A Reason For Unreason: Returns-Based Beliefs In Game Theory

Players cooperate in experiments more than game theory would predict. The authors introduce the 'Returns-based beliefs' approach: the expected returns of a particular strategy in proportion to total expected returns of all strategies. Using a decision analytic solution concept, Luce's (1959) probabilistic choice model, and 'Hyperpriors' for ambiguity in players' co-operability, the approach explains empirical observations in various classes of games including the Prisoner's and Traveler's Dilemmas. Testing the closeness of fit of this model on Selten and Chmura (2008) data for completely mixed 2 ? 2 games shows that with loss aversion, returns-based beliefs explain the data better than other equilibrium concepts.

Provided by: University of Cambridge Topic: Big Data Date Added: Sep 2010 Format: PDF

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