Learning in Games
Learning in Games
The chapter sketches the many forces driving strategy share dynamics in human society, ranging from diploid genetics to individual learning. It then focuses on learning and imitation in strategic interaction. The overall goal is to identify empirically the adaptation processes of humans interacting with each other. After developing parametric models of learning rules and decision rules, the chapter shows how the models can be fit to laboratory data of profit-motivated human subjects playing matrix and bimatrix games. Belief learning models have fitted parameters describing how players’ beliefs respond to new and older evidence, and how strongly actions respond to beliefs. Models mentioned include noisy best response, quantal response equilibrium, weighted fictitious play, and experience-weighted attraction.
Keywords: learning in games, noisy best response, quantal response equilibrium, weighted fictitious play, experience-weighted attraction
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