Equilibrium Behavior in Competing Dynamic Matching Markets

Li, Zhuoshu; Gupta, Neal; Das, Sanmay; Dickerson, John P.

doi:10.24963/ijcai.2018/54

Cited by 4 publications

(10 citation statements)

References 12 publications

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“…We propose the explore-then-Gale-Shapley (ETGS) algorithm inspired by the fundamental idea of exploration-exploitation trade-off for the objective of learning players' preferences and finding the most preferred stable arm. Denote N as the number of players, K as the number of arms, T as the horizon, and ∆ > 0 as the minimum preference gap between the first N + 1ranked arms among all players, where we assume K ≥ N to ensure each player to have chances of being matched like previous works [Liu et al, 2020[Liu et al, , 2021Sankararaman et al, 2021;Basu et al, 2021;Kong et al, 2022;Maheshwari et al, 2022]. Our main result is: Theorem 1.…”

Section: Our Contributionsmentioning

confidence: 99%

“…In Table 1, we compare our new upper bound with related results. Among all previous works, only Liu et al [2020] and Basu et al [2021] derive the player-optimal stable regret guarantees. Though Liu et al [2020] achieve the same order of O(K log T /∆ 2 ) for the same type of regret as ours, their algorithm requires strong assumptions.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Recently, a rich line of works study the bandit learning problem in two-sided matching markets [Liu et al, 2020[Liu et al, , 2021Sankararaman et al, 2021;Basu et al, 2021;Kong et al, 2022;Maheshwari et al, 2022;Ghosh et al, 2022]. There are several players and arms corresponding to two sides of the market participants.…”

Section: Introductionmentioning

confidence: 99%

“…Since more than one stable matching may exist in the market, there are mainly two types of regrets for players: one is the player-optimal stable regret which is defined compared with the players' most-preferred stable matching; and the other is the weaker player-pessimal stable regret defined compared with the players' least-preferred stable matching. Liu et al [2020] first study a centralized setting where a central platform assigns allocations of arms to players in each round. When the horizon T and preference gap ∆ are known, they guarantee the player-optimal stable regret for the centralized ETC algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Player-optimal Stable Regret for Bandit Learning in Matching Markets

Kong¹,

Li²

2023

Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)

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The problem of matching markets has been studied for a long time in the literature due to its wide range of applications. Finding a stable matching is a common equilibrium objective in this problem. Since market participants are usually uncertain of their preferences, a rich line of recent works study the online setting where one-side participants (players) learn their unknown preferences from iterative interactions with the other side (arms). Most previous works in this line are only able to derive theoretical guarantees for player-pessimal stable regret, which is defined compared with the players' least-preferred stable matching. However, under the pessimal stable matching, players only obtain the least reward among all stable matchings. To maximize players' profits, player-optimal stable matching would be the most desirable. Though Basu et al. [2021] successfully bring an upper bound for player-optimal stable regret, their result can be exponentially large if players' preference gap is small. Whether a polynomial guarantee for this regret exists is a significant but still open problem. In this work, we provide a new algorithm named explorethen-Gale-Shapley (ETGS) and show that the optimal stable regret of each player can be upper bounded by O(K log T /∆ 2 ) where K is the number of arms, T is the horizon and ∆ is the players' minimum preference gap among the first N + 1-ranked arms. This result significantly improves previous works which either have a weaker player-pessimal stable matching objective or apply only to markets with special assumptions. When the preferences of participants satisfy some special conditions, our regret upper bound also matches the previously derived lower bound.

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Section: Our Contributionsmentioning

confidence: 99%

Section: Our Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Player-optimal Stable Regret for Bandit Learning in Matching Markets

Kong¹,

Li²

2023

Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)

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“…Largely, these exchanges also make their matching decisions via a combined algorithmic and manual process. These exchanges compete in a variety of ways (e.g., by allowing patient-donor pairs to register in multiple exchange programs); this competition can lead to loss in efficiency [2] as well as sub-optimal changes to individual exchanges' matching polices [19].…”

Section: A Kidney Exchange and Edge Failuresmentioning

confidence: 99%

Improving Policy-Constrained Kidney Exchange via Pre-Screening

McElfresh,

Curry,

Sandholm

et al. 2020

Preprint

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In barter exchanges, participants swap goods with one another without exchanging money; these exchanges are often facilitated by a central clearinghouse, with the goal of maximizing the aggregate quality (or number) of swaps. Barter exchanges are subject to many forms of uncertainty-in participant preferences, the feasibility and quality of various swaps, and so on. Our work is motivated by kidney exchange, a real-world barter market in which patients in need of a kidney transplant swap their willing living donors, in order to find a better match. Modern exchanges include 2-and 3-way swaps, making the kidney exchange clearing problem NPhard. Planned transplants often fail for a variety of reasons-if the donor organ is rejected by the recipient's medical team, or if the donor and recipient are found to be medically incompatible. Due to 2-and 3-way swaps, failed transplants can "cascade" through an exchange; one US-based exchange estimated that about 85% of planned transplants failed in 2019. Many optimization-based approaches have been designed to avoid these failures; however most exchanges cannot implement these methods, due to legal and policy constraints. Instead, we consider a setting where exchanges can query the preferences of certain donors and recipients-asking whether they would accept a particular transplant. We characterize this as a twostage decision problem, in which the exchange program (a) queries a small number of transplants before committing to a matching, and (b) constructs a matching according to fixed policy. We show that selecting these edges is a challenging combinatorial problem, which is non-monotonic and non-submodular, in addition to being NP-hard. We propose both a greedy heuristic and a Monte Carlo tree search, which outperforms previous approaches, using experiments on both synthetic data and real kidney exchange data from the United Network for Organ Sharing.

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MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation

An,

Gao,

Tang

2024

ACM Trans. Inf. Syst.

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Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia, and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) Ignoring personalized spatial- and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users; (2) Insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

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Equilibrium Behavior in Competing Dynamic Matching Markets

Cited by 4 publications

References 12 publications

Player-optimal Stable Regret for Bandit Learning in Matching Markets

Player-optimal Stable Regret for Bandit Learning in Matching Markets

Improving Policy-Constrained Kidney Exchange via Pre-Screening

MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation

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