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We combine economic theory, game theory, and data-driven analysis with computational, experimental, and algorithmic methods.
We study how rules shape incentives, participation, and outcomes — and how they can be improved to make markets more efficient, fair, and resilient.
Our research spans the full landscape of market environments, yet many of our newest insights cluster in four areas:
From bidding to price discovery
We design and analyze mechanisms for trading assets, from auctions to continuous markets. Our work examines how rules influence participation, pricing, and the distribution of gains — in both traditional finance and emerging decentralized systems.
Testing markets before they launch
We use laboratory and field experiments, as well as simulations, to evaluate how people behave in different market designs. This allows us to refine mechanisms and anticipate real-world performance before implementation.
Algorithms in strategic environments
We study how algorithms learn, adapt, and interact with strategic agents — designing systems that are robust to manipulation, fair in outcomes, and aligned with human objectives, while exploring how AI can improve existing markets.
Mechanisms without money
We develop allocation systems for settings where prices cannot or should not be used. Applications include matching refugees to housing, assigning students to schools, and distributing scarce medical resources.
