Paper deep dive
But How Would AI Agents Run a Town's Economy?
Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%
Last extracted: 9/11/2026, 3:31:51 AM
Summary
This paper presents a multi-agent simulation of a closed, money-conserving spatial economy on real Pokhara Lakeside geography, using 100 memory-equipped LLM agents over up to 26 simulated weeks. The study investigates monetary transmission mechanisms by applying demand shocks (tourist influx) and wealth transfers (cash grants). Results indicate that while business revenue increases significantly with demand, wages and prices remain largely static, suggesting a breakdown in monetary transmission at the firm level. Additionally, agents exhibit a near-zero marginal propensity to consume windfalls, leading to a 'frozen' wealth distribution in short horizons that only equilibrates over longer periods. The study highlights that the choice of underlying LLM significantly impacts outcomes, whereas agent memory has negligible effect.
Entities (9)
Relation Signals (7)
LLM Agents → operatesin → Pokhara Lakeside
confidence 98% · We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography
Tourist Demand Shock → causes → Business Revenue Increase
confidence 95% · A 12x tourist demand shock raises business revenue 4.62x
Simulation Horizon → affects → Wealth Distribution Stability
confidence 94% · rho falls to 0.832 at 12 weeks and 0.752 at 26, a horizon-dependence no short study can see
LLM Model Choice → influences → Economic Outcomes
confidence 93% · Swapping the backing LLM moves every outcome we measure (p=0.0039)
Cash Transfer → resultsin → Low Marginal Propensity to Consume
confidence 92% · A randomized cash transfer... shows... marginal propensity to consume 3-4%... indistinguishable from zero
Tourist Demand Shock → doesnotaffect → Wages
confidence 90% · Wages move 1.03x (p=0.42)
Agent Memory → doesnotinfluence → Economic Outcomes
confidence 90% · deleting agents' memory moves none of them detectably
Cypher Suggestions (0)
No Cypher suggestions yet.
Abstract
Abstract:We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running businesses, setting prices) and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1-2 weeks typical of agent-society studies. Across 91 validated runs (2.44M agent decisions, 21.5B tokens), the money stops moving, in a specific and measurable way. A 12x tourist demand shock raises business revenue 4.62x ($p<0.001$), which we decompose exactly into a 1.50x extensive margin (more businesses trading) and a 3.07x intensive margin (more revenue each). Monetary transmission stops there. Wages move 1.03x ($p=0.42$); 0.3% of 3,981 menu items are ever repriced ($p=0.47$). A randomized cash transfer (NPR 5,000 to 20 of 100 agents) shows the same pattern from the opposite direction: 96.7% is still held 311 pulses later, marginal propensity to consume 3-4% by two independent measures, indistinguishable from zero. The wealth distribution is consequently near-frozen at the horizon this literature uses ($\rho=0.964$ over 2 simulated weeks), but not frozen. $\rho$ falls to 0.832 at 12 weeks and 0.752 at 26, a horizon-dependence no short study can see. Matched ablations show which knob actually matters. Swapping the backing LLM moves every outcome we measure ($p=0.0039$); deleting agents' memory moves none of them detectably. A purely social tool fails 94-97% of the time across two model families, compared with ~96% success on economic tools, with no measurable shift away from it. Every headline number is verified twice, by a live validator and by an offline recomputation that reconciles each agent's wealth against its own signed transaction history, and we release the full run corpus for reanalysis.
Tags
Links
- Source: https://arxiv.org/abs/2609.11108v1
- Canonical: https://arxiv.org/abs/2609.11108v1
Trouble viewing inline? Open PDF directly →
Full Text
44,673 characters extracted from source content.
