t tipping enough because of an economic downturn or a social system that undervalues labor. This root cause analysis makes the problem bigger, not smaller, and leads to causes you can’t reasonably solve. Similarly, MECE sounds completely sensible. But in complex problems, the parts are interrelated, not mutually exclusive. We saw from that simple root cause example that you can peel back any problem to deeper and deeper levels—so your list is never comprehensively exhaustive. Even if the waiter includes the national economy in MECE, that’s not enough because the world economy 95

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Price/unit Revenue # Units sold Fixed cost/unit

Profit Cost/unit

Variable cost/unit

Cost # Units sold

Figure 4.2 A typical MECE breakdown.

affects the national economy. Our list grows and grows, far beyond our ability to have an effect on all the items. Figure 4.2 provides an example of a typical MECE. This makes perfect sense—but it’s not a problem breakdown. It describes a situation, not a problem. And it’s a mathematical formula: do all the math and the results equal “profit”? This is a way to find a problem, not break it down. As you fill in these numbers, let’s say you find that your price/unit is much higher than your competitors. Now you’ve found a problem! Good. Time to Think Bigger. Put that problem up top on the Choice Map. Break it down into subproblems. Do the same with all of these methods of situation analysis. If they help you identify a problem to work on, that’s fine. But once you find your problem, don’t skip Step 1 of Think Bigger. From our earlier look at Design Thinking, you might recall the three phases: Analysis, Idea, and Implementation. Design Thinking is fine for the first and last step. Use Think Bigger for the Idea. The various methods we note here are mostly for the Analysis phase. After that, use Think Bigger.

AN IDEA THAT NEEDS A BREAKDOWN

Stories of success in business, especially outsized success, are often told like bad fairy tales: once upon a time, the entrepreneur woke 96

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up with a brilliant, fully formed idea for revolutionizing an industry. He began working hard to implement it, and soon enough he succeeded! By now, we know that’s not how fairy tales work, and that’s not how innovation works either. Yet we keep repeating these stories. Perhaps there is something romantic about the lone genius who can change the world overnight with a single idea. But the reality of such innovations coming into being are always much more mundane. As we’ve seen from our prior case studies of some of history’s biggest ideas, there is always a less remarkable structure in place that has helped create the conditions for innovation to flourish. Remember: the problem and the process of understanding it are central. In Think Bigger, we develop innovative ideas by defining problems and looking for opportunities within those problems. Here is a better story. In 1994, Jeff Bezos, a hedge fund analyst with a computer science background and an interest in technology, started to see business potential in a new, rapidly expanding network called the internet. Here’s the first problem he considered: How can I make money through the internet? He spent several months coming up with ideas and testing their feasibility. First, he thought of a free, electronic message service supported by ads (much like the platform Yahoo would eventually create). Then, he wondered if it might be possible to use the internet for trading stocks (similar to what E-Trade would later do). Still, he wasn’t convinced enough to take on the risk of starting a business around either. Eventually, there was one idea above the rest that stuck with him—the concept of using the internet to sell products directly to consumers.

THE ART OF THE BREAKDOWN

The question Bezos had to address was how he could establish a central marketplace online and become the intermediary between a wide variety of businesses and their customers. He had an ambitious vision, which is key when choosing an innovation problem. And he 97

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served it well by breaking the problem down before attempting to scale up. A major subproblem was that customers would have to be convinced online commerce was safe, convenient, reliable, and inexpensive. What could Bezos sell to help put them at ease? He made a list of twenty possible product categories that included clothes, music, software, office supplies  .  .  . and books. Then he used the following criteria to evaluate their suitability: 1. Was it nonperishable? Could it be sent safely through the mail? 2. Was it consistent? That is, did it remain the same no matter who was selling it? 3. Was it inexpensive enough to be profitable? Could it be purchased cheaply and delivered at low cost?

Books easily met these criteria. Even better, there were already two distributors who stored all the books from every major publisher that he could easily access. Brick-and-mortar bookstores could keep only a few thousand titles in stock, which limited selection. By dealing directly with the two distributors, Bezos could offer customers any book in print. It seemed like an amazing idea— unique and innovative! However, Bezos wasn’t the first to think of this. Several bookstores were already selling online—but that was only part of the fact-finding equation. When Bezos recognized this, he zeroed in to scrutinize their websites and put them to the test: he purchased a $6.04 copy of Cyberdreams by Isaac Asimov from a bookstore in Palo Alto, California, to study its process. Not only did the book take weeks to arrive in Seattle, it was badly damaged by the end of the journey. Aside from the wretched condition of his book, Bezos must have been thrilled—he had just identified a new need: a new subproblem! Another subproblem he identified was where to locate the business. One of the two major book distributors was based in Oregon, so it made sense to set up shop on the West Coast. Bezos considered California, but tax laws made it unappealing. Washington, on the other hand, had no income tax. And his parents lived there. If 98

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he moved home, he could work out of his parents’ garage and be close to the mother lode of books in Oregon. By late 1994, Bezos had made his decision. He left his job in New York and drove to Seattle with his wife. He registered a URL that November, and a few months later he launched Amazon.com. When a customer ordered a book, Bezos bought it from his Oregon distributor at the usual wholesale rate of 50 percent off list price. It arrived in Seattle via UPS a day or two later, and then Bezos repackaged the book and shipped it to the customer, netting a small profit. It took a number of years for Amazon to scale this system for other products and become “The Everything Store” we know today. But Bezos had set his company up for success with an approach to innovation that underscores several of the principles central to Think Bigger. First, he took on something ambitious: developing e-commerce. Second, he found a problem that could be stepped up or down in complexity. Third, by studying what other companies had tried, and noting how and why they failed, he was able to define a problem that he could solve. Fourth, he combined ideas from various fields—computer science, finance, and sales—to develop a new model. In hindsight, the breakdown of a problem might seem obvious. Working backward from the solution, you see the various parts of the problem it solved. But it’s actually quite challenging to do up front without this benefit—the proof of this being that there were not tons of Amazon.coms popping up at the same time. Regardless of the difficulty, as innovators like Naismith and Bezos show, the pursuit can wind up being very much worth the effort. When people jump from problem to solution without pausing to break the problem down, what they gain in speed they tend to lose in quality. No two people will break down the same problem in the exact same way—breakdowns are an act of ideation themselves, and they indicate the components you believe to be key to solving that problem. Sometimes, even banal problems we face in our everyday lives are far more complex than we think at first. It’s clear that the exercise of problem breakdown takes time, thought, and more research than you might realize. 99

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INSIDE AND OUTSIDE THE DOMAIN

Here is an example of problem breakdown. I’ve struggled with this problem all my adult life: How do we make it easier for blind people to plan vacations? You probably have little experience with this problem. If we set out to brainstorm, that’s a big constraint because for that we draw on your personal knowledge. For Think Bigger, it does not matter. The solution will come from outside your own experience. That is why you don’t need to be an expert to generate a useful, novel solution. You do need to understand how to break down the problem. Here is a list of subproblems to the main problem my students came up with in class: • • • • • • • • • • • • • • • • • • • •

How do you use hotel and travel websites? How do you find hotels that can and will accommodate you? How do you assess whether or not the hotel rooms are blind-friendly? How do you research your options when most sources are visual? Where can you read blind-friendly reviews? How can you assess whether a location is safe? How do you check whether or not hotels and other locations are ADA-compliant? How do you navigate crowded airports? How do you read a boarding pass? How do you get a convenient seat on the plane? How do you get a sense of what a place is like? Are there certain places that appeal more to the nonvisual senses? If you get lost, how would you find your way? How do you find a guide? How do you find other blind people and blind-friendly locations? How do you find the most walkable areas? How do you navigate unfamiliar crosswalks and other potential hazards? How do you read signs and instructions? Does Braille use different alphabets? Are local buses, trains or taxis blind-friendly? 100

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• Package trips make planning easier, but do they include blind-friendly activities? • Do you need more time than the sighted for planning, travel, and activities? • If you’re traveling with a service animal, will it be allowed? • If you use a cane, what problems might that bring? • How do you find or negotiate a good price? • How can you feel confident about your choices?

The longer this first list, the better. Don’t leave anything out, even if it might seem mostly irrelevant. Later on you will have a chance to delete items from the list. Once your questions repeat or become trivial, that’s when you stop. Now go back over the list to group similar questions and cut the least important. Then put the list aside. Just think about it. From memory, which questions strike you as most compelling? Go over the list again. Group and cut again. Think again. Repeat. You want to end up with our magic number: no more than five major subproblems. My students ended up with these: • • • • •

How can a blind person navigate a new area that’s not blind-friendly? How do you ensure the hotel and activities are blind-friendly? How do you make the vacation safe? How do you make the vacation affordable? How can you know if the general location is blind-friendly?

This is a very good start. Let’s pause and think back to other forms of problem breakdown. This list is certainly not MECE: the items are not mutually exclusive, and the whole list is not comprehensively exhaustive. And there is no root cause on the list. I already know the root cause: I’m blind! There is no SWOT, no four Ps, or a match to any other template for breakdown. Each Think Bigger breakdown will look completely different depending on the problem itself and the judgment of whoever does the breakdown. With our short list in hand, we next determine whether answering these questions would solve enough of the problem. My rule of 101

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thumb is that to feel comfortable and know when to stop, I must determine: if I were to solve for all these subproblems at once, then the solution would be a significant improvement from what currently exists. The result should be able to make something seemingly impossible feel possible. If the problem is a product, then the new one you ideate should feel significantly better than any product already in the marketplace. For this we talk to experts. These are people who do have experience in the field. But we don’t ask them to brainstorm solutions: we ask them to comment on our problem breakdown. Experts can explain why the problem exists, how others have tried to solve it, and what ideas, activities, and tools already exist to address the problem. For our questions, we want to find professionals in education, advocacy, and policy-making for blind people. A quick internet search leads us to the National Federation for the Blind and the American Council of the Blind. These are the two largest American advocacy organizations for blind people. Those sites alone confirm most of the problems on our list. As for existing help to blind people, we learn which American cities are ADAcompliant in which aspects, and we find museums, parks, and other public places with special services, such as audio tours for blind people. We probe further and find new technologies like Envision Smart Glasses that read menus and signs. Use the internet to also search for individual experts, who are often the staff of blind-oriented organizations. Email or call them. They are often glad to talk about the subproblems on your list. Remember, you’re not asking them a favor. You’re just sitting at their feet and appreciating their wisdom. Experts are usually flattered and willing to give their time. After all, they are passionate about their field. When you speak with experts, do it one-to-one. You want a variety of perspectives. No doubt there will be areas of consensus, but avoid situations that may encourage groupthink. Also, keep in mind that an expert has deep knowledge of certain aspects of the problem, but their expertise also limits their understanding. By talking to them individually, you can more easily spot their biases and underlying assumptions. 102

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Then talk to users: in this case, blind people. Triangulate between the experts and users. That is, say to the experts, “This blind person mentioned this,” and say to the user, “This expert mentioned that.” Keep going until you feel you understand the problem deeply, from many different angles. I am a user, so let me give you some thoughts about these subproblems. There are group vacations for the blind, but these have two flaws: they force you into a group that’s not of your choosing, and for fear of liability, the organizers keep to the safest places and activities. You don’t get anywhere near the same feeling of freedom as a sighted person does on vacation. Apps like Aira and Be My Eyes let you strike out on your own, but they don’t work well enough for me to recommend them to other blind people. Museum and other audio tours are much better. Many are tailored for the blind, and many guides are skilled at working with blind visitors. In my experience, the biggest subproblem is something I rarely hear experts mention: many hotels and cruises don’t allow a blind person to book a solo trip. As soon as you mention you’re blind, they throw up obstacles. Again, they fear liability. So I tend to stay at boutique hotels where I can speak directly to the owner or manager before I get there. I ask for a room with easy access to the elevator or the beach. Smaller hotels tend to be better at service in general, so I can hire a staff member to take me around the area, or even swim with me in the ocean. That also allows me to meet more local people and have an experience that comes closer to what sighted people typically enjoy. For these reasons, if you know someone blind, I highly recommend Sardinia. When you talk to users, aim for the same kind of detail I just told you for myself. You want to know their actual experience, and what they think and feel about it. Begin with the basics: “Do you go on vacations?” or “What’s easiest and what’s most difficult about planning and taking trips?” Let their replies lead you to further questions—and let their responses lead you to further questions. As with the experts, don’t ask for solutions. They will mention good 103

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examples as they talk—the way I did regarding small hotels and Sardinia. It’s also important to think widely about who the users are. Here is a comment on this from a student in my class: The subproblem exercise was really interesting, as it highlighted how even after going through step up/step down, problems can still be too broad. In addition, the categorization aspect allowed us to, at a rudimentary level, identify potential users. Naturally, a lot of the subproblem categories were oriented around the impact of the problem on a specific user (in my group’s case—doctors, patients, government, hospital administrators). The exercise builds a great framework for user interviews and allows us to get a better understanding of how to collect more information about our problem.

In our blind traveler case, hotel managers might be users. Have they had blind guests? And if so, what happened? And what do they think about what happened? Same goes for tour operators, travel agents, even taxi drivers. Look at each subproblem to see who participates in that activity beyond the blind people themselves. Remember that users, like experts, are bound by what they know. I can tell you exactly what problems I face when I travel, and what I think and feel about them. But that’s all. Some of that will overlap with what other blind people say, but each user’s experience is specific to their own situation and desires. It’s impossible to ask all users and get a complete picture of the problem. Keep going until you only hear repetition. When you stop getting new information, you’re done. Now it’s time to step outside. Interviewing and observing your target users are well-known parts of market research. Your aim is “consumer insight.” This insight is valuable, because it will help you dissect the components of the problem—some of which may not readily occur to you. So, I want you to make learning from your users a part of your problem breakdown research. You must avoid confusing consumer insight with solution generation; too often 104

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we think that all we must do is learn from our customers what’s wrong and the solution to the problem will become apparent. If it is a small problem, like product design—for example, the handle on a teacup doesn’t have the right grip—then, consumer research might be enough. But for any complex problem, your users will be ill-equipped to help you with the solution. Although consumer insight is certainly useful for understanding the problem, it is not useful for generating solutions. Think of Henry Ford and the problem he was trying to solve. If Ford had asked his target users in 1907 what they wanted to help their long and inefficient commutes, they would have said they wanted a faster horse and buggy. Experts and users are inside the problem. Everyone else is outside it. People without direct experience in the field will have open minds about it. Experience gives you depth but limits your breadth. Outsiders have less depth but more breadth. They don’t let their knowledge limit their thinking. Ask them the same questions as insiders. They might respond with silly or far-fetched ideas, but often they give a view that users and experts never thought of. Once again, don’t ask for solutions or advice. Immerse them in the field as best you can and ask them for their thoughts. For example, if you could not see, what would be the best way to get around in a place that usually requires a car? How would you navigate a new city? Where would you visit? What experiences would you seek? If a friend or family member could not see, what would you do to help them enjoy their vacation? In my own case, I’ve learned a lot from outsiders. A documentary filmmaker in Hong Kong thought it might be interesting to show a blind person the smells and textures of fish, like shark fins, used in Chinese soups. A Sardinian wanted me to experience how he made sheep’s milk cheese, and he took me to smell a berry plant only found in Sardinia. A harp player on the streets of Paris took my hand so I could feel the instrument’s shape and strings and then his own hands as he played. Since outsiders are less emotionally invested in the problem you’re trying to solve, they tend to have a more open mind. I 105

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personally find that some of my favorite experiences traveling have come about when I connect with outsiders. In the process, I’ve had incredible experiences and made new friends. Above all, I learned that outsiders are very helpful for rethinking a problem from many different angles.

IS THIS A CREATIVITY PROBLEM?

You now have your problem and a set of subproblems. Many times, we start off thinking the problem we’re attempting to solve is too complex and there is no known solution, or no known options— solving it requires creativity. Don’t be surprised if the very process of defining and breaking down your problem allows you to see a solution that already exists for your problem. Your breakdown helped you see it! If that is the case, the first two Steps of Think Bigger is the process by which you can find the choices that you previously did not see. You can stop here and choose a solution. However, many times, your problem remains complex and there are no known solutions for it. Your problem, its breakdown, and the initial ideas you have should not be confused with solutions. At this step, you must be careful not to trick yourself into thinking there is a solution to your problem if there actually is none. Don’t settle for a half-baked solution to speed up the Think Bigger process! Breaking down a problem can be inspiring and can spark in you lots of ideas—but right now, you should take your ideas just as that: sparks! An idea is different from a known solution. You should only stop at this point if you really do have a known solution, that works. If you continue, collect your breakdown and ideas and let me tell you what to do with them.

WHEN DO YOU STOP?

Studying your problem, talking to insiders and outsiders, and rethinking your breakdown will spark ideas for solutions. That’s 106

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inevitable. But don’t let these sparks distract you—or worse, lead you to skip ahead in our Think Bigger steps. It’s too early to jump to conclusions. Instead, write down the solutions that come to you in one place. I call it a “sparking lot,” where you park your sparks for later. They might prove useful, or not, when it comes time to put together a solution to your problem. Until then, write them down and move on. Revision is a key part of the process here. Meaning, don’t just accept your first breakdown. Revise the list at least once. Keep revising until you find that your study and interviews no longer give you further ideas. At that point, here is a way to check if your breakdown is good enough to move on to our next step. I call it the Eighty Percent Test: If I solve all these subproblems, have I solved at least 80 percent of my overall problem? Of course, there is no mathematical way to do this. It’s a judgment call. Ford did something similar when he stopped at that short list of major changes required to make a cheaper car. After that, he went on to make minor improvements year after year. But for his main innovation, the short list was enough. The same was true for Naismith with basketball. He made a short list of innovations. For years after, he, and then many others, continued to make small improvements that helped evolve basketball into the game as we know it today. At this point, found a problem you want to solve. You’ve broken down that problem into a handful of meaningful subparts. You have a fairly good idea of what we know and don’t know about what has and hasn’t worked in the past. It might feel to you, at this point, that you’re ready to start ideating. But before you rush into generating solutions, I am asking you to take a step back and pause. There is one more very important question you must answer before solution generating begins: Why do you want to solve this problem? And, if you were to find the ideal solution to this problem, how do you want the solution to feel?

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W

hat do you really want? Why are you solving this problem? What do you want to get out of this? Now that you have the problem you’re trying to solve and its breakdown, I want you to pause and think about your deepest wants. In this step, you will use your feelings as an aid to help formulate your choosing criteria. This step helps you see the “Big Picture” so that your ideation process is aligned with the most desirable outcome that will ultimately solve your problem. Unlike other methods for innovation, in Think Bigger we look at and identify the wants of at least three different stakeholders— you, the creator; the target; and third parties (competitors and allies). As you identify these wants, you write them down to fill a list that we refer to as the Big Picture (see figure 5.1). Later, you will run through that list to weight each want and create the Big Picture Score. This Big Picture Score will help you in three ways: 1. You will more easily refine your problem breakdown. 2. It serves as the selection criteria when you choose among the multiple ideas from your Choice Map (Step 5). 3. It explains the “why?” behind your idea when you start collecting feedback from others in Step 6.

STEP 3: COMPARE WANTS

Figure 5.1 Big Picture score.

WHAT DOES BILL GATES WANT?

You probably know the story of Bill Gates, one of the most successful innovators in history. But like our microwave question earlier, do you really know the story? Perhaps the story you know follows the common fairytale structure we have seen before: an entrepreneur has a brilliant vision, works hard to make it come true, and achieves outstanding success. You might think this seems like the opposite of Think Bigger. And you would be right—it is. That version of the story actually is a fairytale. The real story is very much a case of Think Bigger.

