发表机构
National Yang Ming Chiao Tung University（国立阳明交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出混合共进化观点博弈（H-COG），结合Friedkin-Johnsen和Phi-4语言智能体，在动态网络中研究观点形成，发现语言智能体导致更高的社会成本，主要源于偏离内在观点而非邻居分歧。
AI 中文摘要
在线网络中的观点形成涉及信念和社会关系的双重变化。分析模型使得研究均衡和社会成本成为可能，但通常将交流表示为固定的数值更新。基于LLM的智能体提供了一种基于语言的替代方案，然而其收敛性和集体效率仍不清楚。我们开发了混合共进化观点博弈（H-COG），在动态重连的K近邻网络中结合了成本最小化的Friedkin-Johnsen智能体（Type-C）和Phi-4语言智能体（Type-L）。我们初始化了50个智能体，其观点来自5,199条关于枪支管制和堕胎的Reddit评论。这些评论使用微调的RoBERTa回归器在连续的[-1,+1]尺度上进行评分，并通过一个混合参数设置每种智能体类型的比例。实验覆盖了九种群体构成、三种初始网络拓扑和两个主题。所有540次运行均在模拟时限内满足收敛标准。在Type-L更新下，共进化网络达到吸引子的可靠性与其在分析更新规则下相同，使得基于均衡的效率比较成为可能。纯Type-L群体的合并无政府状态价格为$5.558 \pm 0.309$，而纯Type-C群体为$1.139 \pm 0.005$。社会成本的分解表明，这一差距主要归因于语言智能体偏离其内在观点，而非与邻居的分歧更大。主要发现在三种初始网络拓扑中保持一致。
英文摘要
Online discussion of political and gender-related issues is often heated, and when opinions in a network draw closer, the convergence is readily taken as genuine consensus. Whether it carries a cost is a question existing methods cannot answer: coevolutionary opinion formation games measure the Price of Anarchy (PoA) of agents that update by numerical rules, while simulations with large language model (LLM) agents report only descriptive indices. We introduce the Hybrid Coevolutionary Opinion Game (H-COG), in which analytical and LLM-driven agents share one network, choose their neighbors by opinion similarity in every round, and hold stances drawn from real Reddit comments on gun control and abortion. To our knowledge, H-COG is the first framework to place Friedkin-Johnsen best-response agents and LLM agents in one coevolutionary game and to measure the social cost and PoA of LLM-driven populations. We prove that on any fixed network, given the LLM agents' opinions, the analytical agents' opinion stage has a unique equilibrium and the social optimum has a closed form, and that the convergence guarantee of Chen et al. for optimistic gradient ascent carries over to H-COG. All runs converge structurally. LLM-driven populations are less polarized yet have about five times the PoA of analytical ones; half of the gap comes from agents being pulled away from their own prior positions, a distance we prove must carry a cost whenever expressed opinions are more concentrated than intrinsic ones. Echo chambers form under every composition and grow out of the rewiring rule rather than the initial topology. Opinions in these populations draw closer largely because agents give up their own positions.
Comments29 pages, 7 figures, 5 tables. A short version appears in the proceedings of CSoNet 2026
