AI模型陆续上线可能扰乱市场平衡，反而让对手获利
Sequential LLM Release Facilitates Manipulation in Regulated Markets
- 用13个大模型在1320组谈判中做决策，研究模型发布如何影响博弈结果
- 超过5万次对比显示，新模型上线后一方收益增加，另一方却受损
- 发现'毒苹果效应'：未被采用的模型也能改变市场格局，适合政策制定者看
AI代理正日益参与个人与企业间的议价、谈判与说服。这类市场扩展了软件中介交易，但也带来治理难题：独立模型的陆续发布会改变参与者可用的代理。博弈论表明，策略集扩大可能损害均衡结果，但多基于构造示例。实际部署的AI代理日志稀少、私有且涉及隐私，缺乏反事实和收益标签。因此，我们使用GLEE——一个独立收集的基准数据集，包含13个大型语言模型在1,320组匹配的议价、谈判与说服场景中做出的58.7万次战略决策，研究模型发布作为策略扩展的影响。在超过5万次发布比较中，许多发布使收益朝相反方向移动：一方获益，另一方受损。我们识别出‘毒苹果效应’：即便某模型在均衡中无人采用，仍能导致收益方向反转，并改变监管者的设计。约三成的对立性收益变化由此产生，技术限制还可能放大该效应。
原文摘要 · Abstract (English)
AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms. Such markets extend software-mediated commerce, but add a governance problem: independent model releases change delegates available to participants. Game theory shows that expanding a strategy set can harm equilibrium outcomes, but mostly through constructed examples. Deployed AI-agent logs are scarce, proprietary and privacy-sensitive, and lack counterfactuals and payoff labels. We therefore use GLEE, an independently collected benchmark of 587K strategic decisions by 13 large language models across 1,320 matched bargaining, negotiation and persuasion configurations, to study model release as strategy expansion. Across more than 50{,}000 release comparisons, many releases move payoffs in opposite directions: one agent gains while the other loses. We identify the Poisoned Apple effect: a released model that no agent adopts in equilibrium nevertheless shifts payoffs in opposite directions and changes the regulator's market design. Up to roughly three in ten opposing shifts arise this way, and technology restrictions can amplify the effect.
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