Incentive Security

Incentive security research focuses on designing and analyzing systems where incentives align participants' actions with desired outcomes, addressing challenges arising from misaligned interests and information asymmetry. Current research explores diverse approaches, including uplift modeling for optimizing incentive allocation under budget constraints, blockchain-based tokenized incentives for federated learning, and reinforcement learning frameworks for modeling principal-agent interactions with boundedly rational principals. This work has significant implications for various fields, improving the efficiency and fairness of online platforms, decentralized systems, and economic interactions by ensuring that incentives effectively drive desired behaviors.