Adaptive Incentive

Adaptive incentive design focuses on dynamically adjusting reward structures to achieve desired outcomes in multi-agent systems, often without complete knowledge of agent behavior. Current research explores this through various approaches, including reinforcement learning algorithms (like multi-agent reinforcement learning and actor-critic methods) and the development of adaptive reward machines to optimize for specific objectives. This field is significant for its potential to improve efficiency and fairness in diverse applications, ranging from federated learning and resource allocation to contract design and environmental control, by aligning individual agent incentives with broader societal goals.