# Agent-Based Modelling Meets Generative AI in Social Network Simulations

arXiv: https://arxiv.org/abs/2411.16031 · HTML: https://mlsanity.com/paper/2411.16031/

## Key facts

- Cited by 26 papers in the map (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 12 of its 32 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## 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, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [The Interconnected Nature of Online Harm and Moderation: Investigating the Cross-Platform Spread of Harmful Content between YouTube and Twitter](https://mlsanity.com/paper/2304.01371/) (2023)

## Built on by (most cited first, 26 of 26)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Mind the Sim2Real Gap in User Simulation for Agentic Tasks](https://mlsanity.com/paper/2603.11245/) (2026)
- [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)
- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [Are LLM-Powered Social Media Bots Realistic?](https://mlsanity.com/paper/2508.00998/) (2025)
- [Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle](https://mlsanity.com/paper/2507.05723/) (2025)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [Simulating and Experimenting with Social Media Mobilization Using LLM Agents](https://mlsanity.com/paper/2510.26494/) (2025)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challenges](https://mlsanity.com/paper/2602.05883/) (2026)
- [PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling](https://mlsanity.com/paper/2602.06030/) (2026)
- [The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions](https://mlsanity.com/paper/2511.08592/) (2025)
- [What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessness](https://mlsanity.com/paper/2509.03827/) (2025)
- [DART: A Structured Dataset of Regulatory Drug Documents in Italian for Clinical NLP](https://mlsanity.com/paper/2510.18475/) (2025)
- [From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media](https://mlsanity.com/paper/2601.15114/) (2026)
- [Evidence-based Distributional Alignment for Large Language Models](https://mlsanity.com/paper/2603.13305/) (2026)
- [Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception](https://mlsanity.com/paper/2603.20750/) (2026)
- [BotVerse: Real-Time Event-Driven Simulation of Social Agents](https://mlsanity.com/paper/2603.29741/) (2026)
- [Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis](https://mlsanity.com/paper/2605.12507/) (2026)
- [Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale](https://mlsanity.com/paper/2606.30801/) (2026)
- [Artificial Intelligence and Modeling & Simulation: An Overview](https://mlsanity.com/paper/2608.00366/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)
- [SCBO: Semantically Coherent Batching and Ordering for LLM-Based Social Surveys](https://mlsanity.com/paper/2609.35250/) (2026)
- [Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulations](https://mlsanity.com/paper/2609.36278/) (2026)
- [From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations](https://mlsanity.com/paper/2610.16498/) (2026)

## Abstract (arXiv)

Agent-Based Modelling (ABM) has emerged as an essential tool for simulating social networks, encompassing diverse phenomena such as information dissemination, influence dynamics, and community formation. However, manually configuring varied agent interactions and information flow dynamics poses challenges, often resulting in oversimplified models that lack real-world generalizability. Integrating modern Large Language Models (LLMs) with ABM presents a promising avenue to address these challenges and enhance simulation fidelity, leveraging LLMs' human-like capabilities in sensing, reasoning, and behavior. In this paper, we propose a novel framework utilizing LLM-empowered agents to simulate social network users based on their interests and personality traits. The framework allows for customizable agent interactions resembling various social network platforms, including mechanisms for content resharing and personalized recommendations. We validate our framework using a comprehensive Twitter dataset from the 2020 US election, demonstrating that LLM-agents accurately replicate real users' behaviors, including linguistic patterns and political inclinations. These agents form homogeneous ideological clusters and retain the main themes of their community. Notably, preference-based recommendations significantly influence agent behavior, promoting increased engagement, network homophily and the formation of echo chambers. Overall, our findings underscore the potential of LLM-agents in advancing social media simulations and unraveling intricate online dynamics.