Institutional Incentive

Institutional incentives research explores how structured rewards and penalties influence individual and collective behavior, aiming to optimize outcomes in various settings. Current research focuses on developing effective incentive mechanisms using machine learning models (e.g., causal forests, bandit algorithms) and game-theoretic frameworks to address challenges like budget constraints, information asymmetry, and strategic agent behavior in diverse applications such as crowdsourcing, federated learning, and supply chain management. This work has significant implications for designing efficient and equitable systems across diverse fields, from online platforms and public policy to collaborative AI and resource allocation.