LLM-Enabled Agent-Based Model Simulates Social Dynamics in AuraSight Scenario
A recent study published on arXiv (2608.00929) presents the GhostField architecture, a hybrid model known as LLM-Enabled Agent Based Network-Dynamic (LAND), designed for social simulation. The research develops a fictional international songwriting competition called AuraSight to explore emerging social dynamics. Over a period of 30 days, 314,244 diverse cyber social agents and human participants exchanged a total of 529,327 messages. The analysis focuses on four layers of social dynamics: ego-network topology, semantic network evolution, coordination dynamics, and influence dynamics. Results reveal that the simulated social interactions generate dynamics through recursive interactions between network topology and narrative processes, rather than solely from individual agents. This paper is classified as a new announcement and can be accessed via the arXiv URL.
Key facts
- The paper is arXiv:2608.00929, announced as a new type.
- The GhostField architecture is a hybrid LLM-Enabled Agent Based Network-Dynamic (LAND) model.
- The AuraSight scenario involves a fictional international song-writing contest.
- The simulation includes 314,244 heterogeneous cyber social agents and human actors.
- A total of 529,327 messages are exchanged over 30 days.
- Four analytical layers are examined: ego-network topology, semantic network evolution, coordination dynamics, and influence dynamics.
- Results show that coordination and influence emerge from recursive interactions between network topology and narrative.
- The study demonstrates that generated social simulations can produce social dynamics.
Entities
Institutions
- arXiv