Prior symmetry, similarity-based reasoning, and endogenous categorization

• 成果类型: Article
• 署名作者: Peski, Marcin
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2010.08.006
• 发表日期: 2011
• 页码: 111-140
• 关键词: Categorization; Invariance; Merging

摘要

This paper presents a rational theory of categorization and similarity-based reasoning. I study a model of sequential learning in which the decision maker infers unknown properties of an object from information about other objects. The decision maker may use the following heuristics: divide objects into categories with similar properties and predict that a member of a category has a property if some other member of this category has this property. The environment is symmetric: the decision maker has no reason to believe that the objects and properties are a priori different. In symmetric environments, categorization is an optimal solution to an inductive inference problem. Any optimal solution looks as if the decision maker categorizes. Various experimental observations about similarity-based reasoning coincide with the optimal behavior in my model. (C) 2010 Elsevier Inc. All rights reserved.