MAXIMS FOR THINKING ANALYTICALLY

The wisdom of legendary

Harvard Professor Richard Zeckhauser

Dan Levy

Foreword by Larry Summers

While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

Maxims for Thinking Analytically: The wisdom of legendary Harvard Professor Richard Zeckhauser

First edition. June 27, 2021

Copyright © 2021 Dan Levy.

Written by Dan Levy.

Cover Image: Mental Power Concept by DrAfter123 via Getty Images.

ISBN

Ebook: 978-1-7353408-9-0

Paperback: 978-1-7353408-8-3

“This book reveals a creative mind and a caring heart. Richard Zeckhauser’s maxims help us become smarter and better. Analysis does not equal paralysis; it leads to wiser decision making. Join the thousands of Harvard students and faculty who have benefited from Richard’s wisdom: read Maxims for Thinking Analytically.”

Iris Bohnet, Harvard Kennedy School Professor, author of What Works: Gender Equality by Design

“Richard Zeckhauser is a savvy producer and consumer in a noisy marketplace of ideas. His maxims are a crash course in thinking effectively.”

Phil Tetlock, University Professor, University of Pennsylvania, author of Superforecasting

“Richard Zeckhauser has few if any equals in insights and wisdom delivered per hour, especially when the subject involves risk and uncertainty. This book makes much of that knowledge available to its readers.”

Mike Spence, Winner of the Nobel Memorial Prize in Economics, New York University Professor

“Maxims for Thinking Analytically was a true eye opener to me. As a professional in a game which is based on probability, it was shocking to realize that I am pretty bad at estimating chances and using probability in everyday life.”

Marion Michielsen, two-time world champion in bridge and frequent bridge partner of Richard Zeckhauser

“Spending an hour talking to Richard Zeckhauser is like having a massive dose of wisdom injected into your brain. It can be hard to keep up. This book provides a sample of that wisdom in small and digestible chunks. It is a treasure.”

Richard H. Thaler, Winner of the Nobel Prize in Economics, University of Chicago Professor, and author of Nudge

To Richard Zeckhauser, for being an extraordinary mentor

To John and Licita Levy, for being extraordinary parents

Table of Contents

Foreword

Acknowledgments

Introduction

Chapter 1 – Thinking Straight

Maxim 1 – When you are having trouble getting your thinking straight, go to an extreme case

Maxim 2 – When you are having trouble getting your thinking straight, go to a simple case

Maxim 3 – Don’t take refuge in complexity

Maxim 4 – When trying to understand a complex real-world situation, think of an everyday analogue

Chapter 2 – Tackling Uncertainty

Maxim 5 – The world is much more uncertain than

you think

Maxim 6 – Think probabilistically about the world

Maxim 7 – Uncertainty is the friend of the status quo

Chapter 3 – Making Decisions

Maxim 8 – Good decisions sometimes have poor outcomes

Maxim 9 – Some decisions have a high probability of a bad outcome

Maxim 10 – Errors of commission should be weighted the same as errors of omission

Maxim 11 – Don’t be limited by the options you have

in front of you

Maxim 12 – Information is only valuable if it can change

your decision

Chapter 4 – Understanding Policy

Maxim 13 – Long division is the most important tool for policy analysis

Maxim 14 – Elasticities are a powerful tool for

understanding many important things in life

Maxim 15 – Heterogeneity in the population explains many phenomena

Maxim 16 – Capitalize on complementarities

Chapter 5 – Living Fully

Maxim 17 – Strive hard not to be envious – see your friend’s success as your gain

Maxim 18 – Eliminate regret

Maxim 19 – Make pleasure-enhancing decisions long in advance, to increase the utility of anticipation

Brief but important life maxims

Living with the Maxims

Appendix A – Gratitude

Appendix B – Contributors

References

Index

About Richard Zeckhauser

About the Author

Notes

FOREWORD

Larry Summers

Charles W. Eliot University Professor and President Emeritus at Harvard University

Richard Zeckhauser is a brilliant man who has made fundamental contributions to economic theory, public economics, decision analysis, behavioral economics, and many other parts of social science. This wonderful book celebrates instead a different and unique Zeckhauser contribution.

Decades ago, Michael Polanyi introduced the concept of tacit knowledge – “that which we know but cannot say.” Tacit knowledge takes many forms: for example, from how to win at bridge to how to have a happy marriage, from how to think clearly about a public policy problem to how to have a satisfying career or how to deal with situations that lack clarity. We all possess tacit knowledge. Few of us possess as much, in as many areas, as Richard Zeckhauser.

This book encapsulates Zeckhauser’s efforts over many years to make his insight and wisdom explicit rather than tacit. The maxims it contains enable us, the readers, to begin to think and live more like Richard – and that means much better. Tale after tale from Richard’s disciples of how his maxims helped them think better make his gifts explicit. To use economic language, this is a public good.

It has been my privilege to know Richard for 53 years. We met first when I was 13 years old, and Richard, a friend of my parents, was kind enough to come and play bridge with me and my brothers. At one level it was not one of Richard’s most successful pedagogical moments. Not being able to remember what had been in our hands after the cards were played, Richard’s references to the possibility of cross ruffs went flying way over our heads.

But in a deeper sense it was a profoundly educational afternoon for me. I appreciated for the first time that complexity could be mastered through the careful application of logic, that strategy should be based on the assumption that rivals would also make strategic choices, and that one often had to make choices knowing that one was more likely than not to be wrong. These lessons have stayed with me in domains far from the card table.

Years later, as a graduate student in economics, I had many conversations with Richard. I learned that it is more fun, more useful, and more interesting to think about the economy than to think about the economics literature. Richard is incapable of picking up an issue of the New York Times without detecting three errors of logic, two situations that were being misanalyzed by their protagonists, and four nonobvious questions worth serious reflection.

A few years later I was in the hospital with an unknown but serious condition. Richard was not my closest friend but he was my most frequent visitor, and his visits were unlike any others. To start with, it was impossible to keep up with the flow of ideas and concepts without being entirely distracted from my worries. Usually the doctors educate the patients and their guests. Not when Richard visited. At the time there were two possible explanations for my symptoms. When told this, Richard inquired about their relative frequency in the population. The more serious diagnosis was about 1/10 as frequent as the less serious one, so I was much more likely to have the better of the two possibilities. Successive interns and residents arrived. None knew the relative frequency and several thought it was entirely irrelevant to my situation. They learned from Richard of the need to pay attention to background probabilities and at least one commented that Richard’s remarks were more educational than anything an attending physician had said in weeks.

In the time of my illness and at several points thereafter Richard taught me by example that it is easy but not so important to support, congratulate, and be with people when they are up. People need their friends and need new friends when they are down. For me as for so many others, Richard Zeckhauser has been the model foul weather friend. It is an important legacy.

This book is a richly deserved tribute to Richard’s wisdom and wit. I promise the reader who will never meet Richard that it is much more. It is a source of explicit guidance on how you can be shrewder and wiser, and ultimately happier and more contributing. Enjoy the privilege I gained at that bridge table in 1968 and have so valued ever since – learning from Richard Zeckhauser.

ACKNOWLEDGMENTS

I would like to acknowledge the help of several people who made this book possible. Alice Heath, a PhD student at the Harvard Kennedy School who was a teaching assistant for Richard Zeckhauser’s course, gave me insightful feedback on every chapter of the book, helped refine my thinking, and made the book more accessible to nontechnical readers. Miriam Avins edited the book superbly and was a very thoughtful partner in this effort. Victoria Barnum provided research assistance, navigated the publishing process, supported me in various ways during the writing process, and helped me have the time needed for writing. Ruth Hütte and Vanessa Levy contributed tremendously to this book’s design.

I would like to thank all of Richard’s coauthors, colleagues, and students. They made this book possible by providing examples, stories, and anecdotes of how they had used Richard’s maxims in their personal and professional lives. In many ways, this makes each of them a coauthor of this book. Contributors’ short bios appear in Appendix B.

Richard frequently identifies individuals who shaped his ideas and helped him look at the world more clearly. Some of them contributed to the book directly. Several of them are no longer alive, but their ideas live on, including in this book. Of particular note are Richard’s mentors Ken Arrow, Fred Mosteller, Howard Raiffa, and Tom Schelling. These individuals, beyond being giants in their fields, were mold breakers in the ways they assessed the world. Richard credits them with teaching him how to think.

I am grateful to others who helped me with research, advice, insight, access to key information, and encouragement, including Gaby Alcalá, Josh Bookin, Akash Deep, Mark Fagan, Maria Flanagan, David Franklin, Catri Greppi, Mae Klinger, Ari Levy, Allison Pingree, Miguel Angel Santos, Carolyn Wood and Wendy Wyatt.

I am grateful to many colleagues who have shaped the way I think about matters related to this book, and who have therefore indirectly contributed to it. They include Alberto Abadie, Arthur Applbaum, Chris Avery, Mary Jo Bane, Daniel Benatar, Joe Blitztein, Iris Bohnet, Jonathan Borck, Filipe Campante, Suzanne Cooper, Jorrit de Jong, Pinar Dogan, Jack Donahue, Susan Dynarski, Greg Duncan, David Ellwood, Doug Elmendorf, Mark Fagan, Carol Finney, Jeff Frankel, John Friedman, Archon Fung, Alan Garber, Steve Glazerman, Rachel Glennerster, Steve Goldsmith, Tony Gomez-Ibañez, Josh Goodman, Stuart Gurrea, John Haig, Rema Hanna, Ricardo Hausmann, Andrew Ho, Daniel Hojman, Kessely Hong, Deborah Hughes Hallett, Anders Jensen, Tom Kane, Felipe Kast, Asim Khwaja, Gary King, Steve Kosack, Maciej Kotowski, Michael Kremer, Robert Lawrence, Jennifer Lerner, Jeff Liebman, Dick Light, Erzo Luttmer, Brian Mandell, Nuno Martins, Janina Matuszeski, Eric Mazur, Nolan Miller, Francisco Monaldi, Mark Moore, Juan Nagel, Jim Ohls, Rob Olsen, Rohini Pande, Lant Pritchett, Juan Riveros, Chris Robert, Dani Rodrik, Soroush Saghafian, Paulo Santiago, Miguel Santos, Malcolm Sparrow, Rob Stavins, Federico Sturzenegger, Arvind Subramanian, Karti Subramanian, Teddy Svoronos, Moshik Temkin, Mike Toffel, Ernesto Villanueva, Rodrigo Wagner, Steve Walt, Julie Wilson, Josh Yardley, and Andrés Zahler.

Most importantly, I am grateful to Richard Zeckhauser. He went much further than providing the ideas in this book. He painstakingly reviewed every chapter twice, and made numerous suggestions that pushed my thinking and improved the book. He was available for several Zoom calls in which he provided me with insights, encouragement, and joy. Furthermore, he has been an incredible mentor. He has provided me with wise advice on important personal and professional decisions, and brought joy to my life in so many ways. I feel indebted to him, and this book represents a tribute on behalf of all who have benefitted greatly from his wisdom and generosity.

Dan Levy

Cambridge, MA

June 2021

INTRODUCTION

The goal of this book is to help you think more analytically, which I hope will lead you to better understand the world around you, make smarter decisions, and ultimately live a more fulfilling life. This is an ambitious goal, and I would have never dared to write such book if I were not building on the ideas of Richard Zeckhauser, a legendary Harvard professor who has helped me and hundreds of others progress toward this goal.

The book is based on “Analytic Frameworks for Policy,” a course Richard has taught at the Harvard Kennedy School for more than four decades. Students learn analytic methods from various fields (economics, decision analysis, behavioral decision, game theory, operations research, etc.), and many describe it as life-changing. Lee Hsien Loong, the prime minister of Singapore, took this course when he came to Harvard as a student many years ago. He recently wrote to Richard: “Your course left such a strong impression that after forty years I can still remember many of the things we discussed. I have often relied on them, and perhaps even more often unconsciously, to make sense of some data or issue, and for this I will always be grateful.”

The book extracts some key ideas of the course, which Richard crystalizes in the form of maxims, one-sentence nuggets of wisdom meant to immortalize an important idea. Richard has been sharing these maxims for many years with his students, colleagues and coauthors, so much so that they remember these maxims long after first hearing them. The maxims covered here represent only a partial list.

Maxims are useful for two reasons. First, they make us think about things that might not be intuitive at first. For example, most of us intuitively associate a good outcome (like “I am happy in my current job”) with a good decision (“I accepted to work for employer X two years ago”), and the maxim “Good decisions sometimes have poor outcomes” can remind us to counter that intuitive notion. Second, they can help us correct behavior when we intuitively know what the right behavior is but yet somehow don’t engage in it. For example, we intuitively know that envy is not good for us but we sometimes fall victim of it, just as tennis players naturally follow the opponent and not the ball. In tennis, the maxim “keep your eye on the ball” helps us overcome our natural tendency. Similarly, the maxim "Strive hard to avoid envy – see your friend’s success as your gain" is meant to remind us of what we know is good for us.

This book is for anyone who wants to think more analytically about the world. Though the book is analytical, it is not mathematical. I have tried hard to write with as little jargon as possible. For readers who want to learn further, I reference some of the underlying academic work in the endnotes and list of references. I hope that you will find value in this book regardless of your prior analytical training. I have seen people from many walks of life and many educational backgrounds – from high school students to physics PhDs – derive value from these maxims.

In the spring of 2020, I sent current and former colleagues, coauthors, and students of Richard’s a list of his most memorable maxims and asked them to submit an anecdote or story of how they put one of the maxims to good use in their personal or professional lives. I received more than 150 submissions. The book is organized around the maxims, with the submissions bringing the maxims to life. Several of the examples in the book are related to COVID-19, one of the most consequential events of the first two decades of this century. How governments and individuals dealt with COVID-19 illustrates the value of the maxims particularly well.

You will learn how to apply the maxims to understand what happens in the world around you, and to make better professional and personal decisions. For example, on the world stage front, you will learn about one key reason for COVID rates to go down that is rarely mentioned but is perhaps more compelling than most that are offered. The same underlying phenomenon helps explain why smoking relapse rates decline over time. You will understand why you should not have been very surprised when Donald Trump won the 2016 U.S. presidential election. On the professional front, you will learn maxims that can help you think about how to choose the people with whom to collaborate, how to decide how much time to spend at work, and many other decisions that play a critical role in your everyday life. On the personal front, you will learn how one of Richard’s colleagues saved money on her wedding by thinking probabilistically, how Richard and his wife Sally made an agonizing health decision that significantly enhanced Sally’s survival probabilities twenty-five years ago, and why Richard asked his mother’s physician to operate on her right away despite this physician’s recommendation to wait for evidence as to whether she had appendicitis or a tumor.

Who is Richard Zeckhauser, you might ask? Richard grew up in Philadelphia and then Long Island. He came to Harvard in 1958 and never left. He graduated from Harvard College (summa cum laude), received his PhD in Economics there, and has taught at the Harvard Kennedy School for more than fifty years. He is an elected fellow of the Econometric Society, the Institute of Medicine (National Academy of Sciences), and the American Academy of Arts and Sciences. In 2014, he was named a Distinguished Fellow of the American Economic Association. Often in collaboration with others, he has published over 300 academic articles, and a dozen books.

But what makes Richard Zeckhauser so extraordinary – to me and to many of the contributors to this book – is his intellectual breadth, wisdom, and generosity. Richard has published academic works in many areas, including decision theory, health, finance, climate change, college admissions, behavioral economics, history, targeting of social programs, public-private partnerships, art history, and literature. He even published a children’s book (The Cockatoo and the Ballerina) and has two more underway. Outside of academia, Richard is a senior principal at Equity Resource Investments, a real estate private equity firm. And to top it off, he has won multiple national championships in contract bridge, a card game that he has played since he was eleven years old and where he regularly competes head-to-head against full-time professional players.

In the words of Alan Garber, provost at Harvard University, Richard is both clever and wise. As you will see in the rest of the book, many of his former students and colleagues recount how Richard helped them make a difficult and important personal or professional decision. His ability to approach problems analytically in the most difficult situations is why I think many of us seek his advice and admire his wisdom.

Richard’s generosity is known to everyone around him. He makes time for his students and his colleagues, recognizes when they need his help or advice, and gives his most valuable resource – time – to them without expecting anything in return. He has frequently invited colleagues and friends to join him and his wife, Sally, to watch a baseball or basketball game, a play at the theater, or a meal at their home. One of his passions as a child was magic, and he often has shared this passion with friends and colleagues by inviting them to a private magic show he organized of some up-and-coming magician he had recently spotted. At the Kennedy School, Richard teaches students who will end up running for some political office, and he tells them “If you run for office one day, I will make a donation to your campaign regardless of whether or not I agree with your political views.” I think this encapsulates both his generosity and his wisdom, mixed with a strong dose of pride in his students.

The rest of this book is organized as follows. Chapter 1 provides you with some maxims to help you think straight (that is, factually, accurately, and truthfully), especially when you are having trouble doing so. Chapter 2 recognizes that the world is a very uncertain place and contains maxims for understanding and dealing with this uncertainty. Chapter 3 introduces maxims to help you make better decisions under uncertainty. Chapter 4 focuses on maxims related to understanding the policy world better, and Chapter 5 describes maxims meant to help you live a more fulfilling and wiser life. The final section (“Living with the Maxims”) distills some key take-aways about the maxims and identifies potential next steps in your journey to become more analytical. Appendix A contains a sample of statements expressing gratitude from the contributors to Richard, and Appendix B contains the list of contributors with their short bios.

I wrote this book because I am convinced that Richard’s wisdom can be helpful to vast numbers of people in the world, many far removed from Cambridge, Massachusetts, and the academic world. I also wrote it as a tribute to him from me and the many others whose lives he has so positively influenced. I hope that you will be enriched by it.

1

THINKING STRAIGHT

We often get stuck when trying to think clearly about a real-world situation. Sometimes, even getting started is hard. When our thinking is stuck important choices get made based on impulse, or on what we did yesterday. Significant value will be lost to ourselves, and to others if we are choosing on their behalf. The maxims presented in this chapter are straightforward principles to help your thinking get underway or to push you forward if you are stuck. These principles sound a common theme: Simplify your situation sufficiently to enable clear thinking and to give you some traction. Then turn back to your actual, more challenging situation, using the insights you gained.

MAXIM 1

WHEN YOU ARE HAVING TROUBLE GETTING YOUR

THINKING STRAIGHT, GO TO AN EXTREME CASE

Mary and Jim want to paint a room together. If Mary painted alone it would take her 2 hours, and if Jim painted alone it would take him 3 hours. How long would it take to paint the room if they paint together? Many high school students (and adults) quickly answer 2.5 hours when they encounter this kind of problem for the first time. Take a minute to think how you would explain to a high school student why this is not the correct answer.

One effective way to see why 2.5 hours is not the right answer is to imagine that Mary alone paints and Jim just watches. How long will it take? Two hours. So if Jim helps Mary paint, it has to take them less than two hours. This is an example of going to an extreme to get your thinking straight. In this case, the extreme we used is that Mary paints and Jim does nothing.[1]

Of all the maxims in this book, this is the most popular among Richard’s coauthors and former students. Broadly speaking, this maxim can be especially helpful for two kinds of situations:

- To understand a concept, problem, or idea better; or

- To assess the best-case or worst-case consequences of a decision you are struggling to make.

Let’s take each of these in turn.

To understand a concept or idea better

Students new to economics sometimes struggle to grasp the concept of the marginal propensity to consume (abbreviated as MPC). MPC is the proportion of additional disposable income (income after taxes and transfers) that an individual consumes. Even harder to understand is what a specific numerical value for MPC implies. In such situations, it helps to go to extreme cases to gain some intuitive feel for what the numbers mean. In this case, since the MPC is a proportion, the two extremes are 0 and 1. An MPC of 0 means that if an individual received an extra $100 of income, she would spend none of it. Think of Warren Buffett. He already has plenty of money to consume what he wants. If Berkshire Hathaway were to pay him an extra $100, he would not change his consumption. At the other extreme, an MPC of 1 means that if an individual received an extra $100 of disposable income, he would spend it all. Think of someone in extreme poverty. An extra $100 could go immediately to satisfy his basic needs. These extreme cases can help ground our understanding of MPC.[2]

Syon Bhanot, one of Richard’s former students and now a faculty member at Swarthmore College, says he uses this maxim every semester to help his students understand many economic concepts that they find challenging at first. “Indeed,” he says, “It is the main weapon in my arsenal when it comes to helping build intuition in students, and I credit Richard by name every time I use it.”

Understanding a concept sometimes involves addressing and correcting a misconception. Barry Nalebuff, one of Richard’s coauthors and a professor at Yale School of Management, does this when teaching negotiation. He noticed people often default to proportional division as a fair solution in a negotiation. For example, imagine Division A of a company is spending $20,000 on a software package and Division B is spending $10,000, but they can buy a joint license for $24,000. How should they split the cost? Most people venture that A should pay double what B pays, so $16,000 and $8,000. But what cooperation is really doing is saving $6,000 and both divisions are needed equally for those savings. They should split those savings equally: Division A pays $17,000 and B pays $7,000.

To help students not yet convinced that proportional division is unfair, he gives an extreme example in a not-so-hypothetical negotiation between a small organic iced-tea startup whom we’ll call David and a large beverage company whom we’ll call Goliath.[3]

Going to an extreme in a negotiation

Barry Nalebuff

In physics and mathematics, one often finds inconsistencies and counterexamples by taking the situation to an extreme. Richard taught me to apply this approach to economics.

Consider the following negotiation scenario. David pays its supplier 19¢ for each plastic bottle. Goliath, a much larger firm, buys a far larger number of the same plastic bottles from the same supplier for 11¢ each. David needs 50 million bottles. Those bottles cost him $9.5 million but would only cost Goliath $5.5 million. They realize they both stand to gain if Goliath buys the 50 million bottles and sells them to David. The question up for negotiation is how the $4 million in savings should be split.

Goliath naturally proposes proportional division: “My sales are $40 billion and your sales are $20 million. That’s a 2000:1 ratio. Thus, I should get $3,998,000 and you can get $2,000.”

But these numbers are so lopsided that even Goliath doesn’t try to defend this absurd division. He charitably offers $1 million for David and $3 million for himself.

David counters that the split should be 50:50. “On what basis?” asks Goliath. Goliath is bringing its gigantic bargaining power to the table. What is David bringing?

“My inefficiencies!” is David’s slingshot reply. He then explains: “Without my high cost, your buying power brings no incremental value.” With the two brought together, $4 million of value is created. Or, to be less flippant, Goliath needs David to access David’s customer base of consumers who want its lightly sweetened organic tea. To apply the value of Goliath’s purchasing power on not just Goliath’s customers (which has already been done), but also to David’s customers, Goliath needs David’s help.

In a happy ending to this tale, the negotiation was rendered moot when Goliath bought David’s company. By way of comparison, this story illustrates one of Richard’s unusual gifts. He is the rare Goliath in his field who doesn’t act like one: he shares the credit evenly with his youthful coauthors – myself included – even when the big insight was really his.

Going to an extreme case can sometimes help you discover the key insight of a problem or situation you are trying to understand. Karen Eggleston, one of Richard’s students and coauthors and now a senior fellow at Stanford University, was working with Professor Victor Fuchs to understand the economic consequences of the demographic transition experienced by the United States and other industrialized countries during the twentieth century.[4] A key pattern in this transition was that people’s retirement years got longer, which created an economic problem as the number of years of productive economic activity no longer represent such a large share of a person’s life. “People cannot expect to finance 20–25 year retirements with 35-year careers,” one economist crudely noted.[5] Karen recalls: “Using Richard’s maxim, I reframed the question to myself as an extreme case: “Would immortality be bad for the economy?” While we clearly had already focused on the imbalance between work lives and retirement – rather than total length of life – as the key economic challenge, this going to an extreme helped to guide my thinking as we constructed our analyses about how longer lives interact with rising living standards.”

One manifestation of going to the extreme occurs when we try to learn from outliers, observations in our data that are much higher (or lower) than the rest of the observations. This is what Jonathan Nelson did when he tried to understand the prices of Renaissance artwork. Jonathan is a professor at Syracuse University in Florence specializing in Italian Renaissance art. He and Richard coauthored a book that applies innovative methods of information economics to the study of art.[6] If you ever needed evidence that Richard is a polymath, this is it!

Going to the extreme by looking at outliers

Jonathan Nelson

Art historians, and especially those who focus on the Renaissance, as I do, often lose sight of the forest for the love of describing trees. We get lost in the branches of individual paintings, artists, or patrons, but that is not the Zeckhauser approach: he creates models. Take our study of art in Renaissance Italy, when major works were invariably made on commission. For economists, the data pool about such transactions is very shallow: a contract here, a payment there, but little of the raw information needed to generate reliable models. Most Renaissance art historians avoid generalizing about finances due to a range of problems. Often, we do not know if payments covered materials, or the exact conversion values of various currencies.

In short, it is hard to think straight about compensation for Renaissance artists, or the cost of their works, but we can gain perspective from the vantage point of outliers. At least three religious paintings by Raphael, the most famous Italian artist in the early 1500s, cost many times more than the norm. In Renaissance Italy, there was surprisingly little price variation of altarpieces due to their size, subject, date, or artist. Nearly two-thirds of the documented examples cost about 100 florins, which corresponds to a bit less than the salary of a high government official in Florence; a full 85 percent of altarpieces cost under 200 florins. The big three by Raphael, in contrast, pulled in between 850 and 1200 florins each! From these extreme cases, we can better understand typical Renaissance pricing mechanisms; for these purposes, it matters little if the figures are off by a few florins.

Together, Richard and I coauthored a short article on “Raphael, Superstar, and His Extraordinary Prices.” This isn’t the place to give away the punchline, but by following Richard’s maxim, we explore why superstar artists receive superlative compensation.

To assess the best-case or worst-case consequences of a decision you are struggling to make

Several of Richard’s former students and coauthors have used this going-to-the-extreme maxim to consider the best-case or worst-case consequences of a decision they are facing. For example, Joachim Neipp used this principle when he was a high-level executive at the pharmaceutical company Bayer in charge of building financial models that would predict the likely consequences of mergers with other companies: “In order to get my thinking straight, I always found it useful to build a model and then drive it to the extreme. Models then had to reflect the draft agreements between Bayer and the company we were considering merging with or acquiring, and the extreme cases were those in which at least one company would suffer a loss. The analyses allowed both companies to see whether the agreements would truly reflect their intentions and understandings of how the partnerships would work. They enabled my company to assess the risks of the partnership, and supported the development of provisions for mutually acceptable risk sharing.”

Xiaochen Fu is one of Richard’s former students at the Kennedy School and now a manager at the Bank of China. When she worked at Agricultural Bank of China, the third largest bank worldwide, she used this maxim to help the bank make its transition to the digital era. At a time when clients were increasingly using smartphones to conduct banking transactions, her bank still had more than 300,000 staff working at 25,000 branches around the country. Some branches found that fewer and fewer clients came in person. She and her staff were struggling to decide how they should adjust the number and location of their branches. “Then I remembered Professor Zeckhauser’s maxim. To find the extreme case, we went through regulations and procedures for all the services provided by a full-function bank branch, in order to identify which services would be very difficult or impossible to deliver online. (For example, the government forbids third-party couriers to deliver physical gold, so clients who want to buy physical gold products must go to branches.) After finding all such services, and considering the needs and preferences of clients served at different branches (for example, senior clients and rural area clients still prefer face-to-face financial services), it became much clearer which branches should be closed, and which ones should be saved. The planning project proved to be cost-efficient, and allowed the bank to adapt to the digital age and better meet the needs of our clients. I reckon that the maxim gave me not only the tools but also the courage to deal with such complicated conditions.” Xiaochen’s account identifies two critical benefits a maxim may bring. It can help you focus on how to approach a problem, and it can give you the courage to take action when you determine the best decision. This is true for many other maxims in this book.

Yinglan Tan, a former student of Richard’s, is the founder and managing partner of a venture capital firm serving Asia. He has used this kind of extreme worst-case scenario approach in his work with clients, particularly in moments of crisis. “When we go through scenarios of how a crisis will impact their businesses, we discuss cash flow and operations from the point of view of the worst-case scenario and work from there. Going to an extreme case especially in challenging times not only grounds our discussion, but also ironically provides a sense of security: if we can pass the most difficult stress test, the rest is manageable.”[7]

A final illustration of this going-to-the-extreme maxim comes from Jack Donahue, a former student and current colleague of Richard’s at the Harvard Kennedy School, and coauthor with him of two books assessing how the public and private sectors can most effectively collaborate in producing public value.[8] Jack credits the idea of considering the extreme case as key to what he considers the biggest policy impact of his career. In 1994, he was a senior policy official at the U.S. Department of Labor. The White House decided it wanted a significant middle-class tax cut, and it was open season for proposals on what form it should take. See box below for how he came up with a policy proposal using this maxim.

Going to the extreme in policy design

Jack Donahue

Bob Reich, the Labor Secretary, asked me to figure out a way to use the tax code to promote worker training. I had immersed myself in the literature enough to know this wouldn’t be easy. So I engaged an ace tax expert named Gene Steurle to help me and my staff develop and vet options. We came up with variant after variant, but each was plagued by two problems. First, there was no easy way to distinguish between additional training that would only happen because of our proposed incentives and training that would have happened anyway. Second, there just wasn’t any bright line between worker training such as vocational training to become a certified nurse assistant, electrician, or computer technician and other kinds of human capital investment such as a college degree. This was problematic because we did not want to subsidize college tuition for families that could afford it. Each time we thought we had a workable tax incentive proposal, Gene would read it, think a bit, and say “Nope, you’re just going to end up paying for college tuition.” (I came to count it as a win if it took him a while to reach that conclusion.)

As it became clear that the administration was serious about a pretty big middle-class tax cut, and as goofy proposals for its nature proliferated from other departments, one evening I took to heart Richard’s maxim of considering the extreme case. What if we didn’t try at all to restrict what kinds of human-capital spending get the tax subsidy, and accepted that most of it would go for college tuition? This would mostly reward families for spending they would have undertaken in any event. But if we limited the subsidy to the lower-income people for whom we most wanted to cut taxes – which was entirely doable – that’s not necessarily a big problem. The additional human-capital investment would be pretty limited, but not nil. And the incidence of the tax cut – lower-income people facing a surge of spending for college or training – would be about right.

The Lifelong Learning Tax Credit was enacted not too long thereafter, and a version of it remains in place. I still don’t expect Richard to entirely approve since the tax cut was not very effective at increasing worker training. But the outcome was definitely better because somebody with Richard’s voice in his head was part of the policy mix.

MAXIM 2

WHEN YOU ARE HAVING TROUBLE GETTING YOUR

THINKING STRAIGHT, GO TO A SIMPLE CASE

Most American readers are familiar with the acronym KISS (Keep It Simple, Stupid), and some readers might be familiar with Occam’s razor.[9] Einstein famously said: “Everything should be made as simple as possible, but no simpler.” While many ideas that Richard discusses in his academic work can be complex or subtle, he has a knack for simplicity. He learned this from mentors, whom he credits for teaching him how to think.

