Replicating Systems, Understanding Mechanisms

This paper surveys social simulation research using Large Language Models (LLMs), arguing that their contributionsshould be evaluated through two distinct objectives: (1) building a simulation as a faithful replication ofa specific real-world system, and (2) building a simulation as a theoretical model of mechanisms that recur acrossmultiple systems. Social simulation has historically pursued these aims separately: constructing detailed models ofparticular systems (e.g., organizations, cities) to test interventions, and developing abstract toy models to identifygeneral principles (e.g., polarization, segregation). The “human-like” capabilities of LLM-based agents hold promisefor both directions, yet their value and challenges differ fundamentally. For the purpose of faithfully replicating specificsystems, LLMs enable agents to embody complex individual attributes and engage in natural language-basedinteractions difficult to model traditionally. The central challenge is validation and calibration against real-world datato yield trustworthy insights for decision-makers. For the purpose of working as theoretical models, LLMs expandthe scope of theoretical modeling by internalizing linguistic interactions (e.g., negotiation, persuasion, rule modification)within agents, making phenomena such as opinion propagation, polarization, and norm formation amenableto formal analysis. A longer-term direction within this objective may be “high-dimensional science”, where detailedmulti-system simulations combined with large-scale predictive models and interpretability techniques may extractgeneral structures in a data-driven manner. Key challenges on the generality path include clarifying the theoreticalstatus of LLM agents and developing methods to extract interpretable principles from complex simulations. Overall,this survey argues that explicitly separating these two classical objectives enables the field to define appropriateevaluation criteria and research agendas for each.