Michael Mauboussin – What Being Wrong Can Teach Us About Being Right
Michael Mauboussin is the author of The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing (Harvard Business Review Press, 2012), Think Twice: Harnessing the Power of Counterintuition (Harvard Business Press, 2009) and More Than You Know: Finding Financial Wisdom in Unconventional Places-Updated and Expanded (New York: Columbia Business School Publishing, 2008). More Than You Know was named one of “The 100 Best Business Books of All Time” by 800-CEO-READ, one of the best business books by BusinessWeek (2006) and best economics book by Strategy+Business (2006). He is also co-author, with Alfred Rappaport, of Expectations Investing: Reading Stock Prices for Better Returns (Harvard Business School Press, 2001).
Visit his site at: michaelmauboussin.com/
Information and circumstances change constantly in the worlds of investing and business. As a consequence, we have to constantly think about what we believe, how well those beliefs reflect the world, and what tools we can use to sharpen our decisions. Because we operate in a world where we can succeed only with a certain probability, we have to learn from our mistakes. Hence, the theme for the Thought Leader Forum in 2016 was “What Being Wrong Can Teach You About Being Right.”
This year’s forum featured a venture capitalist, a computer scientist, an economist who focuses on decisions, and a leading sports executive. Each explored an area of how our thinking and decisions can come up short of the ideal. We heard about how assumptions deeply shape how you assess a company’s potential and how well-intentioned incentive systems can go awry. There was an exploration of how computers, through machine learning, can serve as a new source of knowledge, complementing evolution, experience, and culture. Notwithstanding the potential benefits of augmenting our intelligence through computers, we discussed why we humans have an aversion to algorithms and how to overcome it. And then there is the issue of the old and new guard: how can we convince some who have been successful in an old regime to accept new and better ways of doing things?
The theme of “what being wrong can teach you about being right” has lessons to teach us about naïve realism, man versus machine, and the role of change. Naïve realism is the sense that our view of the world is the correct one. But when confronted with reality, we need to revisit our beliefs.
For example, when we face someone who has beliefs different than ours, we tend to adopt one of three attitudes so that we can perpetuate our position. First, we might assume the other person is merely unequipped with the facts, so simple sharing will swing them to our side. Next, we believe that even with the facts, the other person lacks the mental capacity to see the consequences as we do. We can write off those people. Finally, there may be people who understand the facts as we do but turn their backs on what we perceive to be the truth. We categorize those people as evil.
Machine learning and artificial intelligence are again hot terms. Google DeepMind’s AlphaGo program, which beat a human champion in the board game of Go much sooner than most experts had predicted, is emblematic. The question is how we divide the cognitive work between machines and human judgment. If you are in the information business—and the chances are good this is true if you are reading this—then you must consider carefully how you might integrate computers and humans.
All of this implies change, something we are loathe to do. Changing your mind takes time, effort, and humility. This is especially pertinent when you have been successful in your domain. Strategy in sports is a good analogy. There are traditional ways to do things, and often those ways are effective. But more careful analysis has revealed strategies that fly in the face of conventional wisdom that are clearly better. Defensive shifts in baseball are but one example. Convincing the old guard to change—and eventually, we are all part of the old guard—is a difficult hurdle.
The following transcripts not only document the proceedings, they also provide insights into how you can improve your own ability to learn from mistakes and improve your odds of being right in the future. Bill Gurley suggested that the high valuations for some technology startups (so-called “unicorns’) and the low level of liquidity is a balance that is not tenable. Pedro Domingos explained how computers might be able to complete tasks that are out of the grasp of humans. Cade Massey showed that we don’t readily embrace algorithms but that there is a way to overcome this aversion and improve decisions. And Paul DePodesta suggested that the bias against change has less to do with the game you are playing and more to do with how we humans think.
Michael Mauboussin - What Being Wrong Can Teach Us About Being Right
Good morning. For those of you whom I haven’t met, my name is Michael Mauboussin, and I am head of Global Financial Strategies at Credit Suisse. On behalf of all of my colleagues at Credit Suisse, I want to wish you a warm welcome to the 2016 Thought Leader Forum. For those who joined us last night, I hope you had a wonderful evening. We are very excited about our lineup for today.
I’d like to do a couple of things this morning before I hand it off to our speakers. First I want to highlight the levels at which you might consider today’s discussion about the idea of how being wrong can inform you about being right. I then want to discuss the forum itself, including what you can do to contribute to its success.
You might listen to today’s discussion at three different levels. Some of the points will span multiple levels, but these are some of the ideas that we’ll hear about throughout the day.
The first relates to the ideas of naïve realism. In psychology, this is the human tendency to believe that we see the world around us objectively and that people who disagree with us must be uninformed, irrational, or biased.
The second is man versus machine. This is a theme that is popping up everywhere. What are algorithms good at and what are humans good at? How do we use algorithms to augment our performance? Why do we struggle to defer to algorithms in many settings?
The final is the issue of change. Organizational inertia is a huge issue in many firms. How can firms keep up? How do we integrate new information? What is the psychology of change?
Let’s start with naïve realism. Here’s a cartoon I love: as you can see, there are two armies preparing to square off, and the quote is: “There can be no peace until they renounce their Rabbit God and accept our Duck God.” The picture shows that the flags of