Multi-Agent Social Simulation: Protocolizing LLM-Driven Agent-Based Modeling as a Quantitative Research Method
TL;DR
Results show that protocolized LLM-driven ABM can generate analyzable and empirically assessable outputs across policy-shock, information-intervention, and governance-feedback scenarios.
Abstract
Social and behavioral research often needs to examine policy shocks, information interventions, platform-mediated attention, and governance feedback, but direct experiments on real populations are constrained by ethical risks, intervention costs, and limited repeatability. This study proposes Multi-Agent Social Simulation (MASS), a protocolized form of large language model-driven agent-based modeling (LLM-driven ABM) designed as a low-risk, repeatable, and auditable pre-experimental simulation method for quantitative research. MASS embeds LLMs in an agent-based modeling (ABM) framework and uses role settings, round-based scheduling, information control, background-rule control, structured outputs, harness checks, reason-action logs, and replication manifests to transform open-ended language generation into recordable, checkable, and statistically analyzable agent-round observations. The method is evaluated through the New Jersey–Pennsylvania minimum wage natural experiment, the 2016 UK Brexit digital campaigning context, and the 2023 Zibo barbecue tourism public-opinion event. Results show that protocolized LLM-driven ABM can generate analyzable and empirically assessable outputs across policy-shock, information-intervention, and governance-feedback scenarios. The strongest evidence concerns rule-shock identification, declining undecided share under targeting, and mechanism-chain consistency among governance response, public sentiment, and behavioral intention. MASS is not a substitute for real-world experiments or causal inference; it is a pre-experimental simulation method for mechanism rehearsal, risk identification, counterfactual comparison, and research design preparation.