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Communication and Collusion in Oligopoly Experiments: A Meta-Study using Machine Learning

Author

Listed:
  • Maximilian Andres

    (University of Bremen)

  • Lisa Bruttel

    (University of Potsdam, CEPA)

Abstract

While an influential body of economic literature shows that allowing for communication between firms increases collusion in oligopolies, so far we have only anecdotal evidence on the precise communication content that helps firms to coordinate their behavior. In this paper, we conduct a primary-data meta-study on oligopoly experiments and use a machine learning approach to identify systematic patterns in the communication content across studies. Starting with the communication topics mentioned most often in the literature (agreements, joint benefit, threat of punishment, promise/trust), we use a semi-supervised approach to detect all relevant topics. In a second step, we study the effect of these topics on the rate of collusion among the firms. We find that agreements on specific behavior are decisive for the strong positive effect of communication on collusion, while other communication topics have no effect.

Suggested Citation

  • Maximilian Andres & Lisa Bruttel, 2025. "Communication and Collusion in Oligopoly Experiments: A Meta-Study using Machine Learning," CEPA Discussion Papers 88, Center for Economic Policy Analysis.
    • Handle: RePEc:pot:cepadp:88
    DOI: 10.25932/publishup-68013
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    More about this item

    Keywords

    collusion; communication; machine learning; meta-study; experiment;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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