In this paper we introduce four new learning models: impulse balance learning, impulse matching learning, action-sampling learning, and payof-sampling learning. With this models and together with the models of self-tuning EWA learning and reinforcement learning, we conduct simulations over 12...

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from...

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 2 games and their results are compared with the results from...

In many social dilemmas, individuals tend to generate a situation with low payoffs instead of a system optimum (tragedy of the commons). Is the routing of traffic a similar problem? In order to address this question, we present experimental results on humans playing a route choice game in a...

Theoretical models have had difficulties to account, at the same time, for the most important stylized facts observed in experiments of the Voluntary Contribution Mechanism. A recent approach tackling that gap is Arifovic and Ledyard (2012), which implements social preferences in tandem with an...

We experimentally implement a dynamic public-good problem, where the public good in question is the dynamically evolving information about agents' common state of the world. Subjects' behavior is consistent with free-riding because of strategic concerns. We also find that subjects adopt more...