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June 11, 2026Management Science

Artificial Collusion: Examining Supracompetitive Pricing by Q-Learning Algorithms

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Authors

ABArnoud den BoerJMJanusz M MeylahnMSMaarten Pieter Schinkel

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Overview

Randomized trial examines pricing algorithms for collusion risk in competitors, indicating limited autonomous threat.

Key Points

  • This research investigates whether Q-learning algorithms can lead to autonomous collusion among competitors.
  • Detailed analysis of Q-learning algorithms and their pricing behavior.
  • Examined synchronization of competitors using the same algorithm under identical conditions.
  • Assessment of conditions for collusion risk in algorithmic pricing.
  • Q-learning algorithms often do not meet criteria for autonomous collusion.
  • Supracompetitive pricing observed only under synchronized conditions, requiring explicit cartel agreements.
  • No substantial current threat for competition agencies regarding algorithmic collusion.

Cite This Study

Boer et al. (2026) studied this question.

synapsesocial.com/papers/6a2a512e80c8f91e7f39d8fahttps://doi.org/10.1287/mnsc.2024.08557
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Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1On algorithmic collusion and reward–punishment schemes2024 · 12 citations
  2. 2Remedies for algorithmic tacit collusion2020 · 25 citations
  3. 3Collusion by code or algorithmic collusion? When pricing algorithms take over2020 · 19 citations