The latest Mauboussin on Strategy paper from Legg Mason summarizes what we know and don’t know about assessing risk in complex environments. Mauboussin starts by reiterating Frank Knight’s crucial distinction between risk and uncertainty. Knight defined risk as a situation where we know the underlying probability distribution, but don’t know the outcome. Uncertainty, on the other hand, is a situation where not only the outcome, but even the underlying probability distribution itself is unknown to us. As Nassim Taleb argues persuasively in his book Fooled by Randomness, many people today are modeling uncertain situations using the statistical tools built for risk, which can and has lead to catastrophic errors.

Mauboussin then makes the distinction between endogenous risk, which emerges from a complex system itself, and exogenous risk, which is forced on a system from outside. System dynamics that emerge endogenously, without being precipitated by any outside event, are behind many of the interesting phenomena we see in the markets and society at large today. While endogenous risk is just beginning to be understood, it’s a critical area of research. We’ve looked at some examples of endogenous system dynamics earlier this week with Exploiting the Herd, A Case Study and Exploiting the Herd: Case Study Two. Mauboussin presents some of the common frameworks used to understand endogenous risk:

The first framework is the wisdom of crowds, which writer Jim Surowiecki laid out well a couple of years ago in his book of the same title. The basic idea is simple and somewhat counterintuitive: if you get a diverse group of people together to solve a problem, the group’s answer will typically be better than that of any individual, even an expert. The wisdom of crowds is a more common way of describing a type of complex adaptive system–the heart of the Santa Fe Institute’s work–and is an apt description of the stock market.

The key is that the crowd is only wise under certain conditions. You need agent diversity, an aggregation mechanism, and some sort of incentives. When one or more of these conditions is violated, all bets are off. In human systems, diversity is the most likely condition to be violated. When you take away diversity, the complex system can become fragile and in some cases will lead to large-scale changes. Booms and crashes are good examples of diversity breakdowns in markets. Fads and fashions also illustrate the concept. And that leads to the second framework: diffusion theory.

Technologies, ideas, and illnesses tend to diffuse following an S-curve pattern. So, for example, a new technology will start with only a few adopters, and will grow at a relatively slow rate early on. The rate then accelerates, and the technology takes off. This field has been studied in detail, and is of prime interest to epidemiologists and technologists, just to name two groups. The key point is the growth rate is not stable: it’s low to start, rises, and then slows down again. Also important is that most technologies or ideas don’t diffuse–they simply sputter out.

The final framework is network theory, or how the individual nodes in a network are connected. Network theory bears on a wide variety of phenomena, including your network of friends, transmitters on the power grid, or the spread of disease. In recent years, scientists have made major advances in understanding the nature of networks. We now know that the structure of the network is important in understanding how things get transmitted over the network.

There are two features of these frameworks worth emphasizing. First, they are non-linear. For example, in the case of the wisdom of crowds you can reduce diversity, reduce diversity, and nothing happens. Then you reduce it a bit more and the system reacts violently–the proverbial straw that broke the camel’s back. Many of you know this idea as the tipping point.

That leads to the second feature: lack of proportionality. The size of the perturbation and the outcome are not always linked. Sometimes small perturbations lead to large outcomes, and vice versa. When you combine a lack of linearity with a lack of proportionality, it’s not hard to see that predictions are difficult and cause and effect thinking is often futile.

Read more: Mauboussin on Strategy: Interdisciplinary Perspectives on Risk

Previously:

• Michael Mauboussin: How Do You Compare?
• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

Much of the process of sound decision making rests on our ability to perform appropriate comparisons. Which is a better investment: Google or Yahoo? Which is safer: flying or driving? Which business school is best? Our answers to all of these questions hinge crucially on the basis we use for comparison. Which features are really salient, and which are just noise? Are we looking at a large, objective collection of evidence, or just the recent evidence we have at hand? Are we using our instincts, and predictions of the future, or looking at statistical data from the past? Are we focusing on the ways in which competing alternatives are similar, or the ways in which they differ? What is the relevant timeframe we’re analyzing? Do we care about absolute performance, or relative performance?

Our answers to each of these questions can radically change the outcome of a decision making process, for better or for worse. In his latest Mauboussin on Strategy article, Michael Mauboussin surveys the many behavioral factors that go into forming comparisons, and offers some advice for making comparisons which are appropriate to the situation.

Read more: Mauboussin on Strategy: How Do You Compare?

Previously:

• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

Michael Mauboussin speaks with Columbia’s Ideas at Work magazine about some of the ideas in his recent book, More Than You Know: Finding Financial Wisdom in Unconventional Places.

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.

Read more: Guppies, ants and golf swings: Mental models for investors

Previously:

• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

Individual decisions can be badly thought through, and yet be successful, or exceedingly well thought through, but be unsuccessful, because the recognized possibility of failure in fact occurs. But over time, more thoughtful decision-making will lead to better overall results, and more thoughtful decision-making can be encouraged by evaluating decisions on how well they were made rather than on outcome.

– Robert Rubin, Harvard Commencement Address, 2001

Excerpted from More Than You Know, by Michael Mauboussin.

Legg Mason has just released Michael Mauboussin’s latest paper on strategy. In Common Errors in DCF Models, Mauboussin takes a look at eight common mistakes he sees analysts make when assessing the value of a company using a discounted cash flow model. It’s a great overview of best practices to use in generating the most accurate valuation possible (or the most accurate collection of possible valuations — he takes pains to point out that investing is a probabilistic undertaking, and suggests using scenario and sensitivity analysis to mitigate the risk of putting all your eggs in one valuation basket, so to speak). A great resource for anyone preparing valuations on a company!

Read more: Mauboussin on Strategy: Common Errors in DCF Models

Previously:

• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin