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- W4214720522 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 $$2 times 2$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models." @default.
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- W4214720522 date "2022-02-28" @default.
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- W4214720522 title "Feature-weighted categorized play across symmetric games" @default.
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- W4214720522 doi "https://doi.org/10.1007/s10683-021-09742-7" @default.
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