GAMBIT Lab works at the intersection of game theory, mechanism design and machine learning, building systems that stay fair and robust among strategic agents, with applications ranging from responsible AI to AI in finance.
Analyzing strategic interaction between rational agents, and designing rules and incentives that lead to socially desirable outcomes.
Building learning systems that are fair, accountable and trustworthy when deployed in the real world.
Sequential decision-making under uncertainty, and designing information-revelation strategies to persuade strategic agents.
Applying game-theoretic and machine learning tools to strategic, incentive-driven problems in financial markets and decision-making.