Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement

Frédéric Marty and Thierry Warin
Additional contact information
Thierry Warin: CIRANO - Centre interuniversitaire de recherche en analyse des organisations - UQAM - Université du Québec à Montréal = University of Québec in Montréal

Post-Print from HAL

Abstract: This paper explores algorithmic collusion from both legal and economic perspectives, underscoring the increasing influence of algorithms in firms' market decisions and their potential to facilitate anti-competitive behavior. By employing bandit algorithms as a model—typically used in uncertain decision-making scenarios—we shed light on the mechanisms of implicit collusion that occur without explicit communication. Legally, the primary challenge lies in detecting and categorizing possible algorithmic signals, particularly when they function as unilateral communications. Economically, the task of distinguishing between rational pricing strategies and collusive patterns becomes increasingly complex in the context of algorithm-driven decisions. The paper stresses the need for competition authorities to identify atypical market behaviors. Striking a balance between algorithmic transparency and the prevention of collusion is critical. While regulatory measures could mitigate collusive risks, they might also impede the development of algorithmic technologies. As this form of collusion gains prominence in competition law and economics discussions, understanding it through models like bandit algorithms becomes essential, especially since these algorithms have the potential to converge more rapidly toward supra-competitive prices equilibria

Keywords: Algorithmic Collusion; Machine-Learning; Bandit Algorithms; Antitrust Enforcement; Pricing Strategies (search for similar items in EconPapers)
Date: 2024-10-19
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04745409v1
References: Add references at CitEc
Citations:

Published in Journal of Economy and Technology, 2024, 3, pp.34-43. ⟨10.1016/j.ject.2024.10.001⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement (2023)
Working Paper: Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04745409

DOI: 10.1016/j.ject.2024.10.001

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().