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

Kinga Makiva, Jean-François Bonnefon, Mayada Oudah, Anahit Sargsyan and Tahal Rahwan
Additional contact information
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

Post-Print from HAL

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.

Date: 2025-06-06
References: Add references at CitEc
Citations:

Published in Iscience, 2025, pp.112833. ⟨10.1016/j.isci.2025.112833⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05110666

DOI: 10.1016/j.isci.2025.112833

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().