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

Those of you who have read the introduction to this site know that the term micromotives comes from Thomas Schelling’s book Micromotives and Macrobehavior. This second term, macrobehavior, denotes the oftentimes surprising and complex group behavior that can emerge from even relatively simple patterns of individual behavior. Columbia sociology professor Duncan Watts is leading practitioner of this approach to the study of social science, and has popularized the dynamics of social networks with his books Six Degrees: The Science of a Connected Age and Small Worlds: The Dynamics of Networks between Order and Randomness. He leads the Collective Dynamics Group at Columbia.

From their overview:

The Collective Dynamics Group is a dedicated research effort, led by Professor Duncan Watts, the unifying theme of which is the application of modern mathematical and computational techniques to problems relevant to the social sciences. Examples of current projects include the structure and evolution of social networks, the dynamics of disease epidemics and cultural fads, the role of social information in financial markets, and the use of the Internet as a tool for social science research. The group, which meets weekly, consists of graduate students and post-doctoral researchers from mathematics, sociology, and economics.

Several of the group’s research projects should have important ramifications for those in the business world.

Interpersonal Influence, Contagion, and Collective Decision Making:

People constantly influence each other in all facets of life. Social contagion is the spreading of ideas, rumors, and behavior through a population via interpersonal influences. Collective decisions are generated by a social contagion process which is (often greatly) augmented by the machinery of mass media. Consequently, understanding interpersonal influence is crucial to understanding the behavior of both individuals and groups. Our projects on influence are divided between online experiments and conceptual mathematical models. We have developed a generalized model of contagion that reconciles and extends previously disparate models of contagion from the social and biological sciences. We are interested in standard biological contagion alone since the collective behavior of people is almost always important in how diseases spread. For example, motivated by observations of the SARS outbreak in 2002, we are exploring the effect on a contagion’s spread due to people moving between subpopulations with some frequency. We are also currently developing an online experiment which will explore interpersonal influence in `cultural markets’ (markets for cultural products, such a books, music, celebrity, etc) and how individual behaviors aggregate to produce collective outcomes.

Social Search, Collective Problem Solving, and Organizational Robustness:

The ability to solve problems collectively is central to the long term stability of any group of people, from a small business marketing a new product to nations confronting global economic crises. Real world collective problem solving is inherently a decentralized, distributed activity. When faced with a novel, ambiguous problem defined at the group level, individuals must determine how to coordinate their actions with others by exchanging ideas, knowledge, and questions. A key aspect of this coordination is search. How do invididuals find others who can at least partially answer or rephrase poorly specified problems? We approach this issue of what we call social search by building conceptual models and online experiments. For example, we have constructed a simple, sociologically plausible model of social networks that shows them to be searchable under general conditions. This is the so-called Small World hypothesis, the notion that two random individuals can find a way to connect to each other through a small number of intermediary contacts. For the past few years, we have been running a global small world experiment, where people send email to friends and acquaintances trying to find a sequence of contacts leading to `target’ individuals. In related work, we model modern organizations as reinforced hierarchical networks of individuals searching for information bearers among their peers. Being effective at collective problem solving leads to a tradeoff between specialized efficiency and flexible robustness.

I haven’t had the opportunity yet to read through any of their findings in detail yet, but I’m looking forwad to it.