Do Humans Bargain Differently with AI? Evidence from Alternating-Offer Games

Yuhao Fu, Nobuyuki Hanaki and Haitao Wang

ISER Discussion Paper from Institute of Social and Economic Research, The University of Osaka

Abstract: Artificial intelligence increasingly participates in economic interactions not only as a tool, but also as an autonomous bargaining counterpart negotiating on behalf of firms, platforms, and consumers. Yet little is known about how humans respond psychologically and strategically when bargaining with such agents in dynamic settings. We study this question in a laboratory experiment using a three-stage alternating-offer bargaining game in which participants negotiate in real time with either another human or a GPT-based AI agent. We also introduce a human-beneficiary condition in which the AI agent’s earnings may affect another participant’s payment. Agreements are not reached earlier in human–human bargaining than in human–AI bargaining, but they are reached significantly earlier when the AI’s payoff has human consequences. Human proposers offer more to human opponents than to AI agents, whereas responders become significantly more willing to accept unfair AI offers when AI earnings may benefit another human. These findings suggest that fairness and reciprocity toward AI are weaker and more conditional than toward humans, but partially re-emerge when AI outcomes affect real people. The results have implications for the design of AI negotiation systems and broader human–AI economic interactions.

Date: 2026-04
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Persistent link: https://EconPapers.repec.org/RePEc:dpr:wpaper:1311

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