# Social Simulations: from Agent-Based Modeling to Digital Twins

arXiv: https://arxiv.org/abs/2607.13693 · HTML: https://mlsanity.com/paper/2607.13693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 9 of 9)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [Opinion dynamics: models, extensions and external effects](https://mlsanity.com/paper/1605.06326/) (2016)
- [NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks](https://mlsanity.com/paper/1801.05854/) (2017)
- [Empirically Grounded Agent-Based Models of Innovation Diffusion: A Critical Review](https://mlsanity.com/paper/1608.08517/) (2016)
- [Improving tobacco social contagion models using agent-based simulations on networks](https://mlsanity.com/paper/2207.08254/) (2022)

## Abstract (arXiv)

This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems. Along this trajectory, we discuss the main methodological foundations, applications, advantages, and limitations of each paradigm, highlighting the progressive shift from abstract models designed to investigate general social mechanisms toward increasingly realistic computational representations of specific social systems.