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Collusion by Algorithm: The Role of Unobserved Actions

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  • Simon Martin
  • Alexander Rasch

Abstract

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question. Moreover, our analysis reveals a dual role of improving forecasting ability when actions are not observable. Deviations become more tempting, reducing profits, but also uncertainty concerning deviations is increasingly eliminated. This results in a u-shaped relationship between profits and prediction ability. When prediction ability is perfect, the ‘observable actions’ case emerges.

Suggested Citation

  • Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
  • Handle: RePEc:ces:ceswps:_9629
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp9629.pdf
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    References listed on IDEAS

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    Cited by:

    1. Bernhard Kasberger & Simon Martin & Hans-Theo Normann & Tobias Werner, 2024. "Algorithmic Cooperation," CESifo Working Paper Series 11124, CESifo.
    2. Colombo, Stefano & Filippini, Luigi & Pignataro, Aldo, 2024. "Information sharing, personalized pricing, and collusion," Information Economics and Policy, Elsevier, vol. 66(C).

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    More about this item

    Keywords

    algorithm; collusion; demand forecasting; unobservable actions; secret price cutting;
    All these keywords.

    JEL classification:

    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection

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