Almost Envy-Free Repeated Matching in Two-Sided Markets

Gollapudi, Sreenivas; Kollias, Kostas; Plaut, Benjamin

doi:10.1007/978-3-030-64946-3_1

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…Other work [10,27] has focused on guaranteeing fairness in two-sided platforms over time, rather than in a one-shot setting. Of particular note is the work of Gollapudi et al [16], who consider two-sided EF1 in a dynamic setting, but obtain positive results primarily for symmetric binary valuations, a much more restrictive class of valuations than we consider. Tadenuma [28] studies envy minimization in two-sided matching subject to other notions, including stability, but focuses on ordinal notions of envy and restricts attention to one-to-one matchings.…”

Section: Introductionmentioning

confidence: 86%

Two-Sided Matching Meets Fair Division

Freeman¹,

Micha²,

Shah³

2021

Preprint

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We introduce a new model for two-sided matching which allows us to borrow popular fairness notions from the fair division literature such as envy-freeness up to one good and maximin share guarantee. In our model, each agent is matched to multiple agents on the other side over whom she has additive preferences. We demand fairness for each side separately, giving rise to notions such as double envy-freeness up to one match (DEF1) and double maximin share guarantee (DMMS). We show that (a slight strengthening of) DEF1 cannot always be achieved, but in the special case where both sides have identical preferences, the round-robin algorithm with a carefully designed agent ordering achieves it. In contrast, DMMS cannot be achieved even when both sides have identical preferences.* A preliminary version appeared in the proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 1 Assigning students to courses has been studied before [8,20,7], but these papers typically only consider the preferences of the students. 2 http://csse.utoronto.ca/social-needs-marketplace

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Section: Introductionmentioning

confidence: 86%

Two-Sided Matching Meets Fair Division

Freeman¹,

Micha²,

Shah³

2021

Preprint

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“…Patro et al [42] used round robin allocations to find 𝐸𝐹 1 solution, but without strictly ensuring that product side constraints are satisfied. Gollapudi et al [22] considered two-sided 𝐸𝐹 1 in a dynamic setting, but only for symmetric binary valuations. In our case, however, the expected revenue can not be mapped to binary valuations.…”

Section: Proof Provided In Appendix a □mentioning

confidence: 99%

“…At the end of 𝐿 1 rounds, if some products are not allocated to 𝑅 1 resellers, perform greedy product replacement as mentioned in steps 12-16. Once every product achieves their minimum cardinality, we can allocate 𝑅 2 copies of products subject to user-side cardinality constraints (steps [17][18][19][20][21][22].…”

Section: Seal It Is a Sequential Egalitarian Algorithm (Algorithm 2)mentioning

confidence: 99%

Towards Fair Allocation in Social Commerce Platforms

Gupta

Nagori

Chakraborty

et al. 2023

Proceedings of the ACM Web Conference 2023

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Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products.Our work systematically explores various well-studied benchmarks of fairness-including Nash social welfare, envy-freeness up to one item (𝐸𝐹 1), and equitability up to one item (𝐸𝑄1)-from both theoretical and experimental perspectives. We find that the existential and computational guarantees of these concepts known from the unconstrained setting do not extend to our constrained model. To address this limitation, we develop a mixed-integer linear program and other scalable heuristics that provide near-optimal approximation of Nash social welfare in simulated and real social commerce datasets. Overall, our work takes the first step towards achieving provable fairness alongside reasonable revenue guarantees on social commerce platforms.

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“…Note that in our many-to-one setting, DEF1 is meaningless on the player side because it is always trivially satisfied. Gollapudi et al [2020] studied many-to-many matching in a dynamic setting; their positive results primarily hold for symmetric binary valuations, which are much more restrictive than the valuations that we allow. Patro et al [2020] investigated fairness in two-sided platforms between producers and customers, but assumed that producers are indifferent between customers.…”

Section: Related Workmentioning

confidence: 99%

Fair Division with Two-Sided Preferences

Igarashi¹,

Kawase²,

Suksompong³

et al. 2022

Preprint

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We study a fair division setting in which a number of players are to be fairly distributed among a set of teams. In our model, not only do the teams have preferences over the players as in the canonical fair division setting, but the players also have preferences over the teams. We focus on guaranteeing envy-freeness up to one player (EF1) for the teams together with a stability condition for both sides. We show that an allocation satisfying EF1, swap stability, and individual stability always exists and can be computed in polynomial time, even when teams may have positive or negative values for players. Similarly, a balanced and swap stable allocation that satisfies a relaxation of EF1 can be computed efficiently. In addition, when teams have nonnegative values for players, we prove that an EF1 and Pareto optimal allocation exists, and such an allocation can be found in polynomial time if the valuations are binary.

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Almost Envy-Free Repeated Matching in Two-Sided Markets

Cited by 16 publications

References 20 publications

Two-Sided Matching Meets Fair Division

Two-Sided Matching Meets Fair Division

Towards Fair Allocation in Social Commerce Platforms

Fair Division with Two-Sided Preferences

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