Haifeng Xu Publications

We study the design of efficient dynamic recommendation systems, such as AI shopping assistants, in which a platform interacts with a user over multiple rounds to identify the most suitable product among those offered by advertisers. Advertisers have multi-dimensional private information: their private value from a purchase and private information about the user’s preferences. In each round, the platform displays recommendations; the user learns product characteristics of the shown items and then chooses whether to purchase, exit without purchasing, or submit a new query. These actions generate a stream of feedback—purchase, exit, and follow-up queries—that is informative about the user’s preferences and can be used both to refine future recommendations and to design contingent transfers. We introduce a class of data-driven dynamic team mechanisms that condition payments on realized user feedback. Our main result shows that data-driven dynamic team mechanisms achieve periodic ex-post implementation of the efficient allocation rule. We then develop variants that guarantee participation and deliver budget surplus, and provide conditions under which these properties can be jointly attained.

We study mechanism design in environments where agents have private preferences and private information about a common payoff-relevant state. In such settings with multi-dimensional types, standard mechanisms fail to implement efficient allocations. We address this limitation by proposing data-driven mechanisms that condition transfers on additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our mechanisms extend the classic Vickrey–Clarke–Groves framework. We show they achieve exact implementation in posterior equilibrium when the state is fully revealed or utilities are affine in an unbiased estimator. With a consistent estimator, they achieve approximate implementation that converges to exact implementation as the estimator converges, and we provide bounds on the convergence rate. We demonstrate applications to digital advertising auctions and AI shopping assistants, where user engagement naturally reveals relevant information, and to procurement auctions with consumer spot markets, where additional information arises from a pricing game played by the same agents.

We study mechanism design when agents have private preferences and private information about a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions.

To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the payoff-relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that they achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is affine in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate.

We demonstrate applications to digital advertising auctions and large language model (LLM)-based mechanisms, where user engagement naturally reveals relevant information.

We study mechanism design when agents hold private information about both their preferences and a common payoff-relevant state. We show that standard message-driven mechanisms cannot implement socially efficient allocations when agents have multidimensional types, even under favorable conditions.

To overcome this limitation, we propose data-driven mechanisms that leverage additional post-allocation information, modeled as an estimator of the pay-off relevant state. Our data-driven mechanisms extend the classic Vickrey-Clarke-Groves class. We show that they achieve exact implementation in posterior equilibrium when the state is either fully revealed or the utility is linear in an unbiased estimator. We also show that they achieve approximate implementation with a consistent estimator, converging to exact implementation as the estimator converges, and present bounds on the convergence rate. We demonstrate applications to digital advertising auctions and large language model (llm) - based mechanisms, where user engagement naturally reveals relevant information.