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Abstract

We consider the situation in which individuals in a finite population must repeatedly choose an action yielding an uncertain payoff. Between choices, each individual may observe the performance of one other individual. We search for rules of behavior with limited memory that increase expected pay-off s for any underlying payoff distribution. It is shown that the rule that outperforms all other rules with this property is the one that specifies imita-tion of the action of an individual that performed better with a probability proportional to how much better she performed. When each individual uses this best rule, the aggregate population behavior can be approximated by the replicator dynamic.

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Paper provided by ESRC Centre on Economics Learning and Social Evolution in its series ELSE working papers with number 028.

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Handle: RePEc:els:esrcls:028

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Web page: http://else.econ.ucl.ac.uk/
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Related research

Keywords: social learning; bounded rationality; imitation; multi-armed bandit; random matching; payoff increasing; replicator dynamic.;

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Find related papers by JEL classification:

• C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
• C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other

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