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As we go through the Bill Gates example, I want you to pay special attention to the question of want. At each point we ask: What does Bill Gates want? This might seem especially odd, as Bill Gates is famous for his analytical abilities. Computer software calls for careful logic. What do emotions have to do with it? To make good analytical decisions, you must discard your feelings and think logically. Right? Not even close. Desire colors every decision you make, whatever the situation, whoever you are. And that's true even for our most prolific innovators, including Bill Gates. The real story begins in Seattle, where Gates grew up. He joined the high school computer club and made friends with Paul Allen, who was two years older. There they learned to program BASIC, a simple language invented by two Dartmouth professors to teach computing. Thankfully, their school was fortunate enough to have the latest minicomputer, a DEC PDP, and the club learned how to program BASIC on it. After high school, Allen went to college for two years then dropped out to program small computers for Honeywell in Boston. Gates went close by to Harvard, so the two stayed in communication. In late 1974, Intel released a more powerful chip, the 8080, that the whole computer world recognized as a huge leap forward. Gates and Allen got a copy of the manual and figured out how to put BASIC on it. But they couldn't get the chip itself: Intel only sold it to computer companies. In January of the next year, Allen was walking across Harvard Square to visit Gates when he saw the latest copy of Popular Electronics on a newsstand (see figure 5.2). The cover article showed the Altair—a cheap new computer with the 8080 chip in it. Allen bought a copy and rushed to find Gates. The article said that the Altair was built for the 8080 chip but came without software. The maker of the Altair, MITS, invited programmers to write BASIC software to run it. The race was on. MITS later reported they got fifty calls from programmers who said they were working on it. That included Allen and Gates. Guess who won the race? So was born Micro-Soft—later “Microsoft.” That was the name of the company that Allen and Gates formed to supply the Altair software. Once they got the contract, Allen quit his job and Gates 110

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Figure 5.2 An advertisement for the Altair 8800 computer in Popular Electronics, 1975. Wikimedia Commons.

famously took a leave of absence from Harvard. They moved to Albuquerque, New Mexico, where MITS was based. Let’s see what Bill Gates was thinking at the time. In his book, The Road Ahead, he tells us this: When Paul Allen and I saw that picture of the first Altair computer, we could only guess at the wealth of applications it would inspire. We knew applications would be developed, but we didn’t know what they would be. Some were predictable—for example, programs that would let a PC function as a terminal for a mainframe computer— but the most important applications, such as the VisiCalc spreadsheets, were unexpected. 111

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Now we know that Microsoft succeeded by enabling PCs to run a variety of applications and function independently. However, that was not what Gates envisioned, at least not while he was working on the Altair. He thought he had written software for a terminal that would communicate with a mainframe computer. That was typical for the time: the whole industry saw PCs that way, and there were also thousands of hobbyists tinkering with this technology who had the same idea. Gates wanted everyone to buy an Altair because that meant they would be buying his software too. The hobbyists had other plans. They liked the Altair’s user-friendly software, but they didn’t want to be tied to a single make or model. Instead, they pirated Microsoft BASIC and copied it onto different computers, making it possible for users to transfer applications from machine to machine. This reduced the appeal of the Altair, which meant lower sales for MITS and less money in Gates’s pocket. Gates was outraged. He wrote a now infamous letter to the Homebrew Computer Club, a hobbyist group in Menlo Park, California, that published an influential newsletter. “Most of you steal your software,” he said, going on to threaten that those whose names he learned might “lose in the end.” Pirates were ruining the computer industry, Gates charged, by depriving professionals (like him) of funding that was needed to improve software and, by extension, the industry. The pirates ignored him and Gates gave up, offering to sell the software to MITS for $6,500, but MITS declined. The sales were so bad, they couldn’t afford it. In March 1976, a year after the Altair launch, MITS held the first annual World Altair Computer Convention in Albuquerque. As a last hurrah, Gates showed up. Right away he noticed that users brought other machines too. They all had his version of BASIC on them, which allowed them to swap programs and files because all the computers used the same language. At first, Gates hated this. Hence his letter. But then he realized something staggering: he had a monopoly. All small computers were using his software. That wasn’t bad for Microsoft. It was the opportunity of a lifetime. 112

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This is when Gates famously quit college. Then, he broke his Altair contract and again wrote to the leading computer newsletter. In this letter, he apologized for the first one, thanked the users for adopting his software, and promised them more to come. Then he struck deals with all the leading computer makers to write a version of BASIC for them that worked on different hardware. As new programs appeared, like VisiCalc and WordPerfect (and soon countless others), he simply integrated them into his operating system to make them compatible across machines. That’s the real story of Bill Gates, the famous visionary. But what was his vision, exactly? He wasn’t the first to realize computers would get smaller and more powerful. Gordon Moore gets credit for that, as “Moore’s law” was presented in an article that came out in 1965—when Gates was just ten years old. Similarly, Gates did not create the first small computer—Allen’s job at Honeywell was programming them. And Microsoft wasn’t the first software company—that dates from 1955, the year Gates was born. It’s also true that Gates did not even come up with the idea that software was a separate industry from hardware. It was the users who did that, by copying his software from machine to machine. Gates hated the idea at first. He saw the Altair and his software as a single package. But to his credit, he finally saw that his software, residing on different hardware, was a blessing and not a curse. Now that you know what really happened, let’s go back and ask our original question. What did Bill Gates want? In high school, he wanted to grow up, work in computers, and make a lot of money. Legend has it that he vowed in high school to make a million dollars by the time he was twenty. That wasn’t unusual. It was the dawn of the computer age, and lots of young people dream of getting rich quick. In college, he and Allen talked about starting a software company. The Altair contract gave them that chance. From there, Gates wanted the Altair to dominate the computer market because that meant more sales for his software. The Altair let him down. It was a good living, and he had an important position in the computer industry at a very young age. But it wasn’t enough. He saw that it would not lead to the fortune 113

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he dreamed of. So he switched back to another of his wants: a college degree. At the Altair conference, he then switched what he wanted again. He saw a way to get back into the computer world with an idea even bigger than making Altair software. Let’s look closely at this last switch. He went from what he wanted—an Altair monopoly—to what users and other hardware makers wanted: software that any hardware could use. By taking account of what these two groups wanted, he became the richest person on earth. That’s definitely something he wanted. As we see from the Gates example, you have many wants, and they might be different at different times. You want to solve a problem that matters to you, and you want the solution to come out the way you want. But others matter too. Otherwise, you won’t find a solution, or the solution just won’t work. In this step of Think Bigger, you assess your problem and breakdown as they apply to the wants of three sources: you, the targets of your solution, and third parties who matter most. In his case, that was clear: Gates, computer users, and hardware makers. In your case, chances are it’s not as obvious. That’s why we give your wants their own step in the Think Bigger method (see figure 5.3).

Figure 5.3 The Desires Triangle.

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THE BIG PICTURE SCORE

If you ask someone what they want, they might give you a specific target. For example, “I want to get the Nobel Prize,” “I want to start my own restaurant,” or, in the spirit of Gates, “I want to make a billion dollars before the age of thirty-five.” But in reality, these are only means to an end. Why do you want to start a restaurant? There must be some desire it fulfills. If you don’t get to start a restaurant, is there another outcome that would still fulfill that desire? You can see right away that this search for a desire accounts for the great uncertainty of life. When you apply that to Think Bigger, there are many directions your solution can take to solve the same kind of problem. You might find the problem worth solving, but only certain solutions will fulfill your desire. Have you ever explained a problem to someone, and when they offer a solution, you think, “I don’t want to do that.” That’s because the problem appealed to you, but their solution did not. In Think Bigger, you don’t want to devote time and effort solving a problem you care about then end up with a solution that you don’t want to undertake. Remember Gates’s situation both before and after the Altair conference: you need a solution that your target and third parties want as well. Our Big Picture surfaces all these wants early to help guide every step of your progress toward a solution. Let’s pause to consider the role of emotion in decision-making. You might think that the best decisions are purely rational, with no emotions entangled at all. But that’s impossible. If you think you’re making a decision without emotion, you’re fooling yourself. As the philosopher David Hume tells us, “Reason is, and ought only to be the slave of the passions, and can never pretend to any other office than to serve and obey them.” You can use reason and rational methods of problem-solving but to what end? To get what you want! It’s your wants that explain why you even try to solve a problem in the first place. Even price, that basic building block of economics, is nothing 115

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more than what a buyer wants. You’re willing to pay four dollars for a jar of jam and I’m willing to pay two dollars. But the price is three dollars. So you buy it. I don’t. Why? Because you want the jam more than I do. If neither of us wants any jam at all, we’re willing to pay zero. Decades of research has shown us the countless ways our emotions bias us in our search for information and in its interpretation and processing. Allowing our emotions to dictate the decisionmaking process can lead us to make poor choices. In Think Bigger, we do not attempt to eliminate feelings from the ideation process. Instead, we separate the articulation of feelings from the information gathering and choice creation process. Specifically, we use the Big Picture tool to provide you with a holistic understanding of what the various wants really are. That way, this information about desire is used when choosing among the various ideas you generate from Choice Mapping. The Choice Map is the tool you use for information gathering, processing, and ideation. We will get into that in the next chapter, but for now, I want you to know that in this step feelings, wants, desires, emotions—whatever word you want to use—matter. They are important in the ideation process as they play the important role of providing you with a choosing criterion. As you will see, using the Big Picture Score is a much more comprehensive way of identifying the best idea from the myriad ideas you create from your Choice Map. The three nodes of the Big Picture are you, your targets, and third parties who matter. At the top of the “Big Picture,” you will answer the question “How do I want to feel when I create the ideal solution to my problem?” Note that this question makes it clear that we’re not asking you to leap ahead and come up with a specific solution you want. Of the many possible solutions you might come up with, what emotion should they all have in common? On the bottom left, you answer the question “How do I want my target audience to feel when I solve this problem?” In this case, you should try to understand how an individual or group might be

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directly affected by the solution to your problem. This step requires you to look beyond your own wants for now. So when identifying their wants, take into account the kinds of users you’re targeting for your idea, how their wants are specific to your problem, and how they want the solution to feel. On the bottom right of your “Big Picture,” you answer the question “How do I want my third parties to feel when I solve this problem?” Think widely about two groups: people who might be allies in the solution, and people who can impede it. Who else cares about this problem? Who else cares about your target? Are their competitors in the market that might try and deter you? You won’t be able to satisfy all these wants because some will conflict—for example, allies versus competitors. Think Altair versus other hardware makers for Gates. But you must be aware of these wants to avoid surprises down the road. In your “Big Picture,” you will look among the wants at every angle of the shape and use the Big Picture Score as the tool to tally and weight the most common wants among every party involved. In a perfect world, the solution you create will cater equally to all three groups. But in reality, there will be conflicting wants within each group and across groups. So, in the end, you will have to optimize for the party you want to please most and choose a solution on their behalf. You will come back to use this tool in Step 5. For now, only focus on writing down the wants of those who will influence your problem-solving process. While you must account for the wants of all other parties, your wants as the innovator remain paramount—that’s why they go at the peak of the Big Picture. This is very different from the standard “customer first” approach that dominates many innovation methods. For example, Design Thinking tells you to do extensive customer research to thoroughly understand what they want. That’s fine—and the result fills a corner of the Big Picture. But if you don’t satisfy the wants of the innovator, nothing happens at all. You might have experienced this yourself: a brainstorming session results in a solution. But after that, nothing happens. Why?

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Nobody wanted to do it. The solution did not satisfy their wants. This tends to happen in organizations when the problem comes from above. Imagine some higher-up wants a solution. The group brainstorms a solution that satisfies the higher-up though it doesn’t satisfy the group itself. But no one ever admits it.

THE ROLE OF THE BIG PICTURE SCORE

In the Think Bigger method, the Choice Map is where you record the information about what the problem is and the evidence you have to date that can be used to help create a solution to the problem. Think of the Choice Map as your information-processing tool. The second tool of Think Bigger is the Big Picture Score. This tool takes the Big Picture shape you created and applies a scoring method to help you make a concrete decision. The Big Picture is our shape used for writing down the wants of all parties, the Big Picture Score is the tool we use. In contrast to the Choice Map, the role of the Big Picture Score is to provide the necessary space to identify and express motivations, preferences, and emotions. We often believe that problem-solving should be devoid of emotion, so we go out of our way to suppress our emotion from the process. However, in Think Bigger, we not only surface those emotions, we embrace them, because those things we call biases can actually help us at critical points during the ideation process. Your Big Picture Score will serve two main functions during the Think Bigger process: as your selection criteria and as a check to keep you on the right path. When you doubt whether or not you’re going down a rabbit hole, ask yourself these questions: 1. Are you solving the problem you want to solve (i.e., the Choice Map)? 2. Are you in line—and being consistent with—with your wants?

If you are a visual learner, use the shape in figure 5.4 to find your score. 118

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Figure 5.4 Big Picture scores for different solutions.

PREFERENCES FIRST

You might be wondering, “Isn’t it premature to identify my wants? After all, at this point, all I’ve done is try to understand the problem and I have no options to compare and contrast.” It’s precisely at this time that it’s best for you to step back, pause, and ask yourself, “If I were to find or create the ideal solution, what would it feel like?” Research shows that we’re far better decision-makers when we predefine our criteria—meaning, we do so before identifying our choices and beginning the process of comparing and contrasting. For example, researchers observed that when hiring managers predefined their hiring criteria, they were less likely to hire based on gender stereotypes and more likely to hire based on performance and fit for the role. This holds true, even when considering seemingly analytical tasks like stock picking. A 2007 study examined the experience of 101 stock pickers making investment decisions on a daily basis. For stock pickers who identified and addressed their 119

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more intense feelings—rather than shying away from them—their daily investment returns were higher on average than those who reported less emotional attunement. The study also found that investors who were able to identify and explain their feelings in detail managed to control bias in their choices more effectively. Beyond that, an analysis of several angel investing groups found that investment leaders who used a “gut feel” to describe the blend of analysis, intuition, and emotions involved in their intuitive processes were better able to effectively predict profitable, entrepreneurial investments. Given the uncertainty, social attunement, and emotional challenges involved in starting a business, emotional intelligence should be particularly beneficial in this setting. Emotions are a cornerstone to entrepreneurial success, as revealed by a survey of 65,826 business owners revealing that emotional intelligence (EI) is a stronger predictor of entrepreneurial success than general mental ability (GMA). Similar to Gates, who kept his wants in check and stayed attuned to his inner emotions as he developed Microsoft, you will use your wants to guide you as you frame your problem, reframe it, and search for solutions to get your big idea. When we address our emotions and become aware of them, we are more likely to choose a solution that makes us confident and empowered. That is why Comparing Wants is essential to Think Bigger—this step leans into your emotions and the emotions of others to give you a big picture view of the direction your idea can go.

FILL IN YOUR BIG PICTURE

Identifying underlying wants is not a simple task. Take your time. Here are some words that might help (see table 5.1). As with the problem breakdown, make long lists first. Then narrow them down to three to five options for each. And by the way, we start using the Big Picture Score after breakdown for a deliberate reason. If you do it after the problem statement, you don’t know enough about your problem yet to take this next step. The breakdown 120

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table 5.1 Underlying Desires Accepting

Graceful

Neat

Timely

Airy

Groundbreaking

Noisy

Tolerant

Bright

Hip

Orderly

Unified

Bubbly

Honorable

Outgoing

Unusual

Compelling

Impactful

Patient

Vibrant

Chic

Innovative

Perceptive

Vintage

Detailed

Kind

Quick

Wistful

Dependable

Knowledgeable

Quaint

Witty

Elegant

Lavish

Raw

Youthful

Earthy

Loyal

Rational

Young

Fair

Modern

Safe

Zany

Fancy

Mysterious

Sociable

Zealous

deepens your understanding of the problem and also starts directing your solution toward certain wants of your own, but it happens unconsciously. Now is the time to make those wants conscious.

PLEASING ALL PARTIES

In Gallery 771 of the Metropolitan Museum of Art in New York, there hangs a painting of an elegant and mysterious woman (see figure 5.5). She looks to her left while her body turns slightly in the same direction. Her left hand, at her thigh, directs your eye to the narrow drape of her gown. Her right arm stretches just behind her to rest on the edge of a round table. The room is dark, her gown is black, and her pale skin glows: bare arms, décolletage, and her face in profile like a crescent moon. Madame X, by John Singer Sargent, is one of the most famous portraits in Western art. Gallery 771 bears the name Portraiture in the Grand Manner. But there, you won’t see Sargent’s thirty studies and earlier versions of Madame X in pencil, watercolor, and oil 121

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Figure 5.5 Madame X, the final painting by John Singer Sargent. Wikimedia Commons.

(see figure 5.6). You also won’t see one of the thin straps of her dress drooping off her shoulder because viewers complained that it made her look less than proper (see figure 5.7). Sargent painted over it (see figure 5.8).

Figure 5.6 An alternative sketch of Madame X. Wikimedia Commons.

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Figure 5.7 Another alternative sketch of Madame X with her strap draped. Wikimedia Commons.

Figure 5.8 A third alternative sketch of Madame X with no strap. Wikimedia Commons.

The various versions of Madame X show Sargent’s range of wants in his aim to portray his subject. He began the portrait in 1883, when he was twenty-seven years old and already an established painter. He also worked without a commission, which was a very unusual move for a working artist. His subject, Virginie Amélie Avegno Gautreau, was three years younger and a well-known socialite in Paris. Sargent explained, “I have a great want to paint her portrait and have reason to think she would allow it and is waiting for someone to propose this homage to her beauty.” Gautreau did indeed allow it. We don’t know why, but whatever the reason—and we can guess several—Sargent successfully satisfied her as a third party. The users were the art public, and there he ran into a problem. That drooping strap caused a furor. One critic wrote, “One more struggle, and the lady will be free.” The gallery showing the painting—another third party—took it down. That’s when Sargent repainted the strap. Sargent further explained that he had other wants to fulfill when he painted the drooping strap. He wanted to show the “unpaintable beauty and hopeless laziness” of Madame Gautreau. 123

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He had to give up that want to please his users and the gallery. But not completely—the laziness still comes through. We can see that even in art—that unrivaled bastion of personal expression—a working artist must find balance in the Big Picture Score. Sargent knew that if he just satisfied himself, the Paris art world would shun him, and his career would be over. Artists are free to create whatever they want. But if they want their creations to be understood in the world outside their own heads, they need to evaluate their creation through the lens of the Big Picture Score.

WANTS IN ACTION

As you develop a greater awareness and understanding of your wants, you will become better at avoiding paths that lead to dead ends. This is informative, no matter what kind of problem you’re solving. Let’s try it together. Imagine you’re an award-winning songwriter and you set out to write a new song. That is a big task—your last single was a hit on the Billboard Hot 100 charts and the pressure is on because your record label wants to keep the momentum going. You wonder how to continue your success in songwriting to create another hit. Aside from your record label, your fans expect another catchy tune with meaningful lyrics in this next piece. So, to make it more manageable, you write down your problem and break it down into two subparts. Main Problem: How do I write my next hit song? Subproblems: What entertaining subjects can I write about? What sounds or instruments can I use?

When comparing wants, you must start out by jotting down everything you want from your hit song. After all, how can you write something if you don’t even like it? Recall the shape of your Big Picture—that will help you define your choosing criteria. It is in 124

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the top part of the shape you will use to fill in the lines with boxes next to them. Your list of wants might look like this: • • • • •

I want the song to be catchy. I want the song to match my brand as an artist. I want the song to be relatively easy to record. I want the song to sound folksier than my past work. I don’t want it to be too expensive to record.

This list probably echoes your problem breakdown to some degree. It might even lead you to go back and revise that breakdown! Notice all the adjectives—that’s right, you are describing the idea you want to create. Now, you might be thinking, this sounds like something a songwriter would do without Think Bigger—and you would be correct! Think Bigger is the closest match we can make to how creative thinking happens in our minds. Even for artists, it helps to make explicit what they want from a solution, rather than following along with whatever impulses arise in any moment. Next, write down the wants of your target. It is here you must be as specific as possible. For example, your target can’t simply be people in their twenties. That is too large a scope, as a twenty-oneyear-old is generally quite different from a twenty-nine-year-old. For instance, the twenty-one-year-old might want a pop tune to hear in the club while the twenty-nine-year old might want a song with rock influence that can be played at a bar. How do you find a balance? A good way to understand your target is to interview them or someone who knows a lot about them. The aim is to figure out all the things—big or small—your target would want from a solution and go from there. It is important to note that every target group has wants in common and wants that differ. So, let’s return to the subproblem, “What entertaining subjects can I write about?” Some of your target might prefer songs about romance and fun, while others will want a song about overcoming heartbreak and finding oneself. Not every 125

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solution can satisfy all the wants of your target group. But you need to know what all those wants might be. Then, you can choose strategically, to meet the largest set of wants, or the wants you prefer to meet based on your own personal wants. To help you list your target’s wants, I will provide you with a guide and structure to follow along. To interview your target, or someone who knows your target well, like a musical trends specialist, you will need to narrow your focus. For any problem you solve, the target is always people, even if you’re solving a problem for an organization. If you are finding solutions for an organization, you must ask, who are the people in the organization that will use the solution? Here, I have created a profile for you to fill in—this will help you identify who might be a good fit for interviewing: • Age (person) or size (company) • City or region of primary residence • Socioeconomic status (e.g., income, education levels, occupation, or association memberships) • Brand affinity/product usage (purchasing history and brand loyalties) • Psychological details (personality, lifestyle, political affiliations) • Friends, allies, or competitors • Other relevant details

Once you’ve narrowed down your target—let’s say, in this case, your target is fans of your previous songs, the fans who have been with you from the start, and those who were exposed to you from your last hit—on the right, list what your target wants in a potential solution. These questions can help in your interviews. Note that they mirror what you ask about your own wants: • What do you want this solution to do in an ideal world? • What do you want from a solution (money, recognition, promotion)? • What kinds of solutions do you prefer in the long run (e.g., technical, inexpensive, flashy)? • What do you think the solution should feel like? 126

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• What kind of “aura” or “brand” would you like it to have (easy, edgy, timeless)?  • How do you want others to describe your solution? • What about this problem do you care about most?

Think about what your fans want and start jotting it all down in your Big Picture, this time in the node labeled “Target.” I’ve created a list for you to compare your own to: • • • • •

I want the song to be memorable or “catchy.” I want the song to sound like an extension of the last album. I often prefer songs “you can dance to.” I’d love to hear a collaboration with another of my favorite artists. I prefer that the song not sound auto tuned.

