We present a model of adaptive economic agents that are k periods forward looking. Agents in our model are randomly matched to interact in finitely repeated games. They form beliefs by relying on their past experience in the same situation (after the same recent history) and then best respond to...

In a novel experimental design we study public good games with dynamic interdependencies. Each agent's income at the end of a period serves as her endowment in the following period. In this setting growth and inequality arise endogenously allowing us to address new questions regarding their...

This paper provides new evidence on gender bias in teaching evaluations. We exploit a quasi-experimental dataset of 19,952 student evaluations of university faculty in a context where students are randomly allocated to female or male instructors. Despite the fact that neither students' grades...

In this paper (reinforcement) learning of decision makers that face many different games is studied. As learning separately for all games can be too costly (require too much reasoning resources) agents are assumed to partition the set of all games into analogy classes. Partitions of higher...

We develop a simple model to study the coevolution of interaction structures and action choices in prisoners' dilemma games. Agents are boundedly rational and choose both actions and interaction partners via payoff-biased imitation. The dynamics of imitation and exclusion yields polymorphic...

We present and analyze a local interaction model where agents play a bilateral prisoner's dilemma game with their neighbors. Agents learn about behavior through payoff-biased imitation of their interaction neighbors (and possibly some agents beyond this set). We find that the [Eshel, I., L....

In this paper we provide a framework to reason about limited awareness of the action space in finitely repeated games. Our framework is rich enough to capture the full strategic aspect of limited awareness in a dynamic setting, taking into account the possibility that agents might want to reveal...