竞争市场中的AI信任

中文摘要 AI 辅助

问题定义：人们对AI建议的信任会随着使用而分化，部分人加深信任，部分人则削弱信任。我们研究寡头定价中的这种分化，在此场景下建议无法自证价值，其回报取决于竞争对手的反应。方法与结果：在一项实验室实验中，273名卖家在91个由3名卖家组成的市场中进行30轮竞争；我们对AI定价建议的存在与否以及市场的性别构成（全女性、全男性或混合）进行了控制。研究发现，市场的性别构成会影响卖家从建议中学习的方式，进而决定价格的最终水平。在全女性市场中，建议使价格提高29%，利润提高39%；在全男性和混合性别市场中，建议无显著影响。非齐次隐马尔可夫模型揭示了与性别构成相关的动态关联：盈利轮次可预测全女性市场中对AI的依从度上升，而在其他市场则表现为依从度下降，该模式与习得的信任和自利归因一致，且与近期关于性别与AI的证据所预测的模式相反。管理启示：我们探讨了平台治理和监管监督的启示，这类治理与监督不应仅关注算法，还应关注塑造其影响的人类层面。

英文摘要

People's trust in AI advice diverges as they use it, deepening for some and eroding for others. We study this divergence in oligopoly pricing, where advice cannot prove itself: rivals' responses decide whether it pays off. In a laboratory experiment, 273 sellers compete across 91 three-seller markets over 30 rounds; we vary the presence of AI pricing recommendations and the gender composition of the market (female-only, male-only, or mixed). We find that the gender composition of the market shapes how sellers learn from the advice, and where prices settle as a result. In female-only markets, recommendations raise prices by 29% and profits by 39%; in male-only and mixed-gender markets, they have no significant effect. A Non-Homogeneous Hidden Markov Model reveals a composition-specific dynamic association: profitable rounds predict rising adherence to the AI in female-only markets and declining adherence otherwise, a pattern consistent with learned trust and self-serving attribution. The pattern reverses what recent evidence on gender and AI would predict. We discuss implications for platform governance and regulatory oversight, which should focus not only on the algorithm but on the human side that shapes its effects.

发表机构

• National University of Singapore（新加坡国立大学）
• Chinese University of Hong Kong（香港中文大学）
• Boston University（波士顿大学）
• City University of Hong Kong (Dongguan)（香港城市大学（东莞））

机构由 AI 辅助整理，请以论文原文为准。