Distortion and Risk in Optimal Incentive Contracts
用多任务模型提出绩效测量的两个参数：扭曲和风险，分析它们如何影响激励合同的价值和使用，并应用于研发实验室和非营利组织等场景。
Performance measurement is an essential part of the design of any incentive system. The strength and value of incentives in organizations are strongly affected by the performance measures available. Yet, the characteristics of valuable performance measures have not been well explored in the agency literature. In this paper, I use a multitask model to develop a two-parameter characterization of performance measures and show how these two parameters-distortion and risk-affect the value and use of performance measures in incentive contracts. I show that many complex issues in the design of real-world incentive contracts can be fruitfully viewed as tradeoffs between these two features of performance measures. I also use this framework to analyze the provision of incentives in several specific environments, including R&D labs and nonprofit organizations.