SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
SocioVerse2 adds longitudinal interventions and researcher-controlled loops to generative-agent social simulation.
SocioVerse2 extends SocioVerse 1.0 into a human-AI co-evolutionary social simulation framework. A longitudinal loop evolves environments and forks counterfactual branches, while a controllable research loop treats the study as editable state. Infrastructure provides composable skills, researcher checkpoints, five persona pools, and an environment service over 21 point-in-time signal sources. Seven case studies cover canonical agent-based models, policy processes on real records, and macroeconomic nowcasting beyond the response model's knowledge cutoff.
- Two loops support longitudinal simulation and editable research state.
- Infrastructure uses five persona pools and 21 real-world signal sources.
- Validated on seven case studies, including macro nowcasting past the model cutoff.
- Code, data services, and a workbench are released open source.
Full article236 words · extracted from arxiv.org · click to collapse
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based modeling with real behavioral data. Existing platforms verify collective behavior, align simulated populations with real societies in cross-sections, and employ autonomous agents for the research process. However, two social science requirements remain without systematic support: intervention in the content of a simulation and the researcher's control over the process that produces it. We present SocioVerse2, which extends SocioVerse 1.0 into a human-AI co-evolutionary paradigm built from two loops and one infrastructure. The longitudinal simulation loop simulates the target population with evolving environments and forks counterfactual branches via interventions. The controllable research loop takes the study itself as an editable state and updates state versions via controllable editing. The social science agentic infrastructure carries both loops through composable skills with researcher checkpoints, a population service over five persona pools, and an environment service over 21 real-world signal sources with point-in-time guarantees. We validate SocioVerse2 across three case families and seven case studies, from reproducing canonical agent-based models to modeling policy processes on real records and nowcasting macro-economic indices beyond the response model's knowledge cutoff. With the human-AI co-evolutionary paradigm, these cases go beyond system demonstrations to become substantive studies that investigate frontier questions in their respective disciplines. Code, data services, and a workbench are released as open-source resources.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.24911