Do any of these overlap with the “Wants” we identified in your list? If you see overlap, that’s a good sign! It means you are piecing together the adjectives that will help define your potential solution. Once you’ve started to parse these wants together, it is time to think about who else might matter for the success of your solution. We call these people “Third Parties.” In the case of your next hit song, these people might be your record label, or competing artists. As we mentioned before, your record label is putting the pressure on for you to make a song that will be another hit. They will put constraints on how “folky” your song can be, or maybe they will want it to sound like your previous hits. Maybe competing artists are experimenting with a sound similar to what you’re going for. No matter the case, it’s your job as an innovator to figure out who might have the biggest impact on the success of your solution. And you need to know what they want. As with your target, you want to interview the third parties. Here are some of the factors to account for when deciding who to interview: • Who else cares about this problem? • Who else cares about your target? • Who might be an ally or obstacle for any potential solution? 127

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• Where do they fit in the larger picture? • What do they have to gain or lose from a potential solution?

Let’s narrow down your “Third Parties” to be the record label. Here are some of the questions you can ask either your production manager, the lead for your project, or some of the market experts who work there: • What kind of solution would they not like to see? • What kinds of solutions are they expecting? • Are there certain factors that could aggravate or ingratiate one of these parties to you? • What kind of “aura” or “brand” would most appeal to them (e.g., easy, edgy, timeless)?  • What will they get out of a solution (e.g., money, time, happiness)? • What is their motivation to see you succeed or fail? • Why do they care about this problem? • What might their wants be?

After doing a few rigorous interviews, your list for third parties might look something like this: • My label wants the song to be catchy. • My label wants the song to be cheap to produce (e.g., use synthetic sounds rather than live recordings). • My label would prefer the song sound very similar to my last album which sold well. • Critics would prefer more complex lyrics (i.e. they said the lyrics on my last album were too simple).

Jot them all down in the node of your Big Picture Score labeled as “Third Parties.” Once again, you might notice that some of the wants overlap. Others might be very different. The point is, these lists can help you decide later down the line what trade-offs you can make among the different solutions later on. In our song writing example, as you begin to compose your melody, put the lyrics into 128

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place, and add some beats to the mix, you will likely come up with several ideas. That is good. You must carry multiple ideas forward to complete the Big Picture Score. As you listen to each song—let’s say you’ve come up with three different versions of your new hit single—you will check off how each version tallies up and accommodates your wants as the ideator, the wants of your fans, and the wants of your record label. As you will see in Step 5, you will Choice Map to create myriad ideas that at face value, will all sound useful and novel. Your “Big Picture Score,” will help you take a step back and see (1) how desirable each solution is overall, and (2) which parties you favor for each solution. Ultimately, the Big Picture Score helps you choose an idea based on the party or parties you want to optimize for.

YOUR OWN BIG PICTURE SCORE

You now have your Big Picture. You have all your wants with the boxes to check off beside them. And you have all the wants that align and conflict amongst all parties involved in the process of finding a solution that will succeed. You will use these to calculate your Big Picture Score. It’s natural to have competing or conflicting wants as part of your Big Picture—don’t try to resolve those right now. It is impossible to please everyone about everything, which is why you, the ideator, have to choose. In Think Bigger, the act of choosing comes in phases and the Big Picture is used in each phase. In Step 5, it will serve as your selection criteria when choosing amongst the multiple solutions you will create. In Step 6, as you describe your idea to outsiders, your Big Picture helps you remember and articulate the “why” behind your idea. With your Big Picture Score in hand, you are ready to search for the pieces needed to create the ideal solution for your problem. We will now continue to build your core tool to Thinking Bigger: the Choice Map.

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6 STEP 4: SEARCH IN AND OUT OF THE BOX

THE SEARCH

We now return to the Choice Map, where you have your draft problem and subproblems. It’s time to search for tactics and precedents that solve each subproblem to some degree, somewhere, at some time. So our next question is: How do you search? In this day and age, searching is easy, thanks to Google. I type in my key words and voilà! A treasure trove of information populates before me eyes—sometimes into the millions of hits on even the narrowest of topics I choose to pursue. But Think Bigger aims for something much narrower and more focused: real-life examples of success. Must I sift through millions of websites to find them? Here we get help from an unsung hero of innovation by the name of Lloyd Trotter. In the late 1990s, he became the first AfricanAmerican member of General Electric’s executive committee, when GE was the largest and most successful company on earth. Because of this success, Jack Welch, the CEO at the time, became the most famous business leader in the world. Trotter is known in the field of manufacturing operations for helping GE’s factories become models of efficiency and continuous improvement. He was also a leader of the diversity movement across the corporate world, first at GE

STEP 4: SEARCH IN AND OUT OF THE BOX

and then nationwide. Less well known is the method Trotter used for both achievements—in manufacturing and diversity. So far, I’ve given you lots of examples of innovators who used many of the principles of Think Bigger in practice, consciously or not. Trotter is the first person in the history of innovation to formalize those principles into a clear method that we all can use. As Isaac Newton stood on “ye shoulders of Giants,” the Think Bigger method is itself in many ways a product of great minds and forces that have come before it—with a special debt to the brilliant Lloyd Trotter. I had the good fortune to interview him in the summer of 2020. Here is his story. He started out as an apprentice in the Cleveland Twist Drill Company and was the first Black employee on the payroll. They sent him to night school at Cleveland State for a college degree. During his time there, one of the company’s distributors had a problem with a GE machine—and Trotter easily solved it. When a GE employee came to fix the problem and saw Trotter had already found a solution for it, he was asked to work for the company one day a week to redesign the machine. He did. Eight weeks later, GE offered him a job. So began his GE career as a field service engineer for the lighting business. That was 1970. By 1990, he was president and CEO of GE Industrial, in charge of factories around the world. He recalls that it all began with a simple observation: When I started looking at our factories, there were sixty of them all over the world. And everywhere I went, really—I called it sheer genius. You know, somebody was doing something that no one else was doing. And you know, the thought process was that why aren’t we our own consultants? How can I get a best practice from Europe, as an example, and to the US, or a best practice from Mexico to Europe or whatever? And that led me to say, I need a process with rules. And the process was let’s have every manufacturing unit begin to identify where they think they’re world class. They’re not world

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class as a total entity, but they may do certain things well like inventory turns, which means cash management, or labor utilization. I just wanted to make it simple. One to five, five being a best practice, one being you didn’t really know what you were doing. Zero meant that you didn’t even know what we were talking about. That’s where I put together this matrix, to tell me what you think you’re good at along certain key things for manufacturing: inventory control, cycle times, productivity, you name it.

So was born the Trotter Matrix. Ask anyone who worked at GE in the 1990s, and they’ll remember it. The Trotter Matrix is the direct ancestor of our Think Bigger Choice Map. In terms of application, Trotter used it for one specific purpose, while Think Bigger adapts it for problems of all kinds. Here’s how the Trotter Matrix worked: By putting this matrix together, I had each of the plant managers and their finance guys measure themselves on where they thought they were on a scale of one to five. One meant that you really understood the practice, you knew of the practice, but you really weren’t that good at it, or whatever the measurement was. And then a five was you were best-in-class. You believed that you were in the best in the world, in your world or anybody’s world, at doing it. So, everybody did their self-measurements. I then got all of the finance guys and the manufacturing guys together and we went through it. And what happened was, most people gave themselves high marks when they really shouldn’t have. Which I expected, to be honest. There were two manufacturing units, one in France and one in North Carolina, where I thought the way they graded themselves, they were pretty much god-like. I asked the first team to tell the group why they thought they were that good. They got up and they were laughing. It was pretty clear they hadn’t taken the exercise very seriously. So the second team got up. And before they even started trying to explain why they thought that they were that good, the plant manager said, “I lied. I didn’t take this seriously. I want to take the time 132

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to do a redo and be honest with myself.” And that’s what we did. And then that started it, once we were honest with ourselves.

The Trotter Matrix took off when everyone realized how much it helped them. At first, they thought it was a test, where they would get a score, and that would be the end of it. But Trotter had a very different idea. Over the years, he learned best practices by visiting a variety of plants and taking in the best of what each of them did. That’s what he wanted all of the participants to do in an organized way with each other. If you were a three or less, then you will contact the plant manager who is at the highest category, a four or five, and they will be your coach and mentor to show you how they did it so that you don’t have to go through all of the institutional learning in order to create this. You can grab what you want, steal shamelessly what they had, and you’ll have a coach to do it.

The Trotter Matrix became part of ordinary operations. If you scored high in some category, you had to plan over the next year to teach someone who scored low in that category, and vice versa. At the end of the year, each individual had to report on who they taught and who they learned from. It took less than a year for results to show—and for the CEO of GE, Jack Welch, to notice: We were averaging two percent variable cost productivity. And we took the two percent up to seven percent over a six- to seven-month period. That caught Jack Welch’s eye. We were in a meeting about the three-year plan. And he just blatantly said, “Lloyd, what are you guys doing? What are you hiding? And where is this coming from?” The improvements his finance team showed him for us were over the top, better than any he had seen in his career.

After that meeting, the Trotter Matrix spread throughout GE. It was one of Welch’s secret weapons, as he built a thriving 133

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conglomerate of twenty-four different companies in twenty-four different industries. Critics insisted that conglomerates are inefficient by nature, arguing that there was no reason for NBC TV to be under the same roof as GE’s medical equipment business, and so on for the remaining industries. But Trotter offered a reason, and a great one, why the critics were wrong about conglomerates. The more diverse the range of industries interacting the better because it dramatically expands the range of best practices to steal from one another. This way, everyone learns and improves together. Welch appointed the world’s first Chief Learning Officer, Steve Kerr, who made the Trotter Matrix a core feature of GE’s management education program in Crotonville, New York. As new managers came in for training, they all learned how to use the matrix to “steal shamelessly” from throughout GE. Kerr spread Trotter’s reporting system too, so everyone had to learn or teach the method across the whole company, every year. Trotter, and then Kerr, further expanded the matrix to external sources as well. For example, GE’s quality control program borrowed from Motorola, and it took many elements from Japan’s Kaizen. Trotter used the matrix for diversity too: We started our diversity launch in General Electric; we were nowhere. There was an article where GE falls short, in the New York Times. And I was quoted in that article and got a little bit too flippant. I was the only African-American on the corporate Executive Council at the time. And they asked me a question. And I said, “If I manage my business the way we’re managing diversity, I would have been fired a long time ago.” And guess what, they printed it in the paper, that statement. So, I got a call from Jack. I said, “Are you ready to fire me?” And he said, “No, you’re right. So now what are we gonna do about it?” Well, the first thing, we agreed that we didn’t have the answers. At the time there were three organizations—Xerox, IBM, and I believe it was AT&T—who were being touted as best-in-class in getting affirmative action and diversity going in their corporations. 134

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I took a team, not HR people, of operating people to these places. In my mind, relegating some of these social things to HR is the wrong thing to do. They should help. But it’s an operating problem. And we made it an operating problem that I led. And we went to these places. We took notes. And we listened. And then we took the best of all three of those and then formed it and put it into a culture, our culture, the way we thought our culture could accept it.

We can see that in any form, for any problem, the Trotter Matrix begins with humility. Instead of thinking you know the answer, you admit that others, somewhere, at some time, might know better. Trotter cites that humility as key to his original idea: I had an opportunity to start really from the bottom. You know, be one of those individuals using their hands every day and then, at times, ignored when I had suggestions on how to do things differently. I guess that changed my thinking or reinforced my thinking about where ideas come from.

THE CHOICE MAP IN ACTION

Trotter’s original matrix always had the same problem at the top: How to improve my factory? The subproblems were standard factory functions, and the sources were all his factories. He and Kerr adapted it to apply to other problems, across all GE companies. Here’s an example from the GE Way Fieldbook, by Robert Slater: The service people in the Medical Systems business have learned how to remotely monitor a GE CT scanner as it operates in a hospital, at times detecting and repairing an impending malfunction online, sometimes before the customer even knows a problem exists. Medical Systems shared this technique with other GE businesses— in jet engines, locomotives, Motors and Industrial Systems, and Power Systems—bringing overall improvement to GE businesses as a whole. Now GE businesses can monitor the performance of jet 135

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engines in flight, locomotives pulling freight, paper mills in operation, and turbines in customer power plants. This capability gives GE the opportunity to create a multibillion dollar service business by upgrading installed GE equipment.

Here is another example from the early 2000s to show just how the Trotter Matrix worked in detail. Crotonville gave an incoming management cohort this problem: How can GE Appliances move to online commerce? At the time, GE made appliances of all kinds, from toasters to refrigerators. Amazon and other online stores were just taking off, so GE wondered if they should start selling appliances directly to consumers online. But there’s a catch: GE had always sold to big stores, who then sold the appliances to customers. If they started selling directly to customers, wouldn’t the big stores cut them out and buy appliances from their competitors instead? The Crotonville cohort took on this problem and started a Trotter Matrix (see table 6.1). The draft problem was simple: How to move from wholesale to online commerce in appliances? They then broke down the subproblems into the chain of activities for selling appliances. As we noted before, the problem and subproblems stay in draft until the last moment—when you have a solution that fits them. The Trotter Matrix put the twenty-four GE businesses as column

table 6.1 Trotter Matrix (blank) Problem (Draft): Wholesale to Online Commerce in Appliances Subproblems (draft)

Fin. Plast. NBC Med. Power Mort. Appl. Etc.

Customer identification Customer retention Customer credit Wholesaler retention Customer service Distribution

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headings because that’s where they search first: GE Finance, GE Plastics, NBC TV, GE Medical, GE Power, GE Mortgage, GE Appliances, and so on. The cohort then does team management, to decide who searches on which subproblem, who maintains the chart, whether they get together every evening to update each other, and so on. Then they search. Remember that Crotonville has the records of all GE units and what they scored high on, so that’s the first place to search. The cohort also comes from different GE companies, so they give each other leads that way. They follow up each lead by calling the manager responsible for the successful example and asking exactly how it works. Day by day, they fill in the matrix with successful examples they might want to use for their final combination. That means even the cells are a draft—you don’t know which ones you will use. Here’s our example partially filled in (see table 6.2). Behind each X is a short description of the example, why it works, and how it might apply to this problem. In this example, the cohort stopped. That’s because they found their big idea—and it came from a most unusual place. GE Finance had a product that you could only get online if you were a GE employee, as part of its employee benefits package. So, this appliance cohort asked, “What if we offer appliances online only to GE employees and their families, as a benefit? The big stores won’t get mad—it’s just an internal

table 6.2 Trotter Matrix (partially filled in) Problem (Draft): Wholesale to Online Commerce in Appliances Subproblems (draft)

Fin. Plast. NBC

Customer identification

X

Customer retention

X

Med. Power Mort. Appl. Etc. X

Customer credit Wholesaler retention

X

Customer service

X

Distribution

X

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benefit. There are 300,000 GE employees, with family—that makes a million customers, and they’ll all surely buy appliances. We can work out all the technical details with a million customers and make a profit right away, without alienating the big stores.” Note that they did not solve the original problem. Instead, they solved a smaller version. Their final matrix had this as their final problem statement: GET STARTED IN ONLINE COMMERCE FOR APPLIANCES. That’s what they solved. They did not solve the whole problem—which is fine. You can’t solve every problem in two weeks. You can’t solve every problem in two months. Some problems you can never solve. But the Trotter Matrix showed them how to try. And now, Think Bigger does too. As it turns out, GE accepted this idea—which had such positive outcomes, that a few years in, they expanded it to include employees of companies that did business with GE. That created a highly profitable pool of millions of additional customers—and the big stores never objected. The internal appliance program remained in place for more than fifteen years until GE sold off its appliance business in 2016. On that score, note the last X on the Matrix, Appliances / Distribution. Here’s that idea: Why not have employees pick up their online purchase at the big stores that sell GE appliances? The store gets a cut of the profit. Great! Now, you can see how we make the jump from the Trotter Matrix to the Think Bigger Choice Map. We leave off the columns because we don’t limit our search to GE companies. In practice, GE didn’t either, but they did not show those other sources on the matrix itself. In the Choice Map, we make room for all sources in the same place by putting the tactics we find side-by-side in each row. Here’s how the final Choice Map looks for the same appliance problem (see table 6.3). This is a relatively simple problem with a relatively simple solution. You can see how the Choice Map accommodates great complexity beyond that, with space for lots of different tactics from an infinite variety of sources. There are no columns, so it’s no longer a matrix. That’s why we call it a Choice Map: like a map, it shows 138

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table 6.3 Choice Map Problem: Get Started in Online Commerce for Appliances Subproblems

Precedents

Customer identification

GE Finance: employee benefit

Customer retention

NBC TV: success A

GE Power: success B

Customer credit Wholesaler retention

GE Power: success C

Customer service

GE Mortgage: success D

Distribution

GE Appliances: store pickup

possible directions for you to choose. In our case, it’s the combinations to solve your problem. With his matrix, Trotter pioneered formal search as the main cognitive tool for innovation. Remember: all thinking is an act of memory. To solve a complex problem, your brain probes your shelves of memory to find the right pieces of the puzzle. Search adds more pieces of the right kind on your memory shelves, so you have a greater chance of solving the problem by putting some combination of them together. The Trotter matrix, and now our Choice Map, helps you keep track of these mental steps—because for complex problems, it’s all too much to keep in your head.

CURIOSITY PAYS OFF

You don’t need me to point out that Lloyd Trotter was intensely focused on the task of improving efficiency across GE. His matrix approach was groundbreaking in a number of ways, but one of his most valuable contributions was his insistence on putting people with diverse backgrounds, perspectives, and expertise in the same 139

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room to tackle a problem together. That aspect of his work underscores a key tenet of Think Bigger: looking beyond familiar domains for clues to solve your problem. Leonardo da Vinci is possibly the most iconic example of someone who showed us how creativity benefits from gaining knowledge in domains far beyond familiarity and core expertise. Aside from his most famous paintings, the Mona Lisa and The Last Supper, he was a man who filled up notebooks with countless visionary, innovative ideas. A simple flip through these fifteenth- and sixteenth-century notebooks shows designs for machines that were precursors to modern robots, automobiles, helicopters, airplanes, and military tanks. If you were to visit his home, you would find that da Vinci was a man who was constantly learning about many different things—physics, chemistry, anatomy, engineering, painting, and sculpting, just to name a few. As da Vinci studied these various fields, he was always breaking ideas and materials down into their elemental components, collecting bits of knowledge from different domains, and looking for ways to combine them into innovations. By breaking something down to its fundamentals, he realized that those components could be modified and recombined to create something new. I consider him the first person in recorded history to consciously recognize the value of searching out of domain to generate new ideas. You might say this illegitimate son of a notary and a peasant was simply born a genius. Indeed, we often feel great innovators have a unique quality that makes them polymaths, gaining expertise in multiple fields at once. However, he wasn’t an innovator because he was an expert in numerous individual fields but rather because he took the time to explore new areas of knowledge and make connections where others did not. The ability—and curiosity—to search across many domains of inquiry, collect examples, and later combine them into new innovations is the cornerstone of the Think Bigger method. This process was da Vinci’s real competitive advantage—and like him, you must allow your curiosity to guide you. It is, however, important to create a balance between developing expertise in particular areas and 140

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exploring unrelated areas of curiosity. Also, like da Vinci, when you explore other areas of interest, you’re finding tactics for what works best in other domains and the potential strategies you could use.

