Sách Behavioral Game Theory: Thinking, Learning, and Teaching - Colin F. Camerer - Teck-Hua Ho - Juin Kua

Behavioral Game Theory: Thinking, Learning, and Teaching


Colin F. Camerer - Teck-Hua Ho - Juin Kuan Chong

1 Introduction
Game theory is a mathematical system for analyzing and predicting how humans behave
in strategic situations. Standard equilibrium analyses assume all players: 1) form beliefs
based on analysis of what others might do (strategic thinking); 2) choose a best response
given those beliefs (optimization); 3) adjust best responses and beliefs until they are
mutually consistent (equilibrium).
It is widely-accepted that not every player behaves rationally in complex situations,
so assumptions (1) and (2) are sometimes violated. For explaining consumer choices
and other decisions, rationality may still be an adequate approximation even if a modest
percentage of players violate the theory. But game theory is di®erent. Players' fates
are intertwined. The presence of players who do not think strategically or optimize can
therefore change what rational players should do. As a result, what a population of
players is likely to do when some are not thinking strategically and optimizing can only
be predicted by an analysis which uses the tools of (1)-(3) but accounts for bounded
rationality as well, preferably in a precise way.2
It is also unlikely that equilibrium (3) is reached instantaneously in one-shot games.
The idea of instant equilibration is so unnatural that perhaps an equilibrium should not
be thought of as a prediction which is vulnerable to falsi¯cation at all. Instead, it should
be thought of as the limiting outcome of an unspeci¯ed learning or evolutionary process
that unfolds over time.3 In this view, equilibrium is the end of the story of how strategic
thinking, optimization, and equilibration (or learning) work, not the beginning (one-shot)
or the middle (equilibration).
This paper has three goals. First we develop an index of bounded rationality which
measures players' steps of thinking and uses one parameter to specify how heterogeneous a
population of players is. Coupled with best response, this index makes a unique prediction
of behavior in any one-shot game. Second, we develop a learning algorithm (called
Functional Experience-Weighted Attraction Learning (fEWA)) to compute the path of


equilibration. The algorithm generalizes both ¯ctitious play and reinforcement models
and has shown greater empirical predictive power than those models in many games
(adjusting for complexity, of course). Consequently, fEWA can serve as an empirical
device for ¯nding the behavioral resting point as a function of the initial conditions.
Third, we show how the index of bounded rationality and the learning algorithm can be
used to understand repeated game behaviors such as reputation building and strategic
teaching.
Our approach is guided by three stylistic principles: Precision; generality; and empirical
discipline. The ¯rst two are standard desiderata in game theory; the third is a
cornerstone in experimental economics.
Precision: Because game theory predictions are sharp, it is not hard to spot likely
deviations and counterexamples. 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 distill the deviations into an alternative
theory that is similarly precise as standard theory and can be widely applied.
We favor speci¯cations that use one or two free parameters to express crucial elements
of behavioral °exibility because people are di®erent. We also prefer to let data, rather
than our intuition, specify parameter values.4
Generality: Much of the power of equilibrium analyses, and their widespread use,
comes from the fact that the same principles can be applied to many di®erent games,
using the universal language of mathematics. Widespread use of the language creates a
dialogue that sharpens theory and cumulates worldwide knowhow. Behavioral models of
games are also meant to be general, in the sense that the same models can be applied
to many games with minimal customization. The insistence on generality is common in
economics, but is not universal. Many researchers in psychology believe that behavior
is so context-speci¯c that it is impossible to have a common theory that applies to all
contexts. Our view is that we can't know whether general theories fail until they are
broadly applied. Showing that customized models of di®erent games ¯t well does not
mean there isn't a general theory waiting to be discovered that is even better.