Adversarial competition and collusion in algorithmic markets
File(s)
Accepted version
Author(s)
Rocher, Luc
Tournier, arnaud
de Montjoye, Yves-Alexandre
Type
Journal Article
Abstract
Algorithms are now playing a central role in digital marketplaces, setting prices and automatically responding in real time to competitors’ behaviour. The deployment of automated pricing algorithms is scrutinized by economists and regulatory agencies, concerned about its impact on prices and competition. Existing research has so far been limited to cases where all firms use the same algorithm, suggesting that anti-competitive behaviour might spontaneously arise in that setting. Here we introduce and study a general anti-competitive mechanism, adversarial collusion, where one firm manipulates other sellers that use their own pricing algorithm. We propose a network-based framework to model the strategies of pricing algorithms on iterated two-firm and three-firm markets. In this framework, an attacker learns to endogenize competitors’ algorithms and then derive a strategy to artificially increase its profit at the expense of competitors. Facing a drastic loss of profits, competitors will eventually intervene and revise or turn off their pricing algorithm. To disincentivize this intervention, we show that the attacker can instead unilaterally increase both its profits and the profits of competitors. This leads to a collusive outcome with symmetric and supra-competitive profits, sustainable in the long run. Together, our findings highlight the need for policymakers and regulatory agencies to consider adversarial manipulations of algorithmic pricing, which might currently fall outside of the scope of current competition laws.
Date Issued
2023-05
Date Acceptance
2023-03-16
Citation
Nature Machine Intelligence, 2023, 5 (5), pp.497-504
ISSN
2522-5839
Publisher
Nature Research
Start Page
497
End Page
504
Journal / Book Title
Nature Machine Intelligence
Volume
5
Issue
5
Copyright Statement
Copyright © 2023 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s42256-023-00646-0
Identifier
https://www.nature.com/articles/s42256-023-00646-0
Publication Status
Published
Date Publish Online
2023-05-04