# Towards an LLM-powered Social Digital Twinning Platform

arXiv: https://arxiv.org/abs/2505.10681 · HTML: https://mlsanity.com/paper/2505.10681/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 2 of 2)

- [LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities](https://mlsanity.com/paper/2405.06700/) (2024)
- [Generating Spatial Synthetic Populations Using Wasserstein Generative Adversarial Network: A Case Study with EU-SILC Data for Helsinki and Thessaloniki](https://mlsanity.com/paper/2501.16080/) (2025)

## Built on by (most cited first, 4 of 4)

- [CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation](https://mlsanity.com/paper/2506.21805/) (2025)
- [LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning](https://mlsanity.com/paper/2508.06799/) (2025)
- [LLM-enabled Social Agents](https://mlsanity.com/paper/2605.02335/) (2026)
- [Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networks](https://mlsanity.com/paper/2609.19913/) (2026)

## Abstract (arXiv)

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.