Ultimatum bargaining: Algorithms vs. Humans

Ali Ozkes, Nobuyuki Hanaki, Dieter Vanderelst and Jurgen Willems

Economics Letters, 2024, vol. 244, issue C

Abstract: We study human behavior in ultimatum game when interacting with either human or algorithmic opponents. We examine how the type of the AI algorithm (mimicking human behavior, optimising gains, or providing no explanation) and the presence of a human beneficiary affect sending and accepting behaviors. Our experimental data reveal that subjects generally do not differentiate between human and algorithmic opponents, between different algorithms, and between an explained and unexplained algorithm. However, they are more willing to forgo higher payoffs when the algorithm’s earnings benefit a human.

Keywords: Ultimatum bargaining; Human-ai interaction; Social preferences; Fairness; Equity; Explainability (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524004634

DOI: 10.1016/j.econlet.2024.111979

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