LLM Powered Social Digital Twins: A Framework for Simulating Population Behavioral Response to Policy Interventions

TL;DR

This paper introduces a novel framework for creating Social Digital Twins using Large Language Models to simulate individual behaviors and predict population responses to policies, validated through COVID-19 case studies.

Contribution

The paper presents a domain-agnostic framework for constructing LLM-powered social digital twins that improve behavioral prediction accuracy and enable counterfactual policy analysis.

Findings

Achieved 20.7% reduction in prediction error over baselines.

Demonstrated monotonic, bounded responses to policy changes.

Validated framework in pandemic response with rich observational data.

Abstract

Predicting how populations respond to policy interventions is a fundamental challenge in computational social science and public policy. Traditional approaches rely on aggregate statistical models that capture historical correlations but lack mechanistic interpretability and struggle with novel policy scenarios. We present a general framework for constructing Social Digital Twins - virtual population replicas where Large Language Models (LLMs) serve as cognitive engines for individual agents. Each agent, characterized by demographic and psychographic attributes, receives policy signals and outputs multi-dimensional behavioral probability vectors. A calibration layer maps aggregated agent responses to observable population-level metrics, enabling validation against real-world data and deployment for counterfactual policy analysis. We instantiate this framework in the domain of pandemic response, using COVID-19 as a case study with rich observational data. On a held-out test period, our calibrated digital twin achieves a 20.7% improvement in macro-averaged prediction error over gradient boosting baselines across six behavioral categories. Counterfactual experiments demonstrate monotonic and bounded responses to policy variations, establishing behavioral plausibility. The framework is domain-agnostic: the same architecture applies to transportation policy, economic interventions, environmental regulations, or any setting where policy affects population behavior. We discuss implications for policy simulation, limitations of the approach, and directions for extending LLM-based digital twins beyond pandemic response.