Persona-Based Simulation of Human Opinion at Population Scale

2026-03-27

AI Summary

Existing social simulation approaches predominantly rely on demographic attributes, which inadequately capture individual psychological consistency and thereby limit the fidelity of intervention-effect modeling. This work proposes SPIRIT, a novel framework that introduces semi-structured personality representations—integrating structured psychological traits with unstructured narrative text—into large-scale agent-based simulations to drive context-sensitive, individualized opinion and behavioral responses via large language models. Evaluated on Ipsos KnowledgePanel data, SPIRIT significantly outperforms demographic-only baselines, not only reproducing self-reported survey responses with higher accuracy but also effectively capturing the heterogeneity inherent in human reactions. This advancement marks a paradigm shift from static prediction to dynamic, psychologically grounded simulation of social behavior.

Technology Category

Cognitive Modeling & Cognitive Systems: Simulating Human BehaviorMultiagent Systems: Agent-Based Simulation and Emergent BehaviorKnowledge Representation and Reasoning: Preferences

Application Category

User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

What does it mean to model a person, not merely to predict isolated responses, preferences, or behaviors, but to simulate how an individual interprets events, forms opinions, makes judgments, and acts consistently across contexts? This question matters because social science requires not only observing and predicting human outcomes, but also simulating interventions and their consequences. Although large language models (LLMs) can generate human-like answers, most existing approaches remain predictive, relying on demographic correlations rather than representations of individuals themselves. We introduce SPIRIT (Semi-structured Persona Inference and Reasoning for Individualized Trajectories), a framework designed explicitly for simulation rather than prediction. SPIRIT infers psychologically grounded, semi-structured personas from public social media posts, integrating structured attributes (e.g., personality traits and world beliefs) with unstructured narrative text reflecting values and lived experience. These personas prompt LLM-based agents to act as specific individuals when answering survey questions or responding to events. Using the Ipsos KnowledgePanel, a nationally representative probability sample of U.S. adults, we show that SPIRIT-conditioned simulations recover self-reported responses more faithfully than demographic persona and reproduce human-like heterogeneity in response patterns. We further demonstrate that persona banks can function as virtual respondent panels for studying both stable attitudes and time-sensitive public opinion.

Problem

• persona-based simulation
• human opinion modeling
• population-scale simulation
• individualized trajectories
• social science simulation

Innovation

• persona-based simulation
• large language models
• psychologically grounded personas
• virtual respondent panels
• individualized trajectories