Michael J. Mauboussin is Chief LMCM in 2004, Michael was Chief U.S. Investment Strategist at Credit Suisse. Michael joined CS in 1992 as a packaged food industry analyst, and was repeatedly named to Institutional Investor‘s All-America Research Team and The Wall Street Journal All-Star survey. He 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 Edition (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. He is also co-author, with Alfred Rappaport, of Expectations Investing: Reading Stock Prices for Better Returns (Harvard Business School Press, 2001).

Michael has been an adjunct professor of finance at Columbia Business School since 1993 and is on the faculty of the Heilbrunn Center for Graham and Dodd Investing. In 2009, Michael received the Dean’s Award for Teaching Excellence. He earned an A.B. from Georgetown University. He is also chairman of the board of trustees of the Santa Fe Institute, a leading center for multi-disciplinary research in complex systems theory.

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Morris: In Think Twice, you seem to be convinced — and I agree — that most of the worst decisions are made in haste, without sufficient information, are driven by emotion rather than by reason, and are avoidable. If so, then almost anyone can avoid making such mistakes. True?

Mauboussin: Well, it’s easier said than done. But understanding the types of situations that lead to mistakes is the first step toward managing or mitigating them.

The basic point of the book is that when faced with certain types of situations, our minds will naturally think about the problem one way when there’s a better, more correct, approach. Danny Kahneman’s two systems of the mind are one way to think about it. System 1 is your experiential system. It is fast, automatic, and difficult to train. System 2 is your analytical system. It is slow and purposeful, but malleable. Kahneman’s point is that System 1 generally throws up answers that are right, and System 2 goes along. In certain cases, for example if I ask you to multiply 2,367 times 5,981, you need to recruit your System 2 to answer the problem. You can do it, it’s just you can’t do it automatically.

Think Twice is about situations where System 1 throws up answers that are wrong. Without recruiting System 2, you’ll answer incorrectly. So the idea is to learn about these kinds of situations and have a sense of when you need to slow down and recruit System 2.

There’s a very simple example of how this works. Ask the following question:

A bat and ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost?

If you’re normal, your System 1 shouted an answer in your mind: ten cents. Most people, indeed, answer ten cents. But, of course, that’s wrong. ($0.10 + $1.10 = $1.20) The answer is five cents. Now this is elementary arithmetic, and certainly no challenge. But the structure of the question elicits a quick, incorrect response. My argument is that these kinds of situations are out there and you need to learn to deal with them.

Morris: Here’s one of several passages that caught my eye: “Smart people make poor decisions because they have the same factory settings on their mental software as the rest of us, and that software isn’t designed to cope with many of today’s problems…Beyond the problem of mental software, smart people make bad decisions because they harbor false beliefs.” What to do?

Mauboussin: I argue for three steps: prepare, recognize, and apply. Prepare means that you should learn about the situations and create a mental database of them. In each case, I attempt to clearly identify the situation and explain the science behind it. Recognize acknowledges that these possible mistakes will show up in different guises in your life. You’ll see them in your personal life, your professional life, while watching the news or taking in a ballgame. Apply is about knowing how to deal with the situations so as to slow your process and bring in System 2. Each chapter ends with some concrete action steps you can take to improve your decisions.

Morris: Here’s another. You make no claim to immunity from various cognitive mistakes and duly acknowledge, “I still fall for every one I describe in the book.” You state that your personal goal, when making a choice, “is to recognize when I enter a danger zone while trying to make a decision and to slow down when I do. Finding the proper point of view at the appropriate time is critical.” Here’s a two-part question. First, What are the defining characteristics of the “danger zone” to which you refer?

Mauboussin: A danger zone is when you’re in one of these situations and the decision at hand is consequential. So the first step is being aware of the situation. But even then it’s not a big deal unless the decision is important.

Morris: How best to determine, when approaching or entering one, whether or not a point of view is proper and the timing is right?

Mauboussin: I’m not sure there’s a great answer save being mindful of the circumstances of your decision. Let me give you an example. One of my neighbors read the book and was immediately faced with a situation that dealt with the inside versus outside view. The inside view says that when we decide, we tend to gather information about the situation, combine it with our own inputs, and decide. The outside view considers the problem as an instance of a larger reference class. It basically asks: when other people were in this situation, what happened?

It turns out that people frequently rely too much on the inside view and fail to properly consider the outside view. My neighbor’s situation was a financial proposition. The little voice in his head told him to follow the inside view, which would have entailed an investment. The outside view suggested the investment was not a good idea. He stopped, thought about it, and decided to forgo the investment. It turned out it saved him a tidy sum. There’s a good example. My neighbor was prepared to understand the issue, recognized it in context, and applied the approach to come to the correct decision.

Morris: What is the “halo effect”? Why and how specifically can it create problems?

