用智能体模拟揭示真实威胁比象征威胁更易引发冲突
Realistic threat perception drives intergroup conflict: A causal, dynamic analysis using generative-agent simulations
- 用大模型驱动的虚拟社会模拟，分别操控真实与象征性威胁
- 真实威胁直接加剧敌意，象征威胁仅在无真实威胁时通过内群体偏见起作用
- 非敌对接触可缓解冲突升级，多数群体易积累敌意
人类冲突常归因于物质条件和象征价值受威胁，但二者如何交互及何者主导仍不明确。研究受限于因果控制弱、伦理约束及时间数据稀缺。本文利用大语言模型（LLM）驱动的智能体在虚拟社会中独立调节真实威胁与象征威胁，持续追踪行为、语言与态度。表征分析显示，底层LLM将真实威胁、象征威胁与敌意编码为独立内部状态，且我们的操纵可精准映射并因果引导这些状态。模拟结果提供威胁驱动冲突的因果解释：真实威胁直接提升敌意，象征威胁影响较弱，其作用完全通过内群体偏见中介，且仅在真实威胁不存在时才增加敌意。非敌对的跨群体接触可缓冲冲突升级，结构性不对称使多数群体更易集中敌意。
原文摘要 · Abstract (English)
Human conflict is often attributed to threats against material conditions and symbolic values, yet it remains unclear how they interact and which dominates. Progress is limited by weak causal control, ethical constraints, and scarce temporal data. We address these barriers using simulations of large language model (LLM)-driven agents in virtual societies, independently varying realistic and symbolic threat while tracking actions, language, and attitudes. Representational analyses show that the underlying LLM encodes realistic threat, symbolic threat, and hostility as distinct internal states, that our manipulations map onto them, and that steering these states causally shifts behavior. Our simulations provide a causal account of threat-driven conflict over time: realistic threat directly increases hostility, whereas symbolic threat effects are weaker, fully mediated by ingroup bias, and increase hostility only when realistic threat is absent. Non-hostile intergroup contact buffers escalation, and structural asymmetries concentrate hostility among majority groups.
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