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Categorization in Games: A Bias-Variance Perspective

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Abstract

We develop a framework for categorization in games, applicable both to multi-stage games of complete information and static games of incomplete information. Players use categories to form coarse beliefs about their opponents' behavior. Players best-respond given these beliefs, as in analogy-based expectations equilibria. Categories are related to strategies via the requirements that categories contain a sufficient amount of observations and exhibit sufficient within-category similarity, in line with the bias-variance trade-off. We apply our framework to classic games including the chainstore game and adverse selection games, thereby suggesting novel predictions for these applications.

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  • Jehiel, Philippe & Mohlin, Erik, 2025. "Categorization in Games: A Bias-Variance Perspective," Working Papers 2025:7, Lund University, Department of Economics.
    • Handle: RePEc:hhs:lunewp:2025_007
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    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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