Towards an LLM-powered Social Digital Twinning Platform
This tool addresses societal problem-solving for stakeholders by providing a data-driven simulation platform, though it appears incremental as it integrates existing technologies like LLMs and agent-based modeling.
The authors tackled the problem of simulating complex social systems by developing Social Digital Twinner, an LLM-powered platform that enables natural language interaction for exploring what-if scenarios, demonstrated through a case study on youth school dropouts in Kragero, Norway.
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.