The basic framework for More Than You Know is the idea of consilience among disciplines. Can you talk briefly about that concept?
The one investor who probably best embodies that approach is Charlie Munger, Warren Buffett’s partner at Berkshire Hathaway. Munger’s basic point is the best way to solve problems is to have in your mind a latticework of mental models from various disciplines. He likes to evoke the phrase, “To a person with a hammer, every problem looks like a nail.” In other words, if you have one worldview or one perspective, you’re going to try to solve all problems the same way, and that’s not likely to be very effective.
Consilience is about trying to draw the best ideas from various disciplines so that when you face a business problem or an investing problem, you have the best tools to bring to bear to try to solve that intelligently. A lot of that comes down to finding an apt metaphor or analogy to help think through a problem effectively.
How did this book project come about?
I was on vacation with my family in the summer of 2000, and my wife’s grandfather handed me a copy of Time magazine with an article about Tiger Woods. It described how Tiger Woods won the Masters golf tournament in 1997 by a record margin of 12 strokes. Surprisingly, Woods reviewed his performance and concluded his swing was really awful. So he embarked on a multiyear process to revamp his swing, and as he was doing this his performance really deteriorated. However, he was saying all along, “I’m a better golfer.” And then he burst back onto the scene in 1999–2000 and won an extraordinary number of tournaments.
When I’m reading this article what’s flashing in my mind is the idea of fitness landscapes, which is a concept evolutionary biologists use to describe the fitness of species. The best way to describe a fitness landscape is to envision a mountain range. The metaphor says a species tries to climb up the hills to get to a peak. However, when you get to a local peak you may look out and see that there’s a higher peak somewhere else. So to improve your fitness, you have to go down into the valley and then back up the hill.
That idea of fitness landscapes has a lot of applicability for corporations. You can see many examples of companies that try to get off the local peak onto a higher peak. One example today would be Kodak, where their traditional business is in decline but the digital business is growing rapidly. So they have to go down one side and back up the other side in order to take advantage of that transition.
You give several examples in the book of investing insights from the natural world. Can you talk about a couple of those?
One of my favorite chapter titles is “Guppy Love.” The basic story is that female guppies have a tendency to prefer bright-colored males. However, when experimenters set them up in a contrived situation where some females observed other females choosing dull-colored males, the observing females overrode their own hard wiring and chose the dull-colored males as well. So imitation is clearly something that happens in the animal world.
It’s a very short leap to see how imitation is important in the human world. We’re a very social and imitative species. So what does that mean for the world of business and investing? Well, often when you have a lot of diverse points of view, you get results that are pretty efficient. However, when everyone starts to do the same thing — everyone gets bullish on a stock or bearish on a stock or they get into a particular business fad or trend — it often gets carried to extremes. And those extremes are inflection points that are interesting from a business or investing perspective.
Another favorite example is the ant colony. If you study an ant colony, you will find it has a life cycle — it’s robust, it’s adaptive. However, if you ask any individual ant what’s going on, they have no clue. They’re working with local information and local interaction. I think there’s a very clear parallel to markets. How do markets get to be efficient? The answer is it’s an interaction among a lot of diverse investors. The aggregation mechanism to bring the information together is the stock exchange, and then what emerges from that is the stock market.
The important takeaway is it’s impossible to understand the market by interviewing individual investors because each investor only has a partial piece of the picture. It’s the aggregation that allows the full picture to emerge. What the ant colonies teach us is that in markets, cause and effect are very difficult to pin down. Sometimes we like to think that the experts on TV or the pundits quoted in the Wall Street Journal know what’s going on. They’re really just ants.
There are several places in the book where you debunk conventional wisdom or offer insights that are counterintuitive to most people. Could you give an example of that?
Most of us like to be right more than wrong. The main reason for that is loss aversion, which is a concept in psychology that says for a dollar loss and a dollar gain, we suffer twice as much for the loss as we enjoy the gain. You often hear, “If we’re right about the stock just 51 percent of the time, we’re going to be in really good shape.” And that has nothing to do with how investing works. Real-world investing is about the frequency of correctness and the magnitude of correctness, and both of those in combination are what matter.
You can envision a scenario where you have four stocks in your portfolio; three of them go down a bit, the fourth goes up a lot. That would be a very bad frequency of success but obviously a very good portfolio. So as people think about their portfolios, it’s important to recognize the role of both frequency and magnitude. And that sort of rubs against common sense and conventional wisdom.
In the book’s conclusion you mention some of the things the experts still don’t understand about investing. Can you talk about the directions for future research?
If you look at the world of finance, there are many, many open questions. For example, we don’t really understand how capital markets get to efficiency. There are some theories that are widely used in the world of finance, including mean-variance and no-arbitrage assumptions. I suspect these traditional ideas will eventually be superseded by this idea of complex adaptive systems, or the wisdom of crowds.
I think that the recent developments in neuroscience and decision making are absolutely fantastic. Another area that is really intriguing are the statistical regularities, like the power laws, that have come out of the study of physical systems, like earthquakes. In biological science, we know things like body mass and metabolic rate also follow a power law, a scaling property, and we have ways to explain those phenomena reasonably well. We see many of those same power laws in social sciences, yet we really have no causal mechanisms. So we don’t know why city sizes follow a power law or why the sizes of corporations follow a power law.
The last idea I’d mention is the flight simulator for the mind. One of the challenging things about investing is it’s very difficult to get timely and clear-cut feedback. If you’re a handicapper at the racetrack or you’re a weather forecaster, you get feedback pretty immediately on the decisions that you make, and that helps you calibrate and improve your decision-making process. When you purchase or sell a stock, you really don’t know in a timely fashion whether that decision was a good or a bad one. So an interesting question is whether we could create some sort of artificial environment that allows people to get better feedback on their decisions.
Michael Mauboussin is chief investment strategist at Legg Mason Capital Management and adjunct professor of finance and economics at Columbia Business School.