Feature-weighted categorized play across symmetric games

Marco LiCalzi and Roland Mühlenbernd ()
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Roland Mühlenbernd: Nicolaus Copernicus University Toruń

Experimental Economics, 2022, vol. 25, issue 3, No 11, 1052-1078

Abstract: Abstract Experimental game theory studies the behavior of agents who face a stream of one-shot games as a form of learning. Most literature focuses on a single recurring identical game. This paper embeds single-game learning in a broader perspective, where learning can take place across similar games. We posit that agents categorize games into a few classes and tend to play the same action within a class. The agent’s categories are generated by combining game features (payoffs) and individual motives. An individual categorization is experience-based, and may change over time. We demonstrate our approach by testing a robust (parameter-free) model over a large body of independent experimental evidence over $$2 \times 2$$ 2 × 2 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.

Keywords: Learning in games; Categorization; Similarity (search for similar items in EconPapers)
JEL-codes: C72 C92 D83 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10683-021-09742-7

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