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Adaptive matching for expert systems with uncertain task types

Shah, Virag, Gulikers, Lennart, Massoulié, Laurent and Vojnović, Milan (2020) Adaptive matching for expert systems with uncertain task types. Operations Research, 68 (5). pp. 1403-1424. ISSN 0030-364X

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Identification Number: 10.1287/opre.2019.1954


A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts, as the expert resources are often scarce. The efficient utilization of experts in these platforms is made challenging by the fact that the information available about the parties involved is usually limited. To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches. In our model, the tasks arrive online while the experts are fixed and constrained by a finite service capacity. For this model, we characterize the maximum task resolution throughput a platform can achieve. We show that the natural greedy approach where each expert is assigned a task most suitable to his or her skill is suboptimal, as it does not internalize the aforementioned externality. We develop a throughput-optimal backpressure algorithm which does so by accounting for the “congestion” among different task types. Finally, we validate our model and confirm our theoretical findings with data-driven simulations via logs of, a Stack Overflow forum dedicated to mathematics.

Item Type: Article
Additional Information: © 2020 INFORMS.
Divisions: Statistics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 24 Jun 2022 14:03
Last Modified: 16 May 2024 03:47

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