Department of Industrial Engineering & Decision Analytics [Joint IEDA/ISOM] seminar - Mechanism Design via Market-Clearing Prices for Value Maximizers under Budget and RoS Constraints
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The transition to auto-bidding in online advertising has shifted the focus of auction theory from quasi-linear utility maximization to value maximization subject to financial constraints. We study mechanism design for buyers with private budgets and private Return-on-Spend (RoS) constraints, but public valuations drawn from a continuous distribution, a setting motivated by modern advertising platforms where valuations are predicted via machine learning models.
We extend the Eisenberg-Gale convex program to incorporate RoS constraints and show that its unique solution characterizes a competitive equilibrium with common item-level prices. This equilibrium defines a market-clearing mechanism that is incentive-compatible (truthful reporting of budget and RoS is ex-post optimal) and achieves a tight 1/2-approximation of the first-best revenue benchmark. The equilibrium allocation and price coincide with the outcome of a first-price auction under uniform bidding, yielding a simple implementation. For the online setting, we design a decentralized algorithm that learns the equilibrium multiplier from local feedback alone; both seller's revenue regret and each buyer's total value regret are sub-linear, and we prove matching lower bounds, showing optimality up to logarithmic factors.
Prof. Weiran Shen is a tenure-track associate professor at the Gaoling School of Artificial Intelligence, Renmin University of China. Before joining Renmin University, he was a postdoctoral researcher at the Institute for Software Research (ISR), Carnegie Mellon University, working with Prof. Fei Fang. He obtained his Ph.D. from the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, advised by Prof. Pingzhong Tang, and his B.E. from the Department of Electronic Engineering, Tsinghua University.
His research interests lie at the interface of economics and computation, especially auction and mechanism design, game theory, multi-agent systems, and machine learning.