This paper presents the results from a minimum-effort game in which individuals can observe the choices of others in real time. We find that under perfect monitoring almost all groups coordinate at the payoff-dominant equilibrium. However, when individuals can only observe the actions of their immediate neighbors in a circle network, monitoring improves neither coordination nor efficiency relative to a baseline treatment without real-time monitoring. We argue that the inefficacy of imperfect monitoring is due to information uncertainty, that is, uncertainty about the correct interpretation of a neighbor's actions. Information uncertainty prevents individuals from inferring safely that their group has managed to coordinate from the available information.

A counterexample to the notion that money is memory is provided—one that relies on incomplete information. There exists an implementable allocation for it with money that is not implementable with memory. The result arises because money conveys only a limited amount of information about past actions, which can be beneficial in settings with incomplete information.

In this paper we analyze players' long-run behavior in evolutionary coordination games with imperfect monitoring in a large population. Players can observe signals corresponding to other players' unseen actions and use the proposed simple or maximum likelihood estimation algorithm to extract information from the signals. In the simple learning process we find conditions for the risk-dominant and the non-risk-dominant equilibria to emerge alone in the long run. Furthermore, we find that the two equilibria can coexist in the long run. In contrast, the coexistence of the two equilibria is the only limit distribution under the maximum likelihood estimation learning algorithm. We also analyze the long-run equilibria of other 2x2 symmetric games under imperfect monitoring.

In this paper we study players' long-run behaviors in evolutionary coordination games with imperfect monitoring. In each time period, signals corresponding to players' underlying actions, instead of the actions themselves, are observed. A boundedly rational quasi-Bayesian learning process is proposed to extract information from the realized signals. We find that players' long-run behaviors depend not only on the correlations between actions and signals, but on the initial probabilities of risk-dominant and non-risk-dominant equilibria being chosen. The conditions under which risk-dominant equilibrium, non-risk-dominant equilibrium, and the coexistence of both equilibria emerges in the long run are shown. In some situations, the number of limiting distributions grows unboundedly as the population size grows to infinity.