Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model
cs.AI
Submitted: 2026-08-02
Updated: 2026-09-14
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance and coordinate behavior.
Terminology
Abstract
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.
Sources
- Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations
- From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
- Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems
- From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation
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