Mauboussin: The halo effect was a term coined by a psychologist named Edward Thorndike and was popularized in recent years by Phil Rosenzweig through his excellent book, The Halo Effect. The basic idea is than when we observe success or like something, we attach impossibly good attributes to it. When we observe failure or dislike something, we see nothing but bad attributes.

For instance, Thorndike studied how officers rated their men in the military. If the officer liked a specific solider, he scored him high across all measures, including physical abilities, intellect, discipline, etc. The overall impression dominated the scores for individual attributes.

This creates problems because we tend to think successful companies or individuals are better than they really are, and fail to anticipate reversion to the mean.

Morris: As a father of four and one of the grandfathers of their ten children, I feel a constant temptation to offer advice whenever one of the older ones must make an especially important decision. In your opinion, should someone such as I “think twice” before volunteering advice? Also, to what extent (if any) will someone be willing to accept unsolicited advice from an elder?

Mauboussin: As a father of five kids, I hear you! My policy is to never give advice. I only provide recommendations. Basically, I try to point out the danger zone as I see it and try to slow down the process. I might then offer some thoughts on how to deal with the problem. By walking through the process versus simply getting to the answer, I’m hoping to be able to teach my kids to walk through the process on their own. A recommendation often falls out of the process, which may be roughly equivalent to “advice.” But I try to emphasize the process of making the decision versus solely getting to the answer.

Morris: Now please shift your attention to The Success Equation. When and why did you decide to write it?

Mauboussin: The topic of skill and luck lies at the intersection of a lot of topics I’m interested in. I dedicated a short chapter to the topic in Think Twice, which I knew only scratched the surface. I wrote a separate piece about the topic in the summer of 2010 and it drew a lot of attention, so I knew that there would be a receptive audience.

Perhaps the catalyzing event was a conference hosted by my friend, Scott E. Page, a professor at the University of Michigan. We were able to draw some top flight speakers from the worlds of academia, business, sports, and games. That was a good indicator, too, that there was demand to hear more on this topic.

Finally, there wasn’t much written on skill, luck, and what to do about it. There’s a rich literature in skill acquisition and the role of randomness. But there wasn’t a lot of ready material about how to measure these things relative to one another and to how to decide given relative contributions of skill and luck.

Morris: Were there any head-snapping revelations while writing it? Please explain.

Mauboussin: You know, the idea that has really had an impact on me is the “paradox of skill.” Head-snapping, not really, but powerful nonetheless. It says that in fields that include luck, greater skill can lead to luck having a larger contribution to results. More skill equals more luck. That doesn’t seem right.

I learned about this idea in an essay that the eminent biologist, Stephen Jay Gould, wrote about Ted Williams. In 1941, Williams had a .406 batting average for the season, a feat that has not been replicated. Gould wanted to know why.

Hitting a baseball hurled by a professional pitcher requires enormous skill. But batting averages have held in a steady range over time because pitchers and hitters have improved roughly in lockstep. So an absolute performance in skill is obscured by the lack of relative improvement of either pitchers or hitters.

Batting average includes a component of luck. A player can hit the ball well but fail to get hits because of bad luck, or can hit the ball poorly and fare well because of good luck. Gould’s insight—the paradox of skill—is that as skill increases, the standard deviation of skill narrows within the population of competitors. That means the difference between the best player and an average player trends lower over time. This happens for a few reasons, including the fact that the league draws from a larger pool of talent than it did in the past—the best players from all over the world find their way into Major League Baseball—and training, coaching, and nutritional practices have all gotten better and more uniform.

So let me say this with numbers. The average of all batting averages in MLB is generally in the range of .260 to .270. In 1941, when Williams achieved his feat, the standard deviation was .032. Today it is about .028. Saying this differently, Ted Williams had an average that was 4 standard deviations away from the average, getting him to .406. If a player were to be 4 standard deviations away from average in 2011, he would have hit .380. Awesome, but nowhere near .400.

This basic idea applies elsewhere, including in investing and business. You could say it’s the basis for the efficient market hypothesis.

Morris: To what extent (if any) does the book in final form differ significantly from what you originally envisioned?

Mauboussin: Not that much. I had a pretty good idea of what I wanted to cover. The first part talks about the concepts of skill and luck and why we’re so bad at understanding luck; the second part is about quantifying the roles of skill and luck, including the forms they take and how they change over time; and the final part tells you what you should do about it.

Morris: For those who have not as yet read the book, how do you define “skill” and “luck”?

Mauboussin: This is really important. The definition of skill is pretty much out of the dictionary—“the ability to use one’s knowledge readily in execution or performance.” In other words, you know how to do something and can perform when called upon.

Luck is trickier. I’d say that luck is present when three conditions are satisfied. First, it happens to an individual or group. Second, it can be good or bad. It need not be symmetrical, but it can be good or bad. And finally it is reasonable to believe that another outcome could have occurred.

