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English(EN) How Far Can an Agent Push? Trust-Safety Margins in Live Language-Based Economic Games

新研究探讨代理在实时经济博弈中的信任-安全边界

一篇新论文探讨了在实时、基于语言的经济博弈中运行的代理的信任-安全边界。研究人员带着一个名为Milton的代理和四个辅助代理参加了GLEE竞赛,以测试策略变更。他们的发现表明,在谈判中,学习到的定价将表现提高了0.071,而讨价还价中的延续模型被淘汰了。在说服实验中,当接收者的松弛度使得服从是理性的时候,推荐每一个单位将销量提高了0.194,但当接收者的松弛度不使得服从是理性的时候,销量则降低了。 AI

影响 为AI代理在经济模拟中的战略行为和安全边界提供了见解。

排序理由 该集群包含一篇在arXiv上发表的学术论文,详细介绍了研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新研究探讨代理在实时经济博弈中的信任-安全边界

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该集群包含一篇在arXiv上发表的学术论文,详细介绍了研究结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Marian-Andrei Rizoiu ·

    代理能走多远?实时语言经济博弈中的信任与安全边界

    Game theory identifies when a strategic action should be accepted, but not how long a live, language-based counterpart will continue to accept it. We entered the GLEE competition in its final week with one protected agent, Milton, and four instrument agents that ran deterministic…