26-week simulation reveals LLM town economy stalls

In a paper titled "But How Would AI Agents Run a Town's Economy?", researchers placed 100 memory-equipped LLM agents in charge of a closed, money-conserving spatial economy mapped to Pokhara Lakeside geography for up to 26 simulated weeks across 91 validated runs and 2.44 million decisions. The study found that while a 12x tourist demand shock increased business revenue by 4.62x, monetary transmission abruptly stopped there: wages moved by only 1.03x and just 0.3% of 3,981 menu items were ever repriced. Similarly, when agents were given randomized cash transfers, 96.7% of the funds remained unspent hundreds of steps later, demonstrating a near-zero marginal propensity to consume. Crucially, ablation experiments revealed that swapping the underlying LLM altered every measured economic outcome, whereas deleting agents' memory produced no detectable changes.