题   目：Euclidean Properties of Bayesian Updating

嘉   宾：Kyle  Chauvin，Assistant Professor，NYU Shanghai

主持人：LIM XI ZHI，助理教授，上海交通大学

时   间：2023年4月19日（周三）12:40-14:10

地   点：上海交通大学（徐汇校区）安泰经济与管理学院B207

内容简介：

This  paper introduces a simple, automaton-like model to analyze Bayesian updating  and non-Bayesian heuristics. The model, called a learning rule, combines a set  of belief states with a collection of transition functions over the beliefs.  The primary result is an axiomatic characterization of Bayesian learning  rules, in which beliefs are distributions over a latent state and transitions  follow Bayes’ rule. The second main result characterizes how Bayesian  belief-transitions can be represented as vectors in Euclidean space, equipped  with geometric notions of magnitude and direction. Applications of the  learning rule framework include facilitating belief elicitation from  laboratory subjects.

演讲人简介：

Kyle  Chauvin is an assistant professor of economics at NYU Shanghai. His research  in microeconomic theory and behavioral economics investigates the consequences  of imperfect learning for communication, persuasion, discrimination, and  social networks.

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