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January 27, 2026Canadian Journal of Economics/Revue canadienne d économique0 citations

Innis Lecture: Algorithmic pricing and competition

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RCR. H. Clark

Key Points

  • The aim is to analyze how AI-driven algorithmic pricing affects cartel dynamics and the associated risks.
  • Examine technological advances in AI and their implications for pricing strategies.
  • Identify risks related to algorithmic coordination in cartels.
  • Assess legal implications tied to algorithm-driven collusion.
  • Identified risks include intentional use of algorithms for collusion.
  • Algorithms can act as facilitators of cartel behavior.
  • Autonomous learning by algorithms raises new concerns about collusion.

Abstract

Abstract This article examines how advances in AI‐driven algorithmic pricing are reshaping the nature of cartel formation and coordination. Traditionally, cartels relied on explicit communication, extensive organization, and sustained human effort to reach and maintain agreement while avoiding detection. Recent technological developments now raise concerns that coordination may arise with far less human involvement. In particular, three main risks are identified: the deliberate use of algorithms to implement collusive strategies, the role of algorithms as third‐party facilitators of collusion, and the possibility that algorithms may autonomously learn to collude. While algorithmic pricing can enhance efficiency and competition, the article assesses these risks and considers associated legal implications.

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Cite This Study

R. H. Clark (2026) studied this question.

synapsesocial.com/papers/69785570ccb046adae517802https://doi.org/10.1111/caje.70035
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