{
  "id": 1140,
  "url": "https://arxiv.org/abs/2606.12369v1",
  "title": "Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies",
  "summary": "Large language models (LLMs) are increasingly used as decision-making components in social simulations. This introduces a methodological risk: the simulation may deviate from the explicit behavioral policy defined by the researcher. In online social network (OSN) simulations, action choices shape system dynamics, interaction patterns, and model interpretability. This paper evaluates whether LLM action selectors preserve an interpretable reference policy in an OSN simulation. The reference is a f",
  "authors": "Alejandro Buitrago López, Javier Pastor-Galindo, José A. Ruipérez-Valiente",
  "category": "research",
  "topics": "regulation,safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-10T17:35:32.000Z",
  "fetched_at": "2026-07-14T14:15:03.615Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1140",
  "original_url": "https://arxiv.org/abs/2606.12369v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}