Dynamic Incentive Strategies for Principal‐Agent Coopetition: A Stochastic Approach to Managing Risks and Performance

ABSTRACT This study investigates principal‐agent coopetition (PAC) by developing a comprehensive stochastic dynamical framework that captures the intricate interactions and associated risks between principals and agents in uncertain environments. The research emphasizes the importance of dynamic incentive strategies, focusing on key factors such as equity structure, driving capital, risk aversion degree, and task complexity. By defining and analyzing the dual risks of cooperation and competition within the PAC process, we introduce a novel performance evaluation metric, coordination at risks (CaR), to assess the effectiveness of these strategies. Through extensive numerical simulations, our results highlight the critical need to balance these risks to optimize PAC performance. The study provides actionable insights and practical guidelines for designing dynamic incentive mechanisms that not only align the interests of principals and agents but also mitigate potential risks, fostering sustainable cooperation and enhanced organizational performance in principal‐agent relationships.