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

In many large-scale markets mediated by digital platforms, such as advertising, energy trading, transportation logistics, and spectrum auctions, platforms suggest that participants report true valuations to simplify strategic complexity. This recommendation is widely followed by smaller bidders who lack resources for sophisticated strategic analysis. This paper shows that simple deviations from truthful reporting can improve outcomes for such participants. Using a robust optimization framework, we derive practical bidding strategies for generalized first-price, generalized second-price, and core-selecting combinatorial auctions. Our distribution-free approach maximizes worst-case payoffs over a set of plausible competitor bids, without requiring detailed information about rival behavior. We demonstrate that simple strategies, specifically shading bids on preferred bundles, consistently outperform truthful bidding when bidders have straightforward valuation structures. This insight persists even in complex settings where closed-form optimal policies cannot be derived. The results provide actionable guidance for market participants with limited information who lack resources for deploying sophisticated bidding algorithms, while offering market designers insight into how participants might deviate from recommended strategies.