Position: Simulating Society Requires Simulating Thought

Simulating society with large language models (LLMs), we argue, requires more than generating plausible behavior; it demands cognitively grounded reasoning that is structured, revisable, and traceable. LLM-based agents are increasingly used to emulate individual and group behavior, primarily through prompting and supervised fine-tuning. Yet they often lack internal coherence, causal reasoning, and belief traceability, making them unreliable for simulating how people reason, deliberate, and respond to interventions.