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When AI Enters the Game: Cooperation and Predictability in Repeated Interactions

Author

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  • Alicia von Schenk
  • Victor Klockmann

Abstract

Key MessagesAlgorithms do not inherently promote collusion or increase competitionHuman cooperation declines when strategic counterparts are algorithmsHuman behavior becomes more predictable and aligned with equilibrium selection theory when interacting with algorithmsSocial preferences weaken in human-machine interaction, especially withouta human beneficiaryPolicy should focus on market conditions, not just algorithmic presence

Suggested Citation

  • Alicia von Schenk & Victor Klockmann, 2026. "When AI Enters the Game: Cooperation and Predictability in Repeated Interactions," EconPol Forum, CESifo, vol. 27(02), pages 39-44, April.
    • Handle: RePEc:ces:epofor:v:27:y:2026:i:02:p:39-44
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    References listed on IDEAS

    as
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    4. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    5. Matthias Blonski & Peter Ockenfels & Giancarlo Spagnolo, 2011. "Equilibrium Selection in the Repeated Prisoner's Dilemma: Axiomatic Approach and Experimental Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 3(3), pages 164-192, August.
    6. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
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