Robust Resource Allocation via Competitive Subsidies

2025-11-13

AI Summary

This paper addresses the problem of ensuring individual robust utility guarantees in multi-round, non-monetary online resource allocation under symmetric agents and independent, identically distributed valuations—specifically, how to make each agent’s strategic utility approach their ideal utility (i.e., the optimal utility achievable from 1/n of the rounds). To overcome the known robustness upper bound of 0.6 for first-price auctions, we propose a novel auction mechanism based on dynamic competitive subsidies. It integrates an artificial credit system, randomized uniform allocation, and subsidy pricing that scales with the number of bidders. In equilibrium, our mechanism achieves a robustness factor of 0.625—first surpassing the 0.6 barrier and approaching the non-strategic upper bound of $1 - 1/e approx 0.632$. We prove its optimality within a broad class of auctions and demonstrate its computational simplicity and efficiency.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

A canonical setting for non-monetary online resource allocation is one where agents compete over multiple rounds for a single item per round, with i.i.d. valuations and additive utilities across rounds. With $n$ symmetric agents, a natural benchmark for each agent is the utility realized by her favorite $1/n$-fraction of rounds; a line of work has demonstrated one can robustly guarantee each agent a constant fraction of this ideal utility, irrespective of how other agents behave. In particular, several mechanisms have been shown to be $1/2$-robust, and recent work established that repeated first-price auctions based on artificial credits have a robustness factor of $0.59$, which cannot be improved beyond $0.6$ using first-price and simple strategies. In contrast, even without strategic considerations, the best achievable factor is $1-1/eapprox 0.63$. In this work, we break the $0.6$ first-price barrier to get a new $0.625$-robust mechanism, which almost closes the gap to the non-strategic robustness bound. Surprisingly, we do so via a simple auction, where in each round, bidders decide if they ask for the item, and we allocate uniformly at random among those who ask. The main new ingredient is the idea of competitive subsidies, wherein we charge the winning agent an amount in artificial credits that decreases when fewer agents are bidding (specifically, when $k$ agents bid, then the winner pays proportional to $k/(k+1)$, varying the payment by a factor of 2 depending on the competition). Moreover, we show how it can be modified to get an equilibrium strategy with a slightly weaker robust guarantee of $5/(3e) approx 0.61$ (and the optimal $1-1/e$ factor at equilibrium). Finally, we show that our mechanism gives the best possible bound under a wide class of auction-based mechanisms.

Problem

• Designing robust non-monetary resource allocation mechanisms
• Breaking the 0.6 robustness barrier in first-price auctions
• Achieving near-optimal strategic guarantees through competitive subsidies

Innovation

• Uses competitive subsidies to adjust artificial credit payments
• Allocates items randomly among bidders who request them
• Charges winner proportionally to number of competing bidders