IN DOMAIN, OUT OF DOMAIN

In our Trotter Matrix, note the two categories of tactics. Using the stores that sell appliances for online appliance pickup is “in domain.” That is, the tactic comes from the same domain as the problem: appliances. Selling online, only to employees as a benefit, is “out of domain.” The tactic comes from finance, a domain that’s different from the problem domain. We have seen this distinction before. For Henry Ford, the Oldsmobile stationary line was in domain, and the moving line of the slaughterhouse was out. For Reed Hastings, Blockbuster was in domain and gym membership was out. In Think Bigger, we make these two categories explicit. Here’s a way to remind you to search for both (see figure 6.1). The first two columns of your Choice Map should include the nonredundant best practices in the industry of your problem. Remember, Henry Ford used the Oldsmobile assembly line as his in-domain best practice to speed up the production of the Model T. Those first two columns are where expertise matters most—they provide us with a clear threshold to explore more creative solutions in the space and establish the value added. As the ideator, first building a strong foundation of in-domain best practices helps you set your goalposts before moving on to the next three columns in your Choice Map—the out-of-domain examples of success. In the Choice Map, we prioritize having more out-of-domain examples. This is because those examples make our ideas powerful—think of Ford using moving tracks from the Chicago slaughterhouses and japanning with black lacquer paint. The search for in-domain or out-of-domain examples requires the ideator to ask, “Who has solved this subproblem successfully in the past?” That means anyone, anywhere, anytime, in any way. You 141

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CHOICE MAP PROBLEM

PRECEDENTS

SUBPROBLEMS

IN DOMAIN

OUT OF DOMAIN

Figure 6.1 Choice Map.

don’t seek “current” examples because you have no information on those. The “past” includes yesterday. Trotter might have thought he studied Xerox’s current diversity program, but in reality, he only had information on what they had done so far. That is, the past. So don’t ask, “What is X doing?” Ask, “What has X done?” In each row, your subproblem will be different. Some rows might be entirely out of domain—which is encouraged, especially if your problem is something unknown within that problem definition’s domain. But no row should be entirely in domain. Again, you must always search out of domain for every subproblem—and there is a good reason for this. Using mostly out-of-domain practices in your Choice Map encourages a more innovative solution. My own data analysis shows that for Choice Maps that hold an average of fifteenplus tactics (five tactics per subproblem with at least three of which 142

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are out of domain), their subsequent idea generating is perceived as significantly more creative by peers and colleagues. For in-domain tactics, recall that in Step 2, when you broke down the problem, you consulted experts to gain what you could of their knowledge base to help inform your thinking. Now is the time to ask those same experts for in-domain tactics. We noted that sometimes you get those tactics during your Step 2 interview about problem breakdown, if the expert is hard to reach or willing to talk only once—in which case, it is fine to set out to ask both questions in that one interview. When searching out of domain, you can't rely on experts in the same way. For out-of-domain searching, your tactics can come from anywhere. Later in this chapter, I explain the art of searching out of domain. It’s more difficult than in domain, but as the data shows, it’s the greater source of innovation. The key to generating your best ideas is the diversity and quality of your out-ofdomain tactics. The Choice Map cells are small to keep the map manageable. Make them larger, and the whole map is too big to see all together. In the cell itself, you can just put a shorthand marker: Ford would put “slaughterhouse,” Hastings would put “gym membership.” You then have a more detailed note behind each cell. We call these “micro-notes.” If you use a digital spreadsheet, this is easy to set up. The micro-note is specific and concise about what matters most for that tactic. The micro-note for the slaughterhouse might read “a moving line with stationary workers speeds production.” The one for gym membership might read “flat monthly fees insure steady income independent of use.” Micro-notes specify the tactic and its benefit to your idea. While you certainly are not going to replicate a slaughterhouse or a gym, you might use a moving line or flat membership fee in your own solution. So, now you know the basic mechanics to use the Choice Map for search. To conduct a productive search, you must understand where everything you find will end up. We now begin the art of the search, starting with the mental discipline of Lloyd Trotter’s key insight: “steal shamelessly” from the 143

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success of others. Above all, this is a frame of mind that you have to embrace and practice, which we will cover here. I offer a word here about legal patents and copyright. These rules vary by country, but in general, you need to make sure that you stay within the law. If you can’t afford to pay the patent or copyright fee, don’t use that tactic. In reality, very few tactics have this kind of legal coverage, so don’t let this fear constrain your search. If you find a tactic that you suspect has a patent or copyright, then look further into it. There is also an ethical question: Trotter tells you to “steal shamelessly,” but stealing is a crime! Of course it’s only a metaphor. Patents and copyright cover actual stealing. Outside of that, it’s more a feeling that you’re taking something without permission. Trotter gives us the ethical answer: cite your sources. His whole Trotter Matrix system makes it explicit where each tactic comes from. You can do the same. Matisse, remember, made sure to cite his sources. Picasso did not. We can conclude that Matisse was more ethical than Picasso. For each tactic, it’s important to have evidence that testifies to its merit. You rarely find facts and figures that show a tactic succeeds, so you need other markers. For example, there was no statistical study to show what percentage of gym profit came from the one tactic of flat-fee membership. Instead, Hastings relied on the simple judgment that a flat fee did indeed reduce other penalties, and gyms that used flat fees seemed many and profitable. We see this even in technical fields where there is a lot of data. For example, let’s examine how Google itself came to be. The Google guys, Larry Page and Sergey Brin, started out using AltaVista as a search engine for their doctoral research. AltaVista was the very first program to automate internet searches, using a web crawler that mined every page on the internet. The chief competitor was Yahoo, where humans coded each page. AltaVista was faster, but was Yahoo still the better search engine because of higher quality results? The Google guys relied on their own judgment—and a growing consensus among users—that AltaVista was better. The next 144

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tactic they combined was library author citations. The more times someone else cited your name in a footnote, the higher your author rank. The Google guys decided to apply that to websites, so the more times another site linked to your site, the higher you would rank. That allowed them to display AltaVista results by rank order, rather than randomly. Only then did the Google guys realize they had a great search engine the wider world might want to use. Before that, they were just working on their doctoral research. The last piece of the puzzle was money. How could they make a profit? Yahoo made money as a portal, where it had everything on the same page: email, news, shopping, weather, sports, and anything else you wanted to put there—plus search. They sold banner ads and popup ads that showed up on the same page. This was convenient, but all of that content on the same page made Yahoo slow to load. AltaVista and Google just offered search, and made no money. If they added banners and popups, it would slow down the search. Their biggest advantage over Yahoo was speed. So this was a no go. Then, the Google guys noticed a website called Overture in their search. It was a search engine without many users, but they were selling ads and displaying them—not as banners and popups—but as search results in a nice little list on the side of the page. Interestingly, these ads didn’t compromise search speed. So, the Google guys wrote that feature into Google—and only then did they overtake Yahoo. Nearly twenty years later, ad search still accounts for roughly eighty percent of Google’s revenue. Until the Google guys found the Overture piece, Yahoo remained the most successful search engine. This demonstrates that you search for pieces of what others are doing, not the whole thing. AltaVista solved one piece of the puzzle, library citations solved another piece, and Overture solved a third. You have to use your own judgment as to whether or not the specific tactics you find work well enough for you to borrow it for your Choice Map. Once you find a promising tactic, ask experts if they think it contributes to the idea’s success. That’s how the Google guys singled out AltaVista in the first place—from expert judgment in the field, including their own. Experts are usually happy to comment on what 145

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works in their field. Don’t stop at just “Yes, it works,” or “No, it doesn’t.” Ask the experts why they think that. Not every expert knows everything about their field. Their explanation will reveal to you the depth of their knowledge and understanding of the tactic you ask them about. Ask experts only about tactics within their domain. When you find an out-of-domain tactic, consult an expert from that domain rather than from the domain of your problem. The relationship between out of domain and expertise gives us a distinct angle on diversity. If you do Think Bigger as a team, members from different backgrounds will have different views on what the problem is and how to break it down. The more diversity the better. But expertise matters too. In practice, people with the most experience in the problem’s domain will tend to dominate these two steps. Experience creates a diversity trap. And discrimination prevents some people from getting experience, so valuing people for their experience can reinforce discrimination. In Think Bigger, we handle the problem of diversity by leveling the playing field. First, when you work as a team, always do each task as an individual. That gives everyone an equal shot at contributing. Second, out-of-domain search cancels out the experience advantage. People with less experience in the domain have fresher eyes when searching beyond it. As we see from our many examples, out-of-domain tactics are more innovative than in-domain ones and often come from non-experts. The Choice Map search is a skill you learn that gives you a creative advantage, despite any lack of experience in the problem domain. With repetition, you become expert in doing it. When thinking of diversity in the context of a Choice Map, always remember that the quality of your idea depends on the quality of choices you include in your map.

LEVERAGING DIVERSITY

We’re told, time and time again, that diversity enhances creativity and performance in organizations. Dozens of studies over the last decade 146

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document that almost every measure of group diversity—nationality, education, occupation, geography, or ethnicity—enhances creativity and performance in organizations. Still, we tend to find a certain difficulty involved in leveraging diversity, one that Think Bigger helps to address. As the late Katherine Phillips illustrated in her groundbreaking research, simply bringing people from different backgrounds together to sit at one table is not enough to reap the benefits of diversity. To leverage diversity of thought, you must have a culture of information sharing and conflict resolution where all voices are heard, respected, and understood. When you encourage people who are from different domains to individually fill in the Choice Map with in-domain and out-of-domain precedents and tactics, you will automatically get an array of non-redundant ideas. By conducting an individual search, and coming together to share the tactics and precedents found per row, each group member will be encouraged to share their ideas—no matter how wild or complex they might seem. In practice, I can say that the Choice Map naturally—and effectively—creates a group dynamic that encourages divergent thought and truly leverages the power of diversity.

WHY NOT FAILURE?

Invariably, when I teach the Think Bigger method, some students ask, “Why don’t we study failures too?” It’s a fair question. Starting in childhood, our parents and teachers tell us that we learn the most from failure. We then go on to repeat that as adults, to other adults, whenever anyone fails. The real purpose of saying this is to keep your spirits up and not take failure personally. Failure certainly teaches a moral lesson—that nobody’s perfect. And bouncing back from failure can help build resilience. Unfortunately, though, many people don’t bounce back. If you think about it carefully, it’s not the failure that helps in your personal growth. It’s how you react to it. The failure itself simply teaches the wrong way to do something. And perhaps humility. 147

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If failure was the best teacher, then I would tell you to fail as much as possible in order to learn as much as possible. Failure hurts, and that makes you remember it. But it’s not at all the best teacher. Imagine that you’re a contestant on a TV show where you have to survive on your own for a month. A helicopter drops you off, naked, in the middle of a forest. There’s plenty of water, and the best source of food is the vast variety of mushrooms growing throughout this forest. I offer you two books to improve your chances of survival: an encyclopedia of five hundred varieties of poisonous mushrooms found in the forest or a short guide to ten edible ones. You may choose only one book. Which do you pick? If you want to become an expert in mushrooms, by all means pick the encyclopedia. But you have a very different aim: to survive. You need to build shelter, find fuel for a fire, devise a means to create flame and collect food to sustain yourself. You also need to make something to wear, especially for your feet. In other words, you have a list of pressing subproblems to solve, and the short guide to ten edible mushrooms gives you the answer to one of them. The encyclopedia of poisonous mushrooms offers answers to none. You might also ask, “Don’t I learn best from my own experience rather than the experience of others? Isn’t experience the best teacher?” Let’s return to my hypothetical forest. This time, you have no books. So you set out to learn from experience. You pick a type of mushroom that looks similar to those you’ve eaten before. You try it and get violently ill. What did your experience teach you? Not to eat that mushroom. You try another type. You get sick again. And so on, through the forest. But then you happen upon another contestant. She’s been there two weeks longer than you—and she’s tried twenty-two mushrooms before finally finding one that didn’t make her sick. Do you ask her to point it out to you, or do you say, “No thanks, I want to learn on my own?” Experience can be a very good teacher, but it’s also the slowest. Learning from your fellow survivor on the island will be much faster—and safer—than testing dozens of mushrooms until you chance upon one that works. 148

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Lloyd Trotter would have needed to spend decades running each of his factories to learn a fraction of what he could gather from the accumulated experience of his plant managers. Picasso spent years becoming an expert painter on his own, but there were lots of expert painters with ideas floating around. He leapt forward in innovation by borrowing from other artists, most notably African sculptors. It would have taken him years to master the techniques of African art through experience. Instead, he learned from other artists. In your Think Bigger search, you will find lots of mushrooms, but only the rare edible ones should make it onto your Choice Map. Another frequent question I get is “Won’t the effort to avoid failure make us risk averse? If innovation is always risky, shouldn’t we embrace failure?” It’s true that some entrepreneurs advise wouldbe innovators to “fail early and often.” They treat each new attempt as an experiment, and some experiments fail. But following the Think Bigger method reduces your likelihood of failure by being systematic—even methodical—about the act of innovating. Let’s look at the scientific method, where not all experiments are equal. There are good experiments and bad experiments. I want you to avoid the bad ones. Think Bigger shows you how. Don’t rely on trial and error—that will have you sampling five hundred mushrooms. That’s a bad experiment—inefficient at best, deadly at worst. By contrast, Think Bigger turns up a higher number of good ideas with a reasonable chance for success. Implementing these ideas will be an experiment, yes, but one with greater odds for victory because you’ve built it from a strong foundation of successful tactics. As we know, an estimated 90% of startups fail. While those are useful to know about, they should not go into your Choice Map. You want to increase the odds that the experiment you are going to try will not fail. Isaac Newton did not stand on the shoulders of failed experiments. He stood on the shoulders of past achievements. Using aspects of previous success stories does not automatically eliminate risk. The Netflix launch was an experiment, but it was based on solid tactics. In science, researchers often spend more time constructing an experiment than conducting it. Don’t fear failure, but don’t seek it either. Think Bigger builds your idea from 149

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solid components to give you a better chance that your experiment will actually succeed.

STRATEGIC COPYING

As we’ve said time and time again in this chapter, there is an art to the search—what Trotter calls “stealing shamelessly.” Let’s refer to this as “strategic copying” instead, to reduce the negative implications. While it might sound new, you actually have been “strategically copying” your whole life—right down to the very words you speak. Did you invent your native language? Of course not. You copied it from others! As is the case for most of what you know. Have you ever heard that, “Imitation is the highest form of flattery?” That’s because imitation—its all its forms—allows us to acquire and transmit culture, norms, and social conventions. Yet, the act of imitation can sometimes lead us astray. Experimenters found this out in a comparative study of human toddlers and bonobo apes. Imagine a scientist standing in a room holding a box. A bonobo, with lanky arms hanging to the ground and bright eyes, wanders in and is shown a box with a treat inside it. The experimenter then begins to move his hands and arms in wild gestures, this time opening the box and handing the bonobo the treat. Again, the experimenter makes wild, random gestures with his hands—tracing circles in the air and drawing lines. The bonobo stands and watches with curiosity, waiting patiently to receive the treat from the experimenter, who hands it to the bonobo after his movements. Next, a four-year-old human toddler named Michael walks into the room, with the same experimenter holding the same box and showing the treat to the child. The experimenter then repeats the same gestures in the air as he did with the bonobo, opens the box, and gives Michael the treat. The experimenter repeats his actions again and again as Michael watches him. Later, the experimenter leaves the room and box. Michael is brought back into the room, where he sees the box. Thinking of the 150

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treat inside, Michael goes up to the box and copies the random gestures he saw the experimenter conduct beforehand—tracing circles and lines mid-air—before opening the box and taking the treat. The experimenters then send the bonobo back into the empty room, with the box in the center. The bonobo simply walks up to the box, opens it, and eats the treat. Michael, the human toddler, copied the gestures even though he didn’t need them to get the treat. The bonobo didn’t do any of that. This does not mean that bonobos are smarter than human toddlers. The point is that humans imitate too much sometimes. There are parts you don’t want to imitate. You just to open the box. We can go back over all our examples and see what parts our innovators did not take. A butter churn has a long wooden pole— Nancy Johnson didn’t take that part. The slaughterhouse workers wear white coats—Henry Ford didn’t take that part. Picasso didn’t become a sculptor in wood—he took just the angular facial features from African art. And so on through the history of innovation. People tend to think copying is wrong. Or maybe that it’s considered stealing. While there are, and should be, legal constraints against certain types of stealing, the act of copying is often linked with creativity. More often than not, when we copy, we’re simply strategically replicating what has been done before us, in order to pull out its most important components. For instance, famous authors such as Stephen King (and his son, Joe Hill) might copy entire pages from other books when they are feeling writer’s block. Many authors engage in this practice, using different styles and rhythms than those they are used to, to spark an idea—and, of course, to avoid that most feared image for any writer who has ever tried to write and nothing comes: the empty page. Failure to understand partial copying blocks innovation. You might say to your boss, “Let’s borrow technique A from company Y.” The boss replies, “We’re different from company Y.” Or “We already tried A and it didn’t work.” In the latter case, most likely they tried A + B + C from company Y—that is, they tried to copy too much. They never tried just A. 151

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Breaking down success to just that part you want to borrow is a rare skill that you can develop. Generally, we tend to see success and think we must imitate everything about it or nothing at all. In reality, you’re looking for the particular tactic behind some of a precedent’s success—a sweet spot. You must strategically copy successful tactics, not imitate the entirety of a successful precedent, to target a solution to your problem. In fact, data from my Think Bigger student projects consistently show that students who believe in the value of strategic copying end up creating solutions that industry leaders and their peers judge as more novel and useful.

WHY OLD IDEAS?

Why, you may ask, do we create a Choice Map filled with old ideas? Creativity and innovation mean something new for the future. That’s the opposite of the past. Right? Well, not so fast. Let’s imagine a company wants to make a new cell phone app to support a healthy lifestyle among its users. It decides to crowdsource ideas for the app. Anyone can submit as many ideas as they want. The ideas go into a database, and everyone rates all the ideas. Starting out, you think you have to come up with something attractive to people that’s easy to use and that’s never been tried before. You do some research. After a while, you begin typing up your idea. You stress that this is a regular, easy-to-use app, but it also has some new features never seen before on a health tracker. You submit your idea to the database. Now you look at what other people submitted. You rate each of these different ideas on creativity, purchasing interest, potential profitability, and the clarity of the submission. You see that they fall into two categories: First, some people have described apps that you know from your research already exist, with minor tweaks. Although perfectly functional, there’s not much new. Second, you see the opposite: ideas that are so novel that they depend on technology that either won’t work on a cell phone or doesn’t even exist yet. 152

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Now you notice a third category. Some ideas fall between “practical but not new” and “new but impossible.” You submit your ratings for all three kinds of ideas: practical but not new, new but not practical, and halfway in between. When all the results are in, you wonder: Which of the three kinds got the highest ratings? As it turns out, we know the answer. Two of my colleagues, Olivier Toubia and Oded Netzer, ran a study with over two thousand people who rated more than four thousand ideas. They used a clever tool of language analysis to determine the novelty of each concept. For example, if your idea pairs “health” and “exercise,” the tool shows how often other idea descriptions make the same pairing. If your idea description has lots of pairings identical to other idea descriptions, it gets a low novelty score. But if your pairings are not similar to other idea descriptions—for example, “health” plus “skydiving”—then your idea description gets a high novelty score. After everyone submitted their descriptions, the study then asked participants to rate all the ideas on “creativity.” Which kind of idea got the highest creativity score: the most novel, the least novel, or in between? Toubia and Netzer reported that “ideas that balance well familiarity and novelty, as measured by the combination of ‘ingredients’ in the idea, are judged as more creative.” Something is “familiar” if you’ve seen it before—in Think Bigger terms, that’s a precedent. Creatively solving a problem comes from taking old pieces and combining them into new forms. Remember that the past started a second ago. Has that ingredient ever existed before? If not, it’s not a viable component of a successful innovation.

TRICKS OF THE TRADE

Let’s return to the problem of how exactly to search. You’re fishing in an ocean that covers all of human experience, throughout all time. And you’re not even using a net—you’re spear-fishing, looking for tactics and precedents one by one. Where do you even start? 153

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I mentioned before the value of seeking experts to find in-domain tactics. But no expert knows everything in their field, and you can’t interview all the experts in existence. Your primary source will be written material. For that, a research librarian can be a big help. More than likely, you will use Google to find most of your tactics. It’s become the biggest library in the history of the world. The question you ask determines the quality of your answers. In domain, you look for best practice, like Trotter did. GE’s inventory of best-practice tactics from each of its twenty-four industries made for a rich source of out-of-domain tactics across those industries. Unfortunately, Google doesn’t inventory best-practice tactics the way Trotter did. I just Googled “best practice marketing new products,” and got over a billion hits. When I scroll through the first dozen or so results, I find not a single tactic. I get mostly opinion on what someone claims is a best practice. Why should I believe them? I have to hunt and hunt and hunt to find a single example where someone explains that tactic X helped company Y achieve result Z. Notice the difference between “best practice” and “best-practice tactic.” At GE, you couldn’t just claim you had a best practice and expect everyone to believe you. Trotter verified the results of each unit’s matrix. When you interview experts, this distinction is key. If the expert says, “The best way to achieve Z is to do X,” what do you say next? Ask for an example. Without that, you don’t have a tactic. You have someone’s opinion. The best solution is for your Google search terms to match your subproblem, even as you speak with experts. Long before Google, Ford asked, “How do other automakers reduce production time?” That led him to the Oldsmobile assembly line. That’s an in-domain precedent. He also asked, “How do other industries reduce production time?” That led him to the slaughterhouse moving line and Ford struck gold with an out-of-domain tactic. That is what you must do with Google. Enter your subproblem in terms specific to your domain, and then enter it as a general question to cover all domains. For example, let’s say you’re trying to improve taxi service, and one of your subproblems is “How do we 154

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make taxi fares cheaper for the consumer?” For in-domain tactics, you search similar travel domains where someone solved that problem. For out-of-domain tactics, you strip away the domain. Go back to the subproblem and leave the domain blank: “How do we make [blank] cheaper for the consumer?” Now fill in that blank with a more generic term that does not refer to any specific domain. We call that domain agnostic. For example, you could say, “How do we make a moderately priced service cheaper for the consumer?” Can you see how this immediately opens a larger world of options to consider? Skilled Choice Mappers master all three kinds of out-of-domain search: agnostic, partial, and parallel. Once you get used to doing it, you will unearth lots of good examples. And the more you find, the more selective you can be. This is different from the dilemma of too much choice because you encounter the tactics one by one. Still, it’s wise to keep a longer list of promising leads that you add to each time you find a good tactic. Then go back and study each one further. Take your time. Select only the strongest tactics to enter on your Choice Map. The best way to think outside the box is to literally go into other boxes. Since domains have different levels, you can also go partially out of the box before making a full jump—this would be a partial search. For example, you could say something like “How do we make personal travel cheaper for the consumer?” That’s wider than taxis but still within travel. You can even target an unrelated parallel domain by first making the subproblem agnostic and then thinking of a different domain that fits. For example, first you ask, “How do we make a moderately priced service cheaper for the consumer?” Then you think of other moderately priced services: laundromats, coffee shops, dog-walkers, moving vans, cable television, and so on. Then you search each one: for example, “How do laundromats provide a moderately priced service?” Here are some examples of the three kinds of out-of-domain search. Draft Subproblem: How can we motivate children to learn math? Agnostic: How can we motivate children to learn? 155

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Partial: How can we motivate children to do something that isn’t exciting? Parallel: How can we motivate children to eat healthy food?       What is the best way to get children to brush their teeth every day?       How do museums and libraries manage the behavior of children?       What is the most effective way to get children to do chores? Draft Subproblem: How can we transport donated organs more efficiently? Agnostic: How can we transport delicate objects more efficiently? Partial: How can we transport delicate medical supplies more efficiently? Parallel: How do food companies keep food fresh in transit?       What is the fastest way to ship glass sculptures?       What is the best way to travel with a newborn?       How do bakers transport wedding cakes to the reception? Draft Subproblem: How can we get customers to trust that our food is healthy? Agnostic: How can we get people to trust us? Partial: How can we get customers to trust us? Parallel: How do banks get people to trust them?       How do car salespeople get people to trust them?       How do chiropractors make clients comfortable and trusting?       Who is the most trusted skydiving company, and what do they do?

