We conducted controlled laboratory experiments to investigate how humans adapt the decision rule (DR) they use in repeated strategic interactions in light of new information becoming available. We do so by providing -- along different paths -- more and more information over time, so as to...

We choose between alternatives without being fully informed about the rewards from different courses of action. In making our decisions, we use our own past experience and the experience of others. So the ways in which we interact - our social network - can influence our choices. These choices...

We experimentally investigate coordination games in which cognition plays an important role, i.e., where outcomes are affected by the agents level of understanding of the game and the beliefs they form about each others' understanding. We ask whether and when repeated exposure permits agents to...

Motivated by the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model, leaving endogenous the accuracy of their choice. In some games, increased accuracy is conducive to unstable best-response...

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold...

This paper explores the extent to which people learn in repeated games without feedback, and the extent to which this learning transfers to new games. Current theories of learning model learning as adjustment in behavior in response to feedback about outcomes and payoffs and largely ignore the...

How well do various learning models predict the dynamics of the population distribution of play in a variety of games? We measure and compare the in-sample and out-of-sample prediction performance of seven action-reinforcement learning models as well as Rule Learning for symmetric normal-form...

We test the population rule learning model for symmetric normal-form games, and strongly reject: (i) no rule learning, (ii) no diversity, and (iii) no sophisticated evidence. Further, trembles and herd behavior decline and level-2 behavior increases over time