Feature-weighted categorized play across symmetric games
研究了在连续面对不同一次性博弈时，智能体如何通过将相似博弈分类并采用相同行动来学习，提出了一个基于博弈特征和个体动机的分类模型，并在大量2×2对称博弈实验数据上验证了其优于标准学习模型。
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 <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content"><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.