Here’s a more thorough example of an out-of-domain search. Let’s say my main problem is how to make the subway a better experience. Well, people often have to wait for the subway. One of my subproblems might ask, “How do I reduce the unpleasantness of waiting?” My agnostic search asks about people waiting in general. My partial search asks about people waiting to board transportation—for example, planes and ferries. My parallel search asks what nontransport places make people wait. The parallel search leads me to Disney World. As you wait in line, they have interactive games and activities to occupy your time. They have express lines, batch boarding, funhouse mirrors, and single rider lines. I also found Best Buy, where a single serpentine line funneling toward all the cash registers means you can’t compare wait times and complain. Whole Foods uses color coding for different lines. 156

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On the one hand, a Think Bigger search is a lot of work. But for people who are curious about how things work, searching is fun and exciting. Remember that the single consistent characteristic of creative people is curiosity. For creative people, a Think Bigger search is a joy.

IDEA-WORKING

Google, the library, other sources of written material: these are all vital for search. Yet we began this chapter with techniques to talk to experts because human beings will always know and understand things in ways that mechanical forms never can. Here we return to the question of finding tactics through experts, a technique that greatly widens the circle of relevant people to consult. Google, the library, and other written material will cite names that you can then approach directly. Or perhaps you might know relevant experts already. To find even more, I encourage you to use Idea-Working. Now, you’ve heard the advice about networking over and over I am sure: Meet as many people as you can. Go to as many conferences as possible. Join committees, attend events, take up hobbies, and chat with strangers in the elevator. The next person you meet could be the one you need to know. The concept behind this approach is that success lies in a numbers game, and quantity gives you the best shot at finding someone useful. Social media makes this even easier. Most people approach networking like a lottery, where you keep scratching the surface and hope to hit the jackpot. The purpose of networking is to widen your net. Alternatively, the purpose of Idea-Working is to widen your idea. The standard advice in networking is to get to know the person quickly. In Idea-Working, you don’t want to get to know the person. You want to cut to the chase and ask them something useful about your problem. Think of each relevant person you meet as an expert and follow the same rules I already gave you to help you in your search. Don’t ask them for their opinion on how to solve the problem. Ask them for tactics that solved one of your subproblems. 157

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The key to Idea-Working is how you end your conversation with each person. You should tell them how helpful they’ve been, but you don’t want to take up too much of their time. Is there someone else they know who might be interested to talk to you about your problem? You might get ten names. You might get zero. A good target is three. You then reach out to each of those three. Ask them for tactics, and end the same way. Ask for three more names. Three becomes nine; nine becomes twenty-seven. Your list of tactics grows and grows. Now you can see the difference between networking and IdeaWorking. Networking involves a large number of low-quality conversations. Idea-Working produces a small number of high-quality conversations. Some people brag that their network is more than a thousand strong. For Idea-Working, it’s rare to go beyond twentyseven for any one subproblem. That’s because you will find that the tactics the experts cite begin to repeat. As soon as your new tactics halt, you should stop. I want you to notice as well the length of time you hold onto your contacts. In networking, it’s forever. You keep as many people as possible on your list because you never know when in the future you might want to call on them. In Idea-Working, you talk to the expert once and then never again. There are exceptions, of course, where the expert proves especially helpful, interested, or friendly. For those, ask if you can get back to them as you think of more things as you proceed. For example, you might get a new tactic, and then ask a previous expert to assess whether that tactic was actually a contributing element of success. If you have five subproblems, start a separate Idea-Working stream for each one. The topics might overlap down the road because your subproblems are interrelated. That’s perfectly fine. Feel free to ask the same expert about different subproblems if they have the time and if it’s relevant to their expertise. Do you notice a difference between networking and IdeaWorking? Which is more pleasant? One of the chief reasons people go to business school is to network. But I know from my students that most of them feel they fail at it. When I ask in class, “Who here 158

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is a good networker?” few hands go up. The stories pour out about how awkward and insincere they feel selling themselves. Introverts essentially experience it as a form of social torture. Whenever I explain Idea-Working to the class, I hear a collective sigh of relief. At that point they are starting their Choice Map search. In the weeks that follow, I get a stream of students who pull me aside to say they started Idea-Working and they thank me for teaching it to them. They even use it for traditional networking events. Instead of the usual networking chatter—where do you work, where did you go to school, I have a dog too—they describe their problem briefly and ask if the person can think of any tactics. It might not lead to actual results, but it’s a much more interesting conversation and makes for a better traditional networking contact. Networks have become a major topic of study for social science research. Scholars recognize two kinds of network ties: strong and weak. You can guess what they mean: a strong tie is someone you interact with on a regular basis, while you rarely interact with a weak tie. Idea-Working gives you weak ties. A famous study by Mark Granovetter found that weak ties produce greater knowledge because strong ties tell you what you already know. Many studies since Granovetter’s have confirmed the power of weak network ties. In 2007, Lee Fleming, Santiago Mingo, and David Chen compiled a list of more than thirty-five thousand inventors who had worked on patents with at least one other person. A strong tie meant you worked on that patent with someone you already worked with before on a previous patent. A weak tie meant you worked on that patent with the person for the first time. The results? Weaker ties produce more patents and also more creative ones: meaning, inventions that spanned categories rarely or never seen before. Here’s another weak-tie example. Giuseppe Beppe Soda, Pier Vittorio Mannucci, and Ronald S. Burt studied the list of producers, directors, and writers for the long-running television show Doctor Who. They found that producers often worked on consecutive episodes, but directors and writers did not. They came up with a creativity index, and then judged episodes according to whether or not 159

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the producer, director, and writer worked together before. Their results showed that the less that team worked together before— that is, the weaker the ties—the more creative the episode. We can see that weak ties lead to more out-of-domain tactics, and they give us further guidance to take advantage of diversity too. Strong ties tend to be family, friends, and people you work with. These are typically less diverse than weak ties. Idea-Working gives you a reason and a technique to seek out those more diverse ties. It’s basic human nature that the more different people are, the harder it is to communicate. Idea-Working makes it easy. Even when you reach out to a complete stranger, the nature of your question immediately breaks the ice. You’re not trying to be their friend or even add them to your network. You’re asking an interesting intellectual question and looking up to them as an expert in the subject. Most people are flattered and happy to talk. The more diverse the experts you reach, the more creative your solution, thanks to more out-of-domain tactics you find.

HEDY LAMARR STEPS OUT OF HER BOX

For Think Bigger, an “expert” is someone with experience in the field of your problem or subproblem. Chances are, you’re not an expert in all—or even any—of those fields. You might feel, as a novice, that somehow it’s an act of hubris to take on a problem that experts haven’t solved. Well, we’ve already seen some examples of innovation by novices: Nancy Johnson wasn’t a mechanic or engineer, and the NASA Jet Lab team knew nothing about ventilators. Let me give you another example that you might find even more striking. It’s about the Hollywood movie star Hedy Lamarr. Born in Vienna to a Jewish family, Lamarr became a film star at the age of eighteen. Six years later, MGM Studios brought her to Hollywood and promoted her as “The World’s Most Beautiful Woman.” The year was 1938, and she quickly became one of the film industry’s top leading ladies. In her spare time, she liked to tinker with ideas for inventions: for instance, she tried creating a 160

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new kind of traffic light and soda tablets you could add to water, to name a couple. When she made friends with the eccentric millionaire Howard Hughes, he agreed to fund her experiments. In time, a portable laboratory and a staff of assistants followed Lamarr around everywhere she went—even onto the movie sets. As an Austrian Jew familiar with European politics, she knew that war was coming, and she always kept that possibility in the forefront of her mind. When the war began, the first threat for America was the German U-boat fleet. It ruled the North Atlantic. Without American help, Europe would fall to the Nazis and America would become the next target. So Lamarr set out to direct her tinkering to solve the U-boat problem. U-boats were incredibly difficult to stop because they jammed the radio signals that guided Allied torpedoes. As soon as a torpedo launched, the U-boat would pick up the signal and send out its own signal at the same frequency, directing the torpedo astray. In 1939, Philco released a wireless remote control for radios. Lamarr set out to modify it in such a way as to prevent another signal from jamming it. The Philco remote was a cube six inches wide with a dial on top like a telephone. You dialed the frequency you wanted. But what if the torpedo launched at one frequency and then changed to another one along the way? Before the U-boat could figure out the second frequency, the torpedo would arrive. Lamarr got that idea from the player piano. It works by a mechanism that you wind up; then, on its own, the piano turns a roll of thick paper with holes that match the piano keys. As the paper rolls, it activates different keys. Why not launch the torpedo with a similar roll that moves the radio receiver from one frequency to another? A simple motor mechanism in the torpedo could turn the roll—just like in the player piano. Lamarr was a trained pianist and played duets with her friend George Antheil, a composer known as the “Bad Boy of Music.” He shocked the music world with his symphony Ballet Mécanique: it used sixteen player pianos, two grand pianos, electronic bells, xylophones, bass drums, a siren, and three airplane propellers. Antheil wired the player pianos so they played together. 161

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As the story goes, Lamarr had her aha moment sitting at the piano with Antheil. He hit a key and then she hit the same one at a different octave. Then a different key, and so on. She proclaimed, “Hey look, we’re talking to each other, and we’re changing all the time!” What she did at that moment was strip her subproblem of its domain: how to communicate automatically as the message changes—on the piano, between player pianos, and to the Allied torpedo. Lamarr and Antheil built a portable radio remote control with a thick paper roll that moved among different frequencies as the holes rolled past. They made it for eighty-eight different frequencies in honor of their source, the eighty-eight keys of a piano. They received U.S. patent number 2,292,387 for a “secret communication system.” Alas, the American military ignored the invention. They had their own scientists working away at all kinds of problems and ignored outside innovations. Remember weak and strong ties? But eventually, they did come to their senses. Twenty years later, during the Cuban Missile Crisis, they adopted Lamarr’s frequency-hopping for torpedoes. Later on, it became a key component of other wireless technology as well, including Wi-Fi, Bluetooth, GPS, cordless phones, cell phones, and other digital devices. In 2014, long after they both passed away, Lamarr and Antheil were inducted into the National Inventors Hall of Fame. I offer this story to show you how nonexpert outsiders can learn just enough about a field to put together an innovation for it. I hope you see how Hedy Lamarr’s invention followed the steps of Think Bigger. Even if you’re not an expert, you can do it too.

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A GREAT COMBINATION

Louis Jordan, “King of the Jukebox,” was one of the leading musicians of the era just before rock and roll. One of his most famous songs is “Beans and Cornbread.” These two start out fighting, but then they realize how well they get along, like wieners and sauerkraut hot dogs and mustard sisters and brothers chitlins and potato salad strawberries and shortcake corned beef and cabbage liver and onions red beans and rice bagels and lox sour cream and biscuits bread and butter hot cakes and molasses

Louis Jordan is the poet of great combinations. That’s your aim in this step of Think Bigger: to select from the tactics you found to

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make a great combination. And like beans and cornbread fighting at the start of Jordan’s song, you might first think two tactics can’t possibly go together. But the most creative solutions come from such combinations where at first you can’t see the connection, and then you do—like the Philco remote control and the piano roll in the mind of Hedy Lamarr. You will recall from our basic Think Bigger diagram that the six steps don’t proceed in a straight line. The curved arrows show that you actually go back and forth between the various steps as you proceed. This is especially true in Choice Mapping, when the solution starts coming together. This is hard to see in our examples because there’s no complete record of what the innovator thought at each step. You have at least a thousand thoughts every day, and just one step of Think Bigger can entail many thousands of thoughts. You now understand your problem, which has a fair amount of complexity. You have an idea of the “Big Picture,” and the terms of what you want. A mix of your research and experience helped you identify a collection of tactics that you can draw upon to start imagining and reimagining different combinations until you find one that works. It sounds simple enough! Yet, we know that it is never as seamless as the stories told in hindsight. When we think about the creation of “big” ideas, we often assume that the people behind those “big” ideas have stories that are greater than life. If you sift through all the details and roundabouts, it might surprise you to learn that even the biggest ideas of the biggest personalities were created the same way as any other innovation. They defined and broke down their problem, identified tactics far and wide, and combined them in a useful, novel way that makes sense to those who matter for making the solution a reality. To introduce you to the practice of Choice Mapping, I want to tell you the story of someone who had a really big idea—one of the great revolutionaries of the twentieth century, who not only accomplished something big in his lifetime, but left an indelible mark on future generations. Unlike prior examples described in previous 164

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chapters, this time, I am not talking about a scientific discovery, a piece of art, or a new business. Let’s now look at an “idea” that is used by people across the globe as if it were a familiar technology. I am referring to the strategy of nonviolent civil disobedience. The “father” of this idea was Mahatma Gandhi. If you read the stories about Mahatma Gandhi—of which there are dozens of variations—they will describe to you a man who, in his youth, was extremely shy and a failed lawyer. They might tell you about the difficulties he had in the familial relationship with his father and his wife. They might try to uncover the eccentricities of his mind and heart. But to get his big idea, he did exactly what I am asking you to do. Let me tell you how I believe Gandhi created the idea of nonviolent civil disobedience. As I tell you this story, I focus not on the man and the story of his life but solely the pieces he combined to create his big idea. The problem that Gandhi wanted to solve was, “How do I help the Indian people gain independence from the British Empire?” What were his subproblems? There were three that were critical to solve for: 1. How can a subjugated people take effective action against a mighty power without violence?  2. How can I unite people divided by religion, caste, language, and geographic region?  3. How can I gain support for new ideas in the face of traditional Indian beliefs?

The solution to his first subproblem was inspired by Britain itself. On a trip to London in 1906 as the leader of the Natal Indian Congress in South Africa, he observed the actions of the suffragette movement led by Emmaline Pankhurst. There, a new generation of women pressed for the right to vote through the organized program of getting arrested to make headlines and gain sympathy for their cause. Their main tactics were marches that the authorities declared illegal and hunger strikes that “tore at 165

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Figure 7.1 (left) Emmeline Pankhurst’s daughter Christine Pankhurst and another suffragette pictured with their picket sign; (right) woman being arrested in protests for voting rights.

the hearts of the public.” Upon observing the actions of the British suffragettes, Gandhi famously wrote in an article published in South Africa for the Indian Opinion of November 1906, “Today the whole country is laughing at them, and they have only a few people on their side. But undaunted, these women work on steadfast in their cause. They are bound to succeed and gain the franchise, for the simple reason that deeds are better than words.” He provocatively challenged Indian men to emulate the “manliness” shown by English women. Gandhi adopted the strategy used by Pankhurst and developed it further as a philosophy and set of methods for nonviolent civil disobedience that are known today as Gandhian techniques. For his second subproblem, Gandhi turned to Leo Tolstoy, the great Russian novelist. Tolstoy was a noble, and toward the end of this life he turned his family estate into a classless society. His 166

Figure 7.2 Leo Tolstoy in the woods by his estate.

Figure 7.3 The “Tolstoy Farm,” in South Africa, later named the “Ashram.”

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followers set up Tolstoyan communes to follow his principles of perfect equality among all people, where all could live and work together. Gandhi set up one just like that in South Africa and called it “Tolstoy Farm.” He used it as a way to bring people together in equality and teach them his methods before leading them out to get arrested. Acts of civil disobedience can provoke violence from the police or angry mobs, and the protesters themselves are often tempted to fight back. They need training first, and immersion in the philosophy and techniques of non-violence that Gandhi taught them. For his third subproblem, Gandhi looked to an ancient Indian tradition: the holy man. All the religions of India recognize this figure, and Gandhi started talking, acting and dressing the part. You can see this progression in what he wore. When he first discovered the suffragettes, he dressed in the style of an English lawyer. When he set up Tolstoy Farm, Gandhi dressed like an Indian peasant, just as Tolstoy himself started dressing like a Russian peasant on his classless estate. Gandhi then changed the name of the farm to “ashram” and dressed like an Indian holy man, in a white loincloth and a single white cloth over his naked chest. An ashram is the headquarters of a holy man. Last but not least, he invented a new term to replace “nonviolent civil disobedience:” satyagraha. In Hindi, satya means “truth” and graha means “hold to.” He became a holy man spreading the philosophy and methods of satyagraha throughout the country. Gandhi took entirely disparate tactics—from both the East and the West—and combined them in a way that transcended the boundaries of culture, language, geography, and religion in India. He thus created a movement that encompassed all his subproblems and, ultimately, solved for his main problem. The idea of nonviolent civil disobedience is “big.” It’s powerful. It has become universal and has become the go-to tool for people around the globe calling for justice. This tool was most famously used during the Civil Rights movement in the United States in the 1960. But even today, people continue to pull out this strategy for solving myriad social problems. 168

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Figure 7.4 Gandhi pictured as a young man dressed as a lawyer (left); Gandhi on his Tolstoy Farm dressed in all white as a peasant (middle); Gandhi dressed as a “Holy Man” (right).

