ICML 2026 paper: Predicting the top bidder, not the bid, optimizes utility
New research shows that just knowing *who* is best unlocks efficient online allocation.
In a paper accepted at ICML 2026, researchers Goldner, Mohan, and Tsilivis study consumer utility maximization in an online random-order model. They show that common predictions of agent values are not useful for this problem. Instead, predicting only the identity of the highest-valued agent suffices. Their deterministic truthful mechanism achieves a constant approximation to the optimal full-information solution when predictions are correct (consistency) and a constant approximation to the best implementable solution even when predictions are arbitrarily wrong (robustness).
- Predictions of agent values or optimal value are useless for utility maximization; identifying the highest-valued agent is the key.
- Deterministic truthful mechanism achieves constant consistency (optimal when prediction is correct) and constant robustness (even when prediction is arbitrarily wrong).
- Accepted at ICML 2026, this work provides a practical framework for online allocation problems like ad auctions or resource scheduling.
Why It Matters
Simplifies real-world allocation by requiring only minimal, robust predictions — no need for precise value estimates.