English(EN) Learning to Simulate Individuals from Macro Social Signals

新方法使用预测市场训练大型语言模型进行个体行为模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种名为macro2mind的新方法，利用预测市场数据来训练语言模型模拟个体人类行为。该方法利用市场价格轨迹来推断参与者群体如何解读新闻和更新他们的信念，使模型能够推断他们的互动并将这些互动汇总成价格预测。该系统在SWM-Bench和Polymarket上展示了最先进的方向准确性，并在Humanual、OvertonBench、PRISM和计算机辅助设计应用等四个其他用户模拟基准测试中表现出强大的零样本迁移能力。 AI

影响这种方法可以提高各种应用中模拟用户行为的真实性和多样性，从市场分析到数字孪生。

排序理由该集群包含一篇详细介绍新方法和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yining Zhao, Bushi Liu, Haofei Yu, Zhengyang Qi, Shanyong Wang, Chuyue Li, Yuxiang Liu, Jiaxuan You · 2026-10-07 04:00

从宏观社会信号学习模拟个体

arXiv:2610.07062v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses is either inherited from pretraining or learned from individual-level annotatio…

本文如何被排名

报道来源 [1]

从宏观社会信号学习模拟个体

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