Expedient and Monotone Learning Rules
研究决策者在信息匮乏环境下使用的学习规则，分析绝对便捷性和单调性两个性质，给出必要和充分条件，并发现所有满足这些性质的学习规则都与演化博弈论中的复制者动态相关。
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 are studied: absolute expediency and monotonicity. Both require that some aspect of the decision maker's performance improves from the current period to the next. The paper provides some necessary, and some sufficient conditions for these properties. It turns out that there is a large variety of learning rules that have the properties. However, all learning rules that have these properties are related to the replicator dynamics of evolutionary game theory. For the case in which there are only two actions, it is shown that one of the absolutely expedient learning rules dominates all others. Copyright The Econometric Society 2004.