When building social simulations with LLMs, enriching prompts with decision-relevant context (grounding and norms) can make agent behavior more stable to wording changes while keeping them responsive to actual social conditions.
This paper addresses why LLM agents in social simulations change behavior with minor prompt rewording. The authors propose a framework to systematically enrich context (adding background, grounding, and norms) and measure 'selective robustness'—whether agents ignore meaningless changes but respond to real social shifts.