Expedient and Monotone Learning Rules
This paper considers learning rules for environments in which little prior and feedback information is available to the decision-maker. Two properties of such learning rules, absolute expediency and monotonicity, are studied. The paper provides some necessary and some sufficient conditions for these properties. A number of examples show that there is quite a large variety of learning rules which have these properties. It is also shown that all learning rules that have these properties are, in some sense, related to replicator dynamics of evolutionary game theory.
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