Discovery and Dynamic Consistency

Takashi Hayashi
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Abstract

We provide a simple model in which the decision maker successively discovers elements of the world and expands the state space over time. We propose a dynamic consistency condition that after a new discovery the preference ranking should remain unchanged over acts to which the discovery is irrelevant. Together with other natural axioms, it characterizes a model in which the decision maker's belief evolves over time in order that the marginal distribution of a new belief induced over the old state space coincides with the old belief. It is extended in order to encompass both discovery and learning events, and we characterize the model with an additional property that the decision maker's belief updating follows Bayes' rule when she learns events.
发现和动态一致性
我们提供了一个简单的模型,在这个模型中,决策者连续地发现世界的元素,并随着时间的推移扩展状态空间。我们提出了一个动态一致性条件,即在新发现之后,偏好排序应该保持不变,而不是与发现无关的行为。与其他自然公理一起,它描述了一个模型,在这个模型中,决策者的信念随着时间的推移而演变,以便在旧状态空间上引起的新信念的边际分布与旧信念一致。为了包含发现事件和学习事件,我们对模型进行了扩展,并且我们用决策者在学习事件时的信念更新遵循贝叶斯规则的附加属性来描述模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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