CHOICE MAPPING: THE TOOL

Recall the quote in chapter 1 from the great French polymath, Henri Poincaré, “Invention consists in avoiding the constructing of useless combinations and consists of the constructing of useful combinations, which are in infinite minority.  .  .  . To invent is to discern, to choose.” This quote famously appeared in his 1913 book The Foundations of Science. In it, he further explained that “invention” is choosing useful combinations amid a multitude of useless ones and, “Among chosen combinations the most fertile will often be those formed of elements drawn from domains which are far apart. Not that I mean as sufficing for invention the bringing together of objects as disparate as possible; most combinations so formed would be entirely sterile. But certain among them, very rare, are the most fruitful of all.” Here, Poincaré endorses our Think Bigger emphasis on outof-domain tactics. And he would know. His ability to combine “disparate” elements made him perhaps the most creative scientist who never won a Nobel Prize. For thirty years, from 1882 until his death in 1912, he derived and applied advanced mathematical formulas to an astounding variety of problems: celestial 169

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mechanics, fluid mechanics, optics, electricity, telegraphy, elasticity, thermodynamics, quantum mechanics, gravity, relativity, cosmology, topology, number theory, electromagnetism, differential equations and algebraic geometry. Yet none of these subjects fit a Nobel category. Poincaré may not have won a Nobel Prize, but his work did lead to a Nobel Prize being awarded to one of the greatest scientists in history: Albert Einstein. While working as a patent clerk in the Swiss Patent Office of Bern, Einstein would spend his days sitting at his desk reading proposals for inventions of all kinds. In his downtime between reading patent papers, he would read Poincaré, singing his praises and claiming in his journals, “Poincaré realized the truth [of the relation of everyday experience to scientific concepts] in his book.” Einstein recognized in Poincaré the beauty of searching beyond the parts of the world that interest you and exposing oneself to the unfamiliar. Historians and scientists alike have claimed Einstein as the clerk behind the review of patents for the gravel sorter and the electromechanical typewriter, among others. He also filed more than 50 patents, including patents for the refrigeration system, the sound reproduction system, and the automatic camera. These inventions, and Einstein’s exposure to them, are responsible for triggering his famously powerful thought experiments on relativity. Einstein even refers to the Bern patent office in his 1905 Annus Mirabilis (miracle year) papers as his “worldly cloister,” where he “hatched [his] most beautiful ideas.” You now have a Choice Map that includes your problem, your problem breakdown, and a row of tactics from best practices within industry as well as a number of best practices from entirely different industries. These are the materials that you will use to generate multiple solutions to your problem. In a prototypical 5 × 5 Choice Map, which, in its simplest form, has the potential to give you 3,125 different creative combinations, you start by taking one tactic per row, line up the five tactics—one per subproblem—in your head, and then you ask your mind to imagine how might you combine these tactics to make a solution. Recall the Nobel Prize winning 170

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work from Eric Kandel from chapter 2 of Think Bigger. Your mind works like a giant library system with shelves and shelves of information bits and, anytime you form a thought, you take information bits from different shelves. Imagine how those bits of information can be combined and recombined in nearly infinite ways to create a thought. When Choice Mapping, I am being deliberate at putting in front of you what information bits you have to work with so that you can be deliberative about combining your various tactics. So, I am helping to jog your memory about those tactics, but like any other thought exercise, you are lining up the set of ideas in your head and asking yourself, “What could I imagine?” and, “What could I create if I were to put these pieces together?” Notice how, when Choice Mapping, you are necessarily creating “useful” and “novel” combinations because you are only combining tactics that provide a solution to one of the subproblems you have identified. And, by taking tactics from diverse industries, you are ensuring that the combinations you generate will be both useful and novel. This is not to say that they will all be equally good. Some combinations will be better than others—and the way you decide is by using your Big Picture Score, to compare and contrast which of the ideas you create are in line with the greatest number of your wants. Whenever we engage in the task of coming up with a really good idea, we find ourselves at various points stuck. We hit a wall and try to figure out how to scale that wall to solve the problem. Unlike other methods of innovation, Think Bigger does not rely solely on mind wandering or taking breaks. Think Bigger is not hoping that the pieces will randomly come off the shelves in your memory bank and form connections. Instead, having an informed Choice Map in hand gives you all the relevant pieces that you need. With these pieces in front of you, anytime you are stuck, anytime you see that the current solution in mind is not working, all you must do is identify a different set of tactics from the very same Choice Map and imagine, “How might I put these pieces together in a way that could work?” 171

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TAKE YOUR TIME

As you start to Choice Map, I urge you to resist the temptation to rush to your first good idea. If you’ve done a lot of brainstorming in the past, you might feel at this point that Think Bigger takes too much time. Remember that one reason brainstorming is so popular is that you can get it over with fast. One hour, two hours, maybe a whole day—but after that, you’re done! For quick answers to problems within your experience, that’s fine. But for creativity, that’s a mistake. The legendary basketball coach John Wooden once said to his players, “Hurry up but don’t rush.” In Think Bigger, you can certainly hurry up—by carving out the time in your schedule to do each step one right after the other. But don’t rush. That is, don’t do the step itself so fast you do it badly. This is especially important in Choice Mapping. You have all the elements you need: the problem, subproblems, tactics, and Big Picture. Quick—let’s throw together a solution! But Learning+Memory shows that the quicker the solution, the less creative it is. You need time to let your mind make less obvious connections. I want to show you an exercise that demonstrates the value of persistence, where you don’t settle for the first and most obvious solution. I’ve done this hundreds of times with thousands of students. Take out a pen and paper and think of a toothpick. Now time yourself. In the next two minutes, come up with as many ideas as possible for how to make use of a toothpick. Ready. Set. Go. Two minutes are up. Stop. Label that list #1. Let’s do it again. For another two minutes, make a new list of ideas for using a toothpick. Ready. Set. Go. Two minutes are up. Stop. Label this new list #2.

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The third time’s a charm—so let’s do it again. In two minutes, make yet another list of ideas for how to make use of a toothpick. Ready. Set. Go. Two minutes are up. Stop. Label this list #3. That’s all—three rounds are enough. Now count how many ideas you had in each round. If you’re like most people, you probably had more ideas in the first and second rounds. Now do this: circle the best ideas. Those are the ones you think are the most novel and valuable. Now I ask, “Which list has the most circles?” In your first toothpick round, you probably went after the lowhanging fruit—the most common uses for a toothpick. In the second round, you felt a bit stretched as you struggled to come up with new ideas that you’ve never actually seen a toothpick used for. When I asked you to do the third round, you may have said to yourself, “Not again!” But lo and behold, when I pushed you further, you came through with your best ideas. Most people have the most circles in list #3—those are your best ideas. Mostly, the fewest circles were in list #1. Note that the quality of ideas tends to go up each time you do it. The first list has the most ideas, but the last list has the best ideas. When I teach Think Bigger, I show how data across the board indicates that as the quantity of respondents’ ideas decreases, the quality of the ideas actually goes up. This might lead you to become a bit demotivated—but the key factor in this step is to persist beyond what you think you’re capable of. In Think Bigger, we want better ideas, not more ideas. But to get to those better ideas, you have to persist, keep going, and not be satisfied with your first ideas. The psychologist Brian Lucas ran an expanded version of our toothpick game. He asked his students to come up with ideas—for ten minutes, in two rounds—for what to eat or drink at a Thanksgiving dinner. Before they started, he asked them to predict which round would yield more original ideas. Then they did the two rounds. After, they rated all the ideas on a 1–3 scale for originality. So what were the results? I think you won’t be surprised to hear

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that students predicted they would do better in the first round, but the more original ideas came in the second round. These experiments make an eloquent case for persistence but in an unusual form. We mostly think of persistence as the endurance to complete a task: climbing a mountain, learning to swim, or working until midnight on a major project. We see now that persistence works for ideas too. Your greatest ideas will only come to you when you make yourself comfortable with being uncomfortable. It’s important to keep Choice Mapping. Keep trying different combinations until you find one that is in line with your “Big Picture” Score. Often, we trip ourselves up in the creativity process by reminding ourselves to, “Be creative.” Telling yourself to be more creative only puts more pressure and distracts you from your ability to come up with your best solutions. My colleague Melanie Brucks studies the phenomenon dubbed the “creativity paradox,” where the act of telling someone to think creatively actually makes them less creative. It’s quite common for a teacher or manager to tell you to “think creatively,” or “use your creativity” to solve a problem. Now think: Did that actually help you be more creative? In one study, Brucks had two thousand people use different products—toys like Lego bricks, office supplies like paper clips, and mobile phone apps—to play “creative” games that would put them in a creative mindset. They did this for an hour every day over two weeks. For half the participants, Brucks told them to write down their most creative ideas after playing the game. She told the other half to simply write down their ideas. The results showed that the participants who were told to be “creative” produced far fewer ideas—and far fewer novel ideas. Brucks concluded that the “creativity” mandate adds too much pressure and offers no guidance on how to think creatively in practice. Think Bigger removes both obstacles. It offers clear guidance on how to be creative and applies no pressure to be novel in the process. Quite the opposite. It explains how no singular element of your solution is novel. Paradoxically, this helps you be more

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creative. If you focus on following our Think Bigger steps, the creativity part will take care of itself

CHOICE MAPPING IN PRACTICE

Before we get our hands dirty and practice Choice Mapping, allow me to share with you an example of a Choice Map using one of my favorite modern innovations. This example shows the thinking that goes on between and during our different steps—specifically, how the innovator’s mind moves around the Choice Map and back and forth from their Big Picture Score. Problem, subproblem, tactics, wants, combination: they rise and fall as waves in a current that move you toward your solution. The problem domain is meat: or rather, not-meat. Recent research tells us that meat production accounts for some 15 percent of global warming—and beef is the worst offender. Yet global meat production keeps climbing. As people get richer, they eat more meat. One attempt at a solution has been to make beans and other vegetables look, cook, and taste like meat. So far, that hasn’t worked. The market for such substitutes has grown fast, but it’s still a drop in the bucket. Besides, even their biggest fans concede that these substitutes don’t really look, cook, and taste like meat. Enter Ethan Brown, an environmental engineer with a green sentimentality and a track record of solving hard technical problems in the energy-meets-climate space. With this unique background, he set out to solve the problem: how to make not-meat a viable alternative to meat. His interest in this—that is, his desire—came straight from his own field of environmental science. I was able to interview Brown in early 2020 and get a better understanding of how such a great problem-solver goes about his work. Here now, is the story of Choice Mapping from the mouth of the innovator himself: In fine Think Bigger style, Brown broke down his problem: Problem: How Do I Help Non-Meat Products Replace Meat?

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Subproblems: 1. How to make a healthy alternative that looks, cooks, and tastes like meat? 2. How would I make a meat-like substance without animals? 3. How do I make this alternative the same price as traditional meat?

Right away, you can see that the makers of the existing meat substitutes might have started out with this same breakdown. Brown starts his search and sure enough, for subproblem #1, he finds plenty of tactics that combine various natural vegetable foods, especially soy. These are all the existing substitutes. His search confirms they are not sufficient—they look like meat, but they don’t really cook and taste like meat. But that’s all right. He knows that one subproblem is only part of the story. Perhaps he will find elements to improve the existing substitutes when he searches on the other subproblems. Now he searches for subproblem #2: a meat-like substance without animals. Pause. Hmmm . . . Isn’t that the same search as #1? No. Those vegetable substitutes are not actually “meat-like,” if you look at the science behind them. And Brown is a scientist. He thought, “Perhaps their basic composition is why those substitutes fall short, rather than how we process them.” He now asks himself, “What is meat, really?” He studies that question and finds that it has a chemical structure very different from all the components of the vegetable substitutes. Sure, soy has protein, but it’s not meat protein. So Brown asks, “Has anyone created meat protein without animals?” We can see that Brown reframed subproblem #2 so much so that it now became the problem at the top of a new Choice Map. Problem: How Can a Product Resemble Meat in Composition? Subproblems: 1. 2. 3. 4.

How do I get the same meat proteins from non-meat sources? How do I re-form them into a new substance? How do I get people to accept this as real meat, not a substitute? How do I match the price of traditional meat? 176

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For his search, Brown dove into the scientific literature about the composition of meat and its proteins. That led him to a subset of scientists working on how to break down plant protein and reform it into animal-like structures. That was out of domain from his own background, but in domain for the problem. Then from his own domain, energy engineering, he found the next piece of the puzzle: procedures from hydro fuels that build proteins back up into a new substance. That was out of domain to the problem. That took care of subproblems #1 and #2. For #3, he found a key tactics in the dairy industry—a related domain. In the anti-fat trend of the 1990s, milk gained an unfair reputation as an unhealthy food. The “Got Milk?” ad campaign reversed that image by showing that milk was good for you—and of course, you liked the taste. Could he do the same with his new product? Would he be able to convince the public that his creation wasn’t a substitute for meat—it was meat, just in a different form? The answer was . . . yes. It worked! The result was a product that looked, cooked, and tasted far more like meat than traditional vegetable substitutes. On his innovation, Brown founded the company Beyond Meat in 2009 and its initial public offering came in May 2019. As of July 2022, Beyond Meat has a market cap of $ 2.36 billion making it the world’s 3396th most valuable company. Brown spent an enormous amount of time on the search stage, and because of this, he changed the overall problem along the way. He read everything he could, found early academic research on protein re-formation, and thought about it all day every day—even when he was in the shower and walking his dog. He had a problem, framed it in a unique way, had in- and out-of-domain tactics, and immersed himself in the search. This is what allowed him to create the unique combinatorial solution that’s now Beyond Meat. Although Beyond Meat is doing well, Brown is still working on subproblem #4: “How do I match the price of meat?” A good place to start would be searching for tactics in supply chain methods across all industries. Today, he’s still chugging along, just as passionate, dedicated, and motivated as he was at the beginning, when he went all in on a big idea. We do not know how the idea may turn 177

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out, but as of July 2022, Beyond Meat is the leading meat substitute brand in North America.

NOW YOUR TURN

Let’s do a Choice Map example together. For the purposes of our exercise here, I have picked something that many of us use in our everyday lives—that way, it is familiar. But of course, you can apply this same method to all kinds of problems. Problem: How can I efficiently carry a good computer mouse with my laptop? Subproblem 1: How do I make it easy/convenient to carry? Subproblem 2: How do I make it difficult to lose? Subproblem 3: How do I make the tracking function work smoothly?

To search for tactics, begin in domain. You might find something like a high-tech key-chain charger that solved the first subproblem. It clips onto keys, or a belt, and stores the cord inside the device. A search online will turn up a best-selling lanyard specifically for a computer mouse or stylus. That solves the second subproblem by using an audio input slot as a place to attach it. You might find the Logitech mouse, which works on any surface, is ergonomically designed, and even has customizable buttons. These are all indomain tactics. When you combine these tactics, you might come up with a highly serviceable state-of-the-art piece of equipment. But it won’t be very creative. A lot of people in the tech industry would think it looks familiar. To be more innovative, we look out of domain. For the first subproblem, we look more generally for anything that gave customers something easier to carry. Credit cards! Much more convenient than bills and coins or even a checkbook. But what type of credit card? They’re not all equal. We look further and find that the Discover Card is ultrathin, mostly blank, and has no raised numbers or features. It’s the easiest credit card to carry. 178

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For the second subproblem, we look for anything that found a new way to keep track of stuff. Sticky notes! Bright, colorful, selfadhesive, and lightweight—such simple qualities really help the average consumer keep track of almost anything. The colors especially distinguish it from the typical gray or black mouse. For the third subproblem we ask, “Is the mouse the only way to capture hand movements?” A mousepad is another way, but that’s in domain. Out of domain we find motion sensors for home or office security, or simply to switch lights on and off. The Fibaro motion sensor detects light, heat, motion, and has voice controls—all of which open up possibilities beyond the normal hand movements for a mouse. Here is the Choice Map I built for this example (see figure 7.5). Now we make different combinations. I’ll give you two ideas I had when Choice Mapping for this example. What if we combined the key-chain charger and the stylus lanyard, both of which are in domain, with the motion sensor? We might create a small spherical sensor you place on any surface to

don’t leave a scky residue

Figure 7.5 Choice Map filled in, computer mouse example.

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make the area around it a touchscreen that mimics your computer screen. You can program it to recognize certain finger movements or even voice commands for specific functions like “zoom” or “scroll.” Finally, the device comes clipped onto a lanyard and has a coiled USB clip for easy charging and carrying. Another combination uses the Discover Card, sticky notes, and the Logitech mouse for a minimal mouse style that comes with a self-adhesive sleeve—like a wallet that you attach anywhere on your laptop. Some cell-phone cases have similar thin wallets to hold credit cards. Our extra-thin mouse slides directly into this sleeve and only comes in bright colors, making it harder to lose. These are just two of many possible combinations. I want you to take a few minutes and make some of your own. By taking one tactic from each subproblem row, what can you create? What type of solution can you come up with? Write it down, study it, and consider whether it would work and how. Do this a couple of times. Take it as a chance to practice your combining and reimagining skills and try to create a solution to this problem. After you do this, you have practice in Choice Mapping. The next time you do it, the task will come a bit more easily. As with any complex skill, you get better with practice. You learn to Think Bigger and Bigger.

STAY OPEN-MINDED

Recall that in Step 4, we emphasized the value of bringing together a set of individuals in a team who offer diverse perspectives and knowledge. Diversity helps you make the best version of your Choice Map, in which you have unexpected and counterintuitive tactics. In the Think Bigger process, I am going to give you strategies that help you become more comfortable doing the mental gymnastics of combining seemingly contradictory tactics. That’s how we’re able to come up with those truly novel and useful combinations. Research clearly shows us the value of the unfamiliar. Over eleven years tracking the progress, popularity, and awards won by fashion 180

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designers, data indicates that designers who spent more time living in a foreign country were more likely to win fashion awards. They were also more likely to be recognized as unique and innovative. For example, Chinese-born designer Uma Wang made headlines as her clothes stormed the catwalks of Paris, London, and Milan during Fashion Week—recognized mainly for her contemporary draped designs and innovative pairings of textured fabrics. In an interview with Vogue France, Wang claimed she drew from her Chinese heritage and her work in both Shanghai and London. With these influences, she was able to combine Eastern references and Western counterparts, with a focus on developing new, innovative materials. Similarly, we see the same phenomenon in the designs of Oscar de la Renta, who grew up in the Dominican Republic but also worked as a couture assistant in Paris. Studies show that bicultural and biracial individuals are more likely to perform better at creative problem-solving tasks. When people are exposed to contrasting cultural narratives, it makes them more creative. This is because they have learned to see connections between ideas that would otherwise, in a local context, be viewed in opposition. Now I am not saying that to be creative, you must be biracial, bicultural, or a world traveler. The point is that having a malleable and open mind is what makes those with bicultural backgrounds more creative—and this is a trait any of us can adopt with a few simple tricks embedded in the Think Bigger method. Let’s do an activity that puts you in the right headspace to come up with an idea that’s novel but remains familiar at its core. I want you to take out a pen and piece of paper. Imagine you’re given the task of coming up with ideas for new products that your local university bookstore could sell. First, you’re presented with the image of a fishing pole. The fishing pole is metal and sturdy, equipped with a hand grip, line guides, and a reel. The fishing line is wispy, and it’s weighted down by a small hook at the end. Considering this image, list all of the ideas that come to mind. Then, step back and take a few minutes to think about the image. Afterward, pick up your pen and write down a final idea. Put that list aside and instead imagine a whiteboard and repeat the same process. 181

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Now I want you to go back to the initial list of ideas you had for the fishing pole. As you look back at them, start to contemplate nine objects you’d typically see in a bookstore—a calculator, index cards, mugs, keychains, pens, notebooks, textbooks, a hoodie, or backpack. Then, come up with some new ideas for your final product that the bookstore could sell, keeping in mind the fishing pole and those nine objects. Write these down. Now repeat the previous step for the whiteboard example. For the third and final activity, look again at the list of ideas you came up with for the whiteboard. As you think about your final product, keep in mind nine objects that are rarely found in a bookstore—for instance, a piano, a Swiss Army knife, a hammer, a treadmill, a helmet, jewelry, roller skates, speakers, or a handbag. Great—you have your ideas! Now look down at your lists and compare them. When creativity expert and researcher Justin Berg performed this study, it showed how beginning inputs strongly shape the end outputs when dealing with creative idea development. If you’re anything like the subjects in his studies, the ideas you came up with when presented with an unexpected item for a bookstore, such as a fishing pole, were likely more novel than the useful ideas you came up with when presented with a typical bookstore item, such as a whiteboard. In the third experiment, those presented with the familiar whiteboard, and then nine unfamiliar bookstore items, actually came up with the most creative ideas. They were even rated by bookstore employees and customers as the most novel and useful. It’s important to understand that the first bit of content we’re presented with as we generate new ideas often anchors the trajectory of the novelty or usefulness of our final products. Being presented with the expected or unexpected completely changes the way we go about ideating. That’s why it’s so important for you to have more out-of-domain ideas written in your Choice Map. By having more out-of-domain tactics, you have the chance to put novelty at the front of your big idea while keeping usefulness in mind. And that’s why Choice Mapping is so unique. 182

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STRATEGIC COMBINATION

The art of Choice Mapping first involves strategically combining the cells in your Choice Map. If you’re in a group setting, everyone must first, as individuals, select one set of tactics from each row—circle or highlight them in different colors to keep track. Then, using your highlighted tactics as inspiration, write down a paragraph describing your ideal combination. As you draft your description, ask yourself, “If I had $100,000 to bring my combination to life tomorrow, what would it look like? Would it be a product? A service?” Use your imagination to connect the dots! Once you have your first paragraph written down, I want you to repeat this process again—this time using different tactics from every row. Ask yourself, “How would this look different than the last combination you imagined?” Once you’ve used every tactic in your Choice Map, share each description with your fellow group members and debate whose idea seems the most feasible—while prioritizing novelty. If you’re on your own, share this description with those around you—your roommate, your friends at dinner, your family at the next gathering, your colleagues, or your gym buddy. Beginning the art of Choice Mapping with this process is important because it gives you a threshold. Just know that, at first, you will naturally anchor to an idea that already exists in your head. With the right tools, you can get past this. Let me show you how.

RANDOM COMBINATION

There sometimes comes a moment in Choice Mapping where you feel stuck. Nothing “sparks” in your mind. No combination appears. Here I offer you a way to combine different tactics without first seeing how they go together. I recommend this step even if you do see a good combination already. You’ll see why. If you’ve been working on your own Choice Map through this book, you now have it filled in. If your map has five subproblems 183

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and five tactics for each one, and you take one tactic from each subproblem. Remember, that makes 3,125 possible combinations. You can’t possibly try them all. But do try at least five of them using something like dice, or a random number generator. You can find one easily on Google. Enter five rows and five columns, and it spits out a new combination for you to assess. At first, you might look at that set of five tactics and think, “I’ll never imagine anything from that.” Don’t sell yourself short. Of course you won’t see something right off. The pieces won’t simply snap together in an elegant, logical way. Let the combination incubate in your mind. Here is some encouragement from the philosopher David Hume: “Nothing is more free than the imagination of man; and though it cannot exceed that original stock of ideas furnished by internal and external senses, it has unlimited power of mixing, compounding, separating, and dividing these ideas, in all the varieties of fiction and vision.” Note how Hume extols the power of human imagination, but he keeps it within the limits of your “stock.” Sometimes, when I first explain how the best ideas come from tactics, someone will protest, “But that limits my imagination!” Hume would reply, “Correct.” As Learning+Memory tells us, the only source for your imagination is the stock of what you’ve learned. And remember the problem of too much choice: if you don’t limit the elements you set out to combine, your brain goes into cognitive overload. It either shuts down or falls back on whatever is familiar. Let’s work through the idea of random combination in more detail. From your 5 × 5 Choice Map, let’s say the dice or the random number generator gives you the numbers 2-5-3-1-2. That means you will take the second tactic from subproblem one, the fifth tactic from subproblem two, the third tactic from subproblem three, the first tactic from subproblem four, and the second tactic from subproblem five. Those are the five tactics you now take the time to imagine in combination. I recommend at least five rounds of the number generator to give you five different random combinations to try.

