Beyond Truthful Reporting: Robust Strategies for Worst-Case Payoff Maximization in Large Markets

We study the bidding problem faced by a participant in market-clearing mechanisms that are not incentive-compatible, focusing on settings where the bidder has limited or no information about rivals’ types or strategies. Using a robust optimization framework, we model this uncertainty through an ambiguity set encompassing all possible realizations of rivals’ bids. The bidder maximizes its worst-case payoff over this set, yielding robust bidding strategies independent of distributional assumptions. In particular, we demonstrate the value of this approach in the context of generalized first-price, generalized second-price, and core-selecting combinatorial auctions, where we characterize simple, easy-to-implement robust bidding policies and show that they consistently outperform truthful bidding for bidders with simple valuation structures. Our analysis uncovers a more general insight, that is, shading bids on the target bundle, that persists even in settings where optimal policies cannot be derived in closed form. Together, these results not only show that the robust framework delivers optimal strategies in some settings, but also provide guidance for bidders with limited information participating in complex markets.

History: Martin Bichler, Senior Editor; Pallab Sanyal, Associate Editor.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2025.2127.