Incentive Compatible

Incentive-compatible mechanisms aim to design systems where self-interested agents, such as users or experts, are motivated to act in a way that benefits the overall system, even without direct oversight. Current research focuses on developing algorithms, often within bandit or multi-armed bandit frameworks, that achieve this compatibility while maintaining low regret (minimizing suboptimal choices) and incorporating budget constraints or other real-world limitations. These methods are crucial for applications like online advertising, recommender systems, and online marketplaces, where aligning user behavior with platform goals is essential for efficiency and revenue generation. The development of robust and theoretically sound incentive-compatible algorithms is a significant area of ongoing research with broad practical implications.