标题： 动态随机主观期望效用 显示英文标题

标题： Dynamic Random Subjective Expected Utility

摘要： 动态随机主观期望效用（DR-SEU）允许对来自代理人或代理人种群的选择数据进行建模，这些代理人的关于客观支付相关状态的信念和口味都可以随机演变。 我们的可观测变量，增强型随机选择函数（aSCF），与决策理论中的以往工作不同，可以直接检验代理人的信念是否反映了在他们的私人信息条件下的真实数据生成过程，以及识别可能不正确的信念。 我们给出了代理人在静态和动态设置中满足该模型的公理化特征。 我们考虑了代理人对客观状态演变有正确信念的情况，以及她的信念不正确但未预见的情况是不可能的情况。 我们还区分了动态模型的两个子变体，在静态设置中它们是一致的：演变的SEU，其中精明代理人的效用根据贝尔曼方程演变，渐进学习，其中代理人正在学习她的口味。 我们证明了关于信念错误程度以及关于口味学习速度的简单自然比较静态结果。 在线附录中的辅助结果从技术和概念的角度扩展了菜单选择和随机选择文献中以前的决策理论工作。 显示英文摘要

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.