| title | Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop |
|---|---|
| authors | Igor Itkin |
| date | 2026-07-19 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.11215 |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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-trend of the surrogate error. We validate it on a faithful reimplementation of the LLM macroeconomy EconAgent and seven further named LLM simulations, with agent decisions cloned from genuine LLM elicitations (primarily DeepSeek) for a few dollars; the predicted error trends hold cell by cell, and the two refuted predictions, both on a strongly saturating response and traced to its curvature, are themselves matched quantitatively by the theory with no free parameters. |
cite
@misc{itkin2026poor,
title={Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop},
author={Igor Itkin},
year={2026},
eprint={2608.11215},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.11215}
}