Case studies of cartels and recent theory suggest that repeated communication is key for stable cooperation in environments where signals about others' actions are noisy. However, empirically the exact role of communication is not well understood. We study cooperation under different monitoring...

We study environments in which agents are randomly matched to play a Prisoner's Dilemma, and each player observes a few of the partner's past actions against previous opponents. We depart from the existing related literature by allowing a small fraction of the population to be commitment types....

A learning rule is uncoupled if a player does not condition his strategy on the opponent's payoffs. It is radically uncoupled if a player does not condition his strategy on the opponent's actions or payoffs. We demonstrate a family of simple, radically uncoupled learning rules whose...

We study the role that the option to terminate a relationship has on cooperation in a repeated prisoner's dilemma. While cooperation is, in theory, sustainable with or without this option, we show experimentally that cooperation rates are significantly lower with the option to terminate. Rather...

We present experiments on repeated non-cooperative network formation games, based on Bala and Goyal (2000). We treat the one-way and the two-ways flow models, each for high and low link costs. The models show both multiple equilibria and coordination problems. We conduct experiments under...

Case studies of cartels and recent theory suggest that communication promotes cooperation under imperfect monitoring, where actions can only be observed with noise. We report the results of a laboratory experiment designed to answer the question how much communication is needed to sustain...

While payoff-based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which...

Algorithms play an increasingly important role in economic situations. These situations are often strategic, where the artificial intelligence may or may not be cooperative. We study the deter-minants and forms of algorithmic cooperation in the infinitely repeated prisoner’s dilemma. We run a...

Three kinds of activities happen in a network: agents link or unlink; agents act; agents observe and learn. The existing literature on network formation, on network games and on monitoring and learning in networks studies each of these in isolation. We propose a framework in which these happen...

This paper studies a novel setting in game theory: a player may learn new actions over time by observing the opponent's play. We investigate the impact of such learning behavior in the context of finitely repeated games. In contrast to related literature such as Kreps et al. (1982), we provide a...