Expand or collapse full text
But How Would AI Agents Run a Town’s Economy? Sajal Regmi Note: Primary contributor. Affiliation: Karela Technologies Inc., USA email: sajal@karelatechnologies.com , Siddhartha Pudasaini Affiliation: Karela Technologies Inc., USA and Chetan Phakami Pun Affiliation: Karela Technologies Inc., USA © none Abstract. We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running businesses, setting prices) and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1–2 weeks typical of agent-society studies. Across 91 validated runs (2.44M agent decisions, 21.5B tokens), the money stops moving, in a specific and measurable way. A 12× tourist demand shock raises business revenue 4.62× (p<0.001p<0.001), which we decompose exactly into a 1.50× extensive margin (more businesses trading) and a 3.07× intensive margin (more revenue each). Monetary transmission stops there. Wages move 1.03× (p=0.42p=0.42); 0.3% of 3,981 menu items are ever repriced (p=0.47p=0.47). A randomized cash transfer (NPR 5,000 to 20 of 100 agents) shows the same pattern from the opposite direction: 96.7% is still held 311 pulses later, marginal propensity to consume 3–4% by two independent measures, indistinguishable from zero. The wealth distribution is consequently near-frozen at the horizon this literature uses (ρ=0.964ρ=0.964 over 2 simulated weeks), but not frozen. ρ falls to 0.832 at 12 weeks and 0.752 at 26, a horizon-dependence no short study can see. Matched ablations show which knob actually matters. Swapping the backing LLM moves every outcome we measure (p=0.0039p=0.0039); deleting agents’ memory moves none of them detectably. A purely social tool fails 94–97% of the time across two model families, compared with ∼ 96% success on economic tools, with no measurable shift away from it. Every headline number is verified twice, by a live validator and by an offline recomputation that reconciles each agent’s wealth against its own signed transaction history, and we release the full run corpus for reanalysis. Extended version: not constrained to a conference page limit, with Artifact Availability as its own section. 1. Introduction A growing body of work embeds LLM agents in towns, markets, and organizations and asks what happens (Park et al., 2023; Vezhnevets et al., 2023; Horton et al., 2023). Most run for days or a couple of simulated weeks, report that agents behave in recognizably human ways, and stop there. The question that matters for deploying agentic AI in economic roles is whether resources that enter such a system move: whether demand reaches wages, whether windfalls get spent, whether a population’s wealth is genuinely evolving or sitting still on the only timescale anyone has checked. We built a closed, money-conserving spatial economy of 100 memory-equipped LLM agents on real Pokhara Lakeside geography, working shifts, running or patronizing 762 registered businesses, and transacting under a live price system. Every rupee is accounted for: total money equals the sum of agent balances at each of 41,328 recorded pulses, exactly, across all 91 validated runs (§5). This lets us follow a shock or a transfer rupee by rupee, and run long enough (up to 26 simulated weeks, against the 1–2 weeks typical of comparable work) to ask whether the wealth distribution we observe is stable or just unobserved for long enough. Money enters and stops at the firm. A 12× tourist shock is delivered cleanly (δ=1.00δ=1.00, padj<0.001p_adj<0.001) and revenue rises 4.62×, which decomposes exactly into a 1.50× extensive margin (more businesses trading) and a 3.07× intensive margin (more revenue where they do). Neither reaches workers: wages move 1.03× (p=0.42p=0.42); 0.3% of 3,981 menu items are ever repriced (p=0.47p=0.47). The chain a textbook tourism economy predicts breaks at the second link. Money that reaches households does not circulate. A within-run randomized transfer (NPR 5,000 to 20 of 100 agents) confirms this: 96.7% is still held 311 pulses later, and two independent MPC estimates agree at 3.3% and 4.0%, neither distinguishable from zero. The distribution is frozen, but only if you stop early. At the conventional 2-week horizon, wealth persistence is ρ=0.964ρ=0.964 and only 21% of agents ever change quintile. At 12 weeks (4 runs) ρ falls to 0.832. In a single 26-week run, three times longer than any comparable economic simulation we know of, it drops to 0.752, with the Gini relaxing from 0.674 to 0.643 by week 16. Horizon changes the answer from “frozen” to “slowly equilibrating.” Two further results complete the picture. Swapping the backing LLM moves every outcome at the p=0.0039p=0.0039 floor available with 9 matched seeds; removing agents’ memory moves none of them detectably. And invite_to_talk fails 94–97% of the time across two model families, accounting for 79.6% of all tool failures. Contributions. (1) A validated, released dataset of 91 runs (2.44M decisions, 21.5B tokens), audited by two independent pipelines (§5); (2) an identified mechanism for monetary transmission failure, decomposed into extensive and intensive margins (§6) and corroborated by a randomized transfer experiment (§7); (3) direct evidence that study horizon changes the qualitative conclusion about wealth mobility (§8); and (4) a demonstration that model choice, not memory, drives behavior, alongside a persistent, model-agnostic failure of social coordination tooling (§9). We release the run corpus; the platform itself is not (§12). 