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Rewards and Punishments Help Humans Overcome Biases Against Cooperation Partners Assumed to be Machines

Author

Listed:
  • Kinga Makiva

    (New York University [Abu Dhabi] - NYU - NYU System)

  • Jean-François Bonnefon

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Mayada Oudah

    (New York University [Abu Dhabi] - NYU - NYU System)

  • Anahit Sargsyan

    (New York University [Abu Dhabi] - NYU - NYU System, TUM Technical University of Munich)

  • Tahal Rahwan

    (New York University [Abu Dhabi] - NYU - NYU System)

Abstract

High levels of human-machine cooperation are required to combine the strengths of human and artificial intelligence. Here we investigate strategies to overcome the machine penalty, where people are less cooperative with partners they assume to be machines, than with partners they assume to be humans. Using a large-scale iterative public goods game with nearly 2000 participants, we find that peer rewards or peer punishments can both promote cooperation with partners assumed to be machines, but do not overcome the machine penalty. Their combination, however, eliminates the machine penalty, because it is uniquely effective for partners assumed to be machines, and inefficient for partners assumed to be humans. These findings provide a nuanced road map for designing a cooperative environment for humans and machines, depending on the exact goals of the designer.

Suggested Citation

  • Kinga Makiva & Jean-François Bonnefon & Mayada Oudah & Anahit Sargsyan & Tahal Rahwan, 2025. "Rewards and Punishments Help Humans Overcome Biases Against Cooperation Partners Assumed to be Machines," Post-Print hal-05110666, HAL.
    • Handle: RePEc:hal:journl:hal-05110666
    DOI: 10.1016/j.isci.2025.112833
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