He often tells the story of a moment early in his career when he met with Howard Raiffa. Howard had introduced Richard to the principles of Bayesian decision analysis, such as thinking in terms of probabilities and defining your preferences over uncertain outcomes. Richard entered Howard’s office and proudly said, “Howard, following your instructions, I simplified. I solved the challenging group-decision problem we discussed assuming that there were just three types of agents.” “That is excellent,” Howard replied. “You have made good progress. Now, go back and try it with two.” Howard Raiffa’s passion for the greatest feasible simplicity enabled him to pioneer the fields of game theory and decision analysis, and play a founding role for the field of negotiation analysis.[10]

Richard tells about how two of his other major mentors, Thomas Schelling and Kenneth Arrow (both Nobel Prize winners), sought simplicity. Schelling, for example, regularly employed everyday and easily grasped examples, such as a parent negotiating with a child, as an analogy to a much more complex situation, such as an international negotiation. A Schelling lecture often moved from a simple example, say a stop sign, to a riff on a dozen critical issues that involved a metaphorical stop sign. One element of Arrow’s genius was to see, and then distill, simple principles, where others saw only murky complexities.[11]

Richard provides his own students with disarmingly simple advice when they are faced with a decision or dilemma. Ingrid Ellen, one of Richard’s former students, and now a professor at New York University, recalls: “’I came to Richard for advice when I was deciding whether to pursue a PhD in economics or public policy. I was agonizing. He responded with a question: he asked me whether I was more excited to answer narrow questions very precisely or to study the social and economic policy questions I cared most about but to answer them with somewhat less precision. If I wanted the former, I should pursue a PhD in economics; if the latter, I should pursue a PhD in public policy. The choice was easy. As always, he took a difficult decision and made it very simple. And I have never had any regrets.”

Benjamin Van Roy, a Stanford professor who met Richard when Benjamin was at MIT, uses the simplicity maxim in the field of reinforcement learning. That field is known for impressive feats carried out by complex systems of artificial neural networks that learn through trial and error using massive computational resources. A well-publicized example is a computer program that learns to play just about any game – for example, chess or go or arcade games – better than human world champions.[12] With all the complexity in such games, it can be difficult to understand what works and why, and how to improve things.

He says, “I advocate Richard’s maxims on clarifying thought by going to simple and extreme cases. I don’t think of the two as distinct. To design an algorithm that demonstrates a sophisticated skill, it is helpful to formulate a task that’s simple enough for a human to easily solve while pushing to the extreme the level of skill required of a general algorithmic solution not specialized to the task. Not everyone thinks this way; some find it strange to work on such simple problems when the field is already able to address such complex ones. But I’ve found this to be a fruitful way to develop ideas that can then be ported to complex domains. With these maxims in mind, my teaching tends to focus on such simple and extreme cases.”

He adds, “There is evidence that this spirit has carried on. For example, there is now open-source software for simulating a variety of simple and extreme reinforcement learning tasks. This development was led by Ian Osband, a former student of mine, and serves the research community as a platform for reinforcement learning algorithm design.”

John Horton, a faculty member at the MIT Sloan School of Management and a former student and coauthor of Richard’s, used this simplicity maxim to tackle a question he was trying to understand in the ride-sharing market (Uber, Lyft, etc.): From a societal perspective, should drivers know where a passenger is going when they decide whether to take a trip? On the one hand, knowing that information allows drivers to accept the “good” trips and not the “bad” ones. If many drivers do this, the wait-time for “bad” trips could be very long or, even worse, passengers needing “bad” trips might never get picked up. On the other hand, if drivers differ in their costs (for example, it is cheaper to pick up a passenger going your way as your day is ending), letting them pick and choose among trips can be economically efficient; the trip will go to a driver for whom the trip is more beneficial. Creating a model that fully captures all such relevant parameters can get very complicated very fast.

John simplified the problem by assuming that there are just two kinds of trips, “good” and “bad,” and the two types are equally likely. He also assumed that drivers only have two possible strategies: 1) accept all trips, or 2) accept “good” trips and reject “bad” ones. By simplifying the problem (two kind of trips, two strategies only), he gained insights he would not be able to get otherwise.[13]

“Once I’ve got a foothold on the problem, I can go much farther. If I do need to make it more complex or general, I’m more likely to understand the implications of what this added complexity does. This is just an example, but it’s entirely representative of how I work. I keep simplifying and interrogating the implications until I’ve earned some intuition for how the problem “works.” It’s an extremely useful heuristic for gaining understanding of a world that’s often overwhelmingly complicated.”

Some readers with mathematical inclinations might interpret this maxim as a way of avoiding hard thinking. But as Chris Avery, Richard’s colleague at the Harvard Kennedy School and coauthor of the book The Early Admissions Game, reveals, “Before I met Richard, I thought that it was somehow cowardly – taking the easy way out – to simplify a complicated calculation to gain traction in this way. I learned from Richard that astute choice of technique is always preferable to virtuosic but misguided computation.”[14]

MAXIM 3

DON’T TAKE REFUGE IN COMPLEXITY

This maxim, a close cousin of the previous one, argues that we sometimes get lost in the complexity of a problem to avoid the hard thinking needed to get to its core. Milton Weinstein, who was also mentored by Howard Raiffa, is a former student and coauthor of Richard’s. He served as a professor at the Harvard T.H. Chan School of Public Health for many years, and recalls that when Richard taught analytic methods in the 1970s, he used to have students critique complex dynamic models created by authors who claimed that the strength of their models was their complexity. “The authors argued that the models were so complex that their outputs could produce results that the unaided human brain could not possibly understand intuitively. Richard and I taught just the opposite. While a model can yield a result that might not have been obvious, an analyst’s job is not complete until he or she can decipher the intuition behind the unexpected result – and be able to explain it to decision and policy makers in plain language (maybe with the help of a diagram or two). Models whose results remain a mystery are not useful; models that can be translated into intuitive insights and be broadly understood can be useful.”[15]

Glenn Loury, a former colleague of Richard’s at the Harvard Kennedy School and now a professor at Brown University, recalls how Richard helped him avoid unnecessary complexity: “As an economist, this is my quintessential Zeckhauser maxim. When I worked at the Kennedy School, my office was just a few steps down the corridor from his. So, I would often encounter Richard to discuss ideas. The subject of our fascinating and far-ranging dialogues varied, but typically some tidbit of analytical economics would animate our exchange. The main lesson that I took away from those many conversations with Richard some thirty years ago is captured beautifully by this maxim. And it has had a profound impact on my own work ever since. For I often find myself taking refuge in complexity, when trying to model an economic phenomenon. Invariably, it turns out that when doing so I am really hiding from my failure to identify the critical question at hand. By seeking comfort in complexity, I find myself moving too quickly from analysis to meta-analysis. I end up looking for generality without knowing what are the insights which can most usefully be generalized. This, in a word, is obscurantism and – in no small part due to Richard’s influence – I am dead set against it!”

Lorae Stojanovic, an undergraduate research assistant for Richard, says, “In my own work for Professor Zeckhauser, my first reaction to a difficult problem is often to think about how I might apply a complicated technique to it, instead of really understanding the basic problem at hand. This approach is not necessarily borne from poor intentions: I may be excited to apply something I learned for a class, or think of an interesting new way to approach the problem. But was the added complexity essential to solve the problem? Probably not. As such, many times when I’ve come to Professor Zeckhauser with what I think is an exciting, complex, solution, he has listened carefully to my reasoning and then completely overturned my idea with a much simpler way to do things.”

Sometimes we take refuge in complexity to avoid the analytical work that might be helpful to solve a challenging problem. But as W. Kip Viscusi suggests, this is a grave mistake. Kip is a professor at Vanderbilt University who credits Richard into turning him into an economist when Richard supervised his undergraduate and PhD dissertations more than forty years ago. He has published more than thirty books and 300 articles (many coauthored with Richard), focusing primarily on risk and uncertainty. He recalls using this maxim in the context of analyses where some components cannot be precisely estimated. “Rather than do a reasoned analysis, some people would rather pronounce the problem as being too complex and not amenable to analytic tools. I have found that this maxim is pertinent to almost every risk and environmental regulation issue. The coronavirus crisis of 2020 is the most recent example of where the maxim comes into play. Some have suggested that the estimates of the illness risks and the likely economic consequences are too uncertain to even think about doing an analysis. But this is exactly the kind of situation in which thinking analytically is likely to yield the greatest dividends.”

This embrace of simplicity can also be useful in moving projects forward. Scott Leland, who directs a research center at the Harvard Kennedy School, says Richard uses the simplicity maxim with the senior fellows that come to the Kennedy School for a short period (typically a year). “Our fellows begin the program wanting to write a book on macroeconomic policy, or financial regulation, or healthcare. Their initial efforts are often mired in complexity. Richard has them pick a small but important subtopic and recommends: ‘say something interesting.’ That will become a stand-alone paper. Oftentimes, that’s enough to be a successful fellow. If they write three or four of those, they have chapters that might lead them back to their original goal of writing a book.” Scott adds, “I use versions of this maxim all the time. If I’m planning a meeting, start anywhere – start simply – and build. If I’m planning a project, strip it down to the bare essentials – the simplest case – and flesh out the details as you go.”

The simplicity maxim is important when asking questions to understand the work of others. Ted Parson, a former colleague and coauthor of Richard’s and now a professor at UCLA, said, “Figuring out a simple way to ask something helps get your own thinking clear. Just do the calculation of the potential benefits and costs of asking a simple question, which might be perceived as a stupid question. The downside risk of asking the stupid question is that listeners unfavorably update their prior impressions, and you experience a moment of embarrassment. Both downsides are remediable and pass quickly even on the occasions when you don’t successfully remediate them. The upside benefit is that you actively clarify your own understanding in a way you can’t always achieve without the effort to put it into words. Moreover, you clarify the issue for bystanders; you might help yourself and others gain a new insight; and you gain the gratitude of those others who were also confused. Particularly once you consider the habitual biases against taking little risks, the balance almost always favors asking the question. You also gain a little admiration from others for your courage, but that’s an undeserved bonus: they only think it took courage because they didn’t think through this calculation.”

Gary Orren, a professor at the Harvard Kennedy School, says that the simplicity maxim is violated more than it is followed. “It is common sense, but not common practice. Why is the simplicity maxim easy to understand, but hard to do? For one thing, the issues we work on are often complex—how to deliver affordable high-quality health care, preserve national security in a world with nefarious non-state actors, or manage a globalized economy. Such issues are not easy to simplify concisely. Furthermore, we were actually taught to violate the KISS principle in school. When we were asked in a history exam ‘why did the Roman Empire fall?’ typically we wrote furiously away, filling as many bluebooks as possible. We then let the instructor search for the three or four key points buried in our brain dump. Maybe we were awarded an A in school for that, but we won’t get an A in the real world with that approach. We cannot take refuge in complexity and bulk. Our audiences will not edit our thinking and communications for us. We must edit, that is, simplify them ourselves.”

Simplicity in coaching baseball

Gary Orren

I may have first learned about the power of simplicity on baseball fields, growing up playing baseball in the Midwest and then coaching Little League teams in New England.

Is the hardest thing to do in sports hitting a pitched baseball, as Ted Williams once observed? I tried to teach boys on my teams how to hit a baseball. I vividly remember one summer early in my coaching career when I read several books on the science of hitting, including Ted Williams’ popular guide. I then prepared and delivered to the team a comprehensive presentation complete with illustrations and diagrams.

The following week I was traveling out of town, so my assistant coach took my place at the team practice. He innocently asked the boys what they recalled from my presentation the week before. He witnessed blank stares and heard a few mumbles of “I don’t know.” My presentation was a blur.

When I returned home, I contacted my assistant and asked him how the practice had gone. He told me the truth. He reported that the boys had implicitly given me a flunking grade. Once I recovered from this embarrassing grade report, I admitted to myself that I had overwhelmed them with too much detail. Hitting a baseball that is hurled at you at high speed from a relatively close distance is a daunting task involving a myriad of complicated factors. My thinking and my presentation had been hijacked by all this complexity. I had sprayed a fog gun into the dugout.

I sat down to prepare a new presentation. It was time for me to get my thinking and my teaching straight. The simplicity principle was my lodestar and salvation.

At the next team practice I decided to concentrate on the three factors I thought were most important. “It’s all about the three Hs: Hands, Hips, and Head.” Hands: how you grip the bat with your hands and where you hold your hands in relation to your torso. Hips: how you orient your hips toward the pitcher and swivel them as you swing. Head: how you keep your eyes on the ball all the time, from watching it in the pitcher’s hand to trying to see it touch your bat.

Hands, Hips, and Head. That became our mantra. I simplified the complexities of hitting a baseball into its three core elements. They were digestible and sticky. By the way, a corollary of the simplicity principle is the Rule of Three. Scholarly research studies and anecdotal evidence attest to the special power of three. (For example, all around the world, people say “On your mark, get set, go.”)

That season our baseball team won the championship for 11-year-old boys in their division of the Newton, Massachusetts, Little League.

MAXIM 4

WHEN TRYING TO UNDERSTAND A COMPLEX REAL-WORLD SITUATION, THINK OF AN EVERYDAY ANALOGUE

Negotiation between two countries might be similar in structure to simpler negotiations like those between a couple or between a parent and child, and the latter is easier to grasp at the outset. Richard often uses an actual negotiation with a contractor fixing his home as an example to illustrate complex real-world negotiations like the Iran nuclear deal. As Alice Heath, a PhD student at the Harvard Kennedy School and a multi-year teaching fellow with Richard says: “Big policy problems are often similar in structure to “smaller” problems that we face every day. The main difference is the number of people impacted. But if we see the analogue to an everyday example, we will be able to really understand the problem and then apply our intuition to the bigger problem.”

The analogues we choose might be everyday situations, or ones that are memorable for other reasons. For example, imagine you are contemplating transporting 100 identical items, one by one, over the Niagara Falls using buckets. There are two types of buckets. The first type has been used 100 times and succeeded in 70 of them. The second type has been used 2 times and succeeded only once. What would you do? This is the kind of classic puzzle that Richard gives to his students and colleagues. Pause for a minute to think about it before you read the answer in the next paragraph.

In the absence of any other information or constraints, you should use the second bucket for a few times until you have a better sense of its overall success rate. With the first one, you are reasonably sure that its success rate is close to 70 percent, whereas with the second one you are highly uncertain because it has only been used twice. So you should use the second bucket a number of additional times until you accumulate more evidence and either conclude that the success rate is below 70 percent (in which case you switch to the first bucket), or conclude that the success rate is above 70 percent (in which case you should continue using it).

Maryaline Catillon was on the receiving end of this puzzle. “This image of transporting buckets across the Niagara Falls always comes to mind when I am thinking about choosing between something I am used to (be it a project, idea, product, organization, restaurant, etc.) and an alternative that I have less experience with. The larger the uncertainty, with the same expected probability of success, the more I should try the new bucket. And the optimal length of experimentation depends on whether the old product or the new product does better. I should experiment longer even if the new product looks inferior, because I can always switch back to the old product. However, if I mistakenly conclude that the old product is superior, and discard the new product for good, I will never learn more about the new product. There is no switching back.” Maryaline’s professional work with drug trials for pharmaceuticals no doubt inspired this formulation.

Maryaline goes on, “The Niagara Falls analogy also has very important implications from the perspective of a project manager, a researcher, or anybody managing any sort of portfolio. When making decisions, such as whether to invest time and effort in a project, think of its option value. Projects with more uncertain outcomes are likely to be more interesting, and potentially high impact. Being the first person to explore an area (be it a research topic or a business idea), is potentially much more interesting, but also easier than entering widely explored areas. Higher-uncertainty projects can bring much higher gains.”[16]

Jonathan Borck has deployed this analogue approach effectively in his work as an economic consultant where he frequently needs to communicate with lawyers and judges who don’t speak his language, and in his second job teaching Kennedy School students. “In my work as a consultant and a statistics instructor, I find it valuable to illustrate complex statistical problems using the familiar example of political polling, which many people (at least in my social and professional circles) know well. For example, when trying to convey the concept of sampling error, say when sampling mortgages to evaluate the performance of mortgage-backed securities, I’ll analogize to the inherent uncertainties when polling for an upcoming presidential race.”

Analogues can be powerful when trying to communicate technical ideas to nontechnical people. For example, suppose you are interested in measuring the effect of a microfinance program on reducing poverty in a certain community. You could see if the poverty rate of the people who participated in the microfinance program changed after the program. If the poverty rate went down, you would conclude that the program was effective. If it didn’t go down, you would not. But this is not a good method because many other things could have caused the participants’ poverty rate to decrease. For example, maybe particularly good weather had allowed participants to grow more crops and earn more income. Maybe the general conditions of the economy got better.

So how can we measure the program’s effectiveness? Ideally, we would compare how people who participated in the microfinance program fared after the program with how these same individuals would have fared if they had not participated. The latter is referred to as the counterfactual scenario (what would have happened in the absence of the program), and of course we never get to observe it. In practice, researchers mimic the counterfactual by comparing the group of individuals who participated in the microfinance program with another group of individuals who are otherwise identical except that they didn’t receive the program. This latter group is often referred to as the control group.

So a key step in evaluating the impact of a microfinance program or many other interventions is to be able to form a control group that is truly comparable to the group that received the intervention. A simple analogue is to say that you want to compare apples to apples. I have used this analogue hundreds of times in training policy makers about evaluation of social programs by showing them a slide with two identical groups of apples, and it is incredible how such a simple image helps cement this abstract idea. They refer to this analogue frequently throughout the training when discussing whether a given evaluation method is likely to yield a credible result.

Coming back to our microfinance example, it would not be appropriate to estimate the impact of the program by comparing a group of people who applied and received the program with a group of people who did not even apply to the program, because the two groups are different; it appears that people self-selected into the microfinance group. We would not be able to know whether any difference in their outcomes was due to the program or to the fact that the two groups were different to begin with. This would be like comparing apples and oranges.[17] One way to get around this comparability problem is to conduct a randomized controlled trial (RCT), where we randomly assign some individuals to receive the intervention and others not. If properly designed and conducted, RCTs ensure an apples-to-apples comparison and a reliable measure of the impact of the program.[18]

To close this chapter, Gary Orren, a colleague and friend of Richard’s for over half a century and a faculty member at the Harvard Kennedy School, provides another example of how analogues make ideas memorable.

AmeriCorps and the Swiss Army knife

Gary Orren

AmeriCorps is a government network that unites hundreds of nonprofit organizations throughout the United States (like City Year, Teach for America, and Habitat for Humanity) under a single umbrella of national service. Supported by public sector/private sector partnerships, AmeriCorps is funded by the federal government, corporations, foundations, and individual donors. The corps members, typically between the ages of seventeen and twenty-four, commit a year or two of full-time service to meet critical community needs such as increasing academic achievement, mentoring youth, fighting poverty, and sustaining national parks while also fostering civic engagement and developing their own leadership skills.

Along with many others, I helped build the AmeriCorps program in the early 1990s. Over the years, I have remained a big fan and supporter of this program. Unfortunately, not everyone shares my affection for AmeriCorps. Some people—including some who hold powerful positions—not only oppose the program but have sought to cut its funding and even eliminate it. I have defended and promoted AmeriCorps on Capitol Hill, in corporate boardrooms, and with foundation officials.

In this battle, I have been armed with statistical data, narrative examples, and historical information. To strengthen my argument, I decided to add a new arrow to my quiver: an everyday analogue to AmeriCorps. I thought that an analogy would help me better understand and convey the complex arguments for and against AmeriCorps. It would simplify AmeriCorps to its essential elements. It would clarify the pros and cons about AmeriCorps that my audiences didn’t yet comprehend by comparing it to something familiar that they did understand. I believed that an analogy would paint a picture that would make my message stickier and more lasting.

I wanted an everyday analogue that would mirror the key features of AmeriCorps. AmeriCorps is a program where young people do many valuable things for their communities pretty well at a good price. What is like that? I considered several possibilities and suggestions. I settled on an analogue I saw in a published article that caught my eye: “AmeriCorps is America’s Swiss Army knife.” Like the AmeriCorps program, the Swiss Army knife does many valuable things pretty well (it’s not the best screwdriver, best scissors, or best knife) for a good price.

When I spoke with legislative, corporate, and foundation leaders, I included this analogy in my pitch. When I returned to their offices weeks later, I reintroduced myself and said: “I spoke with you several weeks ago about the AmeriCorps program.” Many of my listeners replied with smiles of satisfaction, “Oh yeah, I remember you. Swiss Army knife.” In the office next door, I heard, “Swiss Army knife.” Up and down the hall I heard, “Swiss Army knife.”

Notice that built into this everyday analogue is another maxim for good analytical thinking: an affordable concession. We should never become so enamored with the strong points in our thinking that we overlook the weakest links in the chain. Furthermore, acknowledging the weakest links is often persuasive when we communicate with others. Concessions convey honesty and objectivity and make what we say more credible.

Although I am a strong proponent of AmeriCorps, I recognize and acknowledge that AmeriCorps has both pros and cons. In truth, many of the young corps members do only a pretty good job in their daily service, just as the Swiss Army knife does many valuable things only pretty well. Frankly, it’s not the best knife, screwdriver, or scissors. Similarly, the young AmeriCorps members are only seventeen, eighteen, nineteen years old and they are not professional school teachers trained to teach literacy, nor are they professional construction workers trained to rehab community centers.

I concluded that my concession was affordable for two main reasons. Experts in the field of national service and people familiar with AmeriCorps were already well aware that many corps members only did a pretty good job. This awareness was not going to disappear from the discussion if I chose not to mention it. Furthermore, I didn’t only acknowledge that the corps members only did a pretty good job. I also emphasized that they did some things extraordinarily well. They served as unique role models for young school children; they were passionately committed to repairing the world; and they brought tireless energy to the enterprise.

The analogy of the Swiss Army knife helped me better understand the strengths and weakness of AmeriCorps and enabled me to convey that understanding more effectively to others.

2

TACKLING UNCERTAINTY

Our world is full of uncertainty, particularly for highly consequential events. Think of the COVID-19 pandemic, the 9/11 terrorist attacks, or the widespread attempts to reform police in the United States following George Floyd’s death. So too for personal events, such as a serious health situation pouncing when one feels fine or an economic opportunity that arrives out of the blue. We sometimes use hindsight to try to make sense of events we never considered, much less predicted. The maxims in this chapter are intended to help you think clearly about a world that is much more uncertain than you recognized before you picked up this book.

MAXIM 5

THE WORLD IS MUCH MORE UNCERTAIN THAN YOU THINK

On Tuesday, November 8, 2016, Donald J. Trump was elected president of the United States. Regardless of your political views, how surprised were you when you heard the news? I have asked this question to hundreds of students at Harvard; a sizable portion were “shocked” or “beyond shocked.” While there are many explanations for their answers, one underlying theme is that they did not think this event could possibly happen.[19] This maxim reminds us that certainty, and near certainty, is an illusion and that the world is a much more uncertain place than most people believe.[20] So the next time you find yourself thinking that some event will happen for sure or that some other event has no chance of happening, pause to remind yourself of this maxim.

The starkest recent illustration of the world as a surprisingly uncertain place, for most people other than experts and science fiction fans, is COVID-19. At the beginning of 2020, few of us considered the possibility, however remote, that our lives and those of everyone in the planet would be upended by a global pandemic. As Marie-Pascale Grimon, a PhD student at the Harvard Kennedy School who served as a teaching assistant in Richard’s course, remarked a few months into the pandemic “Who would have thought a few months ago that we would soon be facing the greatest pandemic in the world since the 1918 Spanish flu? We make predictions and those are helpful to make decisions, but we should always bear in mind that the world is much more uncertain than you or I think.”

We ignore this maxim at our peril. The dangers stretch even into high-level policy making. Jason Furman, a top economic adviser to U.S. President Obama for eight years and Richard’s Kennedy School faculty colleague, recounts that when he was at the White House, “the best political and legislative aides had only two possible probabilities for events occurring: zero and one. One of the top ones would say ‘I was in Congress for years, and I can tell you there is no chance they will pass this’ – weeks before it passed. Or ‘In all my decades in Washington attaching such and such to the bill has never failed to get it passed,’ just before months and months of failure to get it passed.”

This maxim applies not only to large events of a global scale such as a presidential election or the advent of a pandemic, but also to our personal and professional lives. Think about key events that happened in your life in the past few years and assess what chances you would have assigned to these events happening if someone had asked you even a few years earlier. My guess is that for at least one of these events you would have said “there is no way this could possibly happen.” In my own professional life, if you had asked me in 2004 what were the chances that at some point in my life I would be working on a research project in Burkina Faso, I would have said zero. I grew up in Latin America, was living in Washington DC, and all my research projects were in the United States or Latin America, where I was comfortable with the landscape and the language. Yet less than three years later I was getting on a plane to Ouagadougou, a place I had not even heard of a year earlier.

In a similar vein, Anna Dreber Almenberg, professor of economics at the Stockholm School of Economics and one of Richard’s coauthors, recounts that early in 2008 Richard argued to her when she was a visiting PhD student at Harvard that if you wanted to observe risk taking in the “wild,” the world of bridge (a card game that Richard plays with remarkable skill) is a great setting to do so, given that players must make dozens of probability judgments, at least implicitly, within a single session. “That’s how we ended up at the Fall 2008 North American Bridge Championship in Boston, Massachusetts, collecting data on risk taking in bridge and other domains plus DNA samples to link risk-taking to variation in a dopamine receptor gene. Before meeting Richard, I would have assigned a zero probability to the idea that I would ever attend a bridge championship.” Anna ended up not only going to the Championship but also publishing a paper about this topic with Richard and other coauthors a few years later.[21]

When a low probability event occurs (say an underdog wins a sports championship), we tend to come up with reasons why we might have expected it. This phenomenon is often referred to as hindsight bias, a tendency to perceive past events as having been more predictable than they actually were. But if we consider that many events occur in a year, we should expect at least some to be low-probability events. For example, there are many championships in a given year, so we should not be surprised that every year there is a championship outcome in some sport (say tennis, golf, football, etc.) that no one expected.[22]

Recognizing that the world is an uncertain place can improve planning. To illustrate, let’s begin with a historical example provided by William Samuelson, one of Richard’s coauthors and now professor at Boston University.[23] In June 1967, Egypt closed the Suez Canal due to the Arab-Israeli Six Day War. Oil companies scrambled for alternative means of transport, such as chartering additional tankers. As part of its planning, the management team of one oil company insisted that its analysts provide a best-guess target date for when the canal would reopen. The analysts gave a date in July 1969, and the company organized all its transport operations assuming the canal would reopen on that date. As Samuelson recounts, “On the fateful date, the company’s extra charters expired, prevailing charter rates were sky high, and there was no prospect in the near future of the canal reopening.” Had this company recognized the uncertainty surrounding the reopening date of the Suez Canal, it would not have fixated on a single date and let its extra charters expire by then.[24]

Rich Krumholz, who learned about the world of risk and insurance when Richard advised his undergraduate thesis, is now a managing partner of a large chain of restaurants. He credits this maxim with guiding how he has operated businesses and invested during his post-college life. “We cannot know what the future may hold, but it is likely that over the course of our lives, we will experience several low frequency but high severity events. After devastating occurrences like 9/11 or the COVID-19 pandemic, people accurately remark that it was nearly impossible to predict the specific event in advance. However, just because we do not know what type of high severity event will happen, does not mean that we should not prepare our personal lives, businesses, and investment portfolios to withstand these types of dramatic events. Human psychology leads most of us to extrapolate the near past too far into the future. It lulls us into assurance after periods of calm. However, if we live based on this maxim – that the world is much more uncertain than you think – we will be alert to potential risks and conservatively positioned when these severe events inevitably happen.”

Carolyn Kousky, a former student of Richard’s and now executive director of the Wharton Risk Center at the University of Pennsylvania, studies risk management for disasters ranging from hurricanes to wildfires to terrorist attacks. She illustrates the benefits of taking this maxim into account in her work: “Post-disaster, there is a strong push to enact new policies that (had they been in place) would have mitigated the specifics of what was just experienced. But the next disaster is never a perfect replica of the previous disaster. We have to keep our field of vision wider, to understand our full range of futures, and prepare policies that will be robust and effective across the spectrum of possible surprises.” Many people would argue that post 9/11, the U.S. government’s response was overly focused on preventing another attack involving airplanes and not enough attention was paid to preventing other types of terrorist attacks.

Estimating uncertainty is very challenging, even for people who recognize the world is a very uncertain place. Richard illustrates this in his classes by asking students to estimate some quantities drawn at random from a source such as an almanac, as in the table below.

Estimation Exercise

Quantity to be estimated

Your 1% surprise point

Your best guess

Your 99% surprise point

1

Birth rate in Andorra 2020 per 1,000 population

2

Air Distance from Amsterdam to Atlanta, in kilometers

3

Number of votes Hillary Clinton got in the 2016 U.S. Presidential Election from voters in the state of Alabama

4

Total area of Afghanistan (in square kilometers)

5

Number of McDonald’s restaurant locations in the United States, in 2020

6

Population of Algeria, in 2020

7

Life Expectancy at birth in Angola, 2020, in years.

8

Total Exports in Argentina in 2017, in US dollars

9

Number of votes that Mitch McConnell received to win his Kentucky Senate seat in the 2020 Elections

10

Total worldwide net sales of Apple in 2020, in U.S. dollars

Most of us are in the dark for most or all of our guesses. The exercise is meant to force us to express the extent of this uncertainty by asking us to indicate not only our best guess but also a range that we feel pretty confident will contain the true value of the quantity.

Try doing this exercise yourself before proceeding. Fill in the table above or write the numbers on a sheet of paper (without consulting the internet, of course). The 1 percent surprise point represents the lowest value you think this quantity could take. If you were making 100 of these estimations, only once should the true value be below the 1 percent surprise point. Similarly, the 99 percent surprise point is the highest value you think this quantity could take; out of 100 such estimations, only once should the true value be above the 99% surprise point. There should only be a 2 percent chance that the true quantity is outside the range formed by the surprise points (1 percent chance below and 1 percent chance above).

By this stage of the course, students know that the world is very uncertain and that we tend to understate the uncertainty in our predictions and beliefs. So they understand that Richard is out to show them that they will not sufficiently recognize their own level of uncertainty about these quantities. To provide incentives for reasonable guessing, Richard offers a prize, usually a book, to the student with the best guesses. An excellent assessor will have no surprises and tight ranges.

Despite all this, students tend to choose intervals that are too narrow, reflecting overconfidence in their own beliefs. In a typical year 30 percent of the answers fall outside students’ 1%–99% surprise range. Remember, if students were correctly estimating their own uncertainty, only 2% of the answers should be surprises. This means that students are very overconfident about their own knowledge.

Answers to the questions in the table are at the end of this maxim. Make sure you have filled in the table before you look. How many of the correct quantities were outside the ranges you set (either below your 1 percent surprise point or above your 99 percent surprise point)? If more than one, you might be underestimating the uncertainty in your own beliefs, just like many of Richard’s students and probably millions of people around the world.

Experience with estimating probabilities reduces but does not eliminate the penchant for underestimating uncertainty. When students complete the exercise again with a new set of questions, they are surprised about half as often. Jason Furman argues that White House economists, who are used to thinking in probabilistic terms, are typically better than the legislative aides mentioned earlier at recognizing uncertainty. He says that when asked to predict the upcoming jobs number, they would stress uncertainty and give a 90 percent confidence interval. And yet “about 75 percent of the time, I would guess, the number ended up falling within that 90 percent confidence interval (so even they were not sufficiently internalizing the degree of uncertainty).”

There are some endeavors where uncertainty is so large that it becomes central to our understanding of the issue. Bill Hogan, an expert on energy policy and Richard’s Kennedy School colleague for more than four decades, wrote back in 1985: “A policy that depends on accurate oil price forecasts is a policy in trouble…the range of uncertainty is impressive; it is so large that the uncertainty may be the most important feature of the analysis.”

