Work

A Decentralized Matching Theory Framework to Match Data and Algorithms Providers

Year: 2025

Type: article

Abstract: This paper presents a novel decentralized matching algorithm (DEMA) for pairing data and algorithm providers in AI ecosystems. DEMA addresses scalability, stability, and matching utility challenges in large-scale environments. Formulated as a two-sided matching game, our decentralized solution enables autonomous decision-making based on local information. Simulations demonstrate DEMA’s near-optimal matching quality and almost perfect stability. Furthermore, DEMA exhibits excellent scalability with execution times and memory usage growing much more slowly than centralized matching as the number of providers increases.

Source: IEEE Networking Letters

DOI: 10.1109/lnet.2025.3560459

Authors: Chaouki Ben Issaid, Mehdi Bennis

Institution: University of Oulu

Language: English

Topic: Game Theory and Voting Systems

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: European Commission