Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent. We turn a statistical-physics observation into a method: replace each LLM agent by a low-parameter model fitted from a few hundred to a few thousand cheap queries, then run the society at any $N$ on a laptop. Whether this works is decided before the simulation runs, chiefly by what each agent perceives. We introduce an [interaction order x memory] taxonomy that maps perception and memory to an effective theory and a predicted $N$-tr...
| Authors | Igor Itkin |
|---|---|
| Primary category | cs.AI |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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