2. Related Work Generative agent societies. Park et al. (2023) found that LLM agents equipped with memory, reflection, and planning exhibit credible individual and emergent social behavior across roughly two simulated days, extending earlier persona-populated interfaces (Park et al., 2022). Concordia (Vezhnevets et al., 2023) broadens this into a reusable grounding framework and has been deployed at social-network scale (Gao et al., 2023). Agentopia (Wang et al., 2026) pushes life simulation to 10 simulated years with 100 agents, but its coarse time units and well-being objectives leave economic aggregates unmeasured. This line of work demonstrates qualitatively plausible individual behavior, but does not instrument a closed resource system finely enough to test whether aggregate outcomes (prices, wages, wealth) react to shocks the way a real economy does, and horizons remain short. Our platform addresses this directly: each transaction is ledgered, money is conserved and can be independently audited (§5), and runs last 26 simulated weeks. LLMs as economic agents. A distinct strand examines whether LLMs replicate human-subject behavior in surveys, auctions, and bargaining (Horton et al., 2023; Argyle et al., 2023), or whether they can trade autonomously (Li et al., 2023). These studies usually probe a single decision or one narrow mechanism, not 100 agents jointly sustaining an economy over weeks. EconAgent (Li et al., 2024) and EconSimulacra (Hashimoto et al., 2026) come closest, driving macroeconomic and consumer-economy simulations with LLM agents; neither conserves money at the transaction level, which is what licenses the exact margin decomposition in §6. Our results speak to this literature’s core benchmark, the marginal propensity to consume out of a windfall, typically 0.2–0.5 for human households (Johnson et al., 2006; Jappelli and Pistaferri, 2014). Our measured 3–4% is a sharp outlier against it. Multi-agent benchmarks. AgentBench (Liu et al., 2024) and WebArena (Zhou et al., 2024) score agents on a per-episode success criterion. Our setting has no such criterion; instead we follow the aggregate trajectory of a closed economy across thousands of decision rounds, which is why we report macroeconomic and distributional metrics (Gini (Gini, 1912), wealth-rank persistence, wage pass-through) in place of task-level scores. Positioning. This work is an empirical multi-agent-systems contribution, not a capability demonstration. We aim to identify a mechanism behind a specific failure, not to show that LLM agents can complete some task. The platform serves as the apparatus; the dataset and the mechanism are what we contribute. Larooij and Törnberg (2025) identify validation as the weak point of generative agent-based modeling; the conserved-money ledger and two independent validators (§5) are our answer to it. 3. The Environment Two panels. Panel a plots tourists present against pulse index for the baseline and tourism-shock conditions, averaged over 9 seeds; the shock condition rises about fourteen-fold during a shaded window spanning pulses 84 to 143 and returns to baseline afterwards, while the baseline condition stays flat. Panel b is a table of the 91 accepted runs, listing for each of seven arms the conditions covered, the number of seeds, the pulses per run, and the decision model: core arms at 336 pulses on Qwen3.8-27B, layout-control and memory-off arms at 6 seeds, a cross-model arm on gpt-oss-20b, and two long arms at 2,016 and 4,368 pulses. Figure 1. (a) The tourism-shock treatment: a transient 14× rise in tourists present, mean of 9 seeds. (b) The 91 accepted runs analyzed here, across seven arms that share a common seed pool for matched pairing.Two panels. Panel a plots tourists present against pulse index for the baseline and tourism-shock conditions, averaged over 9 seeds; the shock condition rises about fourteen-fold during a shaded window spanning pulses 84 to 143 and returns to baseline afterwards, while the baseline condition stays flat. Panel b is a table of the 91 accepted runs, listing for each of seven arms the conditions covered, the number of seeds, the pulses per run, and the decision model: core arms at 336 pulses on Qwen3.8-27B, layout-control and memory-off arms at 6 seeds, a cross-model arm on gpt-oss-20b, and two long arms at 2,016 and 4,368 pulses. Figure 2. A run rendered over real Lakeside, Pokhara geography; markers are agents at their current place among 762 registered businesses. Map data © OpenStreetMap, via Mapbox.A screenshot of the simulation rendered over a street map of Lakeside, Pokhara, Nepal. Coloured circular markers scattered across the map show the current location of each of the 100 agents among the 762 registered businesses, which are labelled with real business names. The lake occupies the lower left of the frame. The map imagery is third-party material from OpenStreetMap via Mapbox. Setting. The world is modeled on Lakeside, Pokhara, Nepal, with real building footprints and