A Few Queries Go a Long Way: Information-Distortion Tradeoffs in Matching

Amanatidis, Georgios; Birmpas, Georgios; Filos-Ratsikas, Aris; Voudouris, Alexandros A.

doi:10.1609/aaai.v35i6.16642

Cited by 4 publications

(10 citation statements)

References 31 publications

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“…The rationale is that asking the agents for more detailed information about only a few options is still cognitively not too burdensome, and could result in notable improvements on the distortion. This was indeed confirmed in that work for general social choice, and in a follow-up work for several matching problems [Amanatidis et al, 2021a]. Specifically, the latter work shows that it is possible to obtain distortion O(n 1/k ) with O(log n) queries per agent for any constant integer k, and distortion O(1)…”

Section: θ(supporting

confidence: 66%

Don’t Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

Amanatidis,

Birmpas,

Filos-Ratsikas

et al. 2024

SIAM J. Discrete Math.

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In most social choice settings, the participating agents express their preferences over the different alternatives in the form of linear orderings. While this clearly simplifies preference elicitation, it inevitably leads to poor performance with respect to optimizing a cardinal objective, such as the social welfare, since the values of the agents remain virtually unknown. This loss in performance because of lack of information is measured by the notion of distortion. A recent array of works put forward the agenda of designing mechanisms that learn the values of the agents for a small number of alternatives via queries, and use this limited extra information to make better-informed decisions, thus improving distortion. Following this agenda, in this work we focus on a class of combinatorial problems that includes most well-known matching problems and several of their generalizations, such as One-Sided Matching, Two-Sided Matching, General Graph Matching, and k-Constrained Resource Allocation. We design two-query mechanisms that achieve the best-possible worst-case distortion in terms of social welfare, and outperform the best-possible expected distortion achieved by randomized ordinal mechanisms.

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Section: θ(supporting

confidence: 66%

Don’t Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

Amanatidis,

Birmpas,

Filos-Ratsikas

et al. 2024

SIAM J. Discrete Math.

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“…For this class of mechanisms, we showed tradeo s between the necessary amount of information (number of queries) to achieve low distortion [ABFV21]. In follow-up work [ABFV22c,ABFV22a], we showed how our methodology can be adapted and extended to capture several other fundamental social choice models beyond utilitarian voting, including the well-known one-sided matching problem (in which there is a set of agents that have preferences over a same-sided set of items and the objective is to match each agent to a single distinct item so as to maximize the total value of the agents) and several generalizations of it, such as constrained resource allocation (where each agent can be matched to more than one item), two-sided matching (in which each agent has preferences over all other agents and the goal is to create pairs of agents with maximum value for one another), and graph matching (where the preferences of the agents for other agents are partial and de ned according to a directed graph).…”

Section: Computational Social Choicementioning

confidence: 79%

Multi-agent systems for computational economics and finance

Kampouridis

Kanellopoulos

Kyropoulou

et al. 2022

AIC

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In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artificial intelligence, and economic theory. In particular, we focus on the design and analysis of mechanisms for systems involving multiple strategic agents, both from a theoretical and an applied perspective. We present an overview of our group’s activities, as well as its members, and then discuss in detail past, present, and future work in multi-agent systems.

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“…Subsequently, two main directions have been established. Continuing to work with the model introduced by Procaccia and Rosenschein, one line focuses mainly on maximizing welfare subject to normalization assumptions, but without assuming any metric properties, see Amanatidis et al (2021Amanatidis et al ( , 2022a; Caragiannis and Procaccia (2011);Filos-Ratsikas, Micha, and Voudouris (2020). The other line of work studies problem without the normalization assumptions, but assuming that the preferences are metric, i.e., they satisfy the triangle inequality.…”

Section: Related Workmentioning

confidence: 99%

Low-Distortion Clustering with Ordinal and Limited Cardinal Information

Burkhardt,

Caragiannis,

Fehrs

et al. 2024

AAAI

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Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we would like to evaluate clustering algorithms in terms of social cost objectives that are defined using d. This is done using the notion of distortion, which measures how far from optimality a clustering can be, taking into account all underlying metrics that are consistent with the ordinal information available. Unfortunately, the most important clustering objectives (e.g., those used in the well-known k-median and k-center problems) do not admit algorithms with finite distortion. To sidestep this disappointing fact, we follow two alternative approaches: We first explore whether resource augmentation can be beneficial. We consider algorithms that use more than k clusters but compare their social cost to that of the optimal k-clusterings. We show that using exponentially (in terms of k) many clusters, we can get low (constant or logarithmic) distortion for the k-center and k-median objectives. Interestingly, such an exponential blowup is shown to be necessary. More importantly, we explore whether limited cardinal information can be used to obtain better results. Somewhat surprisingly, for k-median and k-center, we show that a number of queries that is polynomial in k and only logarithmic in n (i.e., only sublinear in the number of agents for the most relevant scenarios in practice) is enough to get constant distortion.

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A Few Queries Go a Long Way: Information-Distortion Tradeoffs in Matching

Cited by 4 publications

References 31 publications

Don’t Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

Don’t Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

Multi-agent systems for computational economics and finance

Low-Distortion Clustering with Ordinal and Limited Cardinal Information

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