We show that the equilibrium policy rule in beauty contest models is equivalent to that of a single agent’s forecast of the economic fundamental. This forecast is conditional on a modified information process, which simply discounts the precision of idiosyncratic shocks by the degree of strategic complementarity. The result holds for any linear Gaussian signal process (static or persistent, stationary or nonstationary, exogenous or endogenous), and also extends to network games. Theoretically, this result provides a sharp characterization of the equilibrium and its properties under dynamic information. Practically, it provides a straightforward method to solve models with complicated information structures.

We propose a theory of gradualism in the implementation of good policies, suitable for environments featuring time consistency. We downplay the role of the initial period by allowing agents both to wait for future agents to start equilibrium play and to restart the equilibrium by ignoring past history. The allocation gradually transits toward one that weighs both short- and long-term concerns, stopping short of the Ramsey outcome but greatly improving upon Markovian equilibria. We use the theory to account for the slow emergence of both climate policies and the reduction of global tariff rates.

This paper characterizes the effects of ambiguity aversion under dispersed information. The equilibrium outcome is observationally equivalent to a Bayesian forecast of the fundamental with increased sensitivity to signals and a pessimistic bias. This equivalence result takes a simple form that accommodates dynamic information and strategic interactions. Applying the result, we show that ambiguity aversion helps rationalize the joint empirical pattern between the bias and persistence of inflation forecasts conditional on household income. In a policy game à la Barro and Gordon (1983) with ambiguity-averse agents, the policy rule features higher average inflation and increased responsiveness to fundamentals.