Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 28, 2023 erstellt
Description:
This paper introduces the dual-contract design via Q-learning methods. In contrast to the standard principal-agent problem (a principal and an agent), we emphasize that the dual-contract problem can also be recognized as a dual-principal-agent problem (two principals and an agent). The method utilizes a combination of game theory and reinforcement learning (RL) to create a contract that is both fair and beneficial to multiple sides. In this problem, two principals (e.g., two departments of a headquarters) are jointly responsible for providing the resources and capital for an agent. The two principals may have different objectives and interests, which must be balanced to ensure that the project is completed efficiently and effectively. In this problem, both principals must design a contract that provides the right incentives for the agent to perform the task optimally, however, the conventional mathematical method is hard to illustrate the economic consequence of this dual-contracting problem. The main advantage of using multi-agent Q-learning for dual-contract design is that it allows for solving optimization problems that are both fair and beneficial to both sides. This is because the multi-agent Q-learning algorithm can take into account both sides’ preferences and optimize the contract parameters accordingly. Additionally, using a Q-learning algorithm allows the contract to be adjusted over time as conditions change, ensuring that the contract remains fair and beneficial to both sides in the long term