In medicine, uncertainty looms large although this is not always recognized by the people involved. Atul Gawande, a renowned surgeon, writer, and public health leader, eloquently articulates this point. “The core predicament of medicine — the thing that makes being a patient so wrenching, being a doctor so difficult, and being a part of a society that pays the bills they run up so vexing — is uncertainty. With all that we know nowadays about people and diseases and how to diagnose and treat them, it can be hard to see this, hard to grasp how deeply the uncertainty runs. As a doctor, you come to find, however, that the struggle for caring for people is more often with what you do not know than what you do. Medicine’s ground state is uncertainty. And wisdom — for both patients and doctors — is defined by how one copes with it.”[25]

In thinking about these matters, it is helpful to distinguish between risk, uncertainty and ignorance (see table below). We face risk in situations with known probabilities and known states of the world. For example, if you play roulette in a casino, the states of the world (represented by all the possible slots the ball can land on) are known, and the probability associated with each state (the likelihood that the ball will land in any specific slot) is also known. As Richard has written “Casinos, which rely on dice, cards and mechanical devices, and insurance companies, blessed with vast stockpiles of data, have good reason to think about risk. But most of us have to worry about risk only if we are foolish enough to dally at those casinos or to buy lottery cards to a significant extent.”[26]

Categorization of situations where outcomes are unknown

States of the World

Known

Unknown

Probabilities

Known

Risk

NA

Unknown

Uncertainty

Ignorance

Source: Richard Zeckhauser

When the possible states of the world are known, but the probabilities that those states occur are unknown, we face uncertainty. Uncertainty is a much more common occurrence for most of us than risk. For example, when you are trying to decide which medical treatment to undertake, you might have a good sense of the possible states of the world (cured vs. not cured) for each of the treatments you are considering, but you will not know for sure the probabilities associated with each of these outcomes and will have to make educated guesses about them. In Richard’s words: “Uncertainty, not risk, is the difficulty regularly before us.”

Richard has also written extensively about ignorance, a situation that goes beyond both risk and uncertainty.[27] In situations with unknown probabilities and unknown states of the world, we are simply ignorant. This is the most extreme form of uncertainty. Many of the examples described earlier in this chapter relate to events that we could conceive of before they occurred, even if we thought it exceedingly unlikely that they would occur. Yet, a number of the most consequential events in our lifetimes were not imagined before they happened. The attack on 9/11 was a prime example of ignorance. Planes, which had not been considered as a possible weapon, brought down the World Trade Center, an outcome also generally considered impossible. Richard says, “Ignorance is an important phenomenon, I would argue, ranking alongside uncertainty, and way above risk. Ignorance achieves its importance, not only by being widespread, but also by involving outcomes of great consequence.”

Richard has done fascinating work on ignorance in many spheres, but perhaps none as unconventional as the work he did with Devjani Roy, an English PhD who did a postdoctoral fellowship with Richard at the Harvard Kennedy School and is now at Texas Tech University Health Sciences Center. In this work, they explored ignorance in the world of literature.

Ignorance in literature

Devjani Roy

In the summer of 2013, Richard and I were writing the initial draft of what became our work on ignorance. We were discussing Leo Tolstoy’s novel Anna Karenina, in which Anna, wife to the well-respected, if dour, Alexei Karenin, indulges in a ruinous adulterous affair with the young cavalry officer Count Vronsky. Our discussion veered toward marriage as a decision-making situation; more specifically, we discussed how marriages such as Anna’s, with the stolid and unimaginative Karenin, take place with no thought to future compatibility over a lifetime.

Richard then remarked that marriage is one of life’s most important decisions, possibly one with the greatest impact on health, happiness, and material well-being; yet, when we make the decision, we typically have the least available or the least salient information to enable a robust decision. Marriage, then, is a situation of ignorance.

“Consequential Amazing Developments” (or CADs, to use Richard’s term) are important outcomes the ignorant decision maker could not have envisioned – such as how Anna’s chance encounter with Vronsky at the Moscow railway station that sets off Tolstoy’s novel. I do not know if Anna, impulsive and reckless, would recognize the importance of ignorance in her life. But we all should. Indeed, we might do well to pay attention to Richard’s reflections on information scarcity as we enter marriage and extend these to other important areas of our lives.

Even Richard, who understands the maxim “The world is much more uncertain than you think” better than anyone I know, can underestimate the extent of uncertainty in the world. Jeff Bielicki, a former student of Richard’s and now a professor at Ohio State University, recalls a conversation with Richard in the fall of 2004 when he was his teaching assistant. At this time, the Boston Red Sox, the baseball team beloved by most people in the Boston area including Richard, were doing well during the post-season. But then they lost two games of the American League Championship Series against their arch-rivals, the New York Yankees. “Richard was a bit despondent about the prospects of the Red Sox making it to the World Series. I mentioned that I didn’t think the prospects were as poor as he was stating them, in part justified by how uncharacteristically well many of the Red Sox players had been performing. My case was not helped when the Yankees subsequently clobbered the Red Sox 19-8 (and at Fenway Park!). But the Red Sox turned it around: they won four straight games, advanced to the World Series, and ultimately won it – breaking the 86-year Curse of the Bambino.”[28]

Answers to the estimation exercise[29]

Quantity to be

estimated

Correct

answer

Was the correct answer below your 1% surprise point or above your 99% surprise point?

1

Birth rate in Andorra 2020 per 1,000 population

7 births/1000

2

Air Distance from Amsterdam to Atlanta, in kilometers

7,082

3

Number of votes Hillary Clinton got in the 2016 U.S. Presidential Election from voters in the state of Alabama

729,547

4

Total area of Afghanistan (in square kilometers)

652,230

5

Number of McDonald’s restaurant locations in the United States, in 2020

13,226

6

Population of Algeria, in 2020

42,972,878

7

Life Expectancy at birth in Angola, 2020, in years.

61.3

8

Total Exports in Argentina in 2017, in US dollars

$58.45 billion

9

Number of votes that Mitch McConnell received to win his Kentucky Senate seat in the 2020 Elections

1,222,749

10

Total worldwide net sales of Apple in 2020, in U.S. dollars

$274.52 billion

MAXIM 6

THINK PROBABILISTICALLY ABOUT THE WORLD

Let’s go back to the day Donald Trump was elected president of the United States. When people are asked why they were shocked about the results, they often cite FiveThirtyEight.com, the website founded by Nate Silver that is famous for its predictions of everything from political elections to basketball championships, as a source they used to reach the conclusion that Hillary Clinton would win the election for sure.

It is true that FiveThirtyEight.com predicted that Hilary Clinton was more likely to win than Donald Trump. But the website’s final forecast (published the night before the election) gave Donald Trump a 29% chance of winning. This was based on several factors that the site considered to indicate a high level of uncertainty: the way the electoral college map was playing out, the fact that lots of people were still undecided days before the election, and the fact that polls were very volatile in the weeks preceding the election. While a 29% chance for Trump still meant that Hillary Clinton was a strong favorite to win, this probability was high enough that people should not have been so surprised when Trump won.

Yet many people were shocked with the outcome, including people who were checking the FiveThirtyEight.com website constantly before the election. Jonathan Borck, who works for an economic consulting firm in Boston, was one of them, despite the fact that he is a former teaching assistant of Richard’s and now teaches statistics at the Harvard Kennedy School. “I was shocked by the outcome. But I should not have been, especially given my status as a card-carrying statistician. Never again will I dismiss an event that has a 29 percent chance of occurring.”

Underlying all of this was a fundamental failure either to understand or be willing to grapple with what that 29 percent chance actually means. Mathematically, it means that if you were to repeat the “experiment” of running the election 100 times, you should expect Trump to win 29 times. Of course, the election only happens once, so this idea is a bit abstract. In the case of election forecasting, Nate Silver and his team run thousands of simulations where the election is played out on their computers many times. For each of these simulations, a different result emerges based on the probabilities that each candidate will win a given state and other statistical assumptions. For example, in simulation #1, Trump wins 34 states including the critical swing states Michigan and Wisconsin, whereas in simulation #2, Trump wins 31 states, but this time neither Michigan nor Wisconsin goes to Trump. And so on. For each simulation, a winner is determined based on the states each candidate won and the Electoral College votes each state carries.[30] On the night before the election, after running 10,000 simulations, the FiveThirtyEight team observed that Trump won in roughly 2,900 of these simulations, which is where the 29 percent number came from. As my former colleague Lant Pritchett said at the time, “if there is a 29 percent chance of rain, I would bring an umbrella!”

Note that the correct interpretation of this 29 percent chance of winning the election is different from saying that the polls indicated that 29 percent of people were planning to vote for Trump. While the polls involve a considerable degree of uncertainty, if only 29 percent of sampled voters said they would vote for Trump, he would have had a minuscule chance of winning the election, far less than one tenth of one percent. Even if the polls were extremely inaccurate, say underestimating Trump’s chances by 10 percent, Trump’s chances would have been effectively close to zero.

The 29 percent probability is an example of a subjective probability. This means it is based on judgment and cannot be calculated in the same objective way we can calculate the likelihood of drawing an ace, king, queen or jack, but not the ace of spades, if you pull a card at random from a deck.[31] Subjective probabilities are the only tool available when assessing a very wide range of real-world events (for example, there is a 60 percent chance that the price of a barrel of oil will be above $50 on June 30 of next year). They also apply to personal matters, to beliefs, and to one-time-only events. As an example, you may think that there is a 30% chance you will be promoted in your job next year. Deciding effectively in most important uncertain situations requires an assessment that uses subjective probabilities.[32]

Thinking probabilistically about the world involves three distinct elements:

1)    Understanding what these subjective probabilities actually mean (as we saw above),

2)    Assigning probabilities to many things (events, beliefs, etc.) in our lives, to help us better understand the world around us and make better decisions, and

3)    Updating these probabilities appropriately when relevant new information comes in.

Key elements of thinking probabilistically about the world

We have already examined the first element. Let’s take each of the last two in turn.

Assigning probabilities

When we recognize uncertainty in the world and try to embrace it, we recognize the need to estimate probabilities of events in both the public arena (for example, a certain candidate will win the election, a certain pharmaceutical firm will develop a successful vaccine, the U.S. government will retaliate against the Chinese government for imposing tariffs on American exports, etc.) and one’s personal life (for example, the next bus will be late, I will like my new job in San Francisco, etc.). Assessing these probabilities can help us make better decisions, as we will see below and in the next chapter.

The idea of assigning probabilities to events may strike you as impossible. Many of Richard’s students, friends, and colleagues have made this same claim of impossibility. But after thinking about it and practicing, many think this is the most valuable lesson he ever imparted. Richard is fond of saying that assessing subjective probabilities is no different than hitting a baseball or learning long division; one improves with practice. In fact, Richard encourages his collaborators to practice constantly in everyday situations, such as in assessing the likelihood that Raul will be here on time, or the chances that Lisa will say that she liked the movie.

Erin St. Peter, a former student of Richard’s and now a research analyst at Wharton’s Real Estate Center, recalls a time when Richard invited his team of teaching assistants to lunch to discuss their research ideas. “As our group approached the checkout line of the cafeteria, he passed his credit card to me and asked, ‘What do you think the probability is that the cashier will question the name on the card if you use it to pay for our group?’ Because the world is more uncertain than you think, I knew the answer should not be 0. And because I am a different gender than the cardholder I thought the answer might be higher for me than it would be for a man. I think I said less than 2%. We passed through the line without question. Examples like these remind me of all of the opportunities you have in the day to be curious and to think probabilistically.”

Calibrating subjective probabilities can be particularly challenging when predicting what individuals will do, as Erin’s cashier example illustrates. Joe Newhouse, a faculty colleague who has known Richard from his undergraduate days, notes this challenge in contract bridge where great players are constantly inferring probabilities to decide on the best course of action. “Bridge is a game of drawing inferences from both what the other players do and do not do. It is no surprise that Richard is a past national champion.”

Victor Paci, a lawyer and one of Richard’s partners at a real estate private equity firm, is a subjective probabilities enthusiast, driven by decades of experience. He argues that the underwriting process on real estate transactions requires thinking probabilistically. “Our typical real estate modeling includes a number of underlying assumptions about future events. We make these assumptions based in part on subjective probabilities. For example, we might be contemplating whether to invest in a development company. Once we have made the investment, new opportunities could become available, such as the opportunity to purchase equity interests in the developer’s projects. Because the chance of this new opportunity is entirely speculative at the time that we decide to make the original investment, it would be inappropriate to include it in our base case underwriting and pricing. However, we will nevertheless model it using subjective probabilities, and take our conclusions about its value into account when assessing the attractiveness of the base case investment.”

You might think that it is possible to estimate the probability of some kinds of events, such as who will win an election or whether a firm can execute on a component of a real estate transaction, but hopeless for some other kinds of events. Many national security officials feel that it is a hopeless task to try to quantify things such as the likelihood that Iran will have operable nuclear weapons within two years or that a particular leader will still be in power three years from now. Yet Jeff Friedman, a former student of Richard’s and now a faculty member at Dartmouth College, coauthored six papers with Richard on the role that probabilistic reasoning can play in national security decision-making. Their analyses endorse quantitative estimates, as opposed to verbal terms such as “likely” or “very unlikely” that are the backbone of the security community’s assessment. Friedman lectures every year at military institutions and intelligence agencies. “These experiences are always rewarding, yet I often come away impressed by the sheer magnitude of skepticism that many practitioners direct toward a style of thinking that is so demonstrably useful.” He credits Richard with helping him see not only the usefulness of this way of thinking but also how enjoyable it can be. “The key to learning how to think probabilistically is not just to understand that this endeavor is practically useful, but to realize that it can also be so enjoyable. That is one of the most important contributions that Richard has made through his research, teaching, and mentorship.”

Estimating probabilities can also have big payoffs for personal decisions. Jennifer Lerner is a Harvard professor in the Department of Psychology and the Kennedy School and a coauthor of Richard’s. She and her husband used probabilistic thinking to save money on their wedding.

Thinking probabilistically for personal decisions

Jennifer Lerner

If you go to Martha Stewart’s web page for wedding planning (something I am not advising you do), she asks rhetorically, “Wouldn’t it be great if wedding planning were an exact science?” Unfortunately, the rhetorical question is just a teaser. According to Ms. Stewart’s website, “When it comes to predicting how many guests will attend a wedding, it’s nearly impossible to accurately estimate what percentage of the overall guest list will affirmatively respond to your invite.”[33]

When my (now) husband Brian and I were planning our wedding back in June 1998, my mother held the same view. Unless an invited guest specifically had a reason for not attending, and told us so, my mom believed that every person on the list would attend and we had to plan accordingly.

Enter conflict: My husband and I did not share this view. We wanted to plan probabilistically. We researched average attendance rates based on how far people had to travel and plugged those rates into our algorithm for attendance. My mom was horrified. It seemed to her like a cold-hearted exercise to anticipate that some people would not attend our wedding, even if they hadn’t specifically told us so. And in my Jewish and Italian family where feeding people is a cherished, sacred event, the idea of not having an excess of food, let alone enough dinners for every single person invited, triggered more than mild disagreement.

Rather than argue it out, my husband and I (poor doctoral students at the time who needed to save every penny) decided to keep our probabilistic estimates to ourselves. Only the two of us and the caterer knew that we expected some invitees would not attend. I’m happy to report that it was a wonderful wedding. Some invited guests were not able to attend at the last minute and we had not overpaid.

Notice that estimating subjective probabilities does not mean that your intuition or gut need to be ignored. In fact, they can help you form a fundamentally richer view of the world that recognizes the uncertainty around you. William Samuelson, a professor at Boston University, coauthor, and long-time tennis partner of Richard’s, illustrates this point nicely: “Over the years in both personal and professional conversations, I’ve had people tell me how they made important decisions. Across this sample, there are four or five frequently quoted reasons. Frequently, they say it was based on “their gut” or alternatively based on “their experience.” To which I ask the classic Zeckhauser follow-up question, “And what does your gut/experience tell you about the chances that you’ll be happy with the choice you made?” That usually elicits a “what do you mean” look and sparks a conversation about assessing probabilities and making decisions.”

Thinking probabilistically often involves comparing probabilities of two different events to determine the best path forward. Richard often tells fun stories involving risk and probabilities, and the following one recounted by Maryaline Catillon, one of Richard’s former students and now coauthor, illustrates this idea well: “I was in a national park with my family and cars were not allowed, so we would go around in a bus, and stop from time to time to hike, picnic, and the like. Once we stopped with a group, and went for a picnic. We could see a family of bears approaching from a distance and went back to the bus stop. The bus finally arrived. Ten people were waiting. There were only six available seats and we were the last four. The conductor said, “I am sorry, there are not enough seat belts for all of you.” What do you think was the probability that we would die in a bus accident? And the probability we would be the victim of bears?” Maryaline adds, “Richard sometimes challenges colleagues who think like the bus driver, or the folks who make rules for the bus driver. When facing problems involving competing risks, I often wonder which is the bear and which is the bus.”

Updating probabilities

The third key element of probabilistic thinking is the willingness to update your probabilities, and doing so appropriately in response to new information. The process used to update our probability assessments with new information, sometimes referred to as Bayesian updating, is incredibly useful in many walks of life.[34] See the figure below.

Imagine that tonight your favorite professional basketball team will play against their arch-rivals. Based on their respective records thus far this season and some sports websites you consulted, you initially assess that the probability that your team wins tonight is 40 percent. Decision theorists call this a “prior” probability or simply a “prior.” Two hours before the game you learn that the opposing team’s superstar got injured in a freak accident that day and won’t be able to play tonight. Since the opposing team wins much more often when he plays than when he does not, you update your initial probability to 55 percent. Decision theorists call this a “posterior” probability. It will be your prior probability if you update again in response to additional new information.

The game begins, and your team racks up a 6-point lead at the end of the first half. Maybe now you would update your subjective probability of a victory to 65 percent. Maybe the other team now scores several times and your subjective probability drops back to 55 percent. As the game progresses, your assessments change depending on what’s happening (or more generally, on information you gather). If there are only thirty seconds left, your victory assessment might soar to 80 percent, even if your team is only up by two points and the other team has the ball. If you think this is overly academic, you can check websites such as espn.com, which do exactly this – they report the odds of each team winning as the game evolves).

Bayesian Updating

Some pieces of information are more important than others. Consider again the 2016 U.S. presidential election, and picture yourself watching the coverage on election night. Let’s say you started the day believing there was a 29 percent chance of Trump winning the election (as the FiveThirtyEight forecast indicated). Results start coming in. You soon learn that Florida, well known as a swing state, has been declared for Trump. At that moment, you should update the probability that Trump will win the election to be far more than 29 percent. This is not only because Florida’s 29 electoral votes (out of 270 needed for victory) were now in Trump’s basket, but also because Trump’s winning Florida increases your assessment of the likelihood that Trump will win some similar battleground states. This single snippet of information might update your probability that Trump wins the election to 40 percent.[35] In contrast, suppose you learn that Trump lost New York state. Since New York votes heavily for the Democrats, you never thought that Trump had a chance there. Trump’s loss there conveys no information about his success in other states. These two results (Florida and New York) might be announced with the same fanfare and a big “Breaking News” sign by CNN, but in terms of helping you update your assessment of the probability that Trump will win the election, the Florida result is highly informative; the New York one tells you close to nothing.[36]

The early days of COVID-19 provide an instructive lesson of the benefits of carefully assessing new information in determining how strongly to update your priors. Sometimes you will feel that the new information is potentially inaccurate or that you lack the level of expertise needed to assess it effectively. However, if you must make a decision and further information will not be available, you must still assess how strongly to adjust your prior beliefs. This is nicely illustrated by Stefan Trautmann, coauthor of Richard’s and professor of Behavioral Finance at the University of Heidelberg in Germany.

Updating subjective probabilities in the early days of COVID-19

Stefan Trautmann

In mid-January 2020, a Chinese colleague told me about some novel virus in the city of Wuhan, which I had visited about a year earlier. Shortly afterwards the virus became more prominent also in the German news as the Chinese government put more than 10 million people under quarantine, and shortly after many more. How to think about a virus outbreak in a distant country in some city few Germans had ever heard about? Some virologists in the media suggested that the new virus may not be particularly dangerous, and that the current flu season might kill more people.

Not being a virologist, epidemiologist, or otherwise having medical expertise, how to think about the public-health and economic risks deriving from the virus outbreak? Richard’s way of thinking through events where there is little to guide our risk assessment, suggests putting ourselves in the shoes of those better informed, especially if they have clear incentives. No matter what you think about the risk tolerance of Chinese officials, their approach to public health, or Chinese politics more generally, any government that strictly enforces a full shutdown of an important industrial center and a curfew on millions of people must have relevant information that there are some very severe risks around. Some simple Zeckhauser rules of thinking would have helped many governments to be better prepared when their countries were eventually hit by the COVID-19 outbreak.

Stefan was recommending that nations should have drawn inferences from the significant decision taken by another entity, in this instance China. Doing so represents a more general principle about decision making: When updating probabilistic beliefs, it is important to use all available information that is relevant, including information from the decisions of others.[37]

Sometimes, asking others to give you their assessment of a particular probability can be helpful in refining your own assessment, or to gain useful information about someone else’s views. Suppose you want to know how your friend or daughter is feeling about getting admitted into the college of her dreams. If you ask “Do you think you will get in?” you will receive a yes or no answer (or maybe “I don’t know”). But if instead you ask, “What do you think are the chances you will get admitted?” you may gain more information and will be able to have a richer conversation. This idea is nicely illustrated by coauthor Maciej Kotowski, a professor of economic theory at the University of Notre Dame and former Harvard Kennedy School colleague.

Gaining information by asking about probabilities

Maciej Kotowski

There are many interesting things in my office – colorful stools to sit on, a tub of Lego bricks, and a wall clock of peculiar proportions. However, most visitors’ eyes are drawn to three one-dollar bills clipped to the corner of my whiteboard. These bills do not belong to me. More precisely, they once belonged to me. Now, they’re Richard’s, who despite countless opportunities has not claimed them.

The bills’ story is simple and reveals how useful it can be to use subjective probabilities to guide actions. Much of my research involves pursuing problems with uncertain answers. Occasionally, one is lucky enough to whittle down a problem into a precise conjecture or formal proposition that is either true or not true. But then, what next? Spending days or weeks trying to prove an incorrect conjecture is hardly a good use of time. Equally wasteful is hunting for a counterexample that might not exist. The natural course of action is for a researcher to pursue his “hunch” as to which path is correct. That is, based on the researcher’s understanding of the problem, one path is deemed more likely to lead to a quick resolution.

Typically, subjective probabilities stem from intuition alone. A slightly improved method, perhaps, is to solicit an expert’s opinion. One could ask the expert, “Do you think this theorem is true?” and an answer of “yes” or “no” will follow. Slightly more information about the expert’s opinion can be collected via a small bet. Would the expert be willing to bet a dollar that the theorem is true? Maybe at 2 to 1 or 3 to 1 odds? Such bets are not made to earn money – the sums are trivial. Instead, they are a small price to pay for more information to further refine one’s own assessment of the problem, which will determine which path to follow.

The three dollars represent my debt to Richard’s intuition. He was right on a few bets, and I proved a few theorems.

In the professional world, assessing the probabilities that different courses of action will lead to success, and being willing to update these probabilities, is essential to effective decision making. It can help you choose investments, select collaborators, determine which projects to pursue, etc. It can also help you decide which projects to abandon, as a story from Nils Wernerfelt, a research scientist at Facebook and coauthor of Richard’s, illustrates.

Assessing probabilities to decide which projects to keep

Nils Wernerfelt

Richard has a story from years ago when a young researcher came to him asking for feedback on several promising early-stage projects. Richard reviewed the material and based on his feedback, the researcher made the tough decision to kill some of the projects. I have this image of Richard’s forever ingrained in my mind of him raising his hand and saying: “And that’s when I knew Steve Levitt would be successful!”[38]

As that line suggests, the side of this story that Richard emphasizes is that it’s critical to know when to kill projects.[39] Time and energy are scarce resources, and even if a project of any nature – not just economics papers – starts out strong, we should constantly be thinking probabilistically and updating our expected values. This is a hard but essential skill.

Another side of the story that’s always stayed with me is how important it is to identify sources of high-quality signals (i.e., information about the likelihood that a project will succeed). For Steve Levitt to update his assessments and kill his projects, he had to know where to go to acquire those signals in the first place. This, too, is an essential skill. Richard has been that expert source for many of us across a range of important subjects in our lives (research topics, career choices, Thai restaurants in Cambridge, etc.). I implore the readers of this book to not only take his maxims to heart, but also identify who the Richard Zeckhausers are in their own existences. Speaking from personal experience, finding such a person will be one of the most important moments in their lives.

Most of the time, a decision maker must decide both what information to gather, often at some cost, and when to act rather than wait to gather more information. Consider a student who has been admitted to several colleges. She might decide to call three graduates from her high school now attending the colleges she is seriously considering, or she could visit each college in person. The importance of a decision should affect her expenditures on information gathering. She will spend four years of her life at her chosen college. A trip would provide much better information than three phone calls.[40] Contrast this with her decision of where to do a summer internship. In this case, the decision might be less consequential and hence she might decide not to travel to each of the potential locations.

Thinking probabilistically can be hard. As Howard Kunreuther, emeritus professor and codirector of Wharton Risk Management and Decision Processes Center at the Wharton School, University of Pennsylvania, puts it: “I now believe that even if people understand probability theory, it is challenging to learn how to apply these concepts when making decisions, particularly when dealing with extreme events, such as natural disasters, few people think probabilistically when deciding whether to protect themselves against future losses.”

In sum

Ultimately, thinking probabilistically starts by correctly viewing the world as a highly uncertain place. With that view in mind, the logical prescription is to assign probabilities to the various events that could occur, and then update those probabilities as new information arrives. These updated probabilities are used to make better decisions. This approach is beautifully summarized in the box below by Chris Robert, one of Richard’s former students and a technology entrepreneur.

Thinking probabilistically

Chris Robert

I left Richard’s course with an essentially different view of the world, one that is fundamentally more accurate and powerful, and this idea of viewing the world probabilistically is at the heart of it. Just as Morpheus helped Neo in The Matrix, Richard helped me to see a deeper reality, one in which uncertainty and probability distributions are behind everything that happens. None of us ever fully control outcomes – even our own thoughts and behavior! – but we act to influence probability distributions as best we can. Philosophically, I may view the world as deterministic, but in practice I have to accept that I am far from omniscient and even farther from omnipotent. Accepting that everything is effectively uncertain and that my thoughts, hopes, and actions can at best indirectly influence the world by influencing probability distributions: this has brought me a kind of peace through acceptance, and a greater mastery over the world around me.

What can I do to control an outcome? That’s the wrong question. What can I do to influence the odds? Now that’s productive. That I can work with – in my life and in my job.

MAXIM 7

UNCERTAINTY IS THE FRIEND OF THE STATUS QUO

If you have had the same credit card, bank account, gym membership, car, or toothpaste brand for many years despite the fact that – for at least some of these choices – you suspect there are better alternatives, you exhibit what is known as “status quo bias.” Status quo bias is a term coined by Richard and his coauthor William Samuelson in an article published in 1988, Richard’s most widely cited academic paper.[41] In its pages, the authors document a series of fascinating decision-making experiments that show that people disproportionally do nothing or maintain their current or previous decision, often when an easily assessable alternative would be superior.

Status quo bias challenged a central assumption of economics at the time: that humans are rational decision makers who consistently make objective choices between options to maximize their satisfaction. In their paper, Richard and William Samuelson explored a variety of possible explanations for the bias.

One possible explanation for status quo bias that may be consistent with rational decision making (though the authors argued not the only or even main one) is that when there is uncertainty about the value of the choices we are considering in a given decision, we tend to stick with our initial or previous choice. Consider the rather trivial choice of which toothpaste brand to use. Most of us first favored a brand because our parents or partner were using it, it was the cheapest quality brand, we liked the taste, our dentist recommended it, or some other reason. We suspect that another brand might be preferable to us, but we are uncertain if this is the case. As long as we consider the original brand we chose to be sufficiently good, and the cost of assessing whether to change not worth it, it is rational to stick with our initial choice. “Don’t sweat the small stuff” provides one explanation for status quo bias.

Yet even when alternative options are far superior to the status quo, and not expensive to assess, uncertainty is often a friend of the status quo. A stark example of this occurred in the early days of the COVID epidemic. People arriving at hospitals were dying and, in an effort to save lives, doctors were trying treatments whose effectiveness was unproven at the time. There was considerable uncertainty about which if any of these treatments could save lives.

The gold standard for determining the effectiveness of treatments is to conduct a randomized controlled trial (RCT), also called a clinical trial in medicine. An RCT randomly splits patients into treatment and placebo groups. Proponents of RCTs argued to wait until the trials were completed before recommending the use of any unproven treatment. This was a manifestation of sticking with the status quo in the face of uncertainty.

Conducting an RCT takes a long time and, in the meantime, thousands of patients were dying. Maryaline Catillon, a former hospital director in France and a recent PhD student of Richard’s, recognized that the status quo was not good, and wrote a piece with Richard arguing that RCTs are the appropriate way to get definitive answers, but they have the downside of taking significant time.[42] They argued that while waiting for RCT results to become available, scientists should conduct quicker, albeit less reliable, high-quality observational studies to inform treatment.[43] This is especially true in an environment where large numbers of patients were being treated, so data to conduct these studies were accumulating very rapidly. In sum, it is important to recognize that uncertainty might lead us to stick with the status quo in medical decision making. And we should be particularly sensitive to this tendency when the status quo is not a satisfactory option.

Awareness of status quo bias and its tendency to appear in the face of uncertainty can help you make better personal and professional decisions. Jayendu Patel, a coauthor and former faculty colleague of Richard’s at the Harvard Kennedy School, reported, “Richard, along with William Samuelson, had brilliantly contributed to our understanding of status quo bias driven by fear of uncertainty. I have seen it impact poor decisions, including my own, on when to change jobs/careers and when to move on from bad marriages. During a lunch in the early 1990s with Kennedy School colleagues that included me, the late Dick Neustadt in a private aside offered: ‘change your career focus every decade,’ which I interpreted as wisdom to manage the devils of status quo bias.”[44]

Interestingly, one can take advantage of status quo bias to help people make better choices by setting a default option that will be good for the person making the decision. The field of behavioral economics is replete with examples of using defaults, and more generally choice architecture, to “nudge” individuals toward good choices. For example, defaults have been used to get people to automatically enroll in retirement plans, donate organs, and purchase “green” energy from sustainable sources.[45] Howard Kunreuther, professor emeritus at the University of Pennsylvania, recognized the influence of status quo bias in the field and in his own work: “The use of default options is a key principle of choice architecture and has led to many empirical studies initiated by the Samuelson-Zeckhauser paper.”

While Richard is a big believer in behavioral economics and an important contributor to the field, he believes that training people in decision analysis allows them to confront and combat a large number of well documented biases, and that in the long run this is more effective than using choice architecture to create environments where people are nudged to make the right choices. In his words, “it is better to teach a person how to decide than to nudge a person to make the right decision.”

Needless to say, status quo bias can also be used to lead people toward making poor choices. Remember when you had planned to read, go to the gym, or spend time with your family and instead spent the whole night mindlessly binge-watching a Netflix series because Netflix automatically turned to the next episode without requiring you to leave the couch or even click the remote? If you have ever done this, you were a victim of your own status quo bias reinforced by Netflix’s clever scheme.

