Solving an Infinite Horizon Adverse Selection Model Through Finite Policy Graphs

Authors:
Hao Zhang
Affiliations:
Marshall School of Business, University of Southern California, Los Angeles, California 90089
Venue:
Operations Research
Year:
2012

Citing 6
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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.