Welfare-Optimal Serial Dictatorships Have Polynomial Query Complexity

Serial dictatorship is a simple mechanism for coordinating agents in solving combinatorial optimization problems according to their preferences. The most representative such problem is one-sided matching, in which a set of n agents have values for a set of n items, and the objective is to compute a matching of the agents to the items of maximum total value (a.k.a., social welfare). Following the recent framework of Caragiannis and Rathi (2023), we consider a model in which the agent-item values are not available upfront but become known by querying agent sequences. In particular, when the agents are asked to act in a sequence, they respond by picking their favorite item that has not been picked by agents who acted before and reveal their value for it. Can we compute an agent sequence that induces a social welfare-optimal matching? We answer this question affirmatively and present an algorithm that uses polynomial number (specifically, O(n^5) of queries). This solves the main open problem stated by Caragiannis and Rathi (2023). Our analysis uses a potential function argument that measures progress towards learning the underlying edge-weight information. Furthermore, the algorithm has a truthful implementation by adapting the paradigm of VCG payments.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i13.33494
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
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article

Welfare-Optimal Serial Dictatorships Have Polynomial Query Complexity

Kurt Mehlhorn, Ioannis Caragiannis, Nidhi Rathi
Proceedings of the AAAI Conference on Artificial Intelligence
Quantum Computing Algorithms and Architecture
article

Welfare-Optimal Serial Dictatorships Have Polynomial Query Complexity

Kurt Mehlhorn, Ioannis Caragiannis, Nidhi Rathi
article en

Abstract

Serial dictatorship is a simple mechanism for coordinating agents in solving combinatorial optimization problems according to their preferences. The most representative such problem is one-sided matching, in which a set of n agents have values for a set of n items, and the objective is to compute a matching of the agents to the items of maximum total value (a.k.a., social welfare). Following the recent framework of Caragiannis and Rathi (2023), we consider a model in which the agent-item values are not available upfront but become known by querying agent sequences. In particular, when the agents are asked to act in a sequence, they respond by picking their favorite item that has not been picked by agents who acted before and reveal their value for it. Can we compute an agent sequence that induces a social welfare-optimal matching? We answer this question affirmatively and present an algorithm that uses polynomial number (specifically, O(n^5) of queries). This solves the main open problem stated by Caragiannis and Rathi (2023). Our analysis uses a potential function argument that measures progress towards learning the underlying edge-weight information. Furthermore, the algorithm has a truthful implementation by adapting the paradigm of VCG payments.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(13)
Aarhus University (DK), Max Planck Institute for Informatics (DE)
Openalex Percentile: Top 78%
Quantum Computing Algorithms and Architecture
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Welfare-Optimal Serial Dictatorships Have Polynomial Query Complexity — Kurt Mehlhorn, Ioannis Caragiannis, et al. · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS