Ultimatum bargaining: Algorithms vs. Humans
通过实验研究人们在最后通牒博弈中与人类或算法对手互动时的行为，发现被试一般不区分对手类型，但更愿意在算法收益惠及人类时放弃更高报酬。
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. • Subjects do not differentiate between human and algorithmic opponents. • They might prefer interacting with humans over algorithms in strategic interactions. • Details of the algorithms’ workings and if any explanation is provided are not determinant.