We study the interaction between an agent of uncertain type, whose project gives rise to both good and bad news, and an evaluator who must decide if and when to fire the agent. The agent can hide bad news from the evaluator at some cost, and will do so if this secures her a significant increase...

Global games of regime change - coordination games of incomplete information in which a status quo is abandoned once a sufficiently large fraction of agents attacks it - have been used to study crises phenomena such as currency attacks, bank runs, debt crises, and political change. We extend the...

We study the problem of Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been investigated under a simplifying model where myopically selfish players appear sequentially and act once...

We introduce the algorithmic learning equations (ALEs), a set of ordinary differential equations which characterizes the finite-time and asymptotic behaviour of the stochastic interaction between state-dependent learning algorithms in dynamic games. Our framework allows for a variety of...

Population games describe strategic interactions among large numbers of small, anonymous agents. Behavior in these games is typically modeled dynamically, with agents occasionally receiving opportunities to switch strategies, basing their choices on simple myopic rules called revision protocols...

We study dynamic network games with two types of players (i.e., heterogeneous players) characterized by their different intrinsic preferences. Instead of calculating the Nash equilibrium, we focus on the asymptotic behavior of an asynchronous best response dynamics. We develop a dynamic approach...