This paper aspires to fill a conspicuous gap in the literature regarding learning in games—the absence of empirical … verification of learning rules involving pattern recognition. Weighted fictitious play is extended to detect two-period patterns in … adequately represent the complete learning process of game players. Additionally, standard weighted fictitious play models are …

In this paper, we introduce two new learning models: action-sampling learning and impulse-matching learning. These two … models, together with the models of self-tuning EWA and reinforcement learning, are applied to 12 different 2×2 games and … are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the …

learning. In repeat-choice situations, such learning brings value. If probabilities appear favorable (unfavorable), a choice …, decision makers often prove to be blind to the learning opportunities offered by uncertain probabilities. They forgo …, many make choices contrary to learning. Priming with optimal strategies offers little improvement. Such decision makers …

How do people learn? We assess, in a model-free manner, subjectsʼ belief dynamics in a two-armed bandit learning … unsuccessful choices, than “update up” following successful choices. The profits from following the estimated learning and decision …-rational Bayesian learning model, but comparable to the profits from alternative non-Bayesian learning models, including reinforcement …

Rule learning posits that decision makers, rather than choosing over actions, choose over behavioral rules with …. Past works on rule learning have shown that when playing a single game over a number of rounds, players can learn to form … sophisticated beliefs about others. Here we are interested in learning that occurs between games where the set of actions is not …