A unifying approach to incentive compatibility in moral hazard problems
提出一种统一方法，将局部激励相容下的全局激励相容验证转化为分布函数比较问题，从而利用不确定性选择工具分析高维道德风险，尤其适用于多任务代理场景。
A new approach to moral hazard is presented. Once local incentive compatibility is satisfied, the problem of verifying global incentive compatibility is shown to be isomorphic to the problem of comparing two classes of distribution functions. Thus, tools from choice under uncertainty can be brought to bear on the problem. The approach allows classic justifications of the first-order approach (FOA) to be proven using the same unifying methodology. However, the approach is especially useful for analyzing higher-dimensional moral hazard problems. New and more tractable multi-signal justifications of the FOA are derived and implications for optimal monitoring are examined. The approach yields justifications of the FOA in certain settings where the action is multidimensional, as in the case when the agent is multitasking. Finally, a tractable multitasking model with richer predictions than the popular but simple linear-exponential-normal model is presented.