Michael Mauboussin pictureMichael J. Mauboussin is Chief Investment Strategist at Legg Mason Capital Management. Prior to joining LMCM, Mr. Mauboussin was a Managing Director and Chief U.S. Investment Strategist at Credit Suisse. Mr. Mauboussin joined Credit Suisse in 1992 as a packaged food industry analyst. He is a former president of the Consumer Analyst Group of New York and was repeatedly named to Institutional Investor’s All-America Research Team and The Wall Street Journal All-Star survey in the food industry group.

Mr. Mauboussin is the author of Think Twice: Harnessing the Power of Counterintuition (Harvard Business Press, 2009) and More 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). Mr. Mauboussin and Alfred Rappaport co-authored Expectations Investing: Reading Stock Prices for Better Returns (Harvard Business School Press, 2001).

Mr. Mauboussin 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, Mr. Mauboussin received the Dean’s Award for Teaching Excellence. BusinessWeek’s Guide to the Best Business Schools (2001) highlighted Mr. Mauboussin as one of the school’s “Outstanding Faculty,” a distinction received by only seven professors.

Mr. Mauboussin earned an A.B. from Georgetown University. He is also affiliated with the Santa Fe Institute, a leading center for multi-disciplinary research in complex systems theory, and is on the board of directors of Sermo, an online community for physicians.

Link to podcast here-http://www.thoughtleaderforum.com/default.asp?P=909655&S=945705

Full transcript below:

Michael Mauboussin: Good morning, everybody. If you could grab a seat, we’ll get going this morning. For those
of you I haven’t met or didn’t meet last night, my name is Michael Mauboussin. I’m the Chief Investment Strategist
at Legg Mason Capital Management, and again, on behalf of all my colleagues at LMCM, I want to wish you a
warm welcome to the 2011 Thought Leader Forum. And for those of you who joined us last night, and I think that
was the majority, I hope you had a wonderful evening, and we’re very excited for the lineup today.
I’d like to do a few things this morning before I hand it off to our speakers. First, I want to offer some very high-level
thoughts on prediction and perception. Second, I want to provide a very quick road map for the talks today. Finally,
I want to discuss the forum itself and how you can contribute to its success.
So let me start with some high-level thoughts on prediction and perception. And there are four very quick points
that I’d like to make. The first is what I’ll call structural versus specific. There are certain regularities that we see
that allow us to make structural predictions relatively easily, but that make specific predictions very hard. A classic
example which you can all relate to would be earthquakes.
So we know what the distribution of earthquakes looks like in terms of their frequency and their release of energy,
but it’s very difficult to pinpoint precisely where any particular earthquake is going to happen. It turns out this
relationship applies to an amazingly wide array of areas, including the rank and size of companies, terrorist acts
and mortality, and the frequency in use of words.
Here’s a picture of city sizes in the United States. As you can see, it’s a basic relationship between rank and
population on a log-log scale, and this basic relationship has been consistent over about a 200-year period. The
point is we can’t predict precisely how big any particular city will be in the future, but we have a pretty good sense
of what that distribution is going to look like.
This, by the way, also applies to what people call black swans. If we actually know what the distribution looks like,
it’s hard to say that any particular event, even if it’s an extreme event, is fairly called a black swan. In fact, Nassim
Taleb himself calls these gray swans. So the point is there are a lot of important systems where we can make
pretty good structural predictions, but we’re very hard-pressed to make accurate, specific predictions.
The second thought here is the idea of prediction boundaries. In other words, know where you can and cannot
predict well. And one way to illuminate this is through what we call the luck-skill continuum. On the left, you see
activities that are shaped solely by luck, on the right, shaped solely by skill. The closer an activity lies to the luck
side of the continuum, the harder it is to predict. This includes outcomes in social, political, and economic events,
and most definitely includes, for example, stock market forecasts.
On the flipside, if the activity is mostly skill, predictions tend to be quite good and quite accurate. So when you pose
the questions “are experts good?” or “are experts accurate?” the answer depends to a great degree on the type of
activity they’re predicting, as with anything else.
The third one, which we got last night in spades, is this notion of attention. And in order to predict something, you
generally have to pay attention to the situation specifically. And the problem is that we have limited attention, so we
can only focus on a few things at a time, and if we focus very intently on one aspect of a scene, for example, it will
suppress our attention to other aspects of it.

This slide is literally a screenshot from what Apollo showed last night. This is before and after his magic trick, and,
of course, this is that inattention blindness that we saw. It’s an inability to pick up differences and changes. So you
can see all the changes that went from one scene to the next.
By the way, I don’t know if he did mention this, but if you go home and tell your friends and family about Apollo’s
thing last night, he’s got a special that’s going to be on TV this weekend. It is Sunday night on the National
Geographic Channel at 8:00 p.m. Eastern time. So I’ll give you a little plug for his show, so you can check that out
and watch that with your family.
Viewed side-by-side, obviously the changes here are very clear, but as I was watching that video, my attention was
very, very focused on that trick and I completely missed what was changing in the aggregate background.
The final thought I want to share is something I’m going to call reductive bias. There may be better terms for this,
but the idea is when you’re asked a question about a complex issue, our minds naturally will answer a much
simpler question. Effectively, we answer the complex with the simple. In many cases that’s not a problem, but it can
get us into trouble.
One classic example is markets. Broadly, we tend to characterize markets as much simpler than they really are.
For example, you often hear about the language of normal bell-shaped distributions. We talk a lot about things like
means and standard deviations, and even things like alpha and beta. These fall out of the language of normal
distributions. But, as we know, real market returns are quite different from

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