Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
arXiv:2608.11215v1 Announce Type: new 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.
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[Submitted on 19 Jul 2026]
Title:Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
View a PDF of the paper titled Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop, by Igor Itkin
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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.
Comments: 25 pages, 12 figures. Code and data at this http URL systematic review and pre-registration archived at Zenodo (doi:https://doi.org/10.5281/zenodo.21198322, doi:https://doi.org/10.5281/zenodo.21340310)
Subjects:
Artificial Intelligence (cs.AI); Statistical Mechanics (cond-mat.stat-mech); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Physics and Society (physics.soc-ph)
MSC classes: 82C22, 91D30, 68T50
ACM classes: I.2.11; I.6.5
Cite as: arXiv:2608.11215 [cs.AI]
(or arXiv:2608.11215v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.11215
arXiv-issued DOI via DataCite
Submission history
From: Igor Itkin [view email] [v1] Sun, 19 Jul 2026 08:44:38 UTC (2,381 KB)
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