Title: SOCIA: Joint Structure–Parameter Co-Optimization for Automated Simulator Construction
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Structure–Parameter Optimization, Agentic LLM, Social Simulation, LLM-guided Bayesian Optimization, Parameter Calibration

Abstract: Building credible simulators from data is difficult because structure design, parameter calibration, and out-of-distribution (OOD) robustness are tightly coupled. We introduce SOCIA (Simulation Orchestration for Computational Intelligence with Agents), a framework that treats simulator construction as joint structure–parameter co-optimization: it elicits mechanism-rich blueprints, exposes explicit tunable parameters, and instantiates a calibration schema, producing an executable simulator with built-in calibration hooks. SOCIA couples Bayesian Optimization for sample-efficient point calibration with Simulation-Based Inference for uncertainty-aware fitting; diagnostics trigger targeted structural edits in an outer refinement loop to co-optimize design and parameters under tight budgets. Across three diverse tasks, SOCIA consistently outperforms strong baselines, excelling on both in-distribution (ID) fitting and OOD shift. Ablations that weaken structure, calibration design, or tuning yield near-monotone degradations, underscoring the necessity of unified structure–parameter optimization. SOCIA’s code and data are available here.

TL;DR: This paper presents SOCIA, an LLM-guided framework that unifies structure generation and parameter calibration to automatically construct high-fidelity social simulations.