In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action …-sampling learning, and payoff-sampling learning. With this models and together with the models of self- tuning EWA learning and … reinforcement learning, we conduct simulations over 12 different 2×2 games and compare the results with experimental data obtained …

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 … their results are compared with the results from experimental data. We test whether the models are capable of replicating …

In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action …-sampling learning, and payoff-sampling learning. With this models and together with the models of self- tuning EWA learning and … reinforcement learning, we conduct simulations over 12 different 2×2 games and compare the results with experimental data obtained …

opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their … information they consult before making their choices. We use these data to estimate learning types using finite mixture models … find that learning depends on network position. Participants in more complex environments (with more network neighbors …

This paper presents a non-Bayesian model of social learning in networks in an environment with a finite set of actions …

Recently there has been much theoretical and experimental work on learning in games. However, learning usually means … learning about the strategic behavior of opponents rather than learning about the game as such. In contrast, here we report on … an experiment designed to test whether players learn a) the relevant features of the payoff structure of a 2x2 game (e …

This paper presents an experiment on learning in repeated games, which complements the analysis of players' actual … a priori information. The econometrics hinges on a model built upon Experience Weighted Attraction learning, and the … simultaneous analysis of data on the information gathered and on actions taken by the subjects. Results suggest that learning is a …

reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show these learning algorithms detect … exploitable opportunities more sensitively than humans. Also, learning algorithms respond to detected payoff …