摘要翻译：
动态随机主观期望效用（DR-SEU）允许对从一个或一群agent观察到的选择数据进行建模，这些agent对客观收益相关状态和口味的信念都可以随机演化。我们的可观察的，增广随机选择函数(aSCF)允许，与以往的决策理论的工作不同，直接测试主体的信念是否反映了真实的数据生成过程，条件是他们的私人信息，以及识别可能不正确的信念。我们给出了agent在静态和动态环境下满足模型的公理刻画。我们研究了当代理人对客观状态的演化有正确信念时的情况，以及当她的信念不正确但不可预见的偶发事件是不可能的情况。我们还区分了动态模型的两个子变量，它们在静态环境中是一致的：进化的SEU，在这里复杂的agent的效用根据Bellman方程进化；渐进的学习，在这里agent正在学习她的口味。我们证明了简单和自然的比较静力学结果的程度，不正确的信念，以及在速度学习味觉。在线附录中包含的辅助结果从技术和概念的角度扩展了先前在菜单选择和随机选择文献中的决策理论工作。
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英文标题：
《Dynamic Random Subjective Expected Utility》
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作者：
Jetlir Duraj
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
  Dynamic Random Subjective Expected Utility (DR-SEU) allows to model choice data observed from an agent or a population of agents whose beliefs about objective payoff-relevant states and tastes can both evolve stochastically. Our observable, the augmented Stochastic Choice Function (aSCF) allows, in contrast to previous work in decision theory, for a direct test of whether the agent's beliefs reflect the true data-generating process conditional on their private information as well as identification of the possibly incorrect beliefs. We give an axiomatic characterization of when an agent satisfies the model, both in a static as well as in a dynamic setting. We look at the case when the agent has correct beliefs about the evolution of objective states as well as at the case when her beliefs are incorrect but unforeseen contingencies are impossible.   We also distinguish two subvariants of the dynamic model which coincide in the static setting: Evolving SEU, where a sophisticated agent's utility evolves according to a Bellman equation and Gradual Learning, where the agent is learning about her taste. We prove easy and natural comparative statics results on the degree of belief incorrectness as well as on the speed of learning about taste.   Auxiliary results contained in the online appendix extend previous decision theory work in the menu choice and stochastic choice literature from a technical as well as a conceptual perspective.
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PDF链接：
https://arxiv.org/pdf/1808.00296