一种基于随机信息测评的多项任务委托代理均衡对策方法

A Multi-Task Principal-Agent Equilibrium Method Based on Random Information Evaluation

由于信息的不对称,导致可观测信息的综合测评指数是多维随机变量,参照Holmstrom&Milgrom(1991)模型的基本框架,建立代理人承担n项任务时均衡对策模型,得出最优解.同时进一步探讨了委托人对代理人承担的一部分任务努力程度不可测,一部分任务努力程度间接可测,一部分任务努力程度完全可测的情形,在不失一般性的前提下就代理人承担三项任务时,运用相关系数分析其任务之间的相关性对替代性任务和互补性任务中最优分享系数的影响.结果表明,委托人在制定绩效工资方案时,要均衡考虑风险态度、激励成本和测评方差才能达到期望效果.

Due to asymmetric information,the comprehensive evaluation index of observable information is a multi-dimensional random variable.With reference to the basic framework of Holmstrom and Milgrom （1991） model,this paper establishes the optimal contract countermeasure model for the multi-task agents and gives the optimal solution.For the agent＇s tasks,the level of effort can be either unmeasurable,or indirectly measurable,or directly measurable.Without loss of generality,assuming that the agent takes three types of tasks,this paper uses the correlation coefficient to analyze the impact of the correlation among tasks on the optimal sharing coefficient for alternate tasks and complementary tasks.The result shows that the principal should equally consider risk attitude,incentive costs and evaluation of variance to achieve the desired effect during the development of performance-based compensation program.