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 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.
Highlights
Experimental game theory studies patterns of behavior for agents playing games
We demonstrate our approach with a simple model that simultaneously describes aggregate play over a large set of independent experiments from the literature, each based on a stream of identical 2 × 2 symmetric games
We evaluate the performance of the Feature‐weighted categorized (FWC) model by means of a comparative test against three major learning algorithms: experience weighted attraction (EWA), fictitious play (FP), and reinforcement learning (RL)
Summary
Experimental game theory studies patterns of behavior for agents playing games. In particular, the dynamics and evolution of choices from players who face a stream of one-shot games is usually viewed as an instance of learning: agents refine their play as they gather more experience. Similarity-based reasoning is invoked to explain experimental evidence on the emergence of “conventions” (Rankin et al, 2000; Van Huyck and Stahl 2018) These contributions are consistent with agents learning within the bounds of their coarse understanding or their similarity judgments, but do not tackle the issue of learning categories or similarities for games. Recent works expand agents’ search over wider sets of categorizations: LiCalzi and Mühlenbernd (2019) study the evolution of binary interval partitions over a space of games that can be mapped to a one-dimensional interval; Daskalova and Vriend (2021) model the categorization of the player’s own actions in a one-shot game under reinforcement learning, and test its fit with the experimental evidence. Haruvy and Stahl (2012) provide some experimental evidence that more sophisticated players tend to move from non-belief-based to belief-based rules of play
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