Year: 2022
Type: article
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 2×2 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.
Source: Experimental Economics
Authors: Marco LiCalzi, Roland Mühlenbernd
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,780
OpenAlex ID: W4214720522