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

MBA students are known for being an ambitious lot, and taking every step possible to ensure their success in the business world. It may come as somewhat of a shock, then, that one of the most significant factors in determining not only their long-term earnings, but even the very industry they work in, is essentially out of their control. Paul Oyer of the Stanford Business School shows in recent research that the performance of the stock market during a business student’s studies has a significant impact on their likelihood of going into investment banking, which in turn has a substantial and long-term impact on their earnings. Those graduating during a period of favorable economic conditions enter the workforce with higher salaries, and these effects persist for a surprisingly long time.

To provide a stark illustration, Oyer compares two sets of Stanford Business School classes: the classes of 1986 and 1987 (who graduated prior to the market crash of 1987), and the classes of 1988 and 1989 (who graduated post-crash). In the twenty years since graduation, the pre-crash classes have earned an estimated $85-250 million more than the post-crash classes, which works out to several hundred thousand dollars per student. Even a decade after entering the workforce, the class of 1988 was still making substantially less than students who graduated just a year ahead of them from Stanford.

Another recent study found similar effects for undergraduates as well. Philip Oreopoulos, Till Von Wachter, and Andrew Heisz found that it can take a full decade for the wages of those who graduated during a recession to catchup to those of their peers who graduated during better economic conditions.

The bottom line? cross your fingers, prospective MBAs! Market conditions at graduation have a significant impact on lifetime earnings, but there’s little anyone can do to effect or time the macroeconomic environment. But if you’re serious about managing your future, Oyer does have one recommendation: risk averse students should short a broad stock market index when they begin their studies, as a hedge against a market downturn hurting future wages. If the market heads south, you may be left with less lucrative job offers, but atleast you’ll have profited from your market hedge.

Read more:

• Hello, Young Workers: One Way to Reach the Top Is to Start There [nytimes.com registration required]
• The Making of an Investment Banker: Macroeconomic Shocks, Career Choice, and Lifetime Income, by Paul Oyer
• The Short- and Long-Term Career Effects of Graduating in a Recession: Hysteresis and Heterogeneity in the Market for College Graduates, by Philip Oreopoulos, Till Von Wachter, and Andrew Heisz