We present Social Digital Twinner, an innovative social simulation tool for exploring plausible effects of what-if scenarios in complex adaptive social systems. The architecture is composed of three seamlessly integrated parts: a data infrastructure featuring real-world data and a multi-dimensionally representative synthetic population of citizens, an LLM-enabled agent-based simulation engine, and a user interface that enable intuitive, natural language interactions with the simulation engine and the artificial agents (i.e. citizens). Social Digital Twinner facilitates real-time engagement and empowers stakeholders to collaboratively design, test, and refine intervention measures. The approach is promoting a data-driven and evidence-based approach to societal problem-solving. We demonstrate the tool's interactive capabilities by addressing the critical issue of youth school dropouts in Kragero, Norway, showcasing its ability to create and execute a dedicated social digital twin using natural language.
@article{arxiv.2505.10681,
title = {Towards an LLM-powered Social Digital Twinning Platform},
author = {Önder Gürcan and Vanja Falck and Markus G. Rousseau and Larissa L. Lima},
journal= {arXiv preprint arXiv:2505.10681},
year = {2025}
}
Comments
13 pages, 3 figures, 23rd International Conference on Practical applications of Agents and Multi-Agent Systems (PAAMS 2025)