There are lots of aphorisms involving luck. For example, “luck is where preparation meets opportunity” or “luck favors the prepared mind.” Note that while these sayings convey important and useful messages, they don’t capture luck as I’ve defined it. A cruder test of skill is what’s in your control and luck is what is out of your control. If there’s anything you can do — for example, prepare effectively — then you are exercising skill.

Morris: In the Introduction, you briefly discuss three types of information that are relevant to statistical prediction. What are the defining characteristics of each type?

Mauboussin: Let me first say that this is right out of the work of Danny Kahneman and Amos Tversky. In their famous paper, “On the Psychology of Prediction,” they said that the three types of information that are relevant to prediction are base rates, the specific evidence of the problem presented, and the weighting between the two. More formally, they called the third piece of information “expected accuracy.”

Base rates allow you to assess a large sample of what happened before. Let’s say a friend starts a new business and you want to gauge the probability of its success. The base rate would tell you what percentage of all new business initiatives succeed. If you know nothing specific, you go with the base rate as a prediction.

Specific evidence tells you the details on the incidence you are trying to predict. In this case, is the new business brilliant and differentiated? Or is it trying to crack into a crowded space?

Expected accuracy tells you how much weight you should place on the base rate versus the specific evidence. Here’s the tie in with skill and luck. When the results of an activity have a heavy dose of luck, your prediction should rely heavily on the base rate. When skill determines the outcome, you can place most of the weight on the specific evidence. This is a relatively simple heuristic and yet is violated constantly in business, sports, and markets.

Morris: Why is untangling skill and luck “an inherently tricky exercise?”

Mauboussin: There are a lot of reasons but I think the main one is that we humans have a deep-seated need to understand cause and effect. In fact, through Michael Gazzaniga’s fascinating work on split-brain patients, we know that part of the left hemisphere of your brain is dedicated to creating narratives to link cause and effect. Gazzaniga calls it “the interpreter.” In his experiments, Gazzaniga and his colleagues fed information to the right hemisphere of split brain patients. Now the right brain has limited language ability and, of course, the left brain doesn’t know about the information. But once the left brain observed an action based on that information, it immediately and seamlessly came up with a story to explain what was going on.

If you tell someone that future outcomes combine skill and luck, you will get no resistance. The problem occurs once something has happened. Your mind immediately seeks a plausible story to explain the event, and then it is at ease. But here’s the key: the interpreter doesn’t know anything about luck. Indeed, it evolved in a world where cause and effect were much more straightforward. So once something has happened, we start to get the feeling that it had to have happened that way. We mentally close the case. That leads to what psychologists call “creeping determinism,” the sense that what happened was the only thing that could have happened. That’s ridiculous, of course, but that’s how our minds work.

Morris: By what specific process can they be untangled?

Mauboussin: There are a number of analytical approaches, but probably the most intuitive answer is to look at something called true score theory. I can express the idea in an equation:

observed outcome = skill + luck

In actuality, what we’re doing is a little more technical, but this is the basic idea. So when untangling skill and luck, we need to be able to pin down two of the three variables in the equation. Well, we can observe outcomes, so we’ve got that one. Next, we can often estimate what would happen in an all-luck world. So with an estimate of luck, we can solve for skill. Now we have skill and luck untangled.

Let me give you an example from professional sports. Our observed outcome would be the win-loss records of the teams over the course of a season. Luck would simply assume that each game is settled through a coin toss, which means that win-loss records would follow a binomial distribution. And with those two components, we can estimate the role of skill.

Morris: Decades of research by Anders Ericssson and his colleagues at Florida State University suggests that developing the skills needed to achieve peak performance (whatever the field) requires about 10,000 hours of deep, deliberate practice under expert and strict supervision in combination with at least some luck. What do you think?

Mauboussin: Deliberate practice absolutely works on the skill side of the luck-skill distribution. The key is that your output — say, your piano playing — is nearly perfectly correlated to your skill. As a result, it is possible to provide very concrete and specific feedback.

Now, it turns out that the 10,000 hours figure isn’t chiseled in stone, although it is a very good average. Also, there is an intimation in many of the books on expertise and deliberate practice that innate differences don’t matter. That is not supported by the evidence. Some of us are better at some tasks than others, and that innate advantage does make a difference. But the notion of deliberate practice is very important and powerful.

Morris: You suggest that “stories can obscure statistics.” Please explain.

Mauboussin: The idea is that our minds are very good at understanding and relating to stories, and very poor at understanding and relating to statistics. So if there’s a case where the story seems to suggest one outcome and the statistics another, the story will generally carry the day.

For example, I was speaking with one of my physicians about this. And he said, “yes, here’s a machine that offers a treatment for an ailment. It works 50% of the time. But if I tell a patient that the last person who used this treatment is doing great, he’ll go for the treatment almost every time.” This goes back to Kahneman and Tversky’s discussion of prediction. The base rate is 50%. The specific case is 100%. The weighting should be heavily skewed toward the base rate, but that’s not how people decide.

