Optimal Monitoring Schedule in Dynamic Contracts

Operations Research, accepted for publication

43 Pages Posted: 19 Sep 2017 Last revised: 2 Jul 2020

See all articles by Mingliu Chen

Mingliu Chen

University of Texas at Dallas - Naveen Jindal School of Management

Peng Sun

Duke University - Fuqua School of Business

Yongbo Xiao

School of Economics and Management, Tsinghua University

Date Written: September 16, 2017

Abstract

Consider a setting in which a principal induces effort from an agent to reduce the arrival rate of a Poisson process of adverse events. The effort is costly to the agent, and unobservable to the principal, unless the principal is monitoring the agent. Monitoring ensures effort but is costly to the principal. The optimal contract involves monetary payments and monitoring sessions that depend on past arrival times. We formulate the problem as a stochastic optimal control model and solve the problem analytically. The optimal schedules of payment and monitoring demonstrate different structures depending on model parameters, and may involve monitoring for a random period of time. Overall, the optimal dynamic contracts are simple to describe, easy to compute and implement, and intuitive to explain.

Keywords: Dynamic Contract, Moral Hazard, Principal-agent Model, Optimal Control, Continuous Time, Costly State Verification

JEL Classification: D86, C73, C61

Suggested Citation

Chen, Mingliu and Sun, Peng and Xiao, Yongbo, Optimal Monitoring Schedule in Dynamic Contracts (September 16, 2017). Operations Research, accepted for publication, Available at SSRN: https://ssrn.com/abstract=3038034 or http://dx.doi.org/10.2139/ssrn.3038034

Mingliu Chen

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Peng Sun (Contact Author)

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States

Yongbo Xiao

School of Economics and Management, Tsinghua University ( email )

Beijing, 100084
China

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