Exploring Causal Mechanisms with Generative Agent-Based Models

Research introduces RePair, a new methodology using generative agent-based models (ABMs) to explore how individual-level behavioral rules lead to collective phenomena. This method calibrates simulation environments, translates candidate mechanisms into natural-language rules, assesses their impact through matched interventions, and analyzes behavioral traces. The method's efficacy was tested across four established social-science models and empirical studies.

Understanding the causal links between individual behaviors and large-scale collective outcomes is critical for designing effective policies and interventions. This methodology offers a novel approach to test and validate such mechanisms, potentially improving predictive capabilities and strategic planning in complex systems.

Natural-language rules are capable of producing measurable collective effects within simulations.

Forward consideration, not a verified fact.