多元思维，分裂网络？基于大语言模型的社会模拟中的人格构成、两极分化与集体智慧

中文摘要 AI 辅助

基于大语言模型智能体的模拟社会被分别用于研究在线两极分化和集体智慧，但两者很少在同一系统中同时测量。因此，很难判断一个社会的人格构成是否同时影响这两者，或者减少两极分化是否会以集体能力为代价。我们提出了TraitMix，一种实验设计，其中模拟社交网络的“大五”人格构成（包括特质水平和特质异质性）作为受控实验变量，并在同一轮运行中测量两极分化和集体表现。在涵盖六个有争议话题和六种语言模型的991次百智能体社会模拟中，特质异质性产生了最大的测量效应，对两极分化的两个面产生相反方向的作用：多样化的社会持有更分散的观点，同时更少地分裂成阵营，因此同质社会并非温和，而是共识性的回音室。特质效应并非可加性的，因为宜人性决定了开放性的符号，这种交互在多个模型中重复出现，尽管主要模型的估计受影响力驱动。与本研究旨在测量的权衡相反，没有任何两极分化指标能预测较差的集体表现，而跨领域互动是四个指标中唯一一个在控制聚合身份后其与集体准确性的关联仍然显著的。我们报告了去除两种潜在测量循环性的消融实验、应用于每个模型的归纳门控，以及未通过这些测试的测量指标。

英文摘要

Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the two are rarely measured in the same system. It is therefore difficult to say whether a society's personality composition shapes both, or whether reducing polarization costs collective competence. We present TraitMix, an experimental design in which the Big Five composition of a simulated social network, both trait levels and trait heterogeneity, is a controlled experimental variable, and in which polarization and collective performance are measured in the same runs. Across 991 simulations of hundred-agent societies, spanning six contested topics and six language models, trait heterogeneity has the largest measured effects, acting in opposite directions on two faces of polarization: varied societies hold more dispersed opinions while being less segregated into camps, so homogeneous societies are not moderate but consensual echo chambers. Trait effects are not additive, as Agreeableness determines the sign of Openness, an interaction that replicates across models although the primary model's estimate is influence-driven. Contrary to the trade-off the study was designed to measure, no polarization measure predicts poorer collective performance, and cross-cutting interaction is the only one of four whose association with collective accuracy survives partialling on the aggregation identity. We report ablations removing two potential measurement circularities, an induction gate applied to every model, and the measures that failed them.

发表机构

• German University of Digital Science（德国数字科学大学）

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