Solving a Dynamic Adverse Selection Model Through Finite Policy Graphs
نویسنده
چکیده
This paper studies an infinite-horizon adverse selection model with an underlying Markov information process and a risk-neutral agent. It introduces a graphic representation of continuation contracts and continuation-payoff frontiers, namely finite policy graphs, and provides an algorithm that generates a sequence of such graphs to approximate the optimal policy graph. The algorithm performs an additional step after each value iteration—replacing dominated points on the previous continuation-payoff frontier by points on the new frontier and reevaluating the new frontier. This dominance-free reevaluation step accelerates the convergence of the continuation-payoff frontiers. Numerical examples demonstrate the effectiveness of this algorithm and properties of the optimal contracts.
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