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

2026-08-02

AI Summary

This study investigates whether human fairness and reciprocity are diminished in dynamic negotiations with AI agents compared to human counterparts. Using a three-stage real-time alternating-offer bargaining experiment, the authors compare human–human and human–GPT agent interactions while manipulating whether the AI’s gains benefit a third-party human recipient. The results reveal, for the first time, that individuals exhibit weaker and more conditional social preferences toward AI—making less fair offers and reciprocating its proposals less frequently. However, when the AI’s payoff benefits another human, fairness and reciprocity are partially restored, leading to significantly earlier agreement. Combining behavioral experimentation with GPT-based negotiation agents, this work provides novel evidence on how social norms adapt in human–AI interaction.

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 affects another participant's payoff. 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 remerge when AI outcomes affect real people. The results have implications for the design of AI negotiation systems and broader human-AI economic interactions.

Problem

• human-AI bargaining
• fairness
• reciprocity
• alternating-offer game
• economic interaction

Innovation

• human-AI bargaining
• alternating-offer game
• fairness
• reciprocity
• behavioral experiment