Local envy-freeness in house allocation problems

Beynier, Aurélie; Chevaleyre, Yann; Gourvès, Laurent; Lesca, Julien; Maudet, Nicolas; Wilczynski, Anaëlle

doi:10.1007/s10458-019-09417-x

Cited by 37 publications

(40 citation statements)

References 37 publications

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“…We now turn to the literature in the context of indivisible items. Beynier et al [5] study the fair division problem in the setting of "house allocation": here agents have (strict) preferences over items, and each agent must receive exactly one item. An agent envies another in this setting if she prefers the item received by the other agent over her own.…”

Section: Related Workmentioning

confidence: 99%

On Fair Division with Binary Valuations Respecting Social Networks

Misra¹,

Nayak²

2021

Preprint

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We study the computational complexity of finding fair allocations of indivisible goods in the setting where a social network on the agents is given. Notions of fairness in this context are "localized", that is, agents are only concerned about the bundles allocated to their neighbors, rather than every other agent in the system. We comprehensively address the computational complexity of finding locally envy-free and Pareto efficient allocations in the setting where the agents have binary valuations for the goods and the underlying social network is modeled by an undirected graph. We study the problem in the framework of parameterized complexity.We show that the problem is computationally intractable even in fairly restricted scenarios, for instance, even when the underlying graph is a path. We show NP-hardness for settings where the graph has only two distinct valuations among the agents. We demonstrate W-hardness with respect to the number of goods or the size of the vertex cover of the underlying graph. We also consider notions of proportionality that respect the structure of the underlying graph and show that two natural versions of this notion have different complexities: allocating according to the notion that accounts for locality to the greatest degree turns out to be computationally intractable, while for other notions, the allocation problem can be modeled as a structured ILP which can be solved efficiently. ACM Subject ClassificationTheory of computation → Design and analysis of algorithms; Theory of computation → Social networks

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Section: Related Workmentioning

confidence: 99%

On Fair Division with Binary Valuations Respecting Social Networks

Misra¹,

Nayak²

2021

Preprint

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“…Another related line of work considers settings where the agents constitute a social network and can only observe the allocations of their neighbors (Abebe, Kleinberg, and Parkes 2017;Bei, Qiao, and Zhang 2017;Chevaleyre, Endriss, and Maudet 2017;Aziz et al 2018;Beynier et al 2018;Bredereck, Kaczmarczyk, and Niedermeier 2018). These works place an informational constraint on the set of agents, whereas our model restricts the set of revealed goods per agent.…”

Section: Related Workmentioning

confidence: 99%

Fair Division Through Information Withholding

Hosseini¹,

Sikdar²,

Vaish³

et al. 2020

AAAI

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Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods that addresses pairwise envy by the removal of at most one good. In the worst case, each pair of agents might require the (hypothetical) removal of a different good, resulting in a weak aggregate guarantee. We study allocations that are nearly envy-free in aggregate, and define a novel fairness notion based on information withholding. Under this notion, an agent can withhold (or hide) some of the goods in its bundle and reveal the remaining goods to the other agents. We observe that in practice, envy-freeness can be achieved by withholding only a small number of goods overall. We show that finding allocations that withhold an optimal number of goods is computationally hard even for highly restricted classes of valuations. In contrast to the worst-case results, our experiments on synthetic and real-world preference data show that existing algorithms for finding EF1 allocations withhold a close-to-optimal amount of information.

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“…(1) in LOCALLY ENVY-FREE ALLOCATION (LEFA) each agent a is satisfied if it does not envy any of its neighborsthis models situations where agents do not know or care about the total available amount of resources. This was studied, e.g., by Beynier et al (2019) and Bredereck et al (2018).…”

Section: Introductionmentioning

confidence: 98%

“…Envy-freeness ranks among the most important fairness requirements in the classical resource allocation problem of distributing indivisible items (resources) among agents (Bouveret and Lang 2008;Bouveret, Chevaleyre, and Maudet 2016). There has also been an extensive line of works studying envy-freeness in a more general setting where agents only directly compare themselves to a subset of other agents (Beynier et al 2019;Aziz et al 2018;Bredereck, Kaczmarczyk, and Niedermeier 2018). For instance, employees in a company would only compare themselves and "envy" other employees that are at a comparable level to them in the company's hierarchy.…”

Section: Introductionmentioning

confidence: 99%

Parameterized Complexity of Envy-Free Resource Allocation in Social Networks

Eiben

Ganian

Hamm

et al. 2020

AAAI

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We consider the classical problem of allocating resources among agents in an envy-free (and, where applicable, proportional) way. Recently, the basic model was enriched by introducing the concept of a social network which allows to capture situations where agents might not have full information about the allocation of all resources. We initiate the study of the parameterized complexity of these resource allocation problems by considering natural parameters which capture structural properties of the network and similarities between agents and items. In particular, we show that even very general fragments of the considered problems become tractable as long as the social network has bounded treewidth or bounded clique-width. We complement our results with matching lower bounds which show that our algorithms cannot be substantially improved.

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Local envy-freeness in house allocation problems

Cited by 37 publications

References 37 publications

On Fair Division with Binary Valuations Respecting Social Networks

On Fair Division with Binary Valuations Respecting Social Networks

Fair Division Through Information Withholding

Parameterized Complexity of Envy-Free Resource Allocation in Social Networks

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