Large Language Models can Achieve Social Balance

Social balance is a well-established concept in sociology which dictates how individual interactions can lead a population to become one faction of positive interactions or be divided in two or more antagonistic factions. In this paper, we consider a group of large language models (LLMs) and study how, after continuous interactions, they can achieve social balance. Across three different LLM models, we find that achieving social balance depends on (i) the type of interaction; (ii) whether agents consider homophily or influence from their peers; and (iii) the population size. We characterize how each model achieves social balance with different frequency, diversity of positive or negative interactions, and interaction stability across conditions (i) to (iii). We show that models achieve different notions of social balance and justify their social dynamics differently. Remarkably, the largest model is not necessarily more likely to achieve social balance with more frequency, stability, and diversity than the smaller ones.

@article{cisneros-velarde2025_2410.04054,
  title={ Large Language Models can Achieve Social Balance },
  author={ Pedro Cisneros-Velarde },
  journal={arXiv preprint arXiv:2410.04054},
  year={ 2025 }
}