Competing Bandits in Decentralized Contextual Matching Markets

• URL: http://arxiv.org/abs/2411.11794v2
• Date: Thu, 19 Jun 2025 17:33:38 GMT
• Title: Competing Bandits in Decentralized Contextual Matching Markets
• Authors: Satush Parikh, Soumya Basu, Avishek Ghosh, Abishek Sankararaman,
• Abstract summary: We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for the supply side (aka arms)<n>Motivated by the linear contextual bandit framework, we assume that for each agent, an arm-mean may be represented by a linear function of a known feature vector and an unknown (agent-specific) parameter.<n>We propose learning algorithms to identify the latent environment and obtain stable matchings simultaneously.
• Score: 13.313881962771777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for the supply side (aka arms) with potentially time-varying preferences to obtain a stable match. Motivated by the linear contextual bandit framework, we assume that for each agent, an arm-mean may be represented by a linear function of a known feature vector and an unknown (agent-specific) parameter. Moreover, the preferences over arms depend on a latent environment in each round, where the latent environment varies across rounds in a non-stationary manner. We propose learning algorithms to identify the latent environment and obtain stable matchings simultaneously. Our proposed algorithms achieve instance-dependent logarithmic regret, scaling independently of the number of arms, and hence applicable for a large market.

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