business locations (Figure 2). The registry holds 762 places across seven categories; 735 carry priced menus totaling 3,981 items. One hundred LLM agents populate the town, each with a cash wallet, a bank balance, and an 80 m visibility radius gating what it can perceive. Two clocks. The world clock advances in fixed 60-simulated-minute pulses, at which the platform resolves scheduled dynamics and snapshots system state. The agent clock is event-driven: each agent wakes, reasons, and acts asynchronously within a pulse rather than all 100 acting in lockstep. A 336-pulse (2-week) run is a continuous, staggered stream of reasoning episodes rather than 336 rounds of simultaneous decisions. The corpus totals 2.44M agent decisions and 21.5B tokens (§5). Agents and tools. Every agent shares one specification: an LLM decision policy (§4), persistent memory (ablated in one arm; §9), and a fixed 19-action toolset: navigation, the local economy (buy_food, set_price, start_shift, pay_agent), memory (write_memory, search_memory, reflect), and social coordination (invite_to_talk, accept_invite, leave_conversation). A subset own a business and may set its prices and pay its wages. Every tool call, transaction, price observation, and conversation turn is logged with pulse-level timestamps. Money conservation. Total money (every agent’s wallet plus bank balance) is conserved exactly except through two ledgered external accounts, a tourist inflow pool and a government sink. Money that leaves agent hands accrues to business cash-on-hand and bank balances. 4. Experimental Design Conditions and arms. Every headline run uses the same baseline agent specification (§3); they differ only in world state and shocks. Five treatment conditions alter the tourist arrival rate and two scheduled interventions: baseline (2.0/sim-hour), tourism-low (0.5), tourism-high (6.0), tourism-shock (arrival steps low→ → across pulses 84–143, Figure 1a), and wealth-grant (baseline arrivals plus a transfer to 20 of 100 agents at pulse 24). Three more arms keep the condition fixed and change one factor each: a duplicate world layout (layout-b), agent memory removed (no-memory), and the decision LLM replaced (cross-model). Two arms lengthen baseline to 12 and 26 simulated weeks. Figure 1b lists the full inventory. Seeds are drawn from a single pre-registered pool shared across all arms, which makes the matched-seed pairing used throughout §9 possible. Deviations from pre-registration. Conditions, seed lists, and directional hypotheses were committed before data collection. The planned headline sweep was 5 conditions × 30 seeds, with a budget rule set in advance: fall back to 15 seeds/condition if per-run cost exceeded projections. That call was made before any comparative result was seen. The realized sweep fell below even that fallback: 9 seeds/condition, a 336-pulse horizon instead of 168, self-hosted Qwen3.8-27B-FP8 instead of the planned MiMo, and ablations on memory/layout/cross-model instead of agent count/visibility radius. The cause was a mid-project shift to self-hosted inference, which traded seed count for a longer horizon. We are explicit about this: 9 seeds is too few to rely on between-seed asymptotics, so every headline comparison below is a matched-seed or within-run contrast. Exclusions. Of 98 dispatched runs, 91 are used here. Six were dropped from cross-model because its third model, Mistral-Small-3.2-24B, emits duplicate tool-call identifiers within a single reasoning episode, which the inference server rejects on the follow-up turn; the circuit breaker halted each such run within 4 pulses. We keep this in §9 as a negative result on multi-hop tool-calling robustness. One further run (tourism-low, seed 10091) drew 56 tourist arrivals against a pre-registered 3σ3σ floor of 56.5, even though it completed all 336 pulses and passed every other check. The pre-committed rule, not the analyst, decides that exclusion. 5. Validation Every number in this paper comes from a single committed pipeline that reads the archived run artifacts directly; nothing is hand-computed. Two independent validators gate every run: the platform’s live TypeScript validator checks run-level integrity at completion (invariants, provider health, pulse-series completeness), and an offline Python validator recomputes every headline number a second time, in a second language, from the raw CSV exports. Its most demanding check is a per-agent ledger reconciliation: the change in wallet-plus-bank balance must equal the signed sum of that agent’s own transaction history, exactly, which catches export truncation and accounting bugs a purely aggregate check would miss. Table 1. Independent Python revalidation from raw CSV exports, all 91 accepted runs. Full 19-check breakdown released with the data. Check Runs passing 17 checks (ledger_vs_panel, money_series, all flow_* totals, …) 91/91 each terminal_gini_vs_metrics (warn) 28/91 tokens_vs_ledger (fail) 73/91 In Table 1, 17 of 19 checks, including the ledger reconciliation and every transaction-flow total, pass on all 91 runs with zero exceptions. terminal_gini_vs_metrics (28/91) flags a benign staleness issue: the live metrics.json snapshot reads current world state at export time and can