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Here are some comments from my students about their Choice Map experience: In the past week, I tried very hard to combine my most random, out-of-domain tactics. I thought it would feel impossible or make little sense, but after trying it, I actually came up with more inspiring ideas. In the phase of combination, I found that I was not thinking outside of the box as much as I would have liked. So, I pivoted my method of choosing and decided on the most out-of-the-box tactics in each row to come up with some of my best combinations. It was in the out-of-domain combinations that produced the most promising results for me. Pushing myself to think out of my comfort zone definitely paid off.

Personally, I prefer to start by only combining the out-of-domain tactics in order to encourage the most novel combinations. If you’re working in a group, get your random numbers and have each person come up with their own combination from it. Then show each other the combinations you came up with. You will be amazed at how different the solutions are. Right away, you will also see that Hume was right: limits don’t hamper your creativity. You can then go on to see if there is a way to improve or combine different solutions that the group came up with. Think of this as a sort of combination of combinations. Don’t be afraid to swap pieces in and out from solution to solution. That’s part of the beauty of Choice Mapping—that despite the limitations in place, there are so many possible choices to make. Have the group make a list of all possible combinations and then review them one by one. Pick out the ones that are most novel and solve the problem most fully. Next comes the Combination Template (see figure 7.6). This is a summary of your solution, with just the subproblems and the single tactic for each.

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Figure 7.6 Subproblems and domains.

Build this Mini Map for up to five combinations. More than that creates too much choice. You next assess each Combination Template in light of your Big Picture Score. I’ve found that this quickly brings one or two Combination Templates to the forefront. Sometimes the phases of search and Choice Mapping lead you to somewhat revise the Big Picture Score itself. But if nothing fits, you might have to go back one or more steps to restate your problem or subproblems, search again, or make more Choice Mapping combinations. Time and time again, those I teach this method to are surprised and impressed by how many interesting ideas they get by using dice or the random number generator. Figure 7.7 shows one such idea based on the Combination Template in figure 7.6. Your aim at the end of this step is to have one or two Mini Maps that make a solution you want to implement. But implementation comes after Think Bigger. Our next chapter shows one last step 186

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Figure 7.7 Final Choice Map write-up.

before that, where you take the idea that formed in your mind and expose it to the world.

HOW MANY IDEAS?

Now, you might be asking, “OK, but how many times do I throw the dice? And how many times do I have to reset this random number generator?” Here’s my answer: do it until you have at least three combinations that feel right. In a Random Combination Template, make sure you have your main problem written at the top, your subproblems on the left-hand side, and only the tactics that you randomly rolled. That’s it. Once you have those pieces to make up your Combination Template, write a one-paragraph description of what your tactics combine to create. I want to challenge you—even when you think you’ve completed the minimum Combination Templates to keep up your search. Continue to roll the dice or load your random combination generator to 187

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pull together the cells in your Choice Map. I want you to combine cells in your Choice Map until you have built out as many Combination Templates as you can to compare with your Big Picture Score and see which one is most in line with it. Alternately, you might find ways to combine different elements from each combination that allow you to recombine and create a more ideal-feeling end result. Remember the power of persistence that we learned from Brian Lucas! To get the highest-quality ideas, you must persist beyond what you think is possible. In Think Bigger, my students typically come up with ten to thirty ideas over the course of three hours. Now, that certainly seems like a lot! But, if you’re working in a group setting, the ideas will accumulate rather quickly. You will begin to narrow your list of ideas to those that are simple. Since each combination is theoretically novel and useful, simplicity is a critical secondary criterion as you move forward with several combinatory ideas since many people across domains, geographies, and demographics will need to be able to understand it. Once you have narrowed your combinatory ideas down to at least three, you are ready to choose which idea you will take with you to the final step of Think Bigger. If you find yourself with less than three ideas, create more random combinations until you have three to move forward with.

HOW DO YOU CHOOSE?

As we mention throughout the book, Think Bigger is nonlinear, so it is important to have all your pieces handy from all the steps you completed. This To help you choose which combinations to use in Step 6, bring back the “Big Picture Score” you created in Step 3. Recall that you identified the wants of all parties involved in the success of your idea—your target, yourself, and third parties. Since you and/or your group members will inevitably anchor on the sexy, top-of-mind ideas and circle on them, you are going to constrain

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your ability to choose the best combination to proceed. On top of that, each combination will be better or worse than one or the other for different reasons. So, we go look back to the work done in Step 3 to give us our choosing criteria. Now, take your three to five combinatory ideas—if you have more combinations because you are conflicted, that is fine too. No matter the number of combinatory ideas you have, one by one, you will individually measure each idea against your Big Picture Score lists from Step 3. Recall, each category in your list had a box beside it (see figure 5.1 from chapter 5). That list is what you will run through and check off as you test whose wants each Combination Template falls in line with. When working in a group, you must individually go through the Big Picture Score and check off the boxes on your own before coming back as a group and coming to a consensus. When you have checked off the boxes in each category—the target, yourself, or third parties—make note of which stakeholder is the weakest. If any of your combinatory scores reach a tie, you must look at how many boxes are checked for each stakeholder and determine the one you want to prioritize and optimize for. Which idea scores the highest for all parties? Which idea scores highest overall—and which group does is weighted the most in that high score? The idea you should prioritize, and ultimately should move forward with, should score highly across all parties. If you are a visual learner, use the diagram from figure 5.4 to plot out your scores and see where one category leans heavier than any of the others. We go back to the Big Picture score because it gives you the opportunity to also look across all your Combination Templates and say to yourself, “Well, this idea scored highest with my target but I want it to score a little higher with third parties. Can I take a piece of the other Combination Template that scored higher with my third parties and recombine it with this other idea?” You can mix, match, and optimize your idea by using the Big Picture Score to get the best idea possible.

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Once you’ve completed your Big Picture Scores, take the idea with the highest score and see if your idea: 1. Answers all your subproblems. 2. Collectively improves the current status of competing products in the marketplace (for instance, Pfizer drastically improved the vaccine marketplace by creating an mRNA-based vaccine that was more effective than the antigen-based flu vaccine). 3. Is liked by you and your fellow group members.

You are now ready for your sixth and final step of Think Bigger. You see your idea—now the question is, do others see it too?

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8 STEP 6: THE THIRD EYE

Y

ou now have a raw idea in the form of a Choice Map. The temptation is to rush out and quick! Prototype it, pitch it. Ready, aim, fire! Hold on. Step 6 is the last phase of ideation before you move on to action. In this step, you must do a series of feedback exercises to learn if you have an idea that’s different from the ideas that already exist. If so, how is it different? What possibilities could it create? You will also try to learn if people understand and interpret the idea in your head the way you intend them to. This is a critical step in ideation: deliberately gathering input from others to help give final shape to your idea. Before I begin, let’s play a song.

SCRAMBLED EGGS

Yesterday All my troubles seemed so far away . . .

These eight words are among the most famous song lyrics in music history. Yesterday was an immediate #1 hit for the Beatles in 1966, and since then more than 2,000 other artists have recorded it,

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including Aretha Franklin, Elvis Presley, Marvin Gaye and Frank Sinatra. MTV and Rolling Stone named it best pop song of all time, and BBC radio named it best song of the twentieth century. One Beatle wrote the song: Sir Paul McCartney. During the summer of 2020, I had the good fortune to speak with him, and I asked him about Yesterday. My question was: “How did you get the idea?” It was 1964, and the Beatles already had great success with singles, albums and live performances. McCartney was twenty-two years old. He woke up one morning with a tune in his head. To help remember it, he quickly added nonsensical words: Scrambled eggs Oh my baby, how I love your legs . . .

Imagine my delight as Sir Paul sang to me these original lyrics. With just the tune and those few words, he took his idea to the other Beatles, his producer, other singers, and just about everyone he ran into. He wanted to make sure it was not a song he heard before, that he copied without knowing. He asked if it was familiar—did it remind them of some other song? Note what he did not ask: whether they liked it. The song grew as he asked around. Each time, the song was a little different. He noted how people reacted to it, both in their words and facial expressions. He was feeling his way toward the best version of his idea. While on vacation in Portugal, during a four-hour drive from the airport to his friend’s house, McCartney rolled the melody around in his head and turned his mind to the lyrics. He sang them over and over in his head until they sounded just right—slightly poetic, a bit lethargic, and unmistakably nostalgic. Upon arriving to his friends home, McCartney excitedly grabbed the first guitar he saw and played his melody while singing the lyrics, “Yesterday, all my troubles seemed so far away . . . ” He explained to me that the first time he played the guitar to hear if the lyrics fit with his tune, he played it badly. The only guitar his friend had was a right-handed one and McCartney was lefthanded. But still he realized something very important: the lyrics 192

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fit! Hearing what was in his head played back to him, he now understood how to take his imagined song and make it real. His new idea was ready to show his band and their producers. When he played it for the other Beatles, he asked them how to arrange it for their foursome. They all gave the same reply: McCartney alone should sing it, with nothing but his guitar. But George Martin, the band’s producer, gave them another idea: McCartney should sing it with a string quartet backing his vocals. Surprised by this suggestion, McCartney replied to Martin, “We’re a rock ’n’ roll band! Why would we ever include a string quartet?” Martin replied, “Try it. If you don’t like it, we’ll just take it out.” So they tried it. When McCartney listened to the result, Martin’s idea clicked—it made the piece feel whole. The way McCartney explained it, he and Martin understood the same unspoken things about the song. They had the same idea and they were seeing it play out. The story of “Yesterday” is a good illustration of the Third Eye Test. The ideas McCartney had from his own conception, plus the feedback from others, slowly melded together to become a new and improved thing. Had he not gone to his bandmates, producer, or close friends to hear their input and rework his original idea, the song would never have become what it is today. That’s why for me, the most remarkable part about this story is not that original tune popping into his head. We know how that happens, especially when the mind is relaxed. Tunes often popped into McCartney’s mind while he was lying in bed. Rather, it was his immediate questioning of his idea in such a structured way—a methodical approach that we now know helps to refine the quality of any such idea. When he woke up to a tune in his head, McCartney intuitively knew he had to answer several questions: First, is this tune different from what already exists? If so, how is it different? Second, if I were to create this song, how can I learn what people hear? And, as I learn what people hear, can I learn how to create the song that I want to sing? It can be said, after that initial germ of an idea arose, that McCartney instinctively went through a 193

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quasi-version of what I want to give you next, as our last Think Bigger step. I call it the Third Eye. Before implementing his idea—that is, making the actual recording—McCartney showed the patience and discipline to spend the time to find out if others saw what he saw, felt what he felt, and experienced what he did upon hearing his new idea. You will do the same with your Choice Map. It’s time to put your new idea out to the minds of other people to find out if they see it too.

I SEE WHAT YOU MEAN

Think of the feeling you get when you explain something complex to someone else and they say back, “Oh, now I see.” It feels good to you because you realize that what you imagined can form in someone else’s imagination too. They don’t just understand. They see and feel it too. What they “see” is a complex thought that forms in their mind. Like all thoughts, an emotion comes with it. It evokes the pleasure of success when they see what you see, plus the same emotion you feel about the idea itself. Neuroscience locates this spark of understanding in your working memory, in the prefrontal cortex. It’s the part of the brain behind your forehead. In Hindu and Buddhist philosophy it’s called the Third Eye. You might have seen paintings of gods or saints with a third eye on the forehead. Like much of secular Asian philosophy, modern science confirms this phenomenon. Note that the location is not in what neuroscientists call the visual cortex, which is located at the very back of your head. And yet in English, and many other languages, we say we “see” an idea when it forms in our minds. That’s because visual understanding is actually just as complex as the idea you’re trying to get across. Walt Disney understood this, and in 1958 he made a short film to demonstrate it: 4 Artists Paint 1 Tree. Four of his studio artists all painted the same old oak tree on a mountaintop, all at the same time. The results were completely different in style. You would never know they painted the same exact tree. 194

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In the Third Eye, you will discover that your idea is like each artist’s interpretation of the oak tree. It is subjective to the eye of the beholder—you, who had the idea, and the people you will inevitably test your idea on in this Step. Now, I want you to look out a window and start describing everything you see. Imagine what you see is a photograph, and then divide it into a hundred squares. Describe fully what’s in each square. Then divide it further into a thousand squares. Your description has much more detail. Divide it again, and you’ll realize that it’s impossible to describe everything you see. Like a digital screen, you can keep going until you have a multitude of tiny dots. Describing each dot, and how they combine to form shapes, will take you close to forever. When you look out the window, your visual cortex lets you take in the whole scene—but it then connects to your working memory to view the scene in a meaningful way. Your eyes pull from those thousands of dots and shapes and colors a subset that strikes you at that moment. Those pieces come together in your working memory and you “see” something you can describe in words. The same thing happens when a strong memory pops into your head. They are all versions of the Third Eye. In Asian philosophy, this Third Eye takes on a mystical cast as an organ of enlightenment. But even in English, “enlightenment” has two possible meanings—spiritual or practical. The spiritual version means you connect with some higher power. The practical version means you “see” in the scientific way I describe here. McCartney enlightened me about how he wrote the song “Yesterday.” I understood it, and felt it too. In Think Bigger, the sixth step leads you through a series of exercises that help you see how others see your idea. How is it different from what already exists? What do others focus on when they learn of your idea? How can you use that information to create greater alignment between your intentions and others’ perceptions? This step is not about convincing others your idea has value. It’s not about getting customer feedback so that you can tweak it to make it more attractive to them. That happens after Think 195

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Bigger, in implementation. It’s also not about getting votes. It’s about whether the idea is worth doing. Your friends will vote “yes” because they like you, and strangers will vote “no” because your idea is different from their ideas. Furthermore, it’s not about getting buy-in from potential investors, partners, or other allies in implementing your idea. That too comes after Think Bigger. I give you this list about what the Third Eye is not because all these techniques are standard advice for gathering feedback. Yet all those techniques skip a key step that answers the following questions: Do others actually understand your idea in the way you want them to, both intellectually and emotionally? And does their understanding alter your idea or how you describe it to the outside world? Once you do this step, you will ask yourself, is it worth pursuing? If yes, you’re done with Think Bigger and you can move on to implementation.

DO YOU LIKE IT?

Most people are naturally social. In the modern age, social media has turned this natural desire into an industry. It looks to me, as the mother of a teenager, that anytime a person sees something new, they take a picture and post it on Instagram or some other platform. Then they refresh their device every five minutes to see what new things others posted too—not just for their photos but for the number of views and the comments on them, from simple “likes” to back-and-forths that can go on for days or even months. As a scientist, I wonder: Do these view counts, likes, and comments reflect what people actually think in any meaningful way? I found some answers in the work of Duncan Watts from Microsoft Research. He and his team created an artificial music market on a website they called the Music Lab. There they offered users fortyeight unknown songs by unknown artists to listen to. The site recorded fourteen thousand visitors. But not everyone heard the same thing. Watts placed a fifth of them in an “independent judgment group” that got brief excerpts of each song and rated 196

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each one as to whether they wanted to download it. The rest of the visitors made up eight different “social influence groups.” There, you got the song excerpts but also the past record of how others in your group rated the songs. In the social groups, the songs that received early praise ended up the most liked across the board— and the songs with the lowest early ratings were the least popular. In the social groups, visitors rated a song according to how others rated it before rather than relying on their independent judgment. There are two extreme positions revealed in this study—both objectively good music and objectively bad music influence overall market performance. The tricky thing about “like” and “dislike” in this context is that such emotions are subjective to the end user’s personal taste. Watts’s study eloquently revealed that there is a little truth to both feelings when there is an underlying objective consensus. But that consensus only exists at the extremes on either end of the spectrum. Songs rated as “best” by those in independent worlds were also rated “best” across all worlds—similar to McCartney’s “Yesterday.” Likewise, the songs rated as “bad” among individuals were rated poorly among all groups. Songs rated in the middle of the distribution had less consensus across the multiple social worlds. What we end up seeing is that we often end up weighting social influence more heavily when we don’t know how we feel. If your song was really, really good—like “Yesterday,”—or really, really bad—like “Scrambled Eggs” might have been, no matter which world the song is heard in, there is more consensus. In other words, only in the cases where the songs were in the extremes did the ratings offer any useful information. Most songs—more than 95 percent—were not the outliers. In which case, the judgment of their value was purely subject to social influence. This makes knowing whether a song was “liked” or “disliked” uninformative. Simply asking someone, “Do you like it?” produces superficial reactions at best. At worst, they reflect biases of the moment—how others voted—or deeper biases from longer experience or preference. When someone says they like your idea, that itself isn’t useful because you cannot know the countless possible biases that contribute to their judgment. 197

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I pay special attention to the “like” problem because social media has made it a worldwide habit to rate things willy-nilly. It’s a normal human tendency to want other people to like your idea. The Third Eye seeks something much harder to get at and far more valuable. You must make it clear that you’re not asking others to judge your idea because they will naturally want to do so. Using the Third Eye to test your idea is a complex task that calls for the same degree of thought and care that you gave to the other five steps of Think Bigger.

LISTEN TO YOURSELF

The first step for the Third Eye is to tell yourself your idea. Write it down and then say it aloud. Next, without writing anything down, describe your idea to yourself, again out loud. Then write down what you said as best you can recall. Don’t record it! This is an act of memory. Now edit what you wrote for clarity and meaning. Read it over a few times. Then put it aside. Again from memory, speak out loud your idea and description. Repeat. You will find that your idea and description change each time to some degree. Speech is creative: as the thought of what we want to say finds its way from our brains to our mouths, we see new ways to say it. We then hear what we said and see that it’s different from that initial thought. In a very real way, speech creates thoughts. As you hear yourself speak, you create in your mind an image of your idea. Is it the image you intended? Does it present the feeling you want to convey? Do you see what you wanted to see? As a blind person, I am very aware of the power of speaking your thoughts. Technology today lets me “read” by converting text to speech. So to read, I listen. I can tell right away when the words of the text, spoken aloud, create in my mind the image and feeling the author wants to convey. This is true for everyone, blind and sighted alike—but people think that just reading ideas has the same effect in the mind. It’s not even close! The science of the Third Eye, the 198

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prefrontal cortex, and thought formation back me on this. Speak, hear, think, repeat. Keep in mind the Big Picture and Choice Map Template throughout this step. Both inform the way you communicate your idea. Use the “Big Picture” to remind yourself of what you want to achieve and ask yourself, “How do I want to feel about the solution?” And how will others feel about this solution? In other words, why do you care? Where the Big Picture is meant to remind you of your hopes, the Choice Map Template keeps you grounded in communicating the problem and solution in a concrete way. This will ensure your rhetoric doesn’t get too lofty or tend too much toward just the problem or just the solution. Once you can say your idea out loud to yourself fluently, without looking at your notes— you are ready to say it to someone else. Find someone you trust—this can be a friend, your romantic partner, a coworker, your two-year-old—it can even be your pet! Tell them to simply listen without any reaction. Saying the same words to yourself and to someone else are two very different experiences. In the presence of other people, when we feel they are observing us, we are better able to quiet our internal minds and see ourselves from the perspective of an observer. We see things from their point of view, even when they say nothing at all. The simple knowledge that someone is listening makes us process information through the eyes of an observer. Once you’ve said your idea out loud to someone else, note any difference from when you spoke just to yourself. Did I change the way I worded the idea? How did I phrase one part of the idea compared to how I said it to myself? Did I feel at ease saying the idea to someone else? Make note of the changes you made and how you felt saying your idea and then move on to the next type of feedback in the Third Eye.

WHAT WORKS

You are now ready to approach experts in your domain of interest. But you will not ask them for feedback in the way they’re used to. 199

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You don’t want their judgments about your idea. You want very specific advice for the solution you came up with. Remember that the other person knows nothing about Think Bigger. They might have experience with one or more of the steps because Think Bigger mimics how creative ideas and innovations come to be. But very few people are conscious of these steps, even when they do them. Assume ignorance. Don’t tell them how you got your idea. Cut to the chase and tell them what it is. You aim to get across to others what the problem is, what your solution is, and why it’s important to you. Say that to them right up front: “Can I tell you my idea to see how it sounds to you?” Beware: even that neutral preface might lead them to conclude that the idea sounds, “good,” or “bad.” After you state and describe your idea in the same words you used on yourself before, quickly ask your next question before they get a chance to vote “Yea” or “Nay.” So what questions do you ask, exactly? You can’t say, “Do you see what I see?” because they can’t possibly know what you see. They only know what they see. And you can’t ask questions too different from normal feedback questions or else they will take too long to puzzle through what your question means. Here’s a simple way to proceed that helps tease out from their minds what’s most useful for Think Bigger. We call it What Works. You: Can I describe my idea to you? Answer: Sure. You: (Describe idea) Then, ask: • What worked? Why? • What didn’t work? Why? • How would you improve the idea?