Morris: What is the Luck-Skill Continuum and what is its importance?

Mauboussin: You can imagine a continuum that at one end is all-luck and no-skill, think lotteries and roulette wheels, and at the other end no-luck and all-skill such as chess or a running race. They key is to place activities along the continuum based on the relative contributions of the two.

Where an activity falls tells you a lot about how you should deal with it. For example, you shouldn’t expect persistently good or bad results on the luck side and you should expect persistence on the skill side.

Morris: What are the “many shapes of luck” and what are the defining characteristics of each?

Mauboussin: The main idea is that you can categorize luck as being largely independent, as in a coin toss or the role of a die, or as the result of a social process. Independent models work pretty well in a lot of settings. For example, you can model the hitting of a baseball player or the shooting of a basketball with such a model. Of course, athletic performance isn’t truly independent, but you capture a lot of what’s going on with such a simple model.

If a social process is at play, you get an inherent lack of predictability and massive inequality. This shows up a lot in realms like book, movie, or music sales. It turns out that it’s almost impossible to predict the commercial success of a product if it’s of decent quality to begin. Now once something has succeeded, it will continue to succeed—think of how sequels do. But initial success is nearly impossible to model.

Inequality is the other characteristic. It turns out that products of very similar quality have vastly different commercial results. Economists call this “convexity,” what the rest of us call these winner-take-all, or winner-take-most, markets.

Morris: When planning to use statistics while making an especially difficult decision, what are the most important do’s and don’ts to keep in mind?

Mauboussin: The best advice is to consider the weights you should assign to the base rate versus the specifics. The most common mistake is to overweight the circumstances surrounding the specific case.

Morris: Please cite one example of why untangling skill and luck in each of three quite different categories of human activity. First, business

Mauboussin: In business, it’s really important to remember that strategies succeed with a certain probability. So brilliant strategies can fail and so-so strategies can succeed. Process is essential.

In business, luck arises from the behavior of competitors, customers, and from changing technology. You’re not making decisions with complete information.

Morris: Next, sports

Mauboussin: Sports is by far the most trackable because we have lots of good data and the games tend to be stable. I’d say that it’s important to know the inherent level of luck in the sport. Some sports have outcomes that are close to random while others have a great deal of skill. In sports, too, statistics about players carry different levels of signal about skill. So it’s important to be choosey about which statistics you rely on.

Morris: Finally, investing

Mauboussin: Investing lies near the luck side of the luck-skill continuum primarily because investors, collectively, are very skilled at reflecting information in asset prices. They key here is to focus on the decision-making process. Some processes are better than others, and I discuss the elements of good process in the book.

Morris: What is reversion to the mean and what is it significance?

Mauboussin: Reversion to the mean says that an outcome that is far from the average will be followed by an outcome with an expected value closer to the average. Whenever there’s luck in an activity, there is reversion to the mean.

On the skill side of the luck-skill continuum, there is no reversion to the mean at all. If you are a better tennis player than me, you’ll win every time. But if you’re on the luck side, you should expect total reversion to the mean—the expected value of the next outcome is the average. So not only is reversion to the mean a really important idea, the luck-skill continuum gives you a sense of the rate of reversion to the mean.

Morris: In which respect(s) is good guesswork (as you suggest) an “art”? Please explain.

Mauboussin: While effort to quantify skill and luck has a great payoff, we should always understand that we are dealing with estimates in all but extreme cases. So I believe that understanding skill and luck is very, very helpful, and can materially improve decision making. But it has an element of art because we simply don’t have enough precision to sort luck and skill perfectly. So I use the term “art” not to suggest that you can approach the task any way you want but rather to say that there is still an element of the unknown in what we’re trying to do.

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To read Part 1, please click here.

Michael cordially invites you to check out the resources at these websites:

His website

His Amazon page

His Columbia Business School faculty page

His Santa Fe Institute page

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TAGs: Michael J. Mauboussin: Part 2 of an interview by Bob Morris, Legg Mason Capital Management, Credit Suisse, Consumer Analyst Group of New York, Institutional Investor’s All-America Research Team, Wall Street Journal All-Star, The Success Equation: Untangling Skill and Luck in Business [comma] Sports, and Investing, Harvard Business Review Press, Think Twice: Harnessing the Power of Counterintuition, Harvard Business Press, More Than You Know: Finding Financial Wisdom in Unconventional Places, Columbia Business School Publishing, BusinessWeek, strategy+business magazine, Alfred Rappaport, Expectations Investing: Reading Stock Prices for Better Returns, Harvard Business School Press, Heilbrunn Center for Graham, Dodd Investing, Georgetown University, Santa Fe Institute