Gernot Wagner, a student and coauthor, notes the need to use judgment when assessing whether refraining from action reflects status quo bias. More generally, maxims describing human behavior apply in some contexts, but not others. “Status quo bias is a prime example. It is the friend of not acting, and you ignore it at your peril. But we also see ‘action bias.’ Often political leaders just want to be seen as doing something, anything – leading them to want to act, even if no action is the preferred choice.”[46]

Sometimes governments choose status quo bias because it reflects their understanding of constituents’ preferences. Marijane Luistro Jonsson, a coauthor, notes the disparate responses of nations to COVID-19. Confronting massive uncertainties, most developed nations chose “to implement hard measures and drastic changes like lockdowns. In Sweden, where I live, the approach depended on information dissemination, enabling people to understand and take responsibility for their decisions.” In 2020, Sweden had fewer deaths and better economic performance than Europe as a whole, though it did worse than its Nordic neighbors. The maxims in the next chapter may help you assess the wisdom of Sweden’s approach.

3

MAKING DECISIONS

The last chapter provided guidance to help you understand uncertainty. When outcomes are uncertain, and particularly when stakes are high, making effective choices is hard. Many people grab an action. Other get paralyzed, and simply make no choice, a poor choice in itself. This chapter presents maxims to help you make sound choices when uncertainties are great and the decision is important. These maxims will help you accept that certainty is rarely a feature of consequential decisions. Paradoxically, recognizing this reality can give you the confidence to move forward. Read below to put yourself on the path to more effective decisions.

MAXIM 8

GOOD DECISIONS SOMETIMES HAVE POOR OUTCOMES

If you were asked what was the best decision you made in the past year, what would you say? Annie Duke, former world poker champion, psychologist, and author of two wonderful books on probabilistic thinking and decision making, has asked this question to hundreds of people around the world. The great majority responded with a decision that turned out well. This suggests that many people confuse the quality of the decision (how good was the process we used to make the decision with the information we had at the time?) with the quality of the outcome (how good was what happened after the decision?). Confusing the quality of the decision with the quality of the outcome is so prevalent that poker players have a term for it: resulting.[47]

In statistics classes at Harvard’s Kennedy School a simple table (below) is often used to help students distinguish the quality of the decision from the quality of the outcome.[48] This maxim addresses a specific case of resulting, and warns us that we might end up in the “You were unlucky” box.

Consider the following example inspired by Annie Duke. Imagine you drove to a bar with your partner or roommate. You both had a wonderful time, but you had a few too many drinks whereas your partner just had non-alcoholic Shirley Temples. When it’s time to go home, you ask your partner – who drives as well as you – to drive both of you home. On the way home, a drunk driver hits your car in the back. You and your partner are both fine, but your car will need major repairs.

The decision to ask your partner to drive both of you home was a good one. With the information you had at the time, you reasonably judged that the likelihood of a car accident was higher if you drove rather than your partner drove. Yet the outcome was not good. It was a good decision with a poor outcome.

The maxim applies to a very wide range of situations. For example, imagine you bought a new iPhone. You then needed to decide whether to buy insurance against accidental damage. Being the kind of person who is very careful with your phone, you assessed that the price of insurance was too high given the likelihood that you would have an accident or lose your phone. Three months later, you were at the grocery store; you dropped your phone and it broke.

If you are like most people, the first thought going through your mind is regret about your insurance decision. Worse, you may have recriminations, concluding that not buying insurance was a bad decision.[49] But the fact that you got a bad outcome does not imply that your decision was a bad one. It was a freak accident; you very rarely drop things. This maxim reminds us to judge the quality of your decision by what you knew at the time you made it, not what you found out afterwards.

Posit a more consequential decision. You have to choose between a job in San Francisco or a job in Los Angeles. You have some sense of whether you will get along with your colleagues in each location and how much you will enjoy living in each city, but you are quite unsure. You can make informed judgments about the likelihood of these outcomes, but you don’t know what will ultimately unfold. If you decide to go to San Francisco and end up not liking it, this does not necessarily mean it was a bad decision.

Josh Yardley, a former student of Richard’s, reflects on how learning this maxim in Richard’s course affects how he evaluates his decisions: “Before Richard’s class, my assessment of the quality of a decision was closely – and often unhelpfully – tied to the outcome of that decision. It’s an easy heuristic: If it worked out okay, it must have been a good decision; if not, it must have been a bad one. But this maxim says, “not so fast.” Every time I’m tempted to let the outcome of a decision determine my assessment of that decision, I try to slow my thinking down. How would I judge the process that resulted in that decision before knowing its outcome? And then, how might I update that judgment given the outcome I can now observe? That first question – How was the process? – disciplines my thinking in a way that is incredibly helpful in evaluating decisions made by my favorite sports teams, by my elected officials, and by me.”

The maxim applies to decisions as small and personal as whether to buy insurance for your iPhone and as large and important as how to steer an organization you lead during moments of crisis. In times of crisis, all potential outcomes look bad. If so, you will have to choose among options all of which will lead to bad outcomes and inevitable, but unfair, blame. The goal is to pick the option that you expect will lead to the least bad outcome.

David Ellwood faced this least-bad-option situation when serving as the dean of the Harvard Kennedy School during the 2008 financial crisis. Any budget cut would be painful, and a series of budget cuts even more so. None of the people he would let go would offer understanding. Few would applaud his chosen action. “The problem is often that no good outcomes are available, forcing you to choose among painful paths. Even more troubling, the best path is knowable only after the fact, when the uncertainty is resolved. Long before you know where the bottom would be or how fast things would recover, we had to decide where and how much to cut back, including how many people to furlough or lay off.” We will return to decisions where many outcomes look bad in our next maxim.

If we cannot judge the quality of the decision by the quality of the outcome, how should we judge it? The key is to understand what information you had (or could have had) at the time you made the decision, and then determine whether given this information you chose a path that would maximize the expected value of your decision. The formal procedure for doing this is to draw a decision tree with nodes for choices and chance events, the probabilities associated with each chance event, and the outcomes associated with each branch of the tree. Yes, the probabilities will not be well defined, as are the probabilities of a coin flip or selecting a card. Assessing how likely you are to break your phone or like San Francisco can only be assessed subjectively. That is a major reason why decision making is difficult. But not thinking hard about probabilities, or worse not attending to them, is the path to poor decisions.

While an extensive treatment of decision trees is beyond the scope of this book, the logic that underlies them is simple: When faced with a decision, think about the choices you have and for each of these choices think about what outcomes you associate with each of the scenarios that are uncertain at the moment.[50]

We illustrate the process with a simple decision tree for the decision of whether to buy insurance for your iPhone. Note that for the two alternatives you face (buy insurance or not), you have two scenarios (whether the phone breaks or not) and each scenario has different consequences depending on the choice you made. You can then calculate for each of the two main branches the expected cost (labeled EC). In this hypothetical example, the expected cost of buying insurance ($102.50) is much higher than the expected cost of not buying it ($42.50). Therefore, unless you are extremely risk averse, the rational decision would be not to buy insurance. And for these hypothetical figures, if you break your phone, it would be a bad outcome and it would cost you $850, but this does not mean the decision was a bad one.

Note that the expected cost is what you would expect your cost to be on average if you made this decision many times, say if you were to buy 1,000 smartphones. You might object because you are only buying one phone. Over the course of your lifetime, however, you will make thousands of decisions like this one, so thinking of what will happen on average across many decisions is helpful.

Simple decision tree for buying iPhone insurance

The act of drawing the tree allows you to visualize the choices and uncertainty involved, which can be informative and insightful even if you don’t solve the tree as formally as in the smartphone example.[51] Moreover, even if you do not draw a decision tree, thinking in terms of its components can frequently push you toward superior decisions. And this is what David Ellwood did as dean of the Kennedy School in 2008. “We tried to do what Richard would do: We looked at a variety of scenarios, assigned a rough probably to each, considered how we might achieve the best outcome in that situation, and finally worked backwards to ask which decision seemed likely to give us the best outcomes, knowing full well that we would not select the perfect path with 20-20 hindsight. Ironically, knowing that one cannot possibly know the ideal route actually makes it easier to act when action really is essential.”[52]

Given that good decisions can sometimes result in bad outcomes, why should you go through the trouble of setting up and executing good decision-making processes? The answer is that, on average, good decisions are more likely to result in good outcomes than bad decisions. They cannot ensure good outcomes, but they increase the chances of good outcomes. If you consistently employ good decision processes, you will accumulate better outcomes over the course of many decisions. Over the course of years, this can make a huge difference in your quality of life.

The fact that good decisions sometimes result in bad outcomes should not lead us to assume that our bad outcomes all result from good decisions with an unlucky result. Bad outcomes often flow from bad decisions, particularly when decisions are made more on whim, and impulse outweighs thinking. The key is to judge the quality of the decision separate from the outcome, and ask yourself if you could have done better at the time you made the decision with the information you had. As an example, suppose you don’t like to get wet when you go out. On a day with a 90 percent chance of rain forecast, you decide to leave home without an umbrella. It pours; you get very wet. Unless you have something against umbrellas, this was a bad outcome emanating from a bad decision. Even if it hadn’t rained, bringing the umbrella would still have been the right decision.[53]

Nevertheless, knowing that a good decision can sometimes result in a bad outcome should make us more compassionate with ourselves and with others when bad outcomes occur. This idea is beautifully illustrated by Harold Pollack, a professor and urban public health scholar at the University of Chicago, and one of Richard’s coauthors and former students.

Humanizing insights

Harold Pollack

Because Richard is an unsentimental thinker, it’s easy to overlook his humanizing insights. Good decisions – or at least defensible ones – sometimes produce poor outcomes. A doctor may practice impeccable evidence-based medicine by avoiding (on-average) wasteful scans. On rare occasions, something important will be missed. Out of an abundance of caution, another doctor may order another test, which leads to further intervention, which causes harm. Once life’s lottery has been run, it is difficult to view such sins of omission or commission with the human sympathy their practitioners deserve.

I considered this insight when I read about an unfortunate February 1, 2020, tweet by Surgeon General Jerome Adams: “Roses are red. Violets are blue. Risk is low for #coronarvirus. But high for the #flu So get your #FLUSHOT!”

Two months later, the nation was in a grip of a deadly pandemic. Viewed from that perspective, the surgeon general's admonition appears tragically complacent and wrong. Viewed from the vantage point of when it was written, his admonition appeared much more defensible and routine. The doctor was reminding people to focus on an important, mundane risk, not to be distracted by a frightening future problem. Many of his critics might easily have sent the same tweet. Richard would remind us that a little human sympathy is in order.

A corollary to this maxim is that bad decisions sometimes have good outcomes. Kessely Hong, a former student of Richard’s and now his colleague at the Harvard Kennedy School, illustrates this vividly: “Following the 2008 financial crisis, my husband and I were humbled when we had a free financial advising session offered by a representative of the company managing my husband’s retirement plan. We had just had our third baby in five years, were utterly exhausted, and had never bothered to adjust his retirement plan investment away from the default choice of a money market account. That ended up being fortuitous, as we were shielded from loss when the stock market underwent a sharp drop in 2008. The financial representative discussed this with us, looked us in the eye, and said, “You weren’t smart, you were lucky.” This reminded me of Richard’s class. He then advised us to include a mix of investments in our retirement plan selections going forward, so that we could look forward to the possibility of growth in the long run.”

Elizabeth Linos, a former student of Richard’s and now a professor at University of California, Berkeley, was working for the Greek government when the prime minister made a controversial decision that was followed by a decade-long financial crisis that rattled the world. “Every time I think back to this painful time for my country, or any time someone criticizes the decision, I tell them Richard’s maxim: all we can do is make the best decision with the information we have at the time we have it. We should judge our political leaders by the decisions they make, not the outcomes themselves. And I’m still convinced that was the right decision, probabilistically speaking.”

Rob Stavins, a colleague of Richard’s at the Harvard Kennedy School for more than thirty years, summarizes this maxim well: “One of my greatest frustrations when reading the newspaper is when someone explicitly or implicitly judges the quality of a decision on the basis of its outcomes, rather than judging the quality of the decision in the context of information that was available at the time. A related pet peeve is when politicians and others claim that an improved economy, or decreased pollutant emissions, or some other raw change, is evidence of a policy success. The comparison that ought to be made is not how things have changed from time A to time B, but rather how things are at time B, compared with how they would have been without the policy.”

MAXIM 9

SOME DECISIONS HAVE A HIGH PROBABILITY

OF A BAD OUTCOME

This is an extension of the previous maxim that “Good decisions sometimes have bad outcomes.” There are some decisions, like the one David Ellwood described earlier in this chapter, where many or all your possible choices will result in bad outcomes. The chances that you will be pleased with the outcome of the decision are small, but analytic decision making can still be useful in guiding you to the best possible decision (or least bad one) given the choices you face.[54]

Some good decisions, indeed, are likely to lead to a bad outcome. Suppose you invest $1,000 in a talented close friend’s startup company at the same price he is selling to his immediate family. You recognize it has a good chance to go broke, say 90 percent, but if it succeeds your investment will be worth $50,000. You invest because the lottery is very favorable, and you can afford the loss of $1,000. The likelihood of a bad outcome from this good decision is 90 percent.

Alice Heath, a student of Richard’s at the Harvard Kennedy School and one of his current teaching assistants, experienced this maxim very clearly when she started working with state child welfare agencies, whose mission is to prevent child abuse and neglect. The children and families they work with face very tough circumstances. Unfortunately, there is often no policy choice that a child welfare agency’s leadership can make that is likely to completely prevent abuse or neglect. “Completely preventing abuse or neglect would likely require draconian measures that would not be good for anyone. The best an agency can do is make the choice that has a higher probability of a better outcome relative to the other choices. Even with the best decisions there will still, sadly, be a high chance that some children suffer abuse and neglect. I have seen state legislators and commentators fail to understand this idea over and over, reading every tragic incident as a decision-making failure rather than the result of a set of choices where the best option is not a good option. As a result, state child welfare directors too often have very short terms and agencies lack stable leadership, which only makes things worse for the children and families who need help.”

Another illustration of this maxim comes from Al Carnesale. Al is an expert on national security policy, a decision tree maven, and one of Richard’s former colleagues. He had stints as chancellor at UCLA, provost of Harvard University, and dean of the Harvard Kennedy School. Throughout the Cold War (and, to a large extent, to this day) the United States’ basic strategy to avoid nuclear war has been “nuclear deterrence.” In essence, the United States maintains the capability to retaliate against a nuclear attack with a counterattack so devastating that no nation will choose to attack it. The Soviet Union, and then Russia, also chose the nuclear deterrence strategy.

Al observes: “In short, both the U.S and the Soviet/Russian governments decided that the best way to avoid nuclear war was to maintain thousands of nuclear weapons aimed at the other side, with many on hair-trigger alert. The outcome of this decision thus far has been good: there has been no nuclear war between the two sides. Does this imply that this decision has been a good one? Not necessarily. It might well have been a bad decision and we’ve been just plain lucky.” Al notes that there may have been alternative strategies to avoid nuclear war that had a lower probability of a poor outcome, and that these alternatives might still exist now. He cautions against relying on good luck.

For anyone who knows Richard, one of the best-known illustrations of this maxim is an extremely challenging health decision he and his wife Sally had to make in 1995. That year, Sally had received a diagnosis of stage 3 breast cancer – a cancer that had spread to the lymph nodes. No option was attractive; each involved both arduous treatment and a significant chance of death.

Sally had been told that she had eight positive nodes. One doctor told her that if she had at least ten positive nodes, she would be eligible for a randomized controlled trial (RCT) where one arm was high-dose chemotherapy paired with a bone marrow transplant (BMT). Richard, just to expand options, asked the doctor to get a recount of the nodes. There turned out to be eleven, as some were clustered together and were hard to count. This raised count was bad news, but it made Sally eligible for the trial.

Her doctors observed: “We do not like to do bone marrow transplants, since they have a 4 percent treatment mortality.” Richard and Sally asked: “What gain does a BMT offer in survival probability, assuming one survives the treatment?” The doctors responded: “We do not know. That is why we are conducting this trial.” The results thus far were confidential. After pushing Sally’s doctors further, and having Richard read widely in the medical literature, including the application for the trial, and having him consult with oncologists at multiple institutions, they concluded that the expected long-term survival gain was 10 percent or more.

They learned three more things: An expert from Columbia University had written an article based on limited data that presented a graph suggesting that BMTs would provide better long-term survival, far past the five-year mortality threshold used in most studies. Second, Sally’s hospital had had 0 treatment fatalities to that point, not the overall 4 percent stated. Third, the sister of the statistician on the trial had recently received a BMT for breast cancer. Given all this information, Richard and Sally decided that she would apply to the trial. She did and was randomized to the BMT treatment.

The treatment was awful and put Sally within a whisker of death. Twenty years later, her oncologist stated that in fact the benefits were seen long after the treatment. Twenty-five years later, Sally is totally healthy. Both she and Richard recognize that they were lucky. They can’t be confident that they made the right decision. What they do know is that they made the right decision on the basis of the scraps of information they had at the time.[55]

Part of what makes Richard extraordinary is that even in the face of such a terrible choice in his personal life, when many of us familiar with this maxim would retreat to our base instincts, he is able to bring his analytical skills to make better decisions. As he thought at the time: “I have been training for this decision my entire life.”

MAXIM 10

ERRORS OF COMMISSION SHOULD BE WEIGHTED

THE SAME AS ERRORS OF OMISSION

In August 1997, shortly after Amazon made its public stock offering, Chris put his meager savings together to purchase $2,500 of Amazon stock. The following summer, in 1998, Chris’s Amazon stock is worth slightly more than $20,000, a wonderful outcome but a risky holding. At the same time, Pat, who has followed the amazing progression of Amazon’s stock price over the last year, is considering buying $20,000 worth of its stock for the first time. They each ponder this decision and decide that at that moment Amazon stock is too risky to own. (In fact, Amazon did not have a profitable year until 2003.) Chris sells his total Amazon holding. Pat decides not to buy Amazon and buys less speculative stocks instead.

Anyone owning $20,000 of Amazon stock in August 1998 would have over $3 million in May 2021, a much higher return than for almost any other stock investment during this period. In this example, Chris made an error of commission. He took an action (selling Amazon stock in 1998) that ended up being a mistake. Pat’s error, on the other hand, was one of omission. He failed to take an action (buying Amazon stock in 1998). Both of them would have done better if they had owned Amazon stock in 1998 and held on to it for many years.

The two decisions are equivalent in their material consequences: both Pat and Chris invested $20,000 in stocks that were not Amazon stocks in 1998, and missed out on the monumental gain over the next two decades. But the two individuals perceived their decisions differently. Chris, who sold his stocks, kicks himself regularly for his stupidity. Pat, who didn’t buy the stocks, rarely thinks about it. If you are like most people, you would feel much more regret had you been Chris than had you been Pat.

More generally, errors of commission generally get weighted more heavily than errors of omission, a tendency sometimes referred to as omission bias.[56] Humans seem to be programmed that way. Richard and many other scholars argue that we should make efforts to give them the same weight. What is intuitive – here giving far greater weight to the commission error – is not always right. That is why tennis coaches make a living telling you to keep your eye on the ball; the natural tendency is to watch one’s opponent.

One potential reason behind omission bias is that we often do not know about the losses that arise from errors of omission. For example, if you choose not to take the job in San Francisco, you will never know how it would have turned out. You are less likely to experience regret. Whereas if you had decided to go and things had turned out poorly (an error of commission), the losses to you are much easier to see.[57] There is a rich literature beyond the scope of this book on why this omission bias exists.[58] Indeed, some work suggests that actions, or errors of commission, generate more regret in the short term; but inactions, or errors of omission, produce more regret in the long run.[59]

Yinglan Tan, a former student of Richard’s and founding partner of Singapore-based Insignia Venture Partners, describes this tendency to give more importance to errors of commission in his own work as a venture capitalist: “A deal gone wrong has far more lasting effects on the fund than missing a deal on a company that turned out to be a billion dollars.”

Richard believes that the approach of the U.S. government and many other governments to COVID-19 gave much more importance to errors of commission than to errors of omission. Consider the case of vaccine distributions. The cautious approach that many governments took was to wait until they were fairly certain that the vaccines would work well without unexpected serious side effects. This cautious approach revealed fear of making an error of commission; that is, a more rushed approach could have resulted in some unexpected side effects. But the consequences of not taking quicker action (an error of omission) have been far worse: a large number of preventable COVID infections and deaths.[60]

We now come back to the difficult health decision Richard and Sally made twenty-five years ago. They both recognize that part of what made the decision of undergoing the BMT treatment challenging is that if Sally had died from the treatment, it would have represented an error of commission which, as we just saw, we intuitively yet erroneously tend to weight more heavily than errors of omission. A clear and objective discussion between them, and with their doctors, allowed them to make the best possible choice without worrying about which type of error (omission or commission) would be involved if things turned out poorly.

We end this section with some thoughts from Jeff Frankel, Richard’s faculty colleague at the Harvard Kennedy School and a member of President Bill Clinton’s Council of Economic Advisors. He reflects on his general views about Richard’s maxims, and how failing to heed this particular maxim has resulted in suboptimal environmental policies.

Errors of omission vs. commission in environmental policy

Jeff Frankel

Richard’s maxims are useful corrections to common mistakes in decision analysis that are hidden in what is widely taken to be unassailable common sense. What could be more universally accepted as wise words to live by, in the medical profession and beyond, than the apparent truism, “First, do no harm.” This principle, based on a line in the Hippocratic oath, seems to say, “errors of omission should be weighted zero, relative to errors of commission.” For example, if a doctor contemplates a surgical procedure for a dying patient that has a 90 percent chance of fixing the problem and a 10 percent chance of resulting in immediate death, a literal interpretation of “do no harm” would be to forego the surgery and let the patient die in the near future. Richard would of course say that the right answer from the probability-weighted average of outcomes is to go ahead with the surgery.

By the way, I have no problem if someone wants to justify “do no harm” as a three-word summary of the idea that surgeons might sometimes be too quick to operate. It would serve as a recommendation to lean toward conservative treatment, thereby counteracting the temptation to “apply your hammer to everything that looks like a nail.”

Public policy applications of Richard’s maxim “Errors of commission should be weighted the same as errors of omission” arise in environmental policy. Some, especially in Europe, have a fear of new and unfamiliar technologies in general. The claim that the burden of proof lies with the innovation, rather than symmetrically with the status quo, sometimes goes under the name of the “precautionary principle.” It helps explain the tendency to forget to compare the worst-case risks of the new technology with the known downsides of the old technologies.

One example is genetically modified organisms (GMOs). This policy issue first came to my attention as a member of President Clinton’s Council of Economic Advisers. It is true that a fundamentally new technology tends to pose risks that are unknown. Let us say that there is some small probability of doing some harm (an error of commission). That is no excuse for neglecting to weigh in the balance the known risks of the existing technology (an error of omission). In the case of genetically modified crops, costs of doing without them include greater need for insecticides and possible food shortages in poor countries.

MAXIM 11

DON’T BE LIMITED BY THE OPTIONS

YOU HAVE IN FRONT OF YOU

Decisions sometimes require you to reject the options you were given, and to look for others. Max Bazerman, a colleague of Richard’s and professor at the Harvard Business School, says that many Zeckhauser lectures have influenced him, but that he was particularly struck when Richard presented the “Cholesterol Problem”: Your doctor has discovered that you have a high cholesterol level, namely 220. She prescribes one of many available statin drugs. She says this will generally drop your cholesterol about 10 percent. There may be side effects. Two months later you return to your doctor. Your cholesterol level is now at 195. Your only negative side effect is sweaty palms, which you experience once or twice a week for one or two hours. Your doctor asks whether you can live with this side effect. You say yes. She tells you to continue on the medicine. What do you say?

“I have had problematic lipids in the past, have studied cholesterol, and am not shy. So, as Richard was looking for someone to respond to his query, I went with staying on the statin – publicly. Richard responded, ‘Why don’t you try one of the other statins instead?’ I immediately saw that he was making a great deal of sense, as there may well be equally effective statins that don’t cause sweaty palms or any other side effects. I implemented his insight in many other contexts and documented this idea in my book The Power of Noticing.”

Tony Gomez-Ibañez, another colleague, is fond of recalling that a famous saying among policy analysts is “let me pick your options, and I will make the decision for you,” which illustrates the importance of keeping an eye out for a better option. That is even true in the cholesterol example, where Max had a satisfactory outcome with his present option. He had a chance of finding a more-than-satisfactory option.

MAXIM 12

INFORMATION IS ONLY VALUABLE IF IT CAN

CHANGE YOUR DECISION

Every year, thousands of students apply to colleges and universities. In the United States, applicants typically get notified whether they were admitted or not in a staggered fashion. Some students hear that their favorite college has admitted them early in the process. Knowing whether the least favorite college on their list admits them or not would not change their decision. But yet they still wait to hear from them (and all other colleges they applied to) before making a decision. This sometimes causes unnecessary stress and delays.

Alice Heath, a student and teaching assistant of Richard’s, has successfully used this maxim in her work with governments, where people ask for additional analysis “because it’s interesting.” She said “So during a presentation about program performance a government official might ask to see the age profile of clients served by a home visiting program, or the number of phone calls made by program staff. We would always filter these requests by asking ‘What might we do differently if we had that information?’ and if the answer was nothing, we conclude that collecting this information wouldn’t be worth the time and push back against the request.”[61]

Craig White, a teaching assistant of Richard’s for four years and now general manager at a Boston-based startup, recalls using this maxim when his wife tested positive for COVID-19. “Some in our family insisted we all be tested. I intuitively thought that regardless of the result of the test, our behavior would be the same. In my family’s case, we would be quarantining for fourteen days no matter what. I did not want to waste any tests, and decided not to get tested since the test would have provided no useful information. For full disclosure, no one listened to me and I was the only one in my family who ended up not getting tested!” Craig wrote a doctoral dissertation applying decision theory to medical problems. His family should have listened.

Finally, Richard is fond of illustrating this maxim about information gathering with an experience his mother had at a hospital many years ago. His mother was visiting Richard and Sally from out of town. At three o’clock in the morning, she woke Richard up, saying “You have to take me to the hospital. I am in intense pain.” At the hospital, the doctor said: “I think your mother has a bladder infection. I will put her on an antibiotic. She should feel better.” Two days later, a doctor called back: “Your mother’s specimen did not prove positive. This implies no urinary tract infection. Bring her in immediately.”

They met the surgeon at the hospital, who examined Richard’s mother and ordered some tests. At around 5:00 pm, after the results came back, the surgeon said, “I believe your mother has appendicitis or a tumor. However, her white blood count is lower than what we typically see for appendicitis, and palpating her abdomen, it does not feel like a tumor. I would like to keep her here until tomorrow so we can monitor her symptoms. We will then know a lot more.” Richard responded: “I presume that in either case you will operate, is that correct?” “Yes.” “Then, shouldn’t you operate now, and bring both sets of tools?”[62]

The surgeon proceeded to operate that evening. It turned out that Richard’s mother had a leaky appendix, and peritonitis (infection in the abdomen). Waiting another day would have been dangerous. The doctor, a man in his fifties and a faculty member at Harvard Medical School, observed that no one had ever taught him “Don’t wait for information if it won’t change your decision.”

4

UNDERSTANDING POLICY

The last chapter provided guidance on making decisions in an uncertain world. This chapter provides maxims to guide policy decisions – decisions made by an actor, perhaps a government official, nonprofit leader, or parent on behalf of a group of people. What are sound bases for making such decisions, and for evaluating them? Even if you are not responsible for making policy decisions, the maxims in this chapter can help you better assess the decisions of those who do. At a more profound level, they will help you better understand the world around you, and help you crystalize your thinking on what is good for society.

MAXIM 13

LONG DIVISION IS THE MOST IMPORTANT TOOL

FOR POLICY ANALYSIS

This maxim makes a simple but critical point: In assessing any policy measure in any realm, you should first determine what it achieves, and what resources it requires. With these tallies, you should then use long division, to compute output per unit of input. Suppose you were in charge of determining vaccination sites for a state and giving them resources. To operate a site for a day costs $50,000, and on average it delivers 1,000 vaccinations a day. Thus, the cost of operation is $50 per vaccination. This information is critical if you want to know if it is worthwhile. Would you pay $50 per vaccination? Alternatively, you can think in terms of output per unit of input, such as benefit per $1,000 spent. In this case, 20 vaccinations per $1,000 spent.

The real world is of course more complicated than the simple example above, but the principle of comparing benefits to costs, the basis of what economists call cost-benefit and cost-effectiveness analysis, is still very valuable in helping figure out what policies should be implemented. Coming back to the vaccination example, suppose you oversee many possible sites and have a fixed budget. How should you decide which sites to operate? If all vaccinations are equally valuable, you should start with the site that yields the most vaccinations per $1,000. Then move to the next site with most vaccinations per $1,000 and so on until you exhaust your budget.

It is critical to choose the output correctly. This requires thinking carefully about what you are trying to achieve. In the case of vaccinations, the goal is not to deliver as many vaccines as possible, but to prevent death and severe illness due to the infectious disease. Since the disease differentially affects some groups, say nursing home patients and Hispanic communities, who often have more crowded housing conditions and are often “essential workers” who must report to job sites, you would prevent more severe or fatal cases by prioritizing vaccinations for these groups.

None of this will be easy, but that doesn’t reduce the importance of assessing the costs and benefits involved. Other issues arise when applying this maxim, as Richard himself explains.

Long division in more complicated scenarios

Richard Zeckhauser

Often the input will be something other than dollars, or dollars are a secondary consideration. Let’s say that we decided to turn a five-acre area into a park. We could have shuffleboard courts, a soccer field, tennis courts, etc. If your goal is to maximize total participant recreation hours, you could ask an expert to estimate how many hours of participant time per day you would get per 1,000 square feet from each of these activities. We would then start with the greatest number of hours per 1,000 square feet and work our way down.[63]

Of course, output per dollar spent may not be constant. For most activities, we would expect diminishing returns as we spend more. (This is called diminishing marginal returns – the “margin” in this case is the last dollar spent.) For example, the third nurse hired in a health clinic is likely to generate greater health benefits than the eleventh nurse. Suppose you run a health clinic that can run a variety of activities, such as immunizations, well baby care, annual checkups, etc., and that your output measure is quality-adjusted life years (QALYs).[64] What should you do if you spend $100,000 on the first activity, $200,000 on the second, and $150,000 on the third, and the benefit at the margin is 10 QALYs/$10,000 expenditure from the first, 8 QALYs from the second, and 11 QALYs from the third? You should boost your QALY output by trimming the second, expanding the third, and adjusting the first. Eventually, we would reach the optimum, where each activity yielded say 9.7 QALYs/$10,000 expenditure. To get there, we would always be using long division to determine how many QALYs we got from the last $10,000 spent on the activity.

Often, we have to evaluate an indivisible project, such as building a bridge or an addition to a rapid transit system. Too often we just say that it is important to extend rapid transit. This maxim would say instead that you should use long division to see what you are getting for what you are spending. Suppose the extension might cost $1 billion/year to operate. (That includes interest on the bonds, plus salaries, maintenance, etc.) It would save passengers 500 million minutes a year. It is useful to know that it cost $2/minute saved. Given this statistic, we might decide that the rapid transit extension was not worth it, because people do not value their time at $120/hour.[65]

Frequently, long division of output divided by dollar or by some other input (such as one’s time) is just a way to enable clear thinking about an issue. Even if the numbers are hard to calculate, guesstimating the value of the ratio of outputs to inputs tells us if we are in the ballpark of reasonability. Moreover, if the value is high, we might do more of the activity, perhaps in some other locale. If it is low, we should consider cutting the activity.

