Efficient Dynamic Coordination with Individual Learning

We study how the presence of multiple participation opportunities coupled with individual learning about payoff affects the ability of agents to coordinate efficiently in global coordination games. Two players face the option to invest irreversibly in a project in one of many rounds. The project succeeds if some underlying state variable theta is positive and both players invest, possibly asynchronously. In each round they receive informative private signals about theta, and asymptotically learn the true value of theta. Players choose in each period whether to invest or to wait for more precise information about theta. We show that with sufficiently many rounds, both players invest with arbitrarily high probability whenever investment is socially efficient, and delays in investment disappear when signals are precise. This result stands in sharp contrast to the usual static global game outcome in which players coordinate on the risk-dominant action. We provide a foundation for these results in terms of higher order beliefs.

Equilibrium Selection in Static and Dynamic Entry Games

We experimentally examine equilibrium refinements in static and dynamic binary choice games of complete information with strategic complementarities known as “entry†games. Our aim is to assess the predictive power of two different equilibrium selection principles. In static entry games, we test the theory of global games as an equilibrium selection device. This theory posits that players play games of complete information as if they were playing a related global game of incomplete information. In dynamic entry games, individuals decide not only whether to enter but also when to enter. Once entry occurs it is irreversible. The number of people who have already entered is part of the state description, and individuals can condition their decisions on that information. If the state variable does not indicate that entry is dominated, the efficient subgame perfect equilibrium prediction calls for all players to enter. Further, if there is a cost of delay, entry should occur immediately, thereby eliminating the coordination problem. This subgame perfect entry threshold in the dynamic game will generally differ from the global game threshold in static versions of the same entry game. Nevertheless, our experimental findings suggest that observed entry thresholds in both static and dynamic versions of the same entry game are surprisingly similar. The mean entry threshold in the static game lies below the global game equilibrium threshold while the mean entry threshold in the dynamic game lies above the efficient subgame perfect equilibrium threshold. An important implication of this finding is that if one were to observe only the value of the state variable and the number of people who enter by the end of the game one could not determine whether the static or the dynamic game had been played.

Efficient dynamic coordination with individual learning

We study how the presence of multiple participation opportunities coupled with individual learning about payoffs affects the ability of agents to coordinate efficiently in global coordination games. Two players face the option to invest irreversibly in a project in one of many rounds. The project succeeds if some underlying state variable è is positive and both players invest, possibly asynchronously. In each round they receive informative private signals about è, and asymptotically learn the true value of è. Players choose in each period whether to invest or to wait for more precise information about è. We show that with sufficiently many rounds, both players invest with arbitrarily high probability whenever investment is socially efficient, and delays in investment disappear when signals are precise. This result stands in sharp contrast to the usual static global game outcome in which players coordinate on the risk-dominant action. We provide a foundation for these results in terms of higher order beliefs

Dynamic coordination with individual learning

We study coordination in dynamic global games with private learning. Players choose whether and when to invest irreversibly in a project whose success depends on its quality and the timing of investment. Players gradually learn about project quality. We identify conditions on temporal incentives under which, in sufficiently long games, players coordinate on investing whenever doing so is not dominated. Roughly speaking, this outcome occurs whenever playersʼ payoffs are sufficiently tolerant of non-simultaneous coordination. We also identify conditions under which players coordinate on the risk-dominant action. We provide foundations for these results in terms of higher order beliefs

Efficient Dynamic Coordination with Individual Learning

We study how the presence of multiple participation opportunities coupled with individual learning about payoffs affects the ability of agents to coordinate efficiently in global coordination games. Two players face the option to invest irreversibly in a project in one of many rounds. The project succeeds if some underlying state variable è is positive and both players invest, possibly asynchronously. In each round they receive informative private signals about è, and asymptotically learn the true value of è. Players choose in each period whether to invest or to wait for more precise information about è. We show that with sufficiently many rounds, both players invest with arbitrarily high probability whenever investment is socially efficient, and delays in investment disappear when signals are precise. This result stands in sharp contrast to the usual static global game outcome in which players coordinate on the risk-dominant action. We provide a foundation for these results in terms of higher order beliefs.