mlsanityData to 7 Oct 2026

Agent-based Modeling: Equilibrium, Echo Chambers, and Efficiency in Hybrid Coevolutionary Opinion Games

arXiv 2609.27639 ↗First posted 23 Sep 2026cs.GT cs.MA cs.SIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Opinion formation in online networks involves changes in both beliefs and social ties. Analytical models make it possible to study equilibrium and social cost, but usually represent communication as a fixed numerical update. LLM-driven agents offer a language-based alternative, yet their convergence and collective efficiency remain unclear. We develop the Hybrid Coevolutionary Opinion Game (H-COG), combining cost-minimizing Friedkin-Johnsen agents (Type-C) and Phi-4 language agents (Type-L) in a dynamically rewired K-nearest-neighbor network. We initialize 50 agents with opinions drawn from 5,199 Reddit comments on gun control and abortion. The comments are scored on a continuous [-1,+1] scale using a fine-tuned RoBERTa regressor, and a mixing parameter sets the proportion of each agent type. The experiments cover nine population compositions, three initial network topologies, and two topics. All 540 runs meet the convergence criterion within the simulation horizon. Under Type-L updating, the coevolving network reaches an attractor as reliably as it does under the analytical update rule, making an equilibrium-based efficiency comparison possible. The pooled Price of Anarchy is 5.558 ± 0.309 for purely Type-L populations, compared with 1.139 ± 0.005 for purely Type-C populations. A decomposition of social cost attributes most of this gap to language agents moving away from their intrinsic opinions, rather than to greater disagreement with their neighbors. The main findings are consistent across the three initial network topologies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 34 of its 62 references that are in the map. First posted on arXiv 23 Sep 2026.

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34 of 62 references.

Closest ancestors · 10

Same direction, newest first

  1. 2026AgoraSim: A Hybrid Agent-Based Modeling Framework7 Jul
  2. 2025Large Language Model Driven Agents for Simulating Echo Chamber Formation25 Feb
  3. 2025Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation17 Feb
  4. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society12 Feb
  5. 2025Emergence of polarization in networks of large language model agents9 Jan
  6. 2024Social Opinions Prediction Utilizes Fusing Dynamics Equation with LLM-based Agents13 Sep
  7. 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents25 Apr
  8. 2024Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation26 Feb
  9. 2023Simulating Opinion Dynamics with Networks of LLM-based Agents16 Nov
  10. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 24

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  5. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  6. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  7. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  8. 2024Phi-4 Technical Reportcited by 675
  9. 2020The Pushshift Reddit Datasetcited by 289
  10. 2024OASIS: Open Agent Social Interaction Simulations with One Million Agentscited by 127
  11. 2017Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotationcited by 77
  12. 2019A Large-scale Dataset for Argument Quality Ranking: Construction and Analysiscited by 66
12 more
  1. 2024Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networkscited by 31
  2. 2023Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Mediacited by 30
  3. 2013DELTACON: A Principled Massive-Graph Similarity Functioncited by 17
  4. 2017Minimizing Polarization and Disagreement in Social Networkscited by 16
  5. 2025LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratingscited by 9
  6. 2024Bluesky: Network Topology, Polarization, and Algorithmic Curationcited by 6
  7. 2024Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Datacited by 3
  8. 2026Exploring the Effects of Alignment on Numerical Bias in Large Language Modelscited by 3
  9. 2016Bounds on the Price of Anarchy for a More General Class of Directed Graphs in Opinion Formation Gamescited by 2
  10. 2025PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labelscited by 2
  11. 2018Multiagent Learning for Competitive Opinion Optimizationcited by 1
  12. 2026Dynamics and Convergences for Markov Coevolutionary Opinion Formation Games in Dynamic Social Networkscited by 1

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