Solving an Infinite Horizon Adverse Selection Model Through Finite Policy Graphs

• 成果类型: Article
• 署名作者: Zhang, Hao
• 署名单位: University of Southern California
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1120.1056
• 发表日期: 2012
• 页码: 850-864
• 关键词: monetary-policy; hidden actions; INFORMATION; games; CONTRACTS; income

摘要

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.