In this chapter on Behavioral Game Theory, the authors (Colin Camerer, Teck-Hua Ho, and Juin Kuan Chong) attempt to tackle the “hard” problem in psychology-modeling: *Making accurate, testable, predictions about human behavior.*

“Because game theory predictions are sharp, it is not hard to spot likely deviations and counter-examples. Until recently, most of the experimental literature consisted of documenting deviations (or successes) and presenting a simple model, usually specialized to the game at hand.

The hard part is to distil the deviations into an alternative theory that is as precise as standard theory and can be applied widely.”

Game theory, and especially variants of it like Behavioral and Evolutionary game theory, show the greatest promise of being able to unify the social & behavioral sciences with a mathematical/theoretical foundation — akin to what calculus does for the physical sciences.

However, in its classical form, while Game Theory proved remarkable at giving us advice about what we SHOULD do under certain circumstances, it failed to make predictions about what humans WILL do except in the rarest types of interactions.

Theorizing is good. But, without empirical “checks” it can run the risk of assuming things to be true that are simply not. *(MOST fitness and strength programs/diets are the worst examples of this.)*

“While the primary goal of behavioural game theory models is to make accurate predictions when equilibrium concepts do not, it can also circumvent two central problems in game theory: refinement and selection. Because we replace the strict best-response (optimization) assumption with stochastic better-response, all possible paths are part of a (statistical) equilibrium. As a result, there is no need to apply subgame perfection or propose belief refinements (to update beliefs after zero-probability events where Bayes’ rule is useless). Furthermore, with plausible parameter values, the thinking and learning models often solve the long-standing problem of selecting one of several Nash equilibria, in a statistical sense, because the models make a unimodal statistical prediction rather than predicting multiple modes. Therefore, while the thinking-steps model generalizes the concept of equilibrium, it can also be more precise (in a statistical sense) when equilibrium is imprecise (see Lucas, 1986).”

A particularly interesting application is in *learning*:

“Other phenomena are evidence of a process of equilibration or learning. For example, institutions for matching medical residents and medical schools, and analogous matching in college sororities and college bowl games, developed over decades and often ‘unravel’ so that high-quality matches occur before some agreed-upon date (Roth and Xing, 1994). Bidders in eBay auctions learn to bid late to hide their information about an object’s common value (Bajari and Hortacsu, 2003). Consumers learn over time what products they like (Ho and Chong, 2003). Learning in financial markets can generate excess volatility and returns predictability, which are otherwise anomalous in rational expectations models (Timmerman, 1993). We are currently studying evolution of products in a high-uncertainty environment (electronics equipment) for which thinking-steps and learning models are proving useful.”

*For more, see* Behavioral Game Theory: Thinking, Learning, Teaching, by Colin Camerer, Teck-Hua Ho, and Juin Kuan Chong

*Now go lift something heavy,*

Nick Horton