As an extreme example, the value of a life saved is far higher than an hour of aesthetic pleasure. Thus, placing a defibrillator in a company office should get priority over putting an equally expensive picture in its lobby, even though the former is only expected to be used once on average in a decade, whereas the latter might receive 2,000 admiring views over that period.

In sum, this maxim involves figuring out some metric of “bang per buck” for different options, ordering these, and then choosing the options with the highest bang per buck until the bang becomes worth less than the resources required, or you simply run out of resources. This maxim can enable quick progress in practice, as Erin St. Peter, a former student of Richard’s and now a research analyst at Wharton’s Risk Center and Real Estate Department, discovered in a summer fellowship when she was in graduate school. She had the opportunity to observe and work with a state government and department of transportation to evaluate different strategies to reduce traffic fatalities. The list of projects under consideration or in various stages of completion included road sign changes, speed traps, increased police presence, impaired driving interventions, and the one that struck her as most peculiar – a multi–million dollar wrong-way driving detection system that the state had fully implemented on a highway segment.

“Wrong-way driving traffic fatalities represent about 1 percent of traffic-related deaths nationwide, but it was a top priority for the state at the time. Furthermore, most wrong-way driving fatalities involve impaired individuals, so more general impaired driving interventions might also help reduce these fatalities. Long division, the essence of cost-benefit analysis, was the most important framework I used to evaluate the different interventions. I compared different interventions according to their associated reduction in traffic fatalities per dollar spent.”

Taran Raghuram, a student and current teaching assistant of Richard’s at the Harvard Kennedy School, saw how this maxim applied in his work as a consultant for public school districts in the United States.

Long Division in Public Education

Taran Raghuram

Looking back at my work in public education, long division was certainly the most useful tool we had at our disposal. In my conversations with school district leaders, simple ratios conveyed more meaning and led to better conversations than sophisticated statistical methods ever did.

Whether comparing the extent of resources (staffing ratios/class sizes) or the effectiveness of certain strategies over others (return-on-investment), thoughtful long division provided metrics that were both simple and comparable. One metric we used was how many principals every managing administrator was responsible for. That simple number carried so much weight because we could compare districts to one another and communicate a clear story – if you have forty principals reporting to each administrator and in another district, they have ten, then they probably have a better idea of what’s happening in their schools than you do.

The smartest people I’ve met in public organizations took this maxim to heart. They may have never taken convex optimization or studied algebraic topology, but they were masters at finding a simple but powerful metric for any situation. I think this is possibly Richard’s most underrated maxim for aspiring policy wonks.

As a final example, David Slusky, along with Taran and Richard, have examined patterns of fatalities from the COVID-19 pandemic by looking at 2020 deaths divided by 2019 deaths in three age categories, which had a 21 percent overall increase in deaths. “Long division revealed very different stories for the three groups. About 79% percent of increased deaths in March to December 2020 for those over 75 were due to COVID. For those aged 45 to 75, COVID accounted for 67% of increased deaths. For those under 45, COVID only accounted for 25% of the increase, while nonnatural deaths (auto accidents, overdoses, homicides, suicides) accounted for 50%.”[66]

MAXIM 14

ELASTICITIES ARE A POWERFUL TOOL FOR UNDERSTANDING MANY IMPORTANT THINGS IN LIFE

Elasticity is one of the most important concepts in economics. In general, elasticity tells us how much we expect one quantity to change when another quantity has increased by one unit (typically 1 percent). The most commonly used elasticity is the price elasticity of demand, the original application defined by the great English economist Alfred Marshall in 1890. It tells us the percent reduction in the quantity demanded of a good when the price is increased by one percent, holding everything else constant. A price elasticity of ice cream of -2 means that when the price of ice cream increases by 1 percent, the quantity of ice cream purchased goes down by 2 percent.

More generally, elasticity underpins a central principle in economics, to think at the margin. Economists find marginal thinking to be of extraordinary value.[67] To illustrate, let’s tackle a simplified version of a question that many professionals have had to answer implicitly or explicitly: Do you prefer to spend time at work, or with your family or on personal projects? Economists don’t regard this as a well formulated question because the answer will likely depend on how many hours you are currently working and how many hours you are currently spending with your family or on projects. If you are working few hours and have a considerable need for money, you will likely want to spend more time working. At the other end, if you are working many hours and you rarely see your family, you would probably prefer additional family hours. In sum, to answer this question it is helpful to think about the value of your last (marginal) work hour. In addition, this marginal hour may not be adding much to your income.[68] This is an elasticity-based observation: an additional unit of work time results in a relatively small number of additional dollars earned, and an incremental dollar may not be worth much to you.

Accordingly, when making this decision, you want to think about whether in your current situation (working as many hours as you are now), you would like to spend an extra hour working or with family. Thinking at the margin means thinking precisely in those terms: what value do you derive from that additional (also called marginal) hour? (Incidentally, in determining what you secure at the margin, you are using our long-division maxim).

Marie-Pascale Grimon, a PhD student at the Harvard Kennedy School and a teaching assistant of Richard’s, recently encountered precisely this dilemma: “Often times, I find myself or others around me making decisions based on absolutes instead of thinking marginally. Are those extra five hours of work worth the extra money when compared to spending an extra five hours with family? Of course, having a job and earning a decent living are good things, but usually we forget to ask ourselves whether that additional time spent doing such and such is worthwhile.”

Iris Bohnet, a professor and colleague of Richard’s at the Harvard Kennedy School specializes in behavioral economics. She applied the elasticity concept in some research with Richard related to trust. Imagine that you lost $100 because of a hole in your pocket – how would that make you feel? Now, imagine that you lost $100 because you lent a friend money and he skipped town without repaying the interest-free loan as agreed. How would you feel about this loss?

In either case, you lost $100. But in the first case the loss was caused by bad luck, and in the second by lack of trustworthiness by a friend. If you feel worse in the second scenario, you exhibit what Iris and Richard called “betrayal aversion.” That aversion implies that people care about how the outcome came to be. Specifically, when the probabilities of loss are the same, people who exhibit betrayal aversion prefer the loss to be caused by natural circumstances as opposed to by the actions of another person. In other words, betrayal is worse than bad luck.

Iris and Richard conducted some clever experiments to learn about how people’s willingness to trust changes when the likelihood or cost of betrayal changes.[69]

Betrayal aversion and elasticity of trust

Iris Bohnet

Intrigued by the findings of our initial experiments in the United States, we went out into the world to see how robust betrayal aversion was. It turns out we found it everywhere we went. Many people in China and Vietnam, in Saudi Arabia and Turkey, in Brazil and in Switzerland, also exhibited betrayal aversion, though not always to the same degree as in the United States.

These findings have implications for institutional design. We think of legal systems’ fostering trust by compensating victims for their material losses when they are wronged. But if people are betrayal averse, arrangements that focus on compensating the victims of betrayal for the material losses experienced will not effectively foster trust. For example, in many contractual arrangements, damages are thought to compensate a person for the injury caused by their counterpart, by making the potential victim of breach equally financially well off whether the contract was performed or breached. The more betrayal-averse a person is, the less effective are institutions that decrease the risk of material losses rather than the risk of betrayal.

This insight led to a fruitful research program that kept Richard, various doctoral students and postdoctoral fellows, and me busy for quite some time. It also lent itself to the exploration of another of Richard’s favorite concepts, elasticity. To better capture how people responded to changes in the institutional environment, specifically the cost and the likelihood of betrayal, we introduced the notion of the “elasticity of trust.” It measures how much more people are willing to trust given changes in either the cost or the likelihood of betrayal.

We took the insights from our research to our teaching on negotiation and decision making, in both degree and executive education programs. In true Kennedy School fashion, this led to a request from a government to help them think about their institutions in light of betrayal aversion.

Richard did not join me in the United Arab Emirates, but it was a lot of fun for a young assistant professor to work with Sheikh Mohammed, the prime minister of the United Arab Emirates and the ruler of Dubai, and his cabinet in Al Ain, an oasis close to the Omani border. I taught them about decision making, trust, and negotiation. We discussed Islamic contract law and how it compared to contract law in Switzerland, a civil law country where “pacta sunt servanda” (agreements must be kept), and in the United States where the common law notion of “efficient breach” describes how a party can breach a contract if it finds it more attractive to pay damages than to honor its commitments.

MAXIM 15

HETEROGENEITY IN THE POPULATION

EXPLAINS MANY PHENOMENA

High and increasing health care costs have attracted considerable interest from academics and policy makers over the past thirty years, particularly in the United States. According to the Centers for Medicare and Medicaid Services, U.S. health care spending grew 4.6 percent in 2019, reaching $3.8 trillion, or $11,582 per person. As a share of the nation’s Gross Domestic Product (GDP), health spending accounted for 17.7 percent.[70]

Many studies have attempted to explain why health expenditures are so high in the United States. One key fact that many of these studies tried to unpack is that we don’t all spend the same amount on health care. In other words, there is heterogeneity in health care spending. Indeed, an important finding from these studies is that expenditures vary widely for individuals, tending to rise with age, and also across individuals in a cohort; that is, some people spend much more than others over a lifetime.

Understanding this heterogeneity is key to the design of sensible health policies. For example, a policy that treats all individuals in the same way (for example, by increasing insurance premiums to try to discourage people from accessing unnecessary medical care) is unlikely to reduce overall health care expenditures. On the other hand, a policy that focuses on chronic disease – about half of the U.S. population has one, and 86 percent of health care costs are attributable to chronic disease – would encourage interventions that could reduce the percent of people with chronic diseases or improve the cost-effectiveness of spending devoted to people with chronic diseases.[71] This is a more promising approach. In the 1950s, when acute diseases were more common than chronic diseases, this approach would not have been as effective.[72]

Chris Zook, a former PhD student of Richard’s, wrote his dissertation in the 1970s about the high-cost users of medical care. He used this maxim and data to show that the highest-cost users were people with specific chronic conditions. He said that one of the principles he learned from Richard was the idea that “average behavior” was rarely the norm in large groups. To understand group behaviors, the key was to identify the right relatively homogeneous segments of people who behaved similarly with regard to the phenomenon you were studying. After doing this, you could productively analyze the behaviors of each segment separately. Once that was done, you could reassemble the population to understand its aggregate behavior.

Chris became a partner at Bain and Capital and a best-selling author of books on business strategy. He credits this heterogeneity idea as “central to understanding the micro-economics of business strategy in study after study, ranging from the turnaround of LEGO (where an incredibly small percentage of obsessive users bought a huge share of LEGOs and were critical to understand separately and in a segmented way) to strategy work for a rental car company (where, again, the key was to understand the relatively homogeneous segments of renters in order to plan and market in a way that would optimize car utilization). I recall a healthcare study for a medical supplier that was trying to understand the enormous differences we were observing in the use of IV fluids across patients in intensive care units. It wasn’t until we got the homogeneous segments exactly right, involving some unexpected twists and dimensions, that we could uncover whether the differences were due to the underlying populations, or due to different medical practices. The list goes on and on through my life, across so many vexing problems of trying to understand and predict group behaviors.”

A recent example of heterogeneity in the population relates to contagion. As many of us learned when following the news about COVID-19, the basic reproductive number, R0, is widely used to predict the spread of a disease. This number, a measure of contagion, represents how many people an average infected individual is predicted to infect. Not every sick person will infect the same number of additional people. “Superspreaders” are people who for several reasons are much more likely to infect others, and superspreading events generate a disproportionally large number of infections (as, for example, a February 2020 Biogen convention in Boston).[73]

Lee Hsien Loong, the prime minister of Singapore and a former student of Richard’s, credits understanding the heterogeneity in rates of transmission and in the infected population as important to understand Singapore’s early experience with COVID around May 2020.

Heterogeneity in COVID transmission

Lee Hsien Loong

“The COVID-19 pandemic offers two vivid examples of the importance of understanding heterogeneity. Countries and cities are tracking their R0, the reproduction rate of the virus, and the death rate. These global parameters gloss over important heterogeneity in the population.

Singapore started having COVID-19 cases in January 2020. As the outbreak continued, our R0 stayed well below 1. We took some comfort in this, and attributed it to assiduous contact tracing. But we were also acutely aware that this was a global figure for Singapore, and that the R0 could be much higher in specific settings if the virus spread there. And so it turned out. We have 300,000 migrant workers living in communal dormitories, where the transmission potential is high. A few undetected cases there snowballed into a major outbreak. The R0 in the dormitories was estimated to be between 2 and 3.

However, another heterogeneity has kept our death rates very low. As of the time of writing, our fatality rate is around 0.05 percent, 34 deaths out of about 60,000 COVID-19 cases so far. This may be because many of our cases were migrant workers, who are younger and healthier than our general population. The vast majority have had mild or no symptoms, and only a handful needed ICU care. A similar sized outbreak among our elderly population or nursing homes in those early days would have been quite a different story. Fortunately, now that we have vaccines, we have prioritized the vaccination of the elderly and vulnerable, which has hopefully reduced this risk.”

Recognizing heterogeneity in the population can also help design policies for different segments of the population. For example, according to the Centers for Disease Control, more than 90 percent of COVID deaths in the United States have occurred among those 55 years or older, and fewer than 1 percent were younger than 25 years old.[74] This suggests that nursing homes are in a dramatically different risk category than are schools.

Richard argued from the early days of the pandemic that the failure to attend sufficiently to heterogeneity in risk had profound negative consequences in the United States and other nations. In the early days, we took woefully inadequate measures to protect elderly citizens, particularly those in long-term care facilities. At the opposite end of the spectrum, given the severe learning and mental-health consequences to young students from being out of school, and the relatively low risks to those students, Richard believes that we were far too reluctant to open schools, particularly to younger students and those from families whose children would suffer more from online versus in-person education.

Understanding heterogeneity frequently helps to diagnose problems where behavior changes over time. Early in 2021, COVID-19 infection rates started dropping precipitously in many developed countries. The most frequent explanations were better social distancing, developing herd immunity, and vaccines getting into arms. Heterogeneity, an additional factor, got little attention.

To see how heterogeneity in risk might explain declining COVID infection rates, you can use a very simple hypothetical example and assume that half of the population has a high risk of contracting COVID (say 20 percent) and the other half a low risk (say 1 percent). These differences in risk of infection could be driven by people’s behavior, housing conditions, employment, the virus strains in their locale, etc. Initially the infection rates will be high because a lot of high-risk people would get infected, but as time passes fewer of these people are at risk of infection (since a lot of them already got infected and the risk of COVID reinfection is pretty small), and the overall infection rate starts approximating the infection rate of the low-risk group (1 percent). This simplified example reveals the mechanism behind the phenomenon: high-risk people make the overall infection rate high at the beginning but as time goes by they become less prevalent in the population at risk of getting infected. This drives the overall infection rate down over time.[75]

The above pattern applies to many areas beyond COVID-19. In fact, Richard has documented heterogeneity in risk as a reason why hernia recurrences, smoking relapse rates, and criminal recidivism rates decline over time.[76] This does not eliminate other factors as well – but heterogeneity is important and often neglected.

One of the most interesting settings in which heterogeneity is important is college admissions. In a landmark book, The Early Admissions Game, Richard and his coauthors argue that early admissions, a process by which many applicants gain access to elite colleges in the United States, provides benefits to some students by boosting their chances of admissions, but is not appropriate for everyone.[77]

Some have argued that because applicants from high-income families tend to apply through early admissions (since they are more likely to have the information and resources needed to do so), it offers an unfair advantage to them. “Early-admission programs tend to advantage the advantaged,” Derek Bok, former president of Harvard, said in 2006 when Harvard dropped early admissions (it was later reinstated).[78] Barry Nalebuff, a professor at Yale School of Management and one of Richard’s coauthors, shows how an understanding of heterogeneity can be helpful to assess the fairness of early admissions.

Heterogeneity in college admissions

Barry Nalebuff

Richard likes giving people puzzles to solve. These puzzles aren’t just brainteaser exercises but ways of understanding policy problems. The trick to solving his puzzles is often a combination of marginal analysis combined with understanding heterogeneity in the population.

I particularly remember a puzzle I struck out on. At the time, colleges were debating the fairness of early admissions. Richard noted that Stanford University’s president Hennessy defended the fairness of early admissions by emphasizing that the average SAT score of those admitted early was 210 points higher than the average SAT score of those admitted in the regular pool. This suggested that applicants admitted under early admissions were more qualified than those admitted under regular admissions.

Was that a valid argument? I focused on the fact that early applicants were more likely to come from better schools with better counselors and better test preparation. Thus, the scores of early applicants might be more inflated relative to their innate ability. Too complicated, replied Richard. He provided the key insight that showed the failure of the Stanford president’s argument: To determine if early admission is fair, we need to know if the worst person admitted under early admission is better than the best person denied under regular admission. Think about the margin, not the average!

The early action pool has many students with perfect SAT scores, so the high average might be hiding other early admission students who have much lower scores – lower than those of some students who might be rejected under regular admission. Like so many of Richard’s lessons, the answer is obvious – but only in hindsight.

Yair Tauman, a coauthor with Richard on papers about games involving espionage and deterrence, notes that heterogeneity, combined with imperfect information, can lead to counterintuitive results. He observes that “Russia has a number of ways it could try to disrupt U.S. intelligence efforts.” Intriguingly, he then notes that in some plausible situations, “Russia should deliberately employ less effective disruptive measures. Both countries will take less hostile actions if the signal on Russian capabilities is more reliable.”[79]

MAXIM 16

CAPITALIZE ON COMPLEMENTARITIES

Imagine you are running a summer tennis camp. Assume that your only important inputs are the instructors and the courts. If you start off with a single tennis court, the value of the first instructor is high, and the value of a second instructor is still positive (since you can have two instructors in a single court), but lower. A third instructor is less valuable still. Now imagine that you had two tennis courts available instead of one. All of a sudden, the value of having a second instructor has gone up: the second instructor can now go to the second court and more children can sign up for your camp.

In this situation, increasing the quantity of one input (in this case tennis courts) makes an increase in the quantity of the other input (in this case tennis instructors) more valuable. Another way to see this is that after some point hiring more instructors won’t do you much good unless you have additional tennis courts. So the value of additional instructors will be higher the more tennis courts you have.

The example above illustrates the value of capitalizing on complementarities. Courts and instructors are complements because the value of one depends on the availability of the other. The world is full of situations like this. For example, the value of fertilizer for a farmer is likely to be higher if other inputs (seeds, irrigation, farming practices, etc.) are available. The value of a blackboard in a school will depend on the availability of other school inputs (such as chalk, teachers, classrooms, etc.).

In economics terms, the situations described above exhibit positive cross-partial derivatives.[80] In fact, when Richard teaches this maxim to his students, he refers to it as “capitalize on positive cross partial derivatives,” a much more technical formulation intended to be playful and memorable.

One key application of this maxim arises when we choose a collaborator, for example in an intellectual project or in business. As Richard frequently argues, we often choose people whose skills are similar to our own. The result is that the gains from collaboration are much smaller than if we were to choose people who would bring different capabilities to the undertaking. If two engineers are planning the construction of a bridge, adding a third engineer to the team won’t help as much as adding an architect or a project manager.

Peter Schuck, Richard’s coauthor and former roommate, and now an emeritus professor of Law at Yale Law School, saw this principle in action in his collaboration with Richard: “I suspect that what I learned of Richard’s modus operandi during our first collaboration – a 1970 article in The Public Interest – has been true of many of his collaborations: Richard comes up with the idea – his comparative advantage – and then moves on to his many other projects, leaving it to the coauthor to do most of the more time-consuming fleshing out of his idea.” Richard supports this view: “When you are an economic theorist, you gain more by collaborating with an empiricist than with another theorist. Similarly, if you are good at ideas and bad at follow up, you should collaborate with someone who is good at follow-up.”

Richard is a living example of the benefits of following this maxim. The number of coauthors he has who were trained in different fields is astounding. As François Degeorge, former student and coauthor of Richard’s, and now professor of Finance at the Università della Svizzera Italiana and managing director of the prestigious Swiss Finance Institute, described, “The most important maxim Richard taught me is one I never heard him pronounce but always saw him practice: engage with people from different fields, interests, mindsets, countries, and ages. In the course of our research collaboration, I have enjoyed the privilege of many conversations with Richard. I was always awed by his ability to work with a prodigious number of coauthors, yet give his full attention to our exchanges. This facility stems from Richard’s intellect of course, but also from his deep love of human interaction.”

The benefit of engaging with a wide range of people goes beyond professional collaborations. As Tarek Masoud, Richard’s friend and faculty colleague at the Harvard Kennedy School, colorfully put it, “Long before our leaders and institutions got wise to the importance of diversity and inclusion, Richard made a habit of seeking out and building friendships with people from backgrounds very different from his own. The diversity of the contributors to the present volume is powerful testimony to that fact. It’s also why his databank of personal stories includes such characters as Armenian clothiers, Manhattanite-coatmakers-cum-murderers, feuding Egyptian physicians, and Indian bridge players.”

The maxim applies not only to professional collaborations but also to personal ones. Below, Alice Heath, a PhD student of Richard’s, describes how she used this maxim in the context of what many consider the ultimate form of collaboration: marriage. And Taran Raghuram, a student and teaching assistant of Richard’s at the Harvard Kennedy School, puts this maxim to use not only when choosing with whom to collaborate, but also in shaping how he collaborates.

Positive complementarities in marriage

Alice Heath

I took Richard’s class when I was about a year into a relationship with the person who would become my wife. My wife and I are extremely different in many ways: she likes to make decisions immediately, I like to collect more information and spend time pondering; she loves meat, I am a vegetarian; she is from the U.S., I am from the UK.

Initially these differences caused a fairly large amount of difficulty, but when I learned from Richard that cross-partial derivatives are important I realized I needed to think about our differences as complementarities. When hosting a dinner party, it’s fantastic to have someone who can cook a delicious meat dish and another person who can make vegetables shine: the dinner party is much better than it would be if you had two people who could only cook meat. Similarly, it’s good to have someone to make the quick decisions for decisions where that’s suitable, and someone who can tap the brakes and make sure the important decisions have been properly analyzed.

For me, the essence of cross-partial derivatives is that despite the cost of trans-Atlantic flights, marrying someone quite different from you can be an excellent choice.

Improving collaborations

Taran Raghuram

I deeply admire Richard’s ability to model successful collaboration. He identifies areas of interest where he is less knowledgeable and sees them as an opportunity to produce great value by working with experts who complement his own skills.

So while positive cross-partial derivatives are of course an important analytical tool, they also serve as a personal reminder to adopt a humbler mindset and work with others to have an impact.

Whenever I have a new idea, rather than stew on it endlessly to unlikely “perfection" as I used to, I now solicit feedback much sooner because of this maxim. The refrain “positive cross-partials" in my head reminds me to put aside my own ego and to seek out perspectives to more quickly converge on a better idea.

This semester, I implemented a rule: spend one hour thinking about a new idea, then get feedback on it, either via a short email or a brief conversation with someone more knowledgeable. This has led to interesting and diverse work in several areas from COVID-related analysis to models of journalism.

Finally, Ed Glaeser, a professor of Economics in Harvard’s Department of Economics and Richard’s colleague, describes how this maxim applies to societies as a whole. He observes that much of what individuals achieve in the world is only possible because of complementary inputs from many other individuals.

Positive cross-partial derivatives in cities and the wider world

Ed Glaeser

This maxim is an understatement. Positive cross-partials are the hallmark of human existence. On our own, we are puny creatures who would fare poorly against any large carnivore in the wild. Our individual actions are only effective because they are supported by the actions of thousands of others – today and through history. My writing of these words required humans to invent written language, computers, the internet, the organizing of this book, and thousands of actions throughout history. My efficacy as an economist owes much to the teachers and mentors that I have had over decades, such as Richard Zeckhauser.

I study cities, and positive cross-partials are the reason that people congregate together in urban areas. By acting together, we can do remarkable things. The most important of these are the cascades of collaborative brilliance that have been responsible for humanity’s greatest hits, from Athenian philosophy to the internet.

5

LIVING FULLY

By now, you probably have a sense of Richard as a deeply analytical person who uses logic and clear thinking to understand the world and to make better personal and professional decisions. While the previous chapters have sometimes dealt with how his former students, coauthors and colleagues use some of the analytical maxims in their personal lives, this chapter is devoted to more general maxims about leading a fulfilling life.

Alan Garber, the provost at Harvard University, was a PhD advisee of Richard’s more than thirty years ago. “When I was young, I really admired people for their cleverness, and Richard is the ultimate in clever. As I grew older, I came to admire people more for their wisdom, and that’s why I admire him so much today. His combination of cleverness and wisdom is rarely found." It is precisely because of this wisdom, he adds, that he and other university leaders seek Richard’s counsel so much.

MAXIM 17

STRIVE HARD NOT TO BE ENVIOUS – SEE YOUR

FRIEND’S SUCCESS AS YOUR GAIN

Envy is considered one of the seven deadly sins, and many spiritual and religious leaders warn us against being envious. In the Book of Genesis, envy is said to be the motivation behind Cain’s murder of his brother, Abel. Cain envied Abel because God favored Abel’s sacrifice over Cain’s. Similarly, the Buddha said, “Do not overrate what you have received, nor envy others. He who envies others does not obtain peace of mind.”

While envy is sometimes considered a natural feeling, and some have distinguished between malicious and benign envy, Richard encourages his students and colleagues to avoid envy. He subscribes to Bertrand Russell’s claim that “envy is one of the most potent causes of unhappiness.”[81]

Syon Bhanot, a former student of Richard’s and now a professor at Swarthmore College, says that he learned very early on from Richard that social comparison was not only fruitless, but also deeply damaging. “If we are constantly measuring our self-worth relative to the performance of those around us, we will always feel we fall short. I remember at some point I was discussing an aspect of my dissertation with Richard in his office, and I made a comment about how a fellow PhD student had recently made an important breakthrough in his work, but I was a bit stuck on something. This contrast had me frustrated and it showed. Richard looked at me and said something to the effect of ‘your friend’s success is only a good thing, and yours is coming. So buy him a beer.’”

Syon then added “From that day on I stopped comparing myself to my peers and started to actively take joy in the success of others. Not only has this made me a happier person in my professional life, but also in my personal interactions. Indeed, I have found that rejecting envy helps you appreciate others more, and helps build lasting friendships and relationships. I hope that my friends and family know that I’m their biggest cheerleader and I always will be, thanks to Richard.”

Richard exemplifies this maxim. One of the themes that emerges from his collaborators is how generous he is with his time, advice, and support. He is also incredibly generous to his mentors, and at every turn recognizes their influence on him without a trace of envy for their success.[82] And it would have been easy for him to feel envy for his mentors: Ken Arrow and Tom Schelling won the Nobel Prize in Economics, and Howard Raiffa is broadly recognized as a pioneer in the fields of game theory, decision analysis, and negotiation analysis. Moreover, one of his students (Mike Spence) also won the Nobel Prize in Economics. Richard celebrates and cherishes the laurels these close friends merited and received.

MAXIM 18

ELIMINATE REGRET

In Chapter 3 we looked at a hypothetical example where you rationally decided not to buy insurance for your iPhone, and the phone broke a few months later. This decision illustrated the maxim that good decisions sometimes result in bad outcomes. An important implication of this maxim is that as decision makers we should not regret sound decisions that lead to poor outcomes. In this case, if you made the decision rationally (taking into account the probability of the phone breaking, the different costs involved under the break and no-break scenarios, and your level of risk aversion), you should feel no regret for your decision. You might feel bad for the outcome, but if the decision was wise, it does not become less wise because of an unlucky outcome.[83]

A corollary of the “avoid regret when a good decision leads to a bad outcome” is that you should not take pride in poorly made decisions that work out well. Moreover, even if a bad decision leads to a bad outcome, wallowing in regret is not productive. You want to reflect and learn from the bad decision but then move on. This is what Yinglan Tan, a former student of Richard’s and a Singapore-based venture capitalist, tries to do in his work: “Venture capitalists play the long game. In the process it is inevitable that misses are made or bets don’t pan out. All the same, I have learned not to let any singular decision weigh down on me. Instead, I look at these misses as an opportunity to learn, and this is an exercise I regularly go through with my team. These moments that could have been occupied with regret are greater sources of insight than one would initially suspect, and are critical to shaping how we proceed with finding the best companies to work with moving forward.”

In sum, regret is a wasteful emotion regardless of whether the bad outcome is a result of a good or a bad decision. Realizing you made a mistake and taking action to correct it is good, but dwelling on the mistake does not do any good. Perhaps even worse, our desire to avoid regret in the future might lead us to make suboptimal decisions in the present. In the iPhone example, you might decide to buy insurance not because it makes rational sense to do so, but because you could avoid the regret you would feel were the phone to break in the future. This is no sound basis for making decisions.

In an article about rational decision theory produced as a tribute to Howard Raiffa, the giant in the field of decision analysis who introduced Richard to the field, Richard wrote, “I believe that a conscientious attempt to banish regret from one’s decision calculus is one of the prime benefits of fully embracing decision analysis, of acting as Howard would recommend.”[84]

Anticipation of regret can lead to well-documented departures from rational decision making. Two are worth mentioning: overweighting errors of commission relative to those of omission, a contributor to status quo bias; and the sunk-cost fallacy. Feelings of regret are often far stronger when a bad outcome results from a conscious choice, as opposed to doing nothing. Remember our Amazon stock example, where Chris sold his stock, whereas Pat merely failed to buy it. Chris, we observed, was likely to feel far more regret. His error of commission led to far greater mental discomfort and self-blame than did Pat’s error of omission, though the financial costs were the same. Given this tendency, we are too eager to stand pat across a vast range of circumstances. Thus, status quo bias leads us astray.

Regret avoidance, as this phenomenon is sometimes called, also contributes to the famed sunk-cost fallacy, a popular subject in introductory economics textbooks. The sunk cost fallacy occurs when you use the fact that you have previously invested resources (time, money or effort) on an activity to justify further investment in the activity.[85] You have fallen victim to this fallacy if you have ever followed through with plans to go to the theater just because you already purchased the tickets (even though the weather is foul and the play has received poor reviews). Or you hold on to a bad investment (or even worse, put in additional money) because you had already made the investment. Or you continue a personal relationship, though the relationship has soured, just because the relationship has persisted many years.

While there are many reasons behind our tendency to fall prey to the sunk-cost fallacy, the fear of experiencing regret if we make the wrong choice is an important one. As Mark Thompson, a former student of Richard’s, bridge partner, professor and later co-investor, put it: “We should steer clear of the sunk-cost fallacy: the common tendency to persevere in activities or allocations that have initially been disappointing, because we want to avoid the regret of having made a bad decision or that of feeling that what we did was in vain. This Zeckhauserian precept has been of value in judging to what extent and whether I and my colleagues and company should have persisted in investments, writing projects, research undertakings, and relationships.”

MAXIM 19

MAKE PLEASURE-ENHANCING DECISIONS LONG IN ADVANCE, TO INCREASE THE UTILITY OF ANTICIPATION

Kessely Hong, a former student and now faculty colleague of Richard’s at the Harvard Kennedy School, loves to take vacations with her family. She enjoys hiking at Acadia National Park, eating delicious meals, and visiting historic sites in Spain. She and her family derive even more enjoyment out of these family holidays by talking about these vacation ideas months or sometimes even a year or more in advance. They imagine themselves at the places, savoring the food, and painting a picture of what the vacation will be like.

