# Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model

arXiv: https://arxiv.org/abs/2608.00929 · HTML: https://mlsanity.com/paper/2608.00929/

## 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 17 of its 70 references that are in the map.
- First posted on arXiv 2 Aug 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, 17 of 17)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)
- [A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://mlsanity.com/paper/1910.12941/) (2019)
- [Are LLM-Powered Social Media Bots Realistic?](https://mlsanity.com/paper/2508.00998/) (2025)
- [An Exploratory Analysis of COVID Bot vs Human Disinformation Dissemination stemming from the Disinformation Dozen on Telegram](https://mlsanity.com/paper/2402.14203/) (2024)
- [Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems](https://mlsanity.com/paper/2605.07069/) (2026)
- [Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clustering](https://mlsanity.com/paper/2008.01139/) (2020)
- [From Curious Hashtags to Polarized Effect: Profiling Coordinated Actions in Indonesian Twitter Discourse](https://mlsanity.com/paper/2207.07937/) (2022)
- [Identifying and characterizing superspreaders of low-credibility content on Twitter](https://mlsanity.com/paper/2207.09524/) (2022)
- [The Dual Personas of Social Media Bots](https://mlsanity.com/paper/2504.12498/) (2025)
- [An agent-based model for emotion contagion and competition in online social media](https://mlsanity.com/paper/1706.02676/) (2017)
- [The Impact of COVID-19 on Twitter Ego Networks: Structure, Sentiment, and Topics](https://mlsanity.com/paper/2506.03788/) (2025)
- [Extending the BEND Framework to Webgraphs](https://mlsanity.com/paper/2509.13212/) (2025)
- [BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots](https://mlsanity.com/paper/2601.06154/) (2026)

## Abstract (arXiv)

Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance and coordinate behavior. This paper uses the the GhostField architecture, a hybrid LLM-Enabled Agent Based Network-Dynamic (LAND) model as a social simulation framework to build the AuraSight scenario. In the AuraSight scenario, 314,244 heterogeneous cyber social agents and human actors exchange 529,327 messages over 30 days surrounding a fictional international song-writing contest. We methodologically examine emergent social dynamics across four analytical layers: ego-network topology, semantic network evolution, coordination dynamics and influence dynamics. Our results show how generated social simulations do also produce social dynamics, and how the dynamics of coordination and influence emerge not from individual agents but from the recursive interaction between network topology and narrative exchange.