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100 LLM agents simulate 26-week town economy, revealing monetary stagnation

By PulseAugur Editorial · [1 sources] ·

A recent simulation placed 100 LLM agents in a closed economic model for 26 simulated weeks, using real-world geography and agent memory. The agents were tasked with earning wages, running businesses, and setting prices autonomously. Over millions of decisions, the simulation revealed that monetary transactions significantly slowed down, with wages and prices remaining largely static despite a large demand shock. This extended simulation duration exposed economic coordination and state management failures that are not apparent in shorter-term agent experiments. AI

IMPACT Reveals potential long-term economic coordination failures in multi-agent LLM systems.

RANK_REASON Research paper detailing a novel simulation methodology and its findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

100 LLM agents simulate 26-week town economy, revealing monetary stagnation

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Research paper detailing a novel simulation methodology and its findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · mech.app ·

    100 LLM Agents Running a Town Economy for 26 Weeks: What Breaks When Agents Set Prices and Earn Wages

    <p>A team placed 100 memory-equipped LLM agents in a closed economy simulation on real Pokhara Lakeside geography and ran it for 26 simulated weeks. The agents earned wages, ran businesses, and set prices autonomously. Across 91 validated runs (2.44M agent decisions, 21.5B tokens…

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