We analyze the evolution of behavioral rules for learning how to play a two-armed bandit. Individuals have no information about the underlying pay-off distributions and have limited memory about their own past experience. Instead they must rely on information obtained through observing the per-formance of other individuals. Evolution is modelled using the replicator dynamic with the revision behaviors as replicators. We find that evolution favors a special class of imitative rules. These so-called strictly improving rules (Schlag, 1996) are found to be neutrally stable when facing any two-armed bandit. Further emphasis is put on which rules survive when.
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Paper provided by ESRC Centre on Economics Learning and Social Evolution in its series ELSE working papers with number
029.
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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