## Evolving past Bruce Bueno de Mesquita’s predictions at TED

September 17, 2011 3 Comments

Originally, today’s post was going to be about “The evolution of compassion” by Robert Wright, but a September 3rd Economist article caught my attention. So we will save compassion for another week, and instead quickly talk about prediction human behavior. The Economist discusses several academics and firms that specialize in using game theory to predicting negotiation behavior and quantify how it can be influenced. The article included a companion video highlighting Bruce Bueno de Mesquita, but I decided to include an older TED talk “Bruce Bueno de Mesquita predicts Iran’s future” instead:

I like the discussion of game theoretic predictions in the first part of the video. I want to concentrate on that part and side-step the specific application to Iranian politics at the end of the video.

Bruce Bueno de Mesquita clearly comes from a political science background, and unfortunately concentrates on very old game theory. However, we know from EGT that many of these classical assumptions are unjustified. In particular, Bueno de Mesquita says there are only two exceptions to rationality: 2-year olds and schizophrenics. Of course, this means we have to ignore classical results such as those of Shafir & Tversky [ST92,TS92] and basically the whole field of neuroeconomics.

The speaker also tries to build a case for modeling by scaring us with factorials and prescribing magic power to computers. He gives the examples of being able to keep track of all possible interactions of 5 people in your head, but not of 10. However, as we know from basic complexity theory, working with problems that grow in difficulty as factorials is not possible for computers either. In particular, if Bueno de Mesquita simple argument held, then for 20 people, all the computing power on Earth would not be enough to run his simulations. Thus, the real reason behind the need for computational modeling (or game theory software as the Economist article calls it) is not one of simply considering all interactions. You still need great ideas and beautiful models to cut down the set of possible interactions to ones that can be tractably analyzed.

Of course, the way we actually overcome this ridiculous explosion in complexity is by using problem-specific knowledge to constrain our possible influences and interactions. My favorite graphic of the talk is the influence graph of the president of the United States. Not because it is a new idea, but because understanding the function and design of such networks is central to modern EGT. A classic example is the work on selection amplifiers [LHN05] which showed the weaknesses of hierarchical structures such as then president’s influence network for promoting good ideas.

Although Bueno de Mesquita accuracy of predictions is impressive (although the 90% he cites is also misleading; note that simply predicting the opposite of the expert opinion would yield similar results), his methods are outdated. If we want to take game theoretic prediction to the next step, we must consider realistic bounds on the rationality of agents, reasonably simple feedback and update rules, and computationally tractable equilibria concepts. All of these are more likely to come from work on questions like the evolution of cooperation than think-tanks bigger and bigger ‘game theory software’.

I tried to keep my comments brief, so that you can enjoy your weekend and the video. Please leave your thoughts and analysis of the talk and article in the comments. Do you think evolutionary game theory can improve the predictive power of these classic models? Why or why not?

### References

[LHN05] Lieberman, E., Hauert, C., and Nowak, M.A. (2005). Evolutionary dynamics on graphs. Nature, 433, 312-316.

[ST92] Shafir, E., & Tversky, A. (1992). Thinking through uncertainty: Nonconsequential reasoning and choice. Cognitive Psychology, 24, 449-474.

[TS92] Tversky, A., & Shafir, E. (1992). The disjunction effect in choice under uncertainty. Psychological Science, 3 , 305-309.