To get meaningful answers, you’re looking to talk to people who might know and care about the idea to some degree. To find them, you can use the technique of Idea-Working for these Third Eye questions too. One expert leads to another. Still, don’t be surprised, despite your efforts to keep things neutral, if someone 200

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says they like or don’t like your idea. This kind of opinion typically implies “If I were in your shoes . . .” Of course, what they would do is different from what you would do because you have different desires, abilities, and knowledge. So make it clear you’re talking about yourself. You’re not asking them what they would do to solve the problem. Some people will hear your question as “Will this idea work?” They might rush to give their opinion on that. However expert they might be in the domain, no one can predict the future, especially about new ideas. So if they opine on whether or not it will work (they will mostly say new ideas won’t work), listen politely, and then bring them back to your questions. You’re not asking about the overall idea. You’re asking what part works, what part doesn’t, and if they have any suggestions to improve it. These are unusual questions for most people, so you might have to rephrase them a few times to get the person to understand. Keeping judgment out of the discussion reduces bias too. If the person even just thinks, “I don’t like this idea,” they tend to follow that bias and give you a long list of what doesn’t work. And if the person actually says they don’t like the idea, you then tend to follow your own bias and discount whatever else they say. That makes for a double whammy of confirmation bias. In addition to our three questions, be prepared to answer other questions and explain your idea more fully as needed. Be open to the possibility that these conversations can last longer than you expected. That’s usually a good thing, as it allows you to speak more in depth. In that case, you might reach into your Choice Map and explain the tactics that underlie your solution to one or more subproblems. Try to make it a natural flow rather than a formal presentation of the Think Bigger method. Analyze each conversation right afterward. Did they make any assumptions about the subproblems that you didn’t anticipate? Did they identify a subproblem that you missed? Did they identify a new tactic? Should you replace a current tactic with a different one? Do they identify a different way of thinking about your combination? What are the variations in how you might imagine your idea? 201

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Once again, keep going until you no longer get new inputs of consequence, and your understanding of the idea no longer evolves. At that point, you’re ready for the next form of feedback in the Third Eye.

PLAY IT BACK

I want you to close your eyes. Picture a dog—any dog at all, perhaps your favorite. Now imagine a pair of pants on that dog. Yes, picture the dog wearing pants. I want you to take out a piece of paper and draw that dog wearing pants. Now look at what you drew. Is your dog tall? Or short? Long? Light fur? Dark? And what about the pants? Do they cover two legs or four? Did you color them in? In all one color, polka dots, stripes, or another pattern? Is there a belt, suspenders, or buttons? How much of the leg do they cover? I’ve had thousands of students do this exercise. They have drawn dogs wearing pants on just their back legs, just their forelegs, on all four legs as a single garment, and as two separate ones. The pants appear in multiple colors, styles, designs, and even material— corduroy, for example. The dogs were even more various: short legs, long legs, short tails, long tails, no tails, short hair, long hair, short ears, long ears, short snout, long snout. There is actually an ongoing debate on the internet about what a dog with pants should look like. The instruction, “Draw a dog with pants,” seems simple enough. But different people see different dogs and different pants—and, therefore, different combinations of the two. We might think that when we see something in our mind’s eye and say it out loud that other people see what we see. With the dog and the pants, the only way to know is to see what they draw. In Think Bigger, we can’t see the picture that forms in the mind of the other person. So we can’t see if they see what we see about our idea. We tend to think that what we think is the truth is true. The late social psychologist Lee Ross calls this phenomenon “naïve realism.” We think we’re being logical, objective, and rational—and therefore accurate in our analysis, judgment, and decisions. So we 202

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think that if other people are logical, objective, and rational, they will agree with us and see what we see. But the opposite is the case. Every human brain is different. Everyone’s life experience is different. Everyone’s desires and knowledge are different. You might think you’re being realistic—that is, that your ideas match reality, but that’s impossible. It’s only your interpretation of reality, which will always be different from someone else’s. When two nations play each other in the World Cup, the fans of each country decry the referees for missing all the infractions that the other team commits. Without fail, each fan base swears that the referees are biased against their team. When two groups with opposite political beliefs about an armed conflict watch the same media broadcast, each believes that the reporters are biased against their view. And so on for every possible topic that people can disagree on. Human perception is always subjective. Immanuel Kant’s seminal work on the subject, Critique of Pure Reason, dates from 1781. Since then, psychologists have continued to formally document countless variations on the same theme. Despite this long history of analysis, people will still tell you they are being objective when they give their thoughts. You can’t address this directly with people, but it helps to use subjective language: What does this conjure up in your mind? What does it make you feel? What thoughts does it provoke? You don’t want their cold analysis. You want to learn as much as you can about how your listeners experience your idea. In this phase of the Third Eye, you refine your understanding of your problem through the Third Eye of other people. I call it “Playback.” You do this with a different set of people—in particular, nonexperts. First, explain your idea in less than five minutes. Then tell them: “It would be helpful for me to hear you describe my idea back to me.” Note how this is very different from What Works. Playback is a much shorter, simpler conversation—in two parts. You want them to answer right away, of course. Just say thanks. Don’t discuss it further. Then a day or two later, ask them again. But this time, don’t repeat your own description. Say, “It’s a day later—I wonder, what do you remember? It would be helpful for me if you could describe 203

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my idea back to me.” Don’t warn them ahead of time you will do this. You want the second question to come as a surprise. If the person comes from your problem domain, wait longer between questions. It could be a week, or even a month. This is because their own knowledge and interest will lead them to think about the idea afterward. Wait for that thinking to peter out. You need a period where they haven’t thought at all about the problem, so they have to think back and remember. Playback tells you several things about your idea. How well do you communicate your idea at this stage? What is most memorable about your idea? What are other people’s emotions regarding the idea? Are they enthusiastic, bored, or skeptical? It’s not a test of how accurately they remember your idea—it reveals what your idea makes people remember, and why. When you hear other people describe your idea back to you, they naturally have gaps in their memory. They will not retain the pieces of your idea they don’t recognize—and that is where you strike gold. The pieces of your idea that your Playback partner does not recognize will be changed by them, in order to fill in what’s unfamiliar with the familiar. The differences you observe between what you described versus what is said back to you will open new doors for you. In the words of New York University neuroscientist Joseph E. LeDoux, “Added connections are therefore more like new buds on a branch, rather than new branches.” We use Playback because it helps us expand and build, where feedback is simply designed to solicit advice. Playback is unique because we now understand that learning is more about recognizing and extending preexisting patterns than scribbling over what came before. Highlighting the unique pattern you have constructed (your current idea) and continuing to iterate, little by little, will set you up for greater success. Memory is not a mirror—it’s reconstructive. This is the cornerstone to understanding Learning+Memory. You don’t have a perfect snapshot of every house you’ve lived in or every mailbox you’ve seen. You have bits and pieces that you constantly combine and recombine when organizing the concept in your mind. 204

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This reconstructive process is innately creative. As we know from Eric Kandel, our brains naturally revise and edit patterns—and this process extends beyond recalling singular images to larger, more abstract concepts as well. In the 1930s, the British psychologist Sir Frederic Bartlett asked people to listen to folktales from other countries and then recount these stories at a later date. As you might guess, unfamiliar stories were not remembered as well as familiar stories. More surprising, however, was that errors in memory were not random. Rather, subjects often rewrote similar parts of the stories in their own minds— particularly the parts that made the least sense to them. Bartlett concluded that when facing problems, humans draw upon mental schemata, or shelves of stored knowledge in our brains, to fill in any minor gaps (or possible subproblems) in our memories. Therefore, remembering is an imaginative process that involves building upon past experiences. Once again, this step allows you to alter your idea and how you express it. If people don’t remember what you most want them to, continue to misunderstand, or show any of the emotions you want your idea to provoke, then adjust what you say accordingly. This might change your idea statement, which might change your Choice Map, which might change some part of your full Choice Map— which is useful. It’s all a part of the refining process. And the more artfully this is done, the clearer and more concise your idea will be.

THE THIRD EYE TEST

Our first three exercises in Third Eye gradually expand the range of viewpoints you bring to your solution, through first your owns eyes and then the eyes of others. Our last exercise takes this sequence one step further. You ask other people to re-imagine your idea with complete creative freedom. The natural tendency for recombination from Learning + Memory results in a very common reaction to any new idea. You tell someone your idea, and they say, “Well if I were you. . . . ” This last 205

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piece of the Third Eye encourages exactly this human tendency: to reimagine ideas we hear from others. First, describe your solution in full. Then ask them to change it however they think best. You can ask clarifying questions, but that’s all. It’s not a debate about their version versus your version. You want to find out what they imagine. Do this with several people. Then study their different versions. You will gain insights that lead you to adjust your solution and how you describe it, and even to understand it better yourself. You now understand all the ways in which others can see and imagine the possibilities of your idea—this gives you a Third Eye. As you proceed toward making your idea a reality, you can never predict what might happen. Including whether it will succeed or not. But you do know that at every step of the process, as you make one choice after another, every time you encounter a problem or roadblock, you can go back to our six steps and use them just for that piece. Congratulations! You have now completed the Six Steps of Think Bigger. If after all this, you ask yourself, recalling the Passion Test from Step 1, "Do I still feel excited about the problem I have solved? Do I see the idea and is it worth pursuing?” And the answer to these is a resounding, “Yes!” Now comes the time to implement. The six steps of Think Bigger came about by me using them myself. The problem became how to use what we now know about the creative mind to make a practical method people can use. Now that you know how to do the steps yourself, you can use Think Bigger again and again, as I have. As you do, your skill and bravery will increase. With these tools, I hope you will be able to Think Bigger than you have before, to make dreams you thought far beyond your reach at last come true.

ON BIG IDEAS

We end where we began, with Frédéric Bartholdi and his Statue of Liberty. Did he know, at its opening October 28, 1886, that his sculpture would come to mean what it does today? 206

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His original idea was to celebrate both the American ideals of freedom and their triumph in the Revolutionary War and Civil War through his creation. Right away a different artist, Emma Lazarus, reimagined the Statue of Liberty as a beacon to a better life for immigrants arriving in the harbor—well before the iconic “huddled masses” imagery of her famous poem was drafted. Bartholdi’s France struggled to hold onto the ideals of liberty from the French Revolution, while Lazarus was a descendant of immigrant refugees from the Portuguese Inquisition. Each brought to the same work of art a different imagination that contributed in different ways to its living history. Enter a third creative thinker—Georgina Schuyler. The Schuylers were an old Dutch family from the early days of New York with a noted lineage—Georgina's great-grandfather was Alexander Hamilton. Georgina was an influential philanthropist and patron of the arts. Lazarus’ poem was one of many Schuyler commissioned as part of the effort to raise money for the Statue of Liberty and she ultimately mounted a campaign to add the poem to the base of the structure. She succeeded in 1903—seventeen years after the official opening. Her creative combination effectively multiplied the impact of both the statue and the poem for posterity. Ultimately, what makes a big idea is when others see the idea, connect with it, and make it their own. Each observer of the idea brings their understanding to it so that the idea itself goes well beyond anything the original innovator could have imagined. Lady Liberty is not a big idea because of the effort it took to build her. She is not a big idea because of her location. She isn’t even a big idea because of Emma Lazarus’ words at her base. She is a big idea because she has come to mean something different to every individual who beholds her—yet she remains universal. The idea of Lady Liberty extends well beyond a symbol, showing people the promise of what they can become. She inspires our potential and manifests our dreams. Each of us brings our meaning to her as she reflects hers back on us, and collectively, we all add to the big idea. Millions have entered New York Harbor as immigrants seeking a better life. They each saw Lady Liberty, emotionally connecting to her as 207

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they embarked upon a new life in a new land, and in turn, created their meaning—their own story that became part of the ongoing tale. This continues today in the form of grandmothers who marvel at Lady Liberty as they enter the harbor, movie directors who place the statue prominently in a scene, newcomers on descending planes, who gaze upon her neon glow in the morning sunlight—just as I have on countless morning bike rides. As innovators, we all want to create a big idea in a similar way. We all want to Think Bigger. And while you as an individual creator can’t predict the future, here’s what you can do. You can be clear about the problem you’re trying to solve—and you can gain clarity about why you want to solve the problem. You can also work to understand why solving the problem is valuable to you and how the solution you have come up with works. If you can do all of that, then you’re Thinking Bigger. And someday, you might discover that as others see the intention behind your idea and internalize it in their own lives to solve their individual problem, little by little, the idea scales and scales—and iterates—to become bigger and bigger. For all the “bigness” that we’ve covered in this book—which at times, I know, can be daunting—to some degree, it’s comforting, if not liberating, to know that all the world’s revolutionary innovations are comprised of familiar elements. What has made innovation so elusive is that we’ve been barking up the wrong tree all along. We can’t create new elements; we can only combine and recombine old ones. As Mark Twain put it, “There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely, but they are the same old pieces of colored glass that have been in use through all the ages.” We can find refuge and inspiration in knowing that someone, somewhere, at some time, has already solved most, if not all, the pieces required to puzzle together our next big idea. After all, if Newton was able to stand on the shoulders of giants and change his world, then why can’t we use what we already know and find a 208

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unique combination of solutions to change our world for the better? So go be curious—because the longer you collect puzzle pieces, the easier the puzzle is to solve. You now know a great secret that has been hiding in plain sight: how our minds put together our best ideas. So the next time a problem confronts you, grab your kaleidoscope, give it a few turns, and start to Think Bigger.

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ACKNOWLEDGMENTS

Think Bigger took nearly a decade to complete from first inspiration to final manuscript. Along the way, I had help from many more people than I can thank here. Bill Duggan, for his pioneering work to apply the new science of Learning and Memory to the theory and practice of creative thinking and innovation. Carl Blaine Horton, my Ph.D. student and my right arm, for his dedication to making Think Bigger what it is today and for helping me create the many exercises that make up the course, this book, and the accompanying workbook. Glenn Hubbard, then Dean of Columbia Business School, for appointing me Academic Director the Entrepreneurship Program, which gave me a solid base for my research on innovation. Craig Hatkoff, dear friend and a Founder of the Tribeca Film Festival, who early on helped me make Think Bigger a practical tool rather than just an academic exercise. The current Dean of Columbia Business School, Costis Maglaras, along with Vice Deans Jonah Rockoff, Kent Daniel and Malia Mason, all of whom gave me institutional, intellectual and moral support for testing Think Bigger in the classroom and then gave me the time to write the book. So too for two successive chairs of the Management Division, Adam Galinsky and Stephan Meier.

ACKNOWLEDGMENTS

My fellow faculty in the Management Division gave me a trove of ideas from their research and informal chats, especially Dan Wang, Modupe Akinola, Malia Mason (again), Adam Galinsky (again), and the late, great Kathy Phillips. Think Bigger drew from a vast number of other researchers as well. Here I note just a few that I know personally and helped me beyond their published studies: Melanie Brucks, Brian Lucas, Olivier Toubia, Oded Netzer, Justin Berg, Gita Johar, Jacob Goldenberg, Steven Sloman, Brad Stone, Harry West, Eric Kandel, Richard Axel, Theresa Amabile, Mark Lepper, and Joseph LeDoux. Three leading innovators gave me personal interviews: Sir Paul McCartney, Ethan Brown, and Lloyd Trotter. Albert Bourla, CEO of Pfizer, introduced me Phil Dormitzer and Alessandra Gurman, who told me the mRNA vaccine story. Stacey Boland of NASA told me the COVID ventilator story. Deborah Dugan, for helping me get these interviews and for showing me innovation in action at every turn. Greg Shaw, who provided expertise and advice on the story of Bill Gates. My Think Bigger course benefited from expert coaches who helped students understand to apply the method: Michael Costa, Sharda Cherwoo, Shaun Butnik, and Julie Harris. A very special thanks to Neale Godfrey, the wind beneath my wings and the mentor of mentos in Think Bigger. To Doug Maine for tirelessly expanding the number of people who learned and know about the Think Bigger method. The Innovation Fellows at Columbia Business School provided ongoing support for Think Bigger students: Vince Ponzo, Joan Affleck, Michael Auerbach, Pamela Bell, Michael Costa, Toos Daruvala, Deborah Dugan, Max Engel, R.A. Farrokhnia, Michael Frank, Neale Godfrey, Tom Higbee, Sara Holoubek, Jake Kahana, Shah Karim, Ceci Kurzman Douglas Maine, Milind Mehere, Eduardo Mestre, David Park, Ryan Riegg, Jaime Robertson-Lavalle, Maneesh Sagar, Joanne Wilson, Ed Zimmerman, Stacy Ruchlamer, Mattan Griffel, Richard Harris, Alfred Drewes, Dominick Correale, Evan Bienstock, Kunal Sood, Pamela Bell, Jo Schneierr, Marcus Brauchli, 212

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David Pham, Nick Gernert, Nitzan Hermon, Tiffany Pham, Bob Friedman, Hope Taitz, Melina Denebeim, Jonathan Mariner, Brown Johnson, Woody Driggs, Michel Brogard, Pamela Horn, Meghan Cross Breeden, David Beale, Sham Mustafa, Briana Ferrigno, Allie Surina Dixon, Amy Murphy, Jeff Lagomarsino, Lloyd Trotter, Mark Schneider, Carolina Viegas, Shaun Budnik, Harry West, Chris Hebble, Liz Lindsey, Sean Prendergast, Joao Matias, Terry Lundgren, Jonathan Krane, Matthew Shay, Barry Salzberg, Alice Chen, Brad Hutton, Scott Clemons, Udayan Bose, Ira Shapiro, Martim De Mello, Murli Buluswar, Amol Sarva, Jamie Fialkoff, Kai-Fu Lee, Suzanne Nossel, Muredach Reilly, Walt Mossberg, Jacob Schlesinger, Ghita Sennouni, Geoff Cook, Kevin Chilton, Jamiel Sheikh, Vijay Aggarwal, Molly Himmelstein, Mary Donohue, Sabine Gaedeke Stener, Raj Maheshwari, Mark Brooks, Elizabeta Ealy, Miklos Sarvary, Harold Pincus, Rhett Godfrey, Kristie Kristovski, Nick Gogerty, Shawnette Rochelle, Helen Fisher, Matthew Baron. The Innovation Salon gave Think Bigger students exposure to new ideas in a wide range of innovations, worldwide. Marcus Brauchli helped recruit the speakers. I thank them all: Scott Clemons, Walt Mossberg, Lloyd Trotter, Brad Hutton, Paul Francis, Marc Lore, Matthew Shay, Terry Lundgren, Ira Shapiro, Suzanne Nossel, Jacob Schlesinger, Marcus Brauchli, Dan Wang, Dr. Kai-Fu Lee, J.B. Lockhart, Dan Farrel, J. Allen Brack, Sean Prendergast, Helen Fisher, Amanda Bradford, Lisa Clampitt, Mark Brooks, Geoff Cook, Miklos Savary, Roger McNamee, Nick Gernert, Chris Britt, Paul Johnson, Nick Gogerty, Jamiel Shiekh, General (ret.) Kevin Chilton, Sabine Stener, Jodi McLean, Matthew Baron, Chris Meyer, Harry West, Hod Lipson, Steven Rosenbush, Rabbi Irwin Kula, Tim Ryan, Peter Caldini, Dr. Yasmin Hurd, Sir Alex Halliday, Ethan Brown, David Blei, Anne Bauer, Jerome Pesenti, Danny Meyer, Xavier Rolet, Kara Swisher, Geoff Heal, Mary Jane McQuillen, Ann Fox, Kyle Godfrey, Caitlin Lacroix, Jamiel Sheikh, Dee Charlamagne, Sham Mustafa, Liz Grausam, Sam Schatz, Hawk Newsome, Chivona Newsome, Ed Zimmerman. My PhD students helped hone the Think Bigger method in the classroom and in debates outside: Mike White, Genevieve Gregorich, and Erica Bailey. 213

ACKNOWLEDGMENTS

My research assistants provided heroic assistance throughout the entire Think Bigger Project: Sean Kaczmarek, Eleanor Bentley, Jordan Antebi, and Allie Sinclair. Two high school interns, Corey Brooks and Nalla Sagna, helped on research and served as readers and commentators. Jake Kahana and Jan Simon helped with visual design. Juliana, my tandem biking companion, for all the rides along the Hudson to Lady Liberty and back. Antony Giles, my dear friend, who plays the role of therapist and never allows me to give up. I am counting on you to use your musical talents help me write the Think Bigger song. Myles Thompson, publisher, who has left no stone unturned to help make this book the best it could be. My mother, for your never-ending encouragement and faith in me. Jasmin, my sister, for always being there for me. A special thank you, Dr. Andrew Marks, for not breaking up with me when I ignored you for countless hours to work on this book. And above all, I thank my hundreds of Think Bigger students. You made the method come alive.

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BIBLIOGRAPHY

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