Anticipation of a future reward, such as an upcoming vacation, can be sometimes even more gratifying than the experience itself. This is the idea behind this maxim and what some people have termed anticipation utility. Chris Robert, a former student of Richard’s and founder of multiple tech startups, takes this idea to a new level.

Orchestrating your own happiness through anticipation utility

Chris Robert

Before I came to the Kennedy School, I had studied human happiness and developed some mechanisms for manipulating my own happiness, but Richard really helped me to hone my craft. This idea of harnessing anticipation utility has been so useful that I now accept that the happiness I derive from anticipating many things actually exceeds the happiness I derive from the things themselves. The simple example is a vacation: one can derive months of happiness planning or daydreaming about a vacation – and then the thing itself comes and goes quite quickly (and may not be that great for any number of reasons). I have generalized the concept to most purchases, recognizing that the anticipation can exceed the reality, particularly if I draw it out. So, for example, I have been dreaming about buying a new car for years already, setting just-over-the-horizon milestones to reach before I pull the trigger, even though there’s no rational reason to delay. I have now set it up as a reward for achieving a particularly challenging, multiyear goal, and the savoring of that eventual reward now has multiple dimensions to it. I am reminded all the time that Richard was absolutely right.

Similarly, Robert Lawrence, a faculty colleague and office neighbor of Richard’s at the Harvard Kennedy School, used a retirement investment strategy recommended by Richard, which yielded good results and allowed him to use this maxim to enjoy in anticipating the rewards of these good results. While the specific financial strategy does not apply to everyone, the notion of considering the long-term horizon when making decisions is relevant for all.

Increasing the utility of anticipation of retirement

Robert Lawrence

Almost all finance professionals advise people to take on less risk in their portfolios the closer they are to retirement. One common rule of thumb is the “rule of 100,” which says that you should subtract your age from 100 and the answer will give you the share of stocks you should hold. This was my view until Richard changed my perspective.

There are situations in which you should take on more risk as you build up your savings. As Richard explained in one of our many enjoyable lunches, the conventional view assumes the primary goal of retirement saving is to ensure that you and your spouse will have enough to live on until you both pass away. But it basically ignores the bequest motive for saving, which is a serious omission for those who are in the fortunate position that they’ve already saved enough to support their retirement needs.

Richard follows different rules because he thinks of himself and Sally as adequately provided for. He is thus investing mainly for their children and grandchildren (plus charitable purposes). Since he is dealing with the money of others, it is their risk tolerance and time horizon that is relevant. His beneficiaries are in an ideal position to deal with risk because they have no idea how much they will eventually inherit! So the portfolio he selects pays only modest attention to risk aversion. It is therefore strongly weighted to stocks and some private equities. These holdings are riskier in the short term than bonds, but almost always provide better financial returns in the long term. Sally and Richard’s heirs should do better but even if they do not, they will still know that Sally and Richard remembered them fondly. Their charities should also expect to benefit from this investment strategy.

Thanks to Richard’s influence, I used this investment retirement strategy, which ultimately is something my children and grandchildren are going to benefit from financially. I will enjoy anticipating their benefit for the rest of my life.

While this maxim encourages you to think long and often about an upcoming pleasurable event, you should also avoid thinking long and often about an upcoming negative event. For example, if you have a colonoscopy in ten days, don’t spend every day in anticipation musing about it!

In the same way you can derive pleasure by thinking about an upcoming pleasant event, you can also enjoy remembering a pleasant event. Frederick Schauer, a former faculty colleague at the Harvard Kennedy School, coauthor, and now professor of law at the University of Virginia, makes this point by proposing a closely related maxim: try to maximize the pleasure (and the utility) of the decisions you have already made. “Much of the literature on decision making is focused on making the best (or most utility-maximizing) decision. Richard’s corollary to this is that one should seek to maximize the pleasure and utility of decisions already made. If you are trapped in a bad conversation, or enduring a bad meal, or cannot conveniently leave a bad play or concert or movie, Richard’s maxim teaches that you should make the best of it. And if you are in a good conversation or having a good meal or enjoying a good concert, then the same maxim says to tell yourself and your companions how good it is, all in order to increase the pleasure to be derived from the same experience.”

In a similar vein, Alan Garber, the provost at Harvard University, recalls a dinner conversation between Nobel prize winner Ken Arrow and Richard more than forty years ago that relates to the idea of deriving value from decisions you have already made. The conversation centered around whether it was rational to spend a lot of money on an outstanding but extremely expensive meal. “Ken and Richard both said that they believed in peak experiences. The notion was that the experience of a delicious and very expensive meal would be so exceptional that you would carry the memory of it for a very long time, and that you needed to account for that when you assessed the utility gain from this experience and therefore whether the expenditure was worth it.”[86]

Finally, once you have made a decision, one habit that will increase your well-being is to collect information favorable to your decision (after you have made it), and to muse on the positives. So for example, if you have decided to attend a certain college, try to discover all the great things the college has to offer (such as the faculty doing work in areas you are interested in, the sense of community the school is able to foster, etc.) and give less attention to any shortcomings of the school for you (such as unexciting cafeteria food, expensive housing etc.).

Similarly, keep in mind that you can make auxiliary decisions that will increase the value of your initial decision (though beware of not falling into the sunk-cost fallacy when doing so). “Strive to make the right decision; afterwards you’ll make your decision right” is the advice Richard gave to Paul Resnick, now professor and associate dean at the University of Michigan, who early in his career was choosing between academic job offers at Berkeley and Michigan and a potential offer at the Kennedy School. As Paul looked back on this decision, he said “Twenty-three years later, I am still at the University of Michigan. Along the way, I have made all sorts of ancillary decisions that have increased the utility of the original decision. During my first two years here, my wife and I took a bike tour of the upper peninsula and I went on a five-day bus tour with faculty to learn about my adopted state. I took advantage of having an office next door to Michael Cohen, a mentor to many, by knocking on his door often, and got on a plane to Boston at least once a year to work with Richard in an attempt to compensate for not having an office next to his. Ten years ago, my wife and I bought a home on the river within walking distance of my office, creating a lifestyle that would never have been possible for me in Berkeley or Cambridge.”

BRIEF BUT IMPORTANT LIFE MAXIMS

There are some things you just don’t want to know

Richard once told me a story about viewing a new piece of furniture arriving in his home fairly early in his marriage. Richard asked Sally, “How much did this cost?” Sally replied, “You don’t want to know.”[87] Richard realized that in fact he did not want to know and inquired no further. He and Sally have used this maxim of avoiding knowing what would make you unhappy plenty of times. He gives it some credit for their virtually battle-free marriage. That someone as analytical and rational as Richard, a man who always seems to be seeking information, would take this purposefully not-know approach was baffling to me. Over time, I have come to realize how wise this maxim is, and my wife and I have used it many times with each other and our children, with great positive effects for our family life.

If you focus on people’s shortcomings, you’ll always be disappointed

Tarek Masoud, a faculty colleague of Richard’s specializing in Middle East politics, came up with this maxim that he never directly heard from Richard but that he believes Richard lives by. “Richard is one of the most generous, nonjudgmental people I have ever encountered. For instance, I am chronically late. I have been late to our lunches, to basketball games, and even to a party he once threw in my honor, and he has never once done anything more than gently chide me for it.”

Practice asynchronous reciprocity

William Samuelson, who coauthored several papers with Richard, is a professor at Boston University. He speaks about a principle that might reflect an analytical bent behind Richard’s generosity: “Many years ago, Richard and I were talking about doing favors in the context of cost-benefit analysis. To my recollection, Richard felt very strongly that one should never hesitate to do things for others (even unasked-for things) if you estimated that the discernible benefit to the other was likely to be greater than one’s own cost of the favor. Sometime in the future, the net-beneficial favor might or might not be reciprocated, or the recipient would undertake his own net beneficial favor for someone else. Creating chains of favors was all to the general good. I have always liked this notion of asynchronous reciprocity (my term) because it is satisfyingly rational and not just sentimental or schmaltzy (“paying it forward”). We should all aim to practice it.”

LIVING WITH THE MAXIMS

By this stage, I hope that you have incorporated into your analytical toolkit some maxims that will help you understand the world better, make smarter decisions, and live more fully. I hope you will take some lasting lessons from this book:

- When you are having trouble getting your thinking straight, consider an extreme or simple case. This will often give you the insight you need to move forward. More generally, make a problem as simple as possible without losing its essence – but no simpler.

- The world is full of uncertainty, much more than you think. Almost every important decision you make will be in the face of uncertainty. Therefore, learning to think probabilistically (assessing subjective probabilities of various scenarios and updating these probabilities with new information) is a critical life skill.

- Because of uncertainty, some good decisions will result in poor outcomes. In fact, for some decisions there are no good outcomes. Your job will be to choose the option likely to lead to the least bad outcome. Also, resist the tendency to dislike more the errors resulting from your actions (errors of commission) than the errors resulting from your inactions (errors of omission). These two types of errors are equally bad; what matters is their consequences, not their source.

- Three important concepts to understand policy: Long division (how much bang for the buck) helps you decide among competing policy options. Elasticities (the change in one quantity associated with a change in another quantity) allow you to think at the margin, which is crucial for making good decisions. Look at subgroups within a population to better understand the group’s overall patterns.

- Three ideas for living more fully: Try not to be envious. Do your best to eliminate the detrimental feeling of regret, which lowers your pleasure and leads you to poor decisions. Remember that you can increase the enjoyment you get out of a pleasant experience by anticipating it and recalling it often.

The nature of thinking analytically is a continuum. I hope this book will help you move toward thinking more analytically. Here are some exercises to help you do so:

- Circle or highlight below three to five maxims that you would like to pay particular attention for the next month. For each of them, write a few sentences describing:

- How you could have applied the maxim in a past situation in your personal or professional life;

- How you could apply it in the future; and

- An example or two from this book that will help you remember this maxim

- Put a reminder on your calendar to review your answers every week for the next 5 weeks

- If you are willing to share with the world or would like to see how others are using the maxims, you can:

- Submit some of your entries to: http://bit.ly/submit-mta. We will be making some of these submissions publicly available with your permission.

- Tweet using #ThinkingAnalytically

- Write a maxim of your own for thinking more analytically

My final advice to help you think more analytically can be summarized in the ultimate maxim: Think like Richard. Your life and the world around you will be better for it.

Maxims for Thinking Analytically

1 – Thinking Straight

Maxim 1 - When you are having trouble getting your thinking straight, go to an extreme case

Maxim 2 - When you are having trouble getting your thinking straight, go to a simple case

Maxim 3 – Don’t take refuge in complexity

Maxim 4 - When trying to understand a complex real-world situation, think of an everyday analogue

2 – Tackling Uncertainty

Maxim 5 - The world is much more uncertain than you think

Maxim 6 - Think probabilistically about the world

Maxim 7 - Uncertainty is the friend of the status quo

3 – Making Decisions

Maxim 8 - Good decisions sometimes have poor outcomes

Maxim 9 - Some good decisions have a high probability of a bad outcome

Maxim 10 - Errors of commission should be weighted the same as errors of omission

Maxim 11 - Don’t be limited by the options you have in front of you

Maxim 12 - Information is only valuable if it can change your decision

4 – Understanding Policy

Maxim 13 - Long division is the most important tool for policy analysis

Maxim 14 - Elasticities are a powerful tool for understanding many important things in life

Maxim 15 - Heterogeneity in the population explains many phenomena

Maxim 16 - Capitalize on complementarities

5 – Living Fully

Maxim 17 - Strive hard to avoid envy – see your friend’s success as your gain

Maxim 18 - Do your best to banish regret

Maxim 19 - Make pleasure-enhancing decisions long in advance, to increase the utility of anticipation

Brief but important maxims for a satisfying life

APPENDIX A – GRATITUDE

One of the most gratifying aspects about writing this book has been the enthusiastic response from Richard’s former and current colleagues, coauthors, students, and collaborators. I received many expressions of gratitude toward him in response to my initial request for contributions. Below is a sample of these expressions; a more complete list can be found at:

bit.ly/gratitude-richard

Alan Berger

“I first met Richard 15 years ago when I was a young associate professor at the Harvard Design School. My dean at the time, Alan Altshuler, recognized that my intellectual curiosities and interests were broader than my department‘s capacities and pushed me to engage faculty outside the school. This was at a time when interdisciplinary work in my department was not encouraged. It is not an overstatement to say Richard opened an entire world of interdisciplinary thinking to me. Remarkably, he valued my opinions and respected my pedagogical world, which is set upon much more artistic and subjective ways of working than his field. It’s fair to say I have become more analytical since collaborating with Richard, and he certainly has a better eye for design. We have published several papers together about urban planning and design, and the environment – from mine reclamation, to suburban development and transportation, to coastal resilience. With every paper we write, Richard finds a way to change my thinking and help me see things anew.”

Justice Stephen Breyer

“In Henry IV Shakespeare the old judge says to his college mate, "Those were the days." That they were, Dick -- when we were teaching regulation at the Kennedy School (ranging from elevator regulation in Massachusetts to natural gas regulation in Oklahoma). We'd think of great lines for our joint writings (without regulation will DNA discoveries lead to 10,000 copies of General Franco? Or, mosquito-men?). We learned, we wrote, we taught, we became friends. You have done great work, my friend. And I am delighted this book will be produced about you.”

Cuicui Chen

"When I was planning on going on the job market, my husband and I also started thinking about having a second child. I was not sure if the timing was right. I went to see Richard about this. He said, ‘Cuicui, just do what you think is best for your family; if a school thinks negatively of you just because of your family status, that school will not be worth your time and energy anyway.’ I adopted his advice and am very happy with what I have now.”

Cary Coglianese

“I co-taught with Richard for several years when I was on the faculty at the Kennedy School of Government. I learned as much from Richard as did any student in our jointly taught doctoral research seminar. ‘Aim for grandeur,’ is the maxim I recall Richard saying repeatedly. By that he meant that students should think big in terms of what their research contributes. They should see themselves not as mere technicians but as full participants in a grand scholarly tradition. They should situate their work so that it speaks to timeless questions about the human condition.

I came to see this maxim as one of the secrets of Richard’s remarkable success as a scholar and a teacher. What could be grander than decision making? The field of decision theory in which Richard has been such a pioneer speaks to what is central to all of our lives. Decisions make up our lives. They affect us and they affect others. Research that advances our understanding of decision-making speaks directly to the most vital aspects of the human condition.

Richard has not only empirically illuminated how people make decisions, but he has also developed vital analytic tools for how they can make still better ones, especially in the face of inevitable uncertainty and trade-offs. Through his ever-popular Kennedy School course on ‘Analytic Frameworks for Policy’ – equally grand in its title and its aim – Richard has passed along essential decision tools to generations of students who have gone on to hold crucial positions of leadership around the world."

Suzanne Cooper

“Richard’s intellectual contributions to our understanding of complex policy issues have been extraordinary. That he is a wonderful mentor and colleague only adds to all we have gained from him. And personally, I owe him a great debt for his support and advice in my own professional development, dating back to my first encounter with HKS over 28 years ago. Thank you Richard!”

Victor R. Fuchs

“As one of the outstanding economists of his generation, Richard Zeckhauser has a rare combination of a nuanced command of theory, extensive high quality empirical research, and seminal analyses of public policy problems. I am indebted to him for his invaluable insights in my research in health economics and for his encouragement of my career.”

John Haig

“I took Richard’s class in 1981 as student at Harvard’s Kennedy School. The lessons he taught me – about probability, decision analysis and risk management – have led me to make vastly better decisions throughout my life, both professionally and personally. The methods and ways to frame problems I learned in his class are by far the most influential learnings from any class I have ever taken. I feel honored to now have him as a friend and colleague.”

Jody Heymann

“There are so many things Richard taught me that transformed the quality of work I am able to do that the list of all I am grateful for is long. His teaching came out best in his commitment to helping students solve tough problems and believing in our capacity to make a difference.

Richard gave graduate students in his courses real world problems—complex and messy—and threw us into the water to try and solve them. He showed us all that the tools he taught were within our reaches to use to help people advance public goods, not because we would get perfect answers but because the tools would accelerate progress.

When I had a wild idea as a masters student that I would try and solve a decision tree about what to recommend around breastfeeding to HIV mothers even though no one knew the transmission rate at the time of HIV in breast milk—instead of saying that’s ridiculous, he said that’s a great challenge, let’s take it on. That led to work with the World Health Organization and a career of being willing to take on complex problems.

It also transformed how I would teach. Students I have who are now working to eliminate detention of migrant children in the US, eliminate child marriage globally, and advance equality within and between groups and countries. They represent just one small part of Richard’s real impact across generations. Richard’s faith that bringing values and rigorous analytic methods together could help bring insights to how to address profoundly difficult problems, and that no one was too early in their career to contribute, has made a difference not just for his students but for his students’ students and their generations into the future.”

Elon Kohlberg

“I was introduced to Richard by our mutual friend Jerry Green. When telling me why I would enjoy meeting Richard, Jerry said that Richard had an “unbounded IQ.” I never heard this expression before or after, and I can’t think of a better way to describe him. Richard is a free spirit. His imagination roams with no regard to demarcations of context. His ability to see beyond boundaries is a joy to behold!”

Lee Hsien Loong

“I attended Richard’s course in 1979–1980. I was probably his first student from Singapore. Since then, many more Singapore students on the MPA program have found the course as rigorous and stimulating as I did more than 40 years ago. Many came back to serve in the government, and were inspired to apply the techniques and mindsets they had absorbed in the course.

Singapore tries hard to apply analytical frameworks to policy problems. I believe this has made a material difference in the quality of our public administration. For this, all of us who have had the privilege of having Richard as a teacher and a friend are very grateful.

Your course left such a strong impression that after 40 years I can still remember many of the things we discussed. I have often relied on them, and perhaps even more often unconsciously, to make sense of some data or issue, and for this I will always be grateful."

Todd Rogers

"Richard was the first Harvard Kennedy School (HKS) faculty to reach out to me when I received my job offer to be an assistant professor. We spoke for a long time and I thought "Wow! This guy is such a broad thinker. HKS is going to be amazing." When we began collaborating on a social psychology project about "paltering" he was wrapping up a philosophy journal article, had finished a children's book, and was considering an art history collaboration. Richard is an inspiring model of a creative, curious, fearless, insightful, and open thinker. I'd feel successful if any one of those attributes were used to characterize my thinking."

Benjamin Schneer

“Even though the Kennedy School is full of many very busy people, I have noticed that Richard is always able to make time for his students and his colleagues. When I started as a new assistant professor, we had lunch several times and, as I imagine has been the case for many others, after several long conversations we began working on a paper together. To be more precise, we started work on a new version of an old paper that Richard had conceived of more than twenty years ago. While this seemed unusual to me, Richard assured me it was not unheard of – in fact, he had recently finished another paper that had been on an even longer hiatus. I think this ongoing engagement (and relentlessness) is unique. For those of us starting our academic careers, Richard provides an example of someone who is generous, endlessly creative, and has stayed connected to so many colleagues.”

Jessica Stern

"Richard was my professor and served on my dissertation committee. I remember thinking about how much fun it was to be challenged by him; and how sad it was, when I finished my dissertation, that I might have to lay bare my assumptions without his help. His playful mind, fueled by a fierce curiosity, made him the best possible reader of a dissertation. He also taught me how to be a mentor. If I'm a good teacher today, if I have the hutzpah to display an interest in my students as people, if I've learned to question my own assumptions, it's because of him.”

Anh Tran

“In my first semester as an MPA student at the Kennedy School, I made a huge mistake by deciding to take Richard’s “Analytic Methods.” Awfully unprepared, I understood perhaps 20 to 40 percent of what Richard said. Growing up in an isolated country, I had no idea who was Howard Raiffa, what was game theory, or why baseball’s rules were so complicated here. I was about to drop or fail the class. Probably noticing my constantly miserable face in the crowd, one day Richard told the class a story about how two Vietnamese students did superbly well in his class ten years earlier. Richard did not really link this example to any analytic method or maxim that he was teaching. I now suspect that he even made up the story. But it transformed my confidence that I could pass the course. When the grades were in, Richard emailed me saying he revised his recommendation letter for the doctoral program. This initial boost in self-confidence that Richard gave me has helped me to go a long way from where I was.”

Alexander Wagner

“Whenever I visit Richard, it appears that I have his full attention – even though, objectively, I know that many other coauthors, friends, and colleagues receive equally intense attention. I am not able to “be like Richard” in terms of intellectual abilities. But I can at least aspire to “act like Richard” in terms of generosity with time and goodwill towards all.”

Wendy Wyatt

“For the past thirty years, I have been privileged to observe the enduring impact Richard’s wit and wisdom have had on many generations of students, colleagues, and friends. He has a unique gift for analytic thinking and decision making, combined with a kind and generous heart.”

APPENDIX B – CONTRIBUTORS

Chris Avery is the Roy E. Larsen Professor of Public Policy at the Harvard Kennedy School, and teaches analytic courses in microeconomics and statistics. He studies rating and selection mechanisms, focusing on the college admissions system. His first book, The Early Admissions Game, coauthored with Andrew Fairbanks and Richard Zeckhauser, was published by Harvard University Press in March 2003. In his current research, he studies college application patterns and college enrollment choices for high school students. He completed a PhD in economic analysis at the Stanford Business School and holds prior degrees from Harvard and Cambridge universities.

Max H. Bazerman is the Jesse Isidor Straus Professor of Business Administration at the Harvard Business School. His research focuses on decision making, negotiation, and ethics. He is the author of eleven books and more than 200 research articles and chapters. His awards include an honorary doctorate from the University of London (London Business School); the Life Achievement Award from the Aspen Institute; and the Distinguished Scholar Award, the Distinguished Educator Award, the Organizational Behavior Division’s Life Achievement Award from the Academy of Management.

Alan Berger is a Professor of Landscape Architecture and Urban Design at MIT, where he teaches courses open to the entire student body. He is founding director of MIT’s P-REX lab, a research lab focused on environmental problems caused by urbanization, including the design, remediation, and reuse of waste landscapes worldwide. All of his research, teaching, and practice emphasize the links between urbanization and the loss of natural resources and growth of waste, to help us better understand how to proceed with redesigning intelligent outcomes.

Syon Bhanot is an Assistant Professor of Economics at Swarthmore College, studying behavioral and public economics. He completed his BA at Princeton University in 2006, his MPP at Harvard Kennedy School in 2009, and his PhD in Public Policy at Harvard University in 2015. His areas of research interest include pro-social behavior, development, and personal finance decisions.

Jeff Bielicki is an Associate Professor at Ohio State University and researches issues in which energy and environmental systems and policy interact, specifically on carbon management, renewable energy, and the energy-water nexus. Prior to graduate school, he was a mechanical engineer at Fermi National Accelerator Laboratory, where he primarily worked on devices and infrastructure that produce antiprotons.

Iris Bohnet, the Albert Pratt Professor of Business and Government, is the Academic Dean of Harvard Kennedy School. She is a behavioral economist, combining insights from economics and psychology to improve decision making in organizations and society, often with a gender or cross-cultural perspective. Her most recent research examines behavioral design to de-bias how we live, learn and work.

Jonathan Borck is an Adjunct Lecturer in Public Policy at Harvard Kennedy School. He is also a Vice President at Analysis Group, Inc., in Boston. He specializes in the application of economics and statistics in the areas of finance, antitrust economics, energy, and the environment.

Stephen Breyer is an American lawyer and jurist who has served as an associate justice of the Supreme Court of the United States since 1994. He was an Assistant Professor, Professor of Law, and Lecturer at Harvard Law School, 1967–1994, and a Professor at the Harvard University Kennedy School of Government, 1977–1980.

Albert Carnesale was Chancellor of UCLA from 1997 through 2006, and continues to teach and conduct research on national security policy. Prior to serving at UCLA, he was at Harvard for 23 years, serving as Lucius N. Littauer Professor of Public Policy, Dean of Harvard Kennedy School, and Provost of the University.

Maryaline Catillon completed her PhD in Health Policy at Harvard University. She previously worked as a Hospital Director in France. Her empirical work combines economics, quantitative methods, and data science to study the value of drugs and health care interventions and to contribute to technology, program, and policy evaluation.

Cuicui Chen is an Assistant Professor of Economics at SUNY Albany. She studies how firms and governments respond to environmental and energy challenges. Some of her past and current research topics include investigating the social welfare consequence of the collusion among five German automakers in adopting ineffective vehicle exhaust control technology; empirically identifying market frictions in cap-and-trade programs; designing efficient international climate change agreements that nations have the incentive to join; and forecasting individual nations’ long-term greenhouse gas emissions using machine learning. She obtained her SM in Technology and Policy from MIT and PhD in Public Policy from Harvard.

Cary Coglianese is the Edward B. Shils Professor of Law and Professor of Political Science; and Director, Penn Program on Regulation. Cary Coglianese specializes in the study of administrative law and regulatory processes, with an emphasis on the empirical evaluation of alternative processes and strategies and the role of public participation, technology, and business-government relations in policy making.

Suzanne Cooper is the Academic Dean for Teaching and Curriculum and the Edith M. Stokey Senior Lecturer in Public Policy at Harvard Kennedy School. She previously served as Faculty Chair of the Master in Public Policy program, overseeing all academic aspects of the program. Her teaching focuses on macroeconomics, empirical methods, and policy analysis.

François Degeorge is a Professor of Finance at USI Lugano, and Senior Chair and Managing Director of the Swiss Finance Institute. He is a former President of the European Finance Association and a former Dean of Faculty of Economics at USI. Before teaching at USI, he taught at HEC Paris.

Jack Donahue is the Raymond Vernon Senior Lecturer in Public Policy. He is also Faculty Chair of the MPP Program and the SLATE Curriculum Initiative Co-Chair for Cases and Curriculum. His teaching, writing, and research focus on public sector reform and the distribution of public responsibilities across levels of government and sectors of the economy, including extensive work with the HKS-HBS joint degree program.

Anna Dreber Almenberg is the Johan Björkman Professor of Economics at the Stockholm School of Economics, researching behavioral economics and meta-science.

Karen Eggleston is a Senior Fellow at the Freeman Spogli Institute for International Studies and Deputy Director of the Shorenstein Asia-Pacific Research Center at Stanford University. She earned her PhD in public policy from Harvard University, and is a Faculty Research Fellow of the National Bureau of Economic Research (NBER). Her research focuses on government and market roles in the health sector and Asia health policy, especially in China, India, Japan, and Korea; healthcare productivity; and the economics of the demographic transition.

Ingrid Gould Ellen is the Paulette Goddard Professor of Urban Policy and Planning at New York University and Furman Center Faculty Director. She is author of Sharing America’s Neighborhoods: The Prospects for Stable Racial Integration (Harvard University Press, 2000), and has published numerous articles on housing policy, neighborhoods, and segregation.

David T. Ellwood is the Isabelle and Scott Black Professor of Political Economy at Harvard Kennedy School. He studies poverty, economic mobility and social policy. He served as Dean of the School from 2004 to 2015. Previously he helped lead President Clinton’s welfare reform effort while serving as an Assistant Secretary at the Department of Health and Human Services.

Jeffrey Frankel is the James W. Harpel Professor of Capital Formation and Growth at Harvard Kennedy School. He is a Research Associate at the National Bureau of Economic Research, and on the NBER Business Cycle Dating Committee which officially declares recessions. He served at the Council of Economic Advisers in 1983–1984 and 1996–1999; as CEA Member in the Clinton Administration, Frankel’s responsibilities included international economics, macroeconomics, and the environment. Before coming to Harvard in 1999, he was Professor of Economics at the University of California at Berkeley.

Jeffrey A. Friedman is an Assistant Professor of Government at Dartmouth College. His research focuses on the ways in which risk and uncertainty shape high-stakes decisions, particularly in the domain of national security.

Xiaochen Fu is a senior manager at Bank of China. She previously worked at Agricultural Bank of China, World Bank, and People’s Bank of China. She received her BA in Economics from Peking University and MPA/ID from Harvard Kennedy School. She is also a Finance PhD candidate at PBCSF, Tsinghua University.

Victor R. Fuchs is the Henry J. Kaiser Jr. Professor Emeritus in the departments of Economics and Health Research and Policy at Stanford University. He is also a research associate of the National Bureau of Economic Research, a senior fellow at the Stanford Institute for Economic Policy Research and a fellow at Stanford's Freeman Spogli Institute for International Studies. He applies economic analysis to social problems of national concern, with special emphasis on health and medical care.

Jason Furman is Professor of the Practice of Economic Policy jointly at Harvard Kennedy School and in the Department of Economics at Harvard University. Furman was a top economic adviser to President Obama for eight years, including serving as the Chairman of the Council of Economic Advisers from August 2013 to January 2017, acting as both President Obama’s chief economist and a member of the cabinet.

Alan Garber is the provost of Harvard University, as well as Mallinckrodt Professor of Health Care Policy and a professor of economics in the Harvard Kennedy School and the Faculty of Arts and Sciences. Most recently, he was the Henry J. Kaiser Jr. Professor at Stanford University, where he was also a professor of medicine, and professor (by courtesy) of economics, health research and policy, and of economics in the Graduate School of Business.

Ed Glaeser is the Fred and Eleanor Glimp Professor of Economics in the Faculty of Arts and Sciences at Harvard University, where he has taught since 1992. He regularly teaches microeconomics theory, and occasionally urban and public economics. He has served as Director of the Taubman Center for State and Local Government, and Director of the Rappaport Institute for Greater Boston. He has published dozens of papers on cities’ economic growth, law, and economics. In particular, his work has focused on the determinants of city growth and the role of cities as centers of idea transmission. He received his PhD from the University of Chicago in 1992.

Tony Gómez-Ibáñez is the Derek C. Bok Research Professor of Urban Planning and Public Policy at Harvard University, where he holds a joint appointment at the Graduate School of Design and the John F. Kennedy School of Government. He has taught courses in economics, infrastructure and transportation policy in both schools.

Marie-Pascale Grimon is a researcher at the Swedish Institute for Social Research of Stockholm University. She holds a PhD in Public Policy from the Harvard Kennedy School.

John A. Haigh is Co-Director, Mossavar-Rahmani Center for Business and Government

and Lecturer in Public Policy at Harvard Kennedy School. From 2005 through 2017 he served as the Executive Dean of the Kennedy School, engaging in strategic decisions and overseeing the operating and financial activities of the school.

Alice Heath is a PhD student in Public Policy at the Harvard Kennedy School. She conducts research in labor and public economics, focusing on policy that impacts children and families. Before her PhD, Alice worked as a Mathematics teacher in the UK and with various state public health and child welfare agencies in the US.

Jody Heymann is UCLA Distinguished Professor of Public Policy and Public Health. She is Founding Director of the WORLD Policy Analysis Center, the largest quantitative center analyzing social policy in 193 countries. An elected member of the National Academy of Sciences, Heymann served as Dean of the UCLA School of Public Health.

William Hogan is the Raymond Plank Research Professor of Global Energy Policy, and Research Director of the Harvard Electricity Policy Group (HEPG). Formerly a member of the faculty of Stanford University, where he founded the Energy Modeling Forum (EMF), he is a past president of the International Association for Energy Economics (IAEE).

Kessely Hong is the Faculty Chair of MPA Programs and Lecturer in Public Policy at Harvard Kennedy School. She received the Manuel C. Caballo Award for Excellence in Teaching in 2015 and the Holly Taylor Sargeant Award for Women’s Advancement in 2018. She teaches about negotiating across differences.

