Solving an Infinite Horizon Adverse Selection Model Through Finite Policy Graphs
Operations Research · 2012 · Volume 60 · Issue 4
Abstract
This paper studies an infinite horizon adverse selection model with an underlying Markov information process. It introduces a graphic representation of continuation contracts and continuation payoff frontiers, namely finite policy graph, and provides an algorithm to approximate the optimal policy graph through iterations. 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.
Keywords
stochastic games, dynamic principal-agent model, adverse selection, dynamic programming, graphs
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