Learning to game the system

• 成果类型: Article
• 署名作者: Li, Jin; Mukherjee, Arijit; Vasconcelos, Luis
• 署名单位: University of Hong Kong; Michigan State University; University of Technology Sydney; Universidade Nova de Lisboa
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdaa065
• 发表日期: 2021
• 页码: 2014-2041
• 关键词: Relational contracts; performance-measures; incentive contracts; balanced scorecard; health-care; moral hazard; INFORMATION; targets; CORRUPTION; DYNAMICS

摘要

An agent may privately learn which aspects of his job are more important by shirking on some of them, and use that information to shirk more effectively in the future. In a model of long-term employment relationship, we characterize the optimal relational contract in the presence of such learning-by-shirking and highlight how the performance measurement system can be managed to sharpen incentives. Two related policies are studied: intermittent replacement of existing measures, and adoption of new ones. In spite of the learning-by-shirking effect, the optimal contract is stationary, and may involve stochastic replacement/adoption policies that dilute the agent's information rents from learning how to game the system.