John Horton is an Assistant Professor at MIT Sloan in the Information Technology group and the Richard S. Leghorn (1929) Career Development Professor. Horton’s research focuses on the intersection of labor economics, market design, and information systems. He is particularly interested in improving the efficiency and equity of matching markets.

John received his PhD from the Harvard Kennedy School in Public Policy in 2011.

Elon Kohlberg is the Royal Little Professor of Business Administration at Harvard Business School, where he has been a professor since 1973. His main interests are in game theory and elementary math education.

Maciej H. Kotowski is an economist and an Associate Professor of Economics at the University of Notre Dame, specializing in the study of auctions, market and mechanism design, and matching markets.

Carolyn Kousky is Executive Director of the Wharton Risk Center at the University of Pennsylvania. Dr. Kousky’s research has examined multiple aspects of disaster insurance markets, federal disaster response, and policies for building climate resilience. She has a BS in Earth Systems from Stanford University and a PhD in Public Policy from Harvard University.

Richard Krumholz is a Managing Partner of Delight Restaurant Group, a Taco Bell and Wendy’s restaurant franchisee with 57 restaurants. Previously, Rich was a Principal at The Baupost Group, where he focused on U.S., European, and Brazilian real estate investments. He has a very successful real estate investment track record with more than 30 transactions across all real estate sectors and parts of the capital structure. Rich began his career in the Merchant Banking Division of Goldman Sachs as part of its Real Estate Principal Investment Area. He received an AB with High Honors in Economics from Harvard College.

Howard Kunreuther is James G. Dinan Professor Emeritus of Decision Sciences and Public Policy, and Co-Director of the Wharton Risk Management and Decision Processes Center at the Wharton School, University of Pennsylvania. Howard has a long-standing interest in how society can better manage low-probability, high-consequence events related to technological and natural hazards.

Robert Z. Lawrence is Albert L. Williams Professor of International Trade and Investment at Harvard Kennedy School, a non-resident Senior Fellow at the Peterson Institute for International Economics, and a Research Associate at the National Bureau of Economic Research. He was a member of President Clinton’s Council of Economic Advisors between 1999 and 2000. Lawrence in an international economist who specializes in international trade policy.

Scott Leland is the Executive Director of Harvard Kennedy School’s Mossavar-Rahmani Center for Business and Government, supporting the Center’s research programs, general administration, strategic planning, and day-to-day operations. He is a graduate of Stanford University and has a Master in Public Policy degree from the Harvard Kennedy School.

Jennifer Lerner is the Thornton F. Bradshaw Professor of Public Policy, Decision Science, and Management at Harvard Kennedy School. Drawing insights from psychology, economics, and neuroscience, her research examines human judgment and decision making. In addition to her roles at Harvard, Professor Lerner served in a Pentagon assignment from 2018-2019 as the Navy’s first Chief Decision Scientist.

Elizabeth Linos is an Assistant Professor of Public Policy at UC Berkeley. She is a behavioral scientist and public management scholar. The majority of her research focuses on how to improve government by focusing on its people. Specifically, her studies consider how we can improve diversity in recruitment and selection, how to reduce burnout at work, and how different work environments affect performance and motivation in government. In her research on behavioral public administration, she also studies how to use low-cost nudges to reduce administrative burdens and to improve overall resident-state interactions.

Lee Hsien Loong is the Prime Minister of the Republic of Singapore. He graduated from the Master of Public Administration program at Harvard Kennedy School in 1980.

Glenn Loury is an academic economist and public intellectual whose work has focused mainly on the economics of social policy. Trained at MIT in the 1970s, he has taught at Harvard, Boston, and Northwestern universities, and at the University of Michigan. He is currently the Merton P. Stoltz Professor of the Social Sciences at Brown University.

Marijane Luistro Jonsson is a postdoctoral researcher at Stockholm School of Economics (SSE), where she teaches and conducts interdisciplinary research on the behavioral dimension of sustainability. Among her research foci are cooperation, nudges, and knowledge resistance. She is originally from the Philippines.

Tarek Masoud is a Professor at Harvard Kennedy School, where he researches political development in the Middle East and Muslim-majority countries. He holds an AB from Brown and a PhD from Yale, both in political science.

Barry Nalebuff is the Milton Steinbach Professor at Yale School of Management. A graduate of MIT, a Rhodes Scholar, and a Junior Fellow in the Harvard Society of Fellows, Nalebuff earned his doctorate at Oxford University. In addition to his academic work, Barry and his former student Seth Goldman cofounded Honest Tea.

Joachim Neipp was educated as an economist at the Department of Economics of the University at Heidelberg in Germany. He served as a Research Fellow at Harvard Kennedy School in 1984–1985. In 1987, he joined Bayer and currently serves as its Head of Financial Operations.

Jonathan K. Nelson is a Teaching Professor at Syracuse University Florence and a specialist of Italian Renaissance Art. Publications including book-length studies of Filippino Lippi, Botticelli, Leonardo da Vinci, Michelangelo, Plautilla Nelli, Bernard Berenson, and Robert Mapplethorpe.

Joseph Newhouse completed his PhD in economics and then spent twenty years at RAND working in the economics of health care. He came to Harvard as the John D. MacArthur Professor of Health Policy and Management, with appointments at the Kennedy School, Medical School, and T.H. Chan School of Public Health.

Gary Orren is the V.O. Key, Jr., Professor of Politics and Leadership at Harvard University, where he has taught for the past 52 years. A leading expert on public opinion, politics, and persuasion, he has published six books. He graduated summa cum laude from Oberlin College and received his PhD from Harvard University.

Victor Paci practiced corporate and securities law as a partner in a large Boston law firm until 2005 when he joined ERI, which had been his client for the previous 24 years. At ERI, he participates in the sourcing, structuring, and negotiation of investments and in the management of the firm and its investment partnerships.

Edward A. (Ted) Parson is Dan and Rae Emmett Professor of Environmental Law at UCLA. Parson studies international environmental law and policy, and the impacts and governance of disruptive technologies. He holds degrees in physics from Toronto and in operations research from the University of British Columbia, and a PhD in Public Policy from Harvard. In former lives, he was a classical musician and an environmental activist.

Jayendu Patel leads OurIndia Investing, teaches at Indian School of Business, and consults. His work combines statistics, economics, and behavioral decisions. After earning his PhD from University of Chicago, he taught at Harvard University, Boston College, and Boston University. From 2001–2008, he was Chief Science Officer at Choicestream.

Harold Pollack is an urban public health researcher at the University of Chicago. His current research concerns improved services for individuals at the boundaries of the behavioral health and criminal justice systems. Past President of the Health Politics and Policy section of the American Political Science Association, he has published widely at the interface between poverty policy and public health. His journalism regularly appears in such outlets as the Washington Post, the New York Times, and other popular publications.

Taran Raghuram is a graduate student studying public policy at the Harvard Kennedy School. He is focused on issues of political polarization and national identity in the United States. He previously worked as a consultant for public school districts and is a Fellow with the Program in Education Policy and Governance at the Harvard Kennedy School. He received his BAs in Physics and Economics from Brown University.

Paul Resnick is the Michael D. Cohen Collegiate Professor of Information and Associate Dean for Research and Faculty Affairs at the University of Michigan School of Information. He previously worked as a researcher at AT&T Labs and AT&T Bell Labs, and as an Assistant Professor at the MIT Sloan School of Management. He received the MS and PhD degrees in Electrical Engineering and Computer Science from MIT, and a bachelor’s degree in mathematics from the University of Michigan.

Chris Robert is a technologist, entrepreneur, economist, researcher, and lecturer. He founded Dobility, Inc., which produces SurveyCTO, an electronic data collection platform used worldwide by leading researchers and evaluation professionals. He is involved in a long-term project to evaluate the impacts of microfinance in South India and develops online curriculum for a program to promote the use of evidence in policy-making (initially in Pakistan and India). He has taught statistics and policy analysis at Harvard Kennedy School.

Todd Rogers is a behavioral scientist and Professor of Public Policy at Harvard Kennedy School. His work applies behavioral science insights and methods to understand important social challenges and to develop interventions to mitigate them. He studies how to help families effectively support student success, and how to improve democracy.

Devjani Roy is Managing Director at Texas Tech University Health Sciences, and a former Postdoctoral Fellow at Harvard Kennedy School, where her research focused on the intersection of the behavioral sciences and the humanities.

William Samuelson is Professor of Finance and Economics at the Questrom School of Business at Boston University, His research interests include decision making, negotiation and dispute resolution, game theory, experimental economics, behavioral economics, and competitive bidding. His scholarly articles have appeared in the American Economic Review, Econometrica, Journal of Finance, Management Science, Operations Research, and Quarterly Journal of Economics. He has authored two books: Game Theory and Business Applications and Managerial Economics (9th Edition).

Frederick Schauer, the David and Mary Harrison Distinguished Professor of Law at the University of Virginia, previously served as the Fran Stanton Professor of the First Amendment at the Harvard Kennedy School and as Professor of Law at the University of Michigan. He writes about legal reasoning, constitutional law, freedom of speech and the press, the law and theory and policy of evidence, and the philosophy of law.

Benjamin Schneer is an Assistant Professor of Public Policy at Harvard Kennedy School, where he focuses on American politics. He received his PhD from the Department of Government at Harvard University.

Peter H. Schuck is the Simeon E. Baldwin Professor of Law Emeritus at Yale Law School, Darling Foundation Visiting Professor at Berkeley Law School, and Distinguished Scholar in Residence at NYU Law School.

David Slusky is an Associate Professor of Economics at the University of Kansas. His research focuses on health care, children, infrastructure and environment, and insurance. He received a BS in Physics and International Studies from Yale University and an MA and PhD in Economics from Princeton University.

Erin St. Peter is a Research Analyst at Wharton’s Risk Center and Real Estate Department. Previously, she worked for a Native American community development financial institution, providing direct collateralized mortgages on tribal lands. Erin holds a BA in Economics and Government/Legal Studies from Bowdoin College and an MPP from Harvard Kennedy School.

Rob Stavins is the A. J. Meyer Professor of Energy and Economic Development, Harvard Kennedy School; Director, Harvard Environmental Economics Program; Director of Graduate Studies, Doctoral Programs in Public Policy and Political Economy & Government; Co Chair, Harvard Business School Kennedy School Joint Degree Programs; and Director, Harvard Project on Climate Agreements. He is University Fellow at Resources for the Future; Research Associate at National Bureau of Economic Research, and an elected Fellow of the Association of Environmental and Resource Economists.

Jessica Stern is a Professor at Boston University’s Pardee School of Global Studies. She teaches and writes about perpetrators of violence. Her most recent books are My War Criminal: Personal Encounters with an Architect of Genocide; ISIS: The State of Terror (with JM Berger); and Denial.

Lorae Stojanovic is a third-year undergraduate at Harvard studying economics.

Larry Summers is the Charles W. Eliot University Professor and President Emeritus at Harvard University. He served as the 71st Secretary of the Treasury for President Clinton and the Director of the National Economic Council for President Obama.

Tan Yinglan founded Insignia Ventures Partners in late 2017 and is the Founding Managing Partner. He serves on the International Board of Stars – Leaders of the Next Generation, the Singapore Government’s Pro Enterprise Panel, and the Committee on the Future Economy’s Subcommittee on Future Corporate Capabilities and Innovation. He is also a board member at Hwa Chong Institution and an Adjunct Associate Professor at National University of Singapore.

Yair Tauman is a Professor of economics at Stony Brook University and Dean of the Adelson School of Entrepreneurship at IDC Herzliya, where he also serves as academic director of the Zell Entrepreneurship Program. He obtained his PhD and MS in mathematics at the Hebrew University of Jerusalem, both under the supervision of Nobel Prize winner Robert Aumann.

Mark Thompson is the president of Equity Resources Group, Inc. He joined the small investment firm in 1983. He was a Professor at the Harvard School of Public Health from 1975 to 1983. Mark has been a visiting professor at the Université de Paris and the Universität Bielefeld in North Rhine-Westphalia in Germany.

Anh Tran is a Professor at Indiana University’s O’Neill School, specializing in the governance issues of developing countries. His current research focuses on performance management and leadership in public organizations. His articles have appeared in the American Political Science Review, Journal of Financial Economics, Journal of Law and Economics, Journal of Public Economics, American Economic Journal: Applied Policy, and other leading academic journals. He is currently an Economic Adviser to the Prime Minister of Vietnam. He also serves as Director of the Vietnam Initiatives at IU, a global think tank on development policies for Vietnam.

Stefan Trautmann is a Professor of Behavioral Finance at the University of Heidelberg, Germany. His research interests concern financial decision making under uncertainty, social preferences and ethics, and the psychology of economic decisions more generally. He is an associate editor at several journals publishing research on behavioral decision making.

Benjamin Van Roy is a Professor at Stanford University, where his research and teaching focuses on reinforcement learning. He also leads a DeepMind Research team in Mountain View.

W. Kip Viscusi is an economist who has published more than 30 books and 370 articles, focusing primarily on risk and uncertainty. He has been a tenured professor at Northwestern, Duke, Harvard, and Vanderbilt.

Alexander Wagner is a Professor of Finance at the University of Zurich and Senior Chair at the Swiss Finance Institute. He earned his PhD in Political Economy from Harvard University. Prior to that, he completed studies in economics and law in his hometown Linz, Austria.

Gernot Wagner teaches climate economics at New York University, coauthored Climateshock with the late Marty Weitzman, and writes the Risky Climate column for Bloomberg. Before coming to NYU, he co-founded Harvard’s Solar Geoengineering Research Program and served, for almost a decade, at Environmental Defense Fund, most recently as lead senior economist.

Milton Weinstein is the Henry J. Kaiser Research Professor of Health Policy and Management at the Harvard T.H. Chan School of Public Health. He is best known for his research on cost-effectiveness of medical practices and for developing methods of economic evaluation and decision analysis in health care.

Nils Wernerfelt is a Research Scientist at Facebook. He received his PhD in Economics from MIT, BA in Mathematics from Harvard, and was a Fulbright Scholar at the Toulouse School of Economics.

Craig White is Senior Vice President and General Manager at ConcertAI, a rapidly growing Boston-based startup. Originally trained an electrical engineer, Craig applies his analytical mindset to a plethora of different problems. Currently he oversees and leads a team developing algorithms and analyses used to determine the optimal treatments for patients with cancer.

Wendy Wyatt has been Richard Zeckhauser's administrative assistant for over thirty years. She has also practiced over this period as a speech-language pathologist. Given their disparate skills, Richard and Wendy’s success together shows positive cross-partial derivatives in action.

Josh Yardley is an Adjunct Lecturer of Public Policy at Harvard Kennedy School, where he teaches courses in econometrics and quantitative methods. He also taught courses in program evaluation and policy analysis as an adjunct lecturer at Brown University.

Alexandre Ziegler is Director of the Center for Portfolio Management at the Institute of Banking and Finance of the University of Zurich. His research interests include asset pricing, information acquisition models, and applied game theory. He has extensive experience in quantitative asset management and software development and is the inventor or co-inventor of several patented software innovations.

Chris Zook is a partner with Bain & Company. He was co-head of the Global Strategy practice for twenty years. During his more than twenty-five years at Bain, Chris has specialized in helping companies find new sources of profitable growth. He splits his time between Bain’s Amsterdam and Boston offices.

REFERENCES

Ariely, Dan. Predictably Irrational: The Hidden Forces that Shape our Decisions. 1st ed. New York: Harper Perennial, 2010.

Arkes, Hal R., and Catherine Blumer. “The Psychology of Sunk Cost.” Organizational Behavior and Human Decision Processes 35, no. 1 (1985): 124-140.

Avery, Christopher, Andrew Fairbanks, and Richard Zeckhauser. The Early Admissions Game. Cambridge: Harvard University Press, 2004.

Bohnet, Iris, Fiona Greig, Benedikt Herrmann, and Richard Zeckhauser. “Betrayal Aversion: Evidence from Brazil, China, Oman, Switzerland, Turkey, and the United States.” The American Economic Review 98, no. 1 (2008): 294-310.

Brown, Alyssa. “There's No Lucky Percentage of Guests Who Will Attend a Wedding.” July 3, 2018. https://www.marthastewart.com/7923646/percentage-how-many-guests-attend-wedding.

Catillon, Maryaline and Richard Zeckhauser. “Unleash the Data on COVID-19.” (n.d.). https://www.hks.harvard.edu/centers/mrcbg/news-events/COVID_Zeckhauser.

Centers for Medicare & Medicaid Services. “National Health Accounts Historical.” (n.d). https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.

Central Intelligence Agency. The World Factbook. (n.d.). https://www.cia.gov/the-world-factbook/.

Donahue, John D., Richard Zeckhauser, and Karen Eggleston. The Dragon, the Eagle, and the Private Sector : Public-private Collaboration in China and the United States. Cambridge ; New York, NY: Cambridge University Press, 2020.

Donahue, John D. and Richard Zeckhauser. Collaborative Governance : Private Roles for Public Goals in Turbulent Times. Princeton: Princeton University Press, 2011.

Duke, Annie. How to Decide. Penguin Publishing Group, 2020.

Duke, Annie. Thinking in Bets. Penguin Publishing Group, 2018.

Dreber, Anna, David. G. Rand, Nils Wernerfelt, Justin R. Garcia, Miguel G. Vilar, J.Koji Lum, and Richard Zeckhauser. “Dopamine and Risk Choices in Different Domains: Findings Among Serious Tournament Bridge Players.” Journal of Risk and Uncertainty 43, no. 1 (2011): 19-38.

Gawande, Atul. Complications: A Surgeon's Notes on an Imperfect Science. 1st ed. New York: Metropolitan Books, 2002.

Gibson, Weldon B., and Michael M. Menke. “Meeting the Challenge of an Age of Uncertainty.” Handling and Shipping, Presidential Issue. (1973) 205–210.

Gigerenzer, Gerd. Risk Savvy. East Rutherford: Penguin Publishing Group, 2014.

Gilovich, Thomas, and Victoria Husted Medvec. “The Experience of Regret: What, When, and Why.” Psychological Review 102, no. 2 (1995): 379-395.

Hammond, John S., Howard Raiffa, and Ralph L. Keeney. Smart Choices: A Practical Guide to Making Better Decisions. Boston: Harvard Business Review Press, 2015.

Haven, Cynthia. “Stanford Economist: How Do We ‘Get off this Path of Deficits as Far as the Eye Can See?’” August 2, 2011. https://news.stanford.edu/news/2011/august/shoven-debt-qanda-080211.html.

The Heritage Foundation. “COVID-19 Deaths by Age.” February 17, 2021. https://www.heritage.org/data-visualizations/public-health/covid-19-deaths-by-age/.

Holman, Halsted R. “The Relation of the Chronic Disease Epidemic to the Health Care Crisis.” ACR Open Rheumatology 2, no 3 (2020): 167-173.

Janssen, Sarah. The World Almanac and Book of Facts, 2020. World Almanac Books, 2020.

Kahneman, Daniel. Thinking, Fast and Slow. 1st ed. Harvard Library e-reader collection. New York: Farrar, Straus and Giroux, 2011.

Kaplan, Sarah, and Chris Mooney. “Genetic Data Show How a Single Superspreading Event Sent Coronavirus Across Massachusetts - and the Nation.” The Washington Post, August 25, 2020.

Levitt, Steven D., and Stephen J. Dubner. Freakonomics : A Rogue Economist Explores the Hidden Side of Everything. London: Penguin. 2006.

Levy, Harold O. “Colleges Should Abandon Early Admissions.” January 12, 2017. https://www.insidehighered.com/views/2017/01/12/discrimination-inherent-early-admissions-programs-essay.

Lloyd-Smith, James O., P. Ekkehard Kopp, Wayne M. Getz, and Sebastian J. Schreiber. “Superspreading and the Effect of Individual Variation on Disease Emergence.” Nature 438, no. 7066 (2005): 355-359.

Ma, Siyu, Yair Tauman, and Richard Zeckhauser. “Deterrence Games and the Disruption of Information.” HKS Faculty Research Working Paper Series RWP20-026, (2020).

Nelson, Jonathan, and Richard Zeckhauser. The Patron's Payoff: Conspicuous Commissions in Italian Renaissance Art. Princeton: Princeton University Press, 2004.

Patt, Anthony, and Richard Zeckhauser. “Action Bias and Environmental Decisions.” Journal of Risk and Uncertainty 21, no. 1 (2000): 45-72.

Raiffa, Howard. Decision Analysis : Introductory Lectures on Choices under Uncertainty. 1st ed., Random House, 1968.

Russell, Bertrand. The Conquest of Happiness. New York: H. Liveright, 1930.

Samuelson, William, and Richard Zeckhauser. “Status Quo Bias in Decision Making.” Journal of Risk and Uncertainty 1, no.1 (1988): 7-59.

Shepard, Donald, and Richard Zeckhauser. “Long-Term Effects of Interventions to Improve Survival in Mixed Populations.” Journal of Chronic Diseases 33, no. 7 (1980): 413-433.

Silver, Nate. “National Polls: 2016 Election Forecast.” November 8, 2016. https://projects.fivethirtyeight.com/2016-election-forecast/national-polls/.

Sneed, Annie. “Computer Beats Go Champion for First Time.” January 27, 2016. https://www.scientificamerican.com/article/computer-beats-go-champion-for-first-time/.

Spranca, Mark, Elisa Minsk, and Jonathan Baron. “Omission and Commission in Judgment and Choice.” Journal of Experimental Social Psychology 27, no. 1 (1991): 76-105.

Stokey, Edith, and Richard Zeckhauser. A Primer for Policy Analysis. New York: W. W. Norton & Company, 1978.

Thaler, Richard H. Misbehaving: The Making of Behavioral Economics. 1st ed. New York: W.W. Norton & Company, 2015.

Thaler, Richard H., and Cass R. Sunstein. Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press, 2008.

Weaver, Charles H. “High-Dose Chemotherapy & Stem Cell Transplant for Breast Cancer.” February 3, 2020. https://news.cancerconnect.com/breast-cancer/high-dose-chemotherapy-stem-cell-transplant-for-breast-cancer.

Zeckhauser, Richard. “Howard Raiffa and Our Responsibility to Rationality.” Negotiation Journal 33, no. 4 (2017): 329-332.

Zeckhauser, Richard. “New Frontiers Beyond Risk and Uncertainty: Ignorance, Group Decision, and Unanticipated Themes.” In Handbook of the Economics of Risk and Uncertainty, edited by Mark Machina and Kip Viscusi, xvii-xxix. North Holland: Elsevier, 2014.

Zeckhauser, Richard. “Investing in the Unknown and Unknowable.” In The Known, the Unknown, and the Unknowable in Financial Risk Management, edited by Francis X. Diebold, Neil A. Doherty, and Richard J. Herring, 304-346. Princeton: Princeton University Press, 2010.

Zeckhauser, Richard. “Distinguished Fellow: Reflections on Thomas Schelling.” The Journal of Economic Perspectives 3, no. 2 (1989): 153-164.

Zeckhauser, Richard, and Donald Shepard. “Where Now for Saving Lives?” Law and Contemporary Problems 40, no. 4 (1976): 5-45.

INDEX

action bias, 88

admissions, 5, 139, 140, 183, 208

Amazon, 108, 109, 154

AmeriCorps, 40, 41, 42, 43

Anna Karenina, 58

appendicitis, 4, 117

apples, 39

Arrow, Ken, VI, 151, 161

Avery, Chris, VI, 26, 183

baseball, 6, 32, 33, 34, 59, 66, 181

Bayesian updating, 73

Bazerman, Max, 114

bears, 72

Berger, Alan, 174, 183

betrayal aversion, 129, 130, 131, 216

Bhanot, Syon, 11, 150, 184

Bielicki, Jeff, 59, 184

Bohnet, Iris, I, VI, 129, 130, 184

Bok, Derek, 139

bone marrow transplant, 106

Borck, Jonathan, VI, 37, 62, 185

Boston Red Sox, 59

Bridge, 47, 67, 206

Burkina Faso, 47, 217

bus, 66, 72, 162

Carnesale, Albert, 185

Catillon, Maryaline, 36, 72, 85, 185

Chen, Cuicui, 175, 185

China, 17, 77, 130, 187, 188, 205, 206

cholesterol, 114

Clinton, Bill, 112

Clinton, Hillary, 51, 60, 62

Coglianese, Cary, 175, 186

commission, 15, 100, 108, 109, 110, 111, 112, 113, 154, 167, 172

COVID-19, 3, 44, 45, 49, 75, 76, 77, 88, 110, 116, 126, 135, 136, 137, 138, 205, 207

decision tree, 95, 96, 97, 98, 104, 178

Degeorge, François, 144, 186

Donahue, Jack, VI, 19, 187, 219

Dreber Almenberg, Anna, 47, 187

Duke, Annie, 91, 92

Eggleston, Karen, 13, 187, 206

elasticity, 127

Ellen, Ingrid, 22

Ellwood, David, VI, 95, 98, 103

envy, 2, 150, 151, 173

FiveThirtyEight, 62, 63, 74

Frankel, Jeff, VI, 111, 112

Friedman, Jeffrey, 69

Fu, Xiaochen, 17, 188

Furman, Jason, 46, 54, 189

Garber, Alan, VI, 5, 149, 161, 189

Gawande, Atul, 55

Glaeser, Ed, 147, 189

Gomez-Ibañez, Tony, VI, 115

Grimon, Marie-Pascale, 46, 128, 190

Haig, John, VI, 177

Heath, Alice, V, 34, 104, 116, 145, 190

hernia recurrence, 138

heterogeneity, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 172

Heymann, Jody, 177, 191

hindsight bias, 48

Hogan, William, 54, 191

Hong, Kessely, VI, 101, 156, 191

Horton, John, 24, 191

ignorance, 55, 57, 58, 59, 216

iPhone, 92, 94, 96, 97, 152, 154

Kohlberg, Elon, 178, 192

Kotowski, Maciej, VI, 78

Kousky, Carolyn, 50, 192

Krumholz, Richard, 49, 192

Kunreuther, Howard, 81, 87, 193

Lawrence, Robert, VI, 158

Lee, Hsien Loong, 1, 135, 179, 194

Leland, Scott, 30, 193

Lerner, Jennifer, VI, 69, 70, 193

Levitt, Steve, 80

Lifelong Learning Tax Credit, 21

Linos, Elizabeth, 101, 194

long division, 120, 121, 124, 126, 168, 172

Loury, Glenn, 27, 194

Luistro Jonsson, Marijane, 88, 194

margin, 122, 127, 128, 140, 168

marginal propensity to consume, 10

marriage, I, 58, 59, 145, 164, 178

Masoud, Tarek, 144, 164, 195

Nalebuff, Barry, 12, 139, 140, 195

negotiation, 12, 13, 22, 34, 131, 152, 183, 196, 198

Neipp, Joachim, 16, 195

Nelson, Jonathan, 14, 15, 195

Newhouse, Joe, 67, 195

Niagara Falls, 35, 36

nuclear deterrence, 105

omission, 100, 108, 109, 110, 111, 112, 113, 154, 159, 168, 172

omission bias, 109, 110

Orren, Gary, 31, 32, 40, 196

Paci, Victor, 67, 196

Parson, Ted, 30

Patel, Jayendu, 86, 196

Pollack, Harold, 99, 100, 197

positive cross-partial derivatives, 143, 146, 204

posterior, 73

prior, 3, 31, 73, 76, 183

QALY, 122

Raghuram, Taran, 125, 145, 146, 197

Raiffa, Howard, VI, 21, 22, 26, 152, 154, 181, 207, 209

randomized controlled trials (RCTs), 39, 85, 106

regret, 93, 109, 110, 152, 153, 154, 155, 168, 173

reinforcement learning, 23, 24, 202

Resnick, Paul, 162, 197

resulting, 2, 91, 112, 167

retirement, 14, 87, 101, 158, 159

risk, I, 17, 29, 31, 47, 49, 50, 55, 56, 57, 72, 76, 96, 100, 131, 136, 137, 138, 152, 158, 159, 177, 188, 202, 216

Robert, Chris, VII, 82, 157, 198

Rogers, Todd, 179, 198

Roy, Devjani, 58, 198

Samuelson, William, 48, 71, 83, 84, 86, 165, 198

Schauer, Frederick, 160, 199

Schelling, Thomas, 22, 210

Schneer, Benjamin, 180, 199

Schuck, Peter, 143

Silver, Nate, 62, 63

Singapore, 1, 110, 135, 136, 153, 179, 194, 201

Slusky, David, 126, 199

smoking relapse rates, 4, 138

St. Peter, Erin, 66, 124, 200

status quo, 83, 84, 85, 86, 87, 88, 113, 154, 171, 216

Stavins, Robert (Rob), VII, 102, 200

Stern, Jessica, 180, 200

Stojanovic, Lorae, 28, 200

subjective probability, 64, 73

Suez Canal, 48

Summers, Larry, 1, I, 200

sunk-cost fallacy, 154, 155, 162

surprise point, 51, 52, 54, 60

Swiss Army knife, 40, 41, 42, 43

Tan, Yinglan, 18, 110, 153

Tauman, Yair, 141, 201, 208

tennis, 2, 48, 71, 110, 122, 141, 142

Thinking at the margin, 128

Thompson, Mark, 155, 201

Tolstoy, 58, 59

Tran, Anh, 181, 201

Trautmann, Stefan, 76, 202

Trump, Donald, 4, 61, 62

trust, 129, 130, 131

Uber, 24

uncertainty, I, 7, 29, 36, 37, 44, 49, 51, 52, 53, 54, 55, 56, 57, 59, 62, 64, 66, 71, 82, 84, 85, 86, 90, 95, 98, 167, 176, 188, 202, 216

utility of anticipation, 156, 158, 173

Van Roy, Benjamin, 23, 202

Viscusi, W. Kip, 29, 202

Wagner, Alexander, 181, 202

Wagner, Gernot, 88, 203

wedding, 4, 69, 70, 71, 205

Weinstein, Milton, 26, 203

Wernerfelt, Nils, 79, 80, 203, 206

White, Craig, 116, 203

wrong-way driving, 124

Yardley, Josh, VII, 94, 204

Zeckhauser, Sally, 4, 6, 105, 106, 107, 111, 117, 159, 164

Ziegler, Alexandre, 204

Zook, Chris, 133, 204

ABOUT RICHARD ZECKHAUSER

Richard Zeckhauser is the Frank P. Ramsey Professor of Political Economy, Kennedy School, Harvard University. He graduated from Harvard College (summa cum laude) and received his PhD there. He is an elected fellow of the Econometric Society, the Institute of Medicine (National Academy of Sciences), and the American Academy of Arts and Sciences. In 2014, he was named a Distinguished Fellow of the American Economic Association. His contributions to decision theory and behavioral economics include the concepts of quality-adjusted life years (QALYs), status quo bias, betrayal aversion, and ignorance (states of the world unknown) as a complement to the categories of risk and uncertainty. Many of his policy investigations explore ways to promote the health of human beings, to help markets work more effectively, and to foster informed and appropriate choices by individuals and government agencies. Zeckhauser has published more than 300 articles. Apart from academics, Zeckhauser is a Senior Principal at Equity Resource Investments, a real estate private equity firm. He has won multiple national championships in contract bridge.

ABOUT THE AUTHOR

Dan Levy has taught quantitative methods, policy analysis, and program evaluation at the Harvard Kennedy School for more than 15 years. He has conducted evaluations of social programs in a wide range of settings including Burkina Faso, Indonesia, Jamaica, Mexico, Niger, Tanzania, and the United States. He currently serves as the faculty director of the Public Leadership Credential, the Harvard Kennedy School's flagship online learning initiative. He has won several teaching awards, including the university-wide David Pickard Award for Teaching and Mentoring. He wrote Teaching Effectively with Zoom: A practical guide to engage your students and help them learn. His teaching was featured in a book titled Invisible Learning by David Franklin. He grew up in Venezuela and received his PhD in Economics from Northwestern University.

NOTES

[1] The correct answer to this problem is that it would take them 1 hour and 12 minutes. Mary paints at a speed of 1/2 room per hour and Jim paints at a speed of 1/3 room per hour. So their combined speed is 5/6 room per hour (i.e., 1/2 + 1/3). This means it would take them 6/5 hours to paint the room, which is equal to 1 hour and 12 minutes. Incidentally, the real-world answer is likely different. If there are gains (or losses) from working together (say because they help or distract each other), the real-world answer will be lower (or higher) than the textbook answer. It is frequently helpful to think of a parallel problem in a different context, preferably a familiar context. For example, consider Martha and Joe driving cars. Martha can cover 100 miles in two hours. Joe can do so in three hours. If they were both driving, how long would it take them to drive a total of 100 miles? The problem seems simpler because we are used to thinking in terms of miles per hour. Martha clearly drives at 50 mph and Joe at 33.33. Together, they cover 83.33 miles in an hour. Thus, it would take them one and one fifth hours, or one hour and 12 minutes, to drive a total of 100 miles.

[2] The key challenge to many people for thinking about MPC is understanding what marginal means. It means looking at what happens with the last unit. Economists find marginal thinking to be of extraordinary value. Thus, they talk about marginal costs in production for firms, and marginal allocation of time for individuals. We will delve into more details about marginal thinking in Chapter 4.

[3] The inspiration for this story came from Honest Tea, a bottled organic tea company that Barry founded with one of his students (Seth Goldman) and later sold to The Coca Cola Company.

[4] Karen is also a coauthor, along with Jack Donahue, of Richard’s 2021 book The Dragon, the Eagle and the Private Sector. This collaboration reflects the principle of capitalizing on complementarities, which is discussed in Chapter 4. The book addresses public-private collaboration in China and the United States. Karen is fluent in Chinese and highly knowledgeable about China. Richard and Jack know little about China and virtually no Chinese. Karen’s unique skills made the book possible.

[5] Cynthia Haven, “Stanford Economist: How Do We ‘Get off this Path of Deficits as Far as the Eye Can See?’” August 2, 2011. https://news.stanford.edu/news/2011/august/shoven-debt-qanda-080211.html.

[6] Jonathan K. Nelson and Richard J. Zeckhauser, The Patron’s Payoff: Conspicuous Commissions in Italian Renaissance Art (Princeton: Princeton University Press, 2004). Their second book, The Risky Business of Renaissance Art, will be published in 2022.

[7] Of course, as Yinglan recognizes, worst-case analyses should not be your guideline. Rather, they often enable you to avoid extra work. If you come up short with the most difficult stress test, you do not simply quit. You then deal with the challenging problem of conjecturing the probability distribution of possible outcomes, and weighing each by its consequences. See Chapters 2 and 3 for more on this.

[8] The two books are John D. Donahue and Richard J. Zeckhauser, Collaborative Governance : Private Roles for Public Goals in Turbulent Times (Princeton: Princeton University Press, 2011) and John D. Donahue, Richard Zeckhauser, and Karen Eggleston The Dragon, the Eagle, and the Private Sector : Public-private Collaboration in China and the United States (Cambridge ; New York, NY: Cambridge University Press, 2020).

[9] KISS is a design principle noted by the U.S. Navy in 1960 that states that most systems work best if they are kept simple rather than made complicated; therefore, simplicity should be a key goal in design, and unnecessary complexity should be avoided. The phrase has been associated with aircraft engineer Kelly Johnson. Occam’s razor or law of parsimony is the problem-solving principle that “entities should not be multiplied without necessity” or more simply, “the simplest explanation is usually the right one.” The idea is attributed to English Franciscan friar William of Ockham (c. 1287–1347), a scholastic philosopher and theologian who used a preference for simplicity to defend the idea of divine miracles. This philosophical razor advocates that when presented with competing hypotheses about the same prediction, one should select the solution with the fewest assumptions. This is not meant to be a way of choosing between hypotheses that make different predictions.

[10] Richard often refers to his mentor Howard Raiffa as the “Johnny Appleseed” of game theory and decision analysis, given how many people Raiffa influenced to work on these fields. Johnny Appleseed was an American pioneer nurseryman who introduced apple trees to several midwestern states in the United States. He became an American legend due to his kind, generous ways, his leadership in conservation, and the symbolic importance he attributed to apples.

[11] Arrow’s prize-winning work demonstrated the existence of market-clearing equilibria. That they exist is hardly a surprise to any shopper. But Arrow saw that plausible conditions, readily grasped by all, could assure its existence. He worked reverse magic, showing that outcomes that would seem feasible and desirable were not always possible, in his famed work on his Impossibility Theorem. This profound theorem showed that five reasonable goals for representative government, where individuals vote to produce a group decision, could not be assured to be satisfied.

[12] In 2015, a computer beat Fan Hui, the European champion of Go, a 2,500-year-old board game. This was seen as a big achievement in artificial intelligence, given that Go is known to be extremely complex, which had made it difficult for computers to master. See https://www.scientificamerican.com/article/computer-beats-go-champion-for-first-time/.

[13] In this case, solving the model yields the insight that the ratio of good trips (which he calls “peaches”) to bad trips (which he calls “lemons”) is the fraction of drivers who accept all rides. At this stage, he also applied the going-to-extreme maxim from earlier in this chapter: “As I think more about this, it makes sense: if everyone is picky, the ratio of peaches to lemons = 0, which means it’s all lemons (because they always get kicked back to the pool of riders). If peaches/lemons = 1, the trips are equally likely, which makes sense, because no lemons get kicked back when no one is picky. Note that here, I’m going to the extreme cases of all picky or no picky drivers.”

[14] Christopher Avery, Andrew Fairbanks, and Richard Zeckhauser, The Early Admissions Game: Joining the Elite (Cambridge: Harvard University Press, 2004).

[15] This does not mean that all models need to be simple. Richard says, “Appropriate simplification is the great art of modeling.” As Alexandre Ziegler, Richard’s coauthor and Director of the Center for Portfolio Management at the University of Zurich, reflects, “As one would expect from Richard, this sentence contains much more than its eight words would suggest. Its first two words alone embody the balancing act required in most modeling exercises. Too much simplification, and the model will omit key drivers of the phenomenon under study. Too little simplification, and the model will likely be unsolvable. Thus, while the maxim is formulated conservatively in terms of art, it is about much more than art. Appropriate simplification is what modeling is all about, and the maxim provides a useful criterion to judge a model. It is a great guide when reading papers and listening to seminar or conference presentations.”

[16] In the decision theory literature, this is known as the two-armed bandit problem. There are two different one-armed bandit machines. These are gambling machines where you insert tokens, pull a handle, and get a payoff if you are lucky. The payoff distributions from the two machines differ and are unknown. You are given 1,000 tokens to play. You will get to keep all the winnings. You must decide as you go along which machine to play. In the early going, when you have very little experience, unless its performance is much worse, you should not just abandon one machine and stick with the other.

[17] This analogue can be used to illustrate more complex concepts in program evaluation. For example, when trying to understand why comparing people who were offered the microfinance program with a group of people with similar observable characteristics might be problematic, we can say that this is like comparing two groups of apples that look alike but might not be alike. For example, maybe there are differences between the two groups that we don’t get to observe, such as whether they have rotten spots inside or how well they taste.

[18] RCTs are not without critics. We briefly talk about RCTs and some of their limitations in Chapter 2.

[19] There is a strong behavioral explanation behind this finding, another central focus of Richard’s research and teaching. At Harvard, support for Clinton over Trump was overwhelming. People make probability judgments primarily on what they can observe, as opposed to evidence from prediction markets or national polls. This phenomenon, first identified by psychologists Amos Tversky and Daniel Kahneman, is an example of the availability heuristic. Events easily brought to mind are termed “available.” Available events play a significantly disproportionate role in affecting judgments, such as probability assessments like the one in this Trump-Clinton example.

[20] The expression “certainty is an illusion” is borrowed from Gerd Gigerenzer, Risk Savvy: How to Make Good Decisions (East Rutherford: Penguin Publishing Group, 2014).

[21] Anna Dreber, David G. Rand, Nils Wernerfelt, Justin R. Garcia, Miguel G. Vilar, J. Koji Lum & Richard J. Zeckhauser. “Dopamine and Risk Choices in Different Domains: Findings Among Serious Tournament Bridge Players.” Journal of Risk and Uncertainty, 43, no.1 (2011):19-38.

[22] Interestingly, how likely you are to observe an outcome you would have classified as unlikely varies by context. Consider the world of tennis. Because a few players tend to dominate the sport, you would be surprised if an unseeded player were to win Wimbledon. Indeed, this has happened only twice in the history of the men’s tournament (Becker in 1985 and Ivanisevic in 2001). Contrast this with people who get elected president of the United States. At 30 years old, most people look very unlikely to become president of the United States. There are a few people (like Joe Biden or George Bush, for example) who might have had at age 30 a higher than a 1 in 100 chance to become president one day. But there are dozens, perhaps hundreds, more people with odds much lower than 1 in 100. When we add all these small odds together, they might combine to account for, say, more than a 50 percent chance of becoming president. Hence the likelihood that any specific one of them will become president is less than 1 in 100, but the likelihood that someone from that large group will is more likely than not. Richard employs this low-probability-but-many-players analysis to refute a common fallacy he calls the Eisenhower Phenomenon. At age twenty-one, Dwight David Eisenhower was just entering West Point, coming off two years of post-high-school work in undistinguished positions. He graduated West Point at age twenty-four with an average academic record, and with disciplinary blemishes. Richard’s student, also age twenty-four, says: “In every way at age twenty-four I am more distinguished than was Eisenhower. Thus, if he became president then I should be able to become president.” The fallacy in this reasoning is that at age twenty-four Eisenhower had a tiny chance to become president, say one in 100,000. Richard’s student’s odds may be much better, say 1 in 10,000. But that hardly means that he should be able to become president.

[23] The original example comes from W. B. Gibson and M. M. Menke “Meeting the Challenge of an Age of Uncertainty,” Handling and Shipping, Presidential Issue (1973): 205-210.

[24] Incidentally, in March 2021, a large ship got stuck in the Suez Canal and blocked passage for several days. This caught the world by surprise and generated losses of billions of dollars.

[25] Atul Gawande, Complications: A surgeon's notes on an imperfect science (New York: Metropolitan Books, 2002).

[26] Richard Zeckhauser, “New Frontiers Beyond Risk and Uncertainty: Ignorance, Group Decision, and Unanticipated Themes.” in Handbook of the Economics of Risk and Uncertainty, eds. Mark Machina and Kip Viscusi (North Holland: Elsevier, 2014), xvii-xxix.

[27] See for example Richard Zeckhauser, “Investing in the Unknown and Unknowable.”, in The Known, the Unknown, and the Unknowable in Financial Risk Management, eds. Francis X. Diebold, Neil A. Doherty, Richard H. Herring (Princeton: Princeton University Press, 2010), 304-346.

[28] The Curse of the Bambino was a superstition that arose after the Boston Red Sox failed to win the World Series for many years after 1918. After the 1919 season, the Red Sox sold star player Babe Ruth (nicknamed “The Bambino”) for $125,000 to the New York Yankees. Before then, the Red Sox had been one of the most successful professional baseball franchises, and after the sale they went without a title for nearly a century.

[29] Sources: Sarah Janssen, The World Almanac and Book of Facts 2020, (World Almanac Books, 2020). Central Intelligence Agency, The World Factbook, n.d.https://www.cia.gov/the-world-factbook. and airmilescalculator.com.

[30] In the United States, the president and vice president are not elected directly by citizens. Instead, they’re chosen by “electors” through a process called the Electoral College. The Electoral College consists of 538 electors. A majority of 270 electoral votes is required to elect the President. For most states, state laws mandate the candidate who receives most votes shall receive all of that state’s electors. For example, in the 2016 Presidential Election, Hillary Clinton got 8,753,788 votes in California vs. Donald Trump’s 4,483,810. This meant that Hillary Clinton won the state of California and got all of California’s 55 Electoral College votes.

[31] That probability is 29 percent, namely 15 chances (relevant cards) out of 52 (total cards). People are much less likely to be surprised or biased when the underlying probability is objective. Unfortunately, as Richard often reminds us, objective probabilities rarely play a significant role in the real world.

[32] This does not mean that to the appropriate use of subjective probabilities just means pulling numbers from the air. You can judge whether someone is good at assessing subjective probabilities by assessing their record over many estimations or predictions. For example, if a weather forecaster predicted a 10% chance of rain on 30 days last year, we would expect rain to have occurred in 3 of these days. If rain occurred in say 12 of those days, that suggests that the forecaster is not very accurate at predicting rain. The forecaster can fall short on another dimension: failing to provide useful information. Take the extreme case of a forecaster in a locale where it rains on 15% of the days. Every day she states: “The probability of rain today is 15%.” She will predict the percent of days experiencing rain over the year reasonably accurately but will provide no useful information.

[33] Alyssa Brown, “There’s No Lucky Percentage of Guests Who Will Attend a Wedding.” July 03, 2018. https://www.marthastewart.com/7923646/ percentage-how-many-guests-attend-wedding.

[34] This is a term used in Bayesian Statistics, a field of statistics in which a probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. Bayesian statistical methods use Bayes’ theorem to compute and update probabilities after obtaining new data. The field is named after Reverend Thomas Bayes, who formulated a specific case of Bayes’ theorem in a paper published in 1763.

[35] Some media outlets did this kind of updating during the November 2020 election night. See for example: https://www.nytimes.com/2020/ 02/03/upshot/needle-iowa-caucuses-faq.html.

[36] Note that it would be informative if Trump lost in New York by a much greater (or lower) margin of votes than expected. If he lost by a lower margin than expected, you might interpret this as a signal that he will do better than you expected on election night, and you would increase your prior probability that he will win the overall election.

[37] An intriguing example involves purchasing an asset, such as a parcel of land, from a well-informed party. You think the land is undervalued at the proposed price, but the selling party is willing to part with it at that price. You draw the appropriate inference that she does not think it is undervalued.

[38] Steve Levitt is a world-renowned economist, perhaps most famous in the world outside of economics for publishing the New York Times best-selling book titled Freakonomics : A Rogue Economist Explores the Hidden Side of Everything. (London: Penguin. 2006.) coauthored with Stephen J. Dubner.

[39] A greater willingness to kill projects also enables one to take on projects with great potential payoffs but only modest chances for success. They can be terminated swiftly when early feedback indicates that their prospects are bleak. Therefore, wasteful expenditures can be avoided.

[40] You might also end your trip-based information gathering if you absolutely love the first college that you visit. You would do so if you judge that the costs of continuing to gather information about other colleges are larger than the likely benefits you will get in terms of informing your decision of what college to choose.

[41] William Samuelson and Richard Zeckhauser, “Status Quo Bias in Decision-Making,” Journal of Risk and Uncertainty 1, no.1 (1988): 7-59.

[42] Maryaline Catillon and Richard Zeckhauser, “Unleash the data on COVID-19”; http://www.modaotonline.com/sites/default/files/centers/mrcbg/Catillon-Zeckhauser 10-18-20.pdf

[43] Such a study, for example, might look at the experience that some major hospitals had had with treatments for particular types of patients that differed across hospitals.

[44] Richard Neustadt, a former Harvard Kennedy School professor, was an expert on the presidency, and a famed advisor to presidents.

[45] The use of nudges in policy contexts has spread widely in recent years. Many governments, including the United States, have established nudge units at the federal level. See the famed books Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011), Richard Thaler, Misbehaving: The Making of Behavioral Economics (New York: W.W. Norton & Company, 2015), Cass Sunstein and Richard Thaler, Nudge: Improving Decisions About Health, Wealth, and Happiness (New Haven: Yale University Press, 2008), and Dan Ariely, Predictably Irrational: The Hidden Forces that Shape our Decisions (New York: Harper Perennial, 2010).

[46] See Anthony Patt and Richard Zeckhauser, “Action Bias and Environmental Decisions.” Journal of Risk and Uncertainty 21, no. 1 (2000): 45-72.

[47] See for example Annie Duke, Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts (Penguin Publishing Group, 2018).

[48] Inspired by Annie Duke, How to Decide: Simple Tools for Making Better Choices (Penguin Publishing Group, 2020).

[49] In fact, point-of-purchase insurance, as when you buy an appliance or an airplane ticket, tends to be substantially overpriced. Only the product seller can offer the insurance. The seller is a monopolist and can be expected to price high.

[50] Classic references on decision trees include Howard Raiffa, Decision Analysis : Introductory Lectures on Choices under Uncertainty (Random House, 1968). Edith Stokey and Richard Zeckhauser, A Primer for Policy Analysis (New York: W. W. Norton & Company, 1978). A less technical treatment of decision trees can be found in John S. Hammond, Howard Raiffa, and Ralph L. Keeney, Smart Choices: A Practical Guide to Making Better Decisions (Boston: Harvard Business Review Press, 2015).

[51] Many people routinely insure new appliance purchases, though the insurance prices are many times the expected payoffs. Collision insurance for automobiles is more complex than appliance insurance, since there are varying levels of deductible. For a typical owner, higher deductible policies are more fairly priced, since bad drivers tend to opt for low deductibles. Just pondering initial outlay relative to returns is a strong first step when thinking about any decision involving money. Decision-tree-thinking will make you do it.

[52] An extremely poor strategy, but frequently taken, is to put off a decision just because it is difficult, even when you do not expect new information to arrive.

[53] You might object by arguing that you did not check the weather forecast, and hence the decision could have been a good one with the information the person had at the time. Checking the weather forecast is quick and almost costless nowadays, so it is reasonable to assume that this information was (or at least could have been) easily available at the time the decision was made.

[54] To use a golf analogy, you should assess outcomes relative to par. David could foresee no happy outcome, whatever he decided, even with luck. A doctor is treating a patient with a cancer-ridden organ. Life without the organ will be unpleasant, but not removing the organ will significantly shorten life expectancy. Neither decision leads to a happy outcome. However, one will be superior in expectation based on the patient’s situation and preferences.

[55] Interestingly, not long after Sally’s transplant, BMTs were discontinued as a treatment for breast cancer. Recently, however, long-term follow-up studies have shown a 15 percent survival gain from the high-dose chemotherapy, with rescue via a BMT, consistent with Sally’s oncologists’ conjecture of expected better survival over the long term. Source: Charles H. Weaver, “High-Dose Chemotherapy & Stem Cell Transplant for Breast Cancer.” February 3, 2020, https://news.cancerconnect.com/breast-cancer/high-dose-chemotherapy-stem-cell-transplant-for-breast-cancer.

[56] See for example: Mark Spranca, Elisa Minsk, and Jonathan Baron, “Omission and Commission in Judgment and Choice.” Journal of Experimental Social Psychology 27, no. 1 (1991): 76-105. Available at https://www.sas.upenn.edu/~baron/papers.htm/oc.html

[57] The common expression “Better the devil you know than the devil you don’t know” is illustrative, and our discussion would say misguided. It simply places more weight on errors of commission – switching to a new potential harm – than those of omission.

[58] Incidentally, omission bias helps to promote status quo bias, the subject of Richard’s most widely cited paper (with Bill Samuelson).

[59] See Thomas Gilovich and Victoria Husted Medvec, “The Experience of Regret: What, When, and Why.” Psychological Review 102, no.2 (1995): 379–395. https://doi.org/10.1037/0033-295X.102.2.379

[60] Similarly, the U.S. government tried to avoid an error of commission when it hesitated to recommend that vaccinated people could stop wearing masks outdoors (where the risk of transmission was very low). In this case, the consequences of the error of commission (an unlikely surge in COVID cases among vaccinated people) seem less severe than the consequences of the error of omission (that is, vaccinated people wearing masks promoted vaccine hesitancy among people who did not see the point of getting vaccinated if they would still have to wear masks).

[61] It is harder to argue conclusively, but Alice’s insight about limiting information acquisition also applies if there is a slight chance the new data will change a decision. The cost of acquisition and analysis must be weighed against the expected gains if a decision is altered. Note also, if there is only a slight chance that new information will tip a decision from A to B, it is likely that the advantage of B over A in that case will also be modest. As an example, you called the three closest stores for the price of a specific dishwasher. They differed by less than $10. Calling the fourth closest store could turn up a better price, but the expected gain is probably not worth the modest hassle of the phone call.

[62] Richard admits he might not have said “bring both set of tools,” but later used it to polish the story for pedagogic purposes.

[63] This example uses just one goal (maximize total participant recreation hours) for ease of exposition. A real-life example is likely to be more complicated but the principle of looking at units of output per unit of input still applies.

[64] The quality-adjusted life year is a generic measure of disease burden, including both the quality and the quantity of life lived. One QALY equates to one year in perfect health. QALY scores range from 1 (perfect health) to 0 (dead). Richard coauthored an article in 1976 (“Where now for saving lives?” by Richard Zeckhauser and Don Shepard, Law and Contemporary Problems, 1976), where this term was first used. It is now a widely used measure in economic evaluation to assess the value of health interventions.

[65] If there are benefits other than time saved that are hard to quantify, we could ask ourselves how large these benefits must be for us to change our view that the rapid transit extension is not worth it.

[66] This example also illustrates the importance of looking at sub-groups when trying to understand the overall behavior of a population. See the maxim about heterogeneity later in this chapter.

[67] Richard reports on his intake interview in 1962 for a summer job with Alain Enthoven, his boss who headed the Systems Analysis (“Whiz Kids”) office in the Pentagon. After a stimulating, conceptually based discussion of Richard’s undergraduate thesis. Enthoven (a deep-thinking economist who went on to a distinguished career as a Stanford professor) concluded: “Do you know how to add, subtract, multiply and divide? Do you understand marginal analysis?” Richard answered “yes.” “That is what you’ll need to do well here.” The fabled Whiz Kids operation only added one tool – namely marginal analysis – to what kids everywhere learn in primary school. Knowing how and when to apply those tools, and to what variables, was the key to its success. Making marginal analysis part of your way of thinking is likely to have huge payoffs in your life.

[68] Note that thinking in terms of averages may not help much. To illustrate, suppose you run a successful retail store. Others take care of ordering, bookkeeping, etc. Your job is to operate the store. You are open 6 days a week, from 9 a.m. to 7 p.m. For 60 hours work you reap $3,000 in profits, which means $50 on average per hour. You are happy with this average rate per hour. However, not many people come in from 6 p.m. to 7 p.m., and most of them would come earlier if necessary. You run a two-week experiment changing hours to 9 a.m. to 6 p.m., and profits dropped by 4 percent. Thus, those last 6 hours were only yielding you $120, or $20/hour. Not worth your time. You decide to close your store at 6 p.m. Moreover, over the long run, you will probably win back some disappointed customers who came at 6:30 p.m. during the experiment.

[69] See for example: Iris Bohnet, Fiona Greig, Benedikt Herrmann and Richard Zeckhauser, “Betrayal Aversion: Evidence from Brazil, China, Oman, Switzerland, Turkey, and the United States.” The American Economic Review 98, no. 1 (2008): 294-310.

[70] Source: Centers for Medicare & Medicaid Services. “National Health Accounts Historical.” (n.d). https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.

[71] Source: Halsted R. Holman, “The Relation of the Chronic Disease Epidemic to the Health Care Crisis.” ACR Open Rheumatology 2, no 3 (2020): 167-173.

[72] Relatively inexpensive drugs to control high blood pressure and cholesterol played a significant role in reducing the toll of chronic diseases since the 1950s. Richard has argued that given their extraordinary benefits per dollar of cost, the appropriate copayment for these drugs should be 0.

[73] Sarah Kaplan and Chris Mooney, “Genetic Data Show How a Single Superspreading Event Sent Coronavirus Across Massachusetts - and the Nation.” The Washington Post, August 25, 2020.

[74] Source: The Heritage Foundation. “COVID-19 Deaths by Age.” February 17, 2021, https://www.heritage.org/data-visualizations/public-health/ covid-19-deaths-by-age/.

[75] There is also heterogeneity among infected individuals’ ability to spread the disease, say due to population density and poor social distancing, and in their likelihood of suffering severe illness once infected. Why did we not include these aspects of heterogeneity from the outset? We were following the maxims to avoid complexity and start with a simple case (see Chapter 1), namely just one type of heterogeneity, the risk of getting infected.

[76] One of Richard’s articles on this topic is: Donald Shepard and Richard Zeckhauser, “Long-Term Effects of Interventions to Improve Survival in Mixed Populations.” Journal of Chronic Diseases 33.no.7 (1980): 413-433.

[77] Christopher Avery, Andrew Fairbanks, and Richard J. Zeckhauser, The Early Admissions Game (Cambridge: Harvard University Press, 2005). Richard’s scholarly work has leveraged this heterogeneity principle in other settings as well, including behavior of savings and asset prices, society’s aggregate time preferences, and choice of optimal health policies when the underlying population is heterogeneous.

[78] See for example Harold O. Levy, “Colleges Should Abandon Early Admissions”, Inside Higher Ed, January 12, 2017. https://www. insidehighered.com/views/2017/01/12/discrimination-inherent-early-admissions-programs-essay

[79] Based on Siyu Ma, Yair Tauman, and Richard Zeckhauser. "Deterrence Games and the Disruption of Information." HKS Faculty Research Working Paper Series RWP20-026, (August 2020).

[80] In economics, the classic example is that two inputs – capital (K) and labor (L) – are used to produce some output. The production function is f(K,L). A positive cross partial derivative (i.e., fKL>0) means that the marginal productivity of labor (how much extra output you get with an extra unit of labor) will increase the higher the quantity of capital. Similarly, it also means that the marginal productivity of capital (how much extra output you get with an extra unit of capital) will increase the higher the quantity of labor. In countries like the United States, the productivity of labor is high, partly as a result of being complemented by so much capital. For example, a worker with a backhoe is much more productive than an equally skilled person with a shovel. Capital and labor typically have positive cross partial derivatives.

[81] Bertrand Russell, The Conquest of Happiness (New York: H. Liveright, 1930).

[82] Among other examples, see Richard Zeckhauser, “Howard Raiffa and Our Responsibility to Rationality.” Negotiation Journal 33, no. 4 (October 2017): 329-332. and “Distinguished Fellow: Reflections on Thomas Schelling,” Journal of Economic Perspectives 3 no.2 (Spring 1989): 153-164.

[83] Of course, the outcome may lead you to reconsider your assessment of the probability of the phone breaking (that is, maybe you are not as careful with your phone as you thought you were). More generally, one element of effective decision-making is assessing probabilities accurately. If a decision comes out favorably, it is more likely that you assessed probabilities effectively. If assessment is part of the decision, as it almost always is, then the quality of the outcome should influence your judgment of the quality of a decision. Richard calls this hindsight insight.

[84] Richard Zeckhauser, “Howard Raiffa and Our Responsibility to Rationality.” Negotiation Journal 33, no. 4 (October 2017): 329-332.

[85] Hal R. Arkes and Catherine Blumer, “The psychology of sunk costs.” Organizational Behavior and Human Decision Processes, 35, no.1(1985): 124-140.

[86] Richard, thinking back on that conversation, observed that pairing that meal with a special occasion, such as an important birthday or anniversary, will make it more memorable, hence a greater source of reflective pleasure in the future.

[87] Refusing to reveal information about one-dimensional matters, such as cost, reveals that the worst case must apply. Job interviewers at Harvard Law School cannot ask about an applicant’s grades. But they can ask: “Anything else you wish to tell me?” Then students can reveal their class standing. Obviously, a student in the top 10% would reveal that. So students in the second decile would also want to reveal their class standing (since they would now be the highest ranked students whose grades the employer does not know). The unraveling would continue until only those with the poorest grades would stay silent. Sally understood this unraveling phenomenon and recognized that Richard would know it well. Sally was setting a precedent for concealment that was beneficial for both.

Table of Contents

Foreword

Acknowledgments

Introduction

Chapter 1 – Thinking Straight

Maxim 1 – When you are having trouble getting your thinking straight, go to an extreme case

Maxim 2 – When you are having trouble getting your thinking straight, go to a simple case

Maxim 3 – Don’t take refuge in complexity

Maxim 4 – When trying to understand a complex real-world situation, think of an everyday analogue

Chapter 2 – Tackling Uncertainty

Maxim 5 – The world is much more uncertain than you think

Maxim 6 – Think probabilistically about the world

Maxim 7 – Uncertainty is the friend of the status quo

Chapter 3 – Making Decisions

Maxim 8 – Good decisions sometimes have poor outcomes

Maxim 9 – Some decisions have a high probability of a bad outcome

Maxim 10 – Errors of commission should be weighted the same as errors of omission

Maxim 11 – Don’t be limited by the options you have in front of you

Maxim 12 – Information is only valuable if it can change your decision

Chapter 4 – Understanding Policy

Maxim 13 – Long division is the most important tool for policy analysis

Maxim 14 – Elasticities are a powerful tool for understanding many important things in life

Maxim 15 – Heterogeneity in the population explains many phenomena

Maxim 16 – Capitalize on complementarities

Chapter 5 – Living Fully

Maxim 17 – Strive hard not to be envious – see your friend’s success as your gain

Maxim 18 – Eliminate regret

Maxim 19 – Make pleasure-enhancing decisions long in advance, to increase the utility of anticipation

Brief but important life maxims

Living with the Maxims

Appendix A – Gratitude

Appendix B – Contributors

References

Index

About Richard Zeckhauser

About the Author

Notes

Table of Contents

Foreword

Acknowledgments

Introduction

Chapter 1 – Thinking Straight

Maxim 1 – When you are having trouble getting your thinking straight, go to an extreme case

Maxim 2 – When you are having trouble getting your thinking straight, go to a simple case

Maxim 3 – Don’t take refuge in complexity

Maxim 4 – When trying to understand a complex real-world situation, think of an everyday analogue

Chapter 2 – Tackling Uncertainty

Maxim 5 – The world is much more uncertain than you think

Maxim 6 – Think probabilistically about the world

Maxim 7 – Uncertainty is the friend of the status quo

Chapter 3 – Making Decisions

Maxim 8 – Good decisions sometimes have poor outcomes

Maxim 9 – Some decisions have a high probability of a bad outcome

Maxim 10 – Errors of commission should be weighted the same as errors of omission

Maxim 11 – Don’t be limited by the options you have in front of you

Maxim 12 – Information is only valuable if it can change your decision

Chapter 4 – Understanding Policy

Maxim 13 – Long division is the most important tool for policy analysis

Maxim 14 – Elasticities are a powerful tool for understanding many important things in life

Maxim 15 – Heterogeneity in the population explains many phenomena

Maxim 16 – Capitalize on complementarities

Chapter 5 – Living Fully

Maxim 17 – Strive hard not to be envious – see your friend’s success as your gain

Maxim 18 – Eliminate regret

Maxim 19 – Make pleasure-enhancing decisions long in advance, to increase the utility of anticipation

Brief but important life maxims

Living with the Maxims

Appendix A – Gratitude

Appendix B – Contributors

References

Index

About Richard Zeckhauser

About the Author

Notes