🤖 AI Summary
This paper investigates the optimal dynamic compensation design for a principal hiring a risk-averse agent over a finite horizon. It formulates a continuous-time principal–agent model where incentive provision is modeled as a mixed stochastic control and optimal stopping game. Methodologically, the analysis integrates continuous-time contract theory, stochastic optimal control, and numerical simulation. The key contribution is the identification of an endogenous timing mechanism for payments—particularly sign-on bonuses—that enables *front-loaded* incentives, thereby departing from conventional *ex-post* reward paradigms. Furthermore, the study reveals significant nonlinear effects of the principal–agent disparity in patience (i.e., discount rates) and the number of bonus opportunities on both the principal’s value and the structure of optimal incentives. These findings provide novel theoretical foundations and actionable insights for designing dynamic contracts in practice.
📝 Abstract
We study a continuous time contracting model in which a principal hires a risk averse agent to manage a project over a finite horizon and provides sequential payments whose timing is endogenously determined. The resulting nonzero-sum interaction between the principal and the agent is reformulated as a mixed control and stopping problem. Using numerical simulations, we investigate how factors such as the relative impatience of the parties and the number of bonus payments influence the principal's value and the structure of the optimal bonus payment scheme. A notable finding is that, in some contractual environments, the principal optimally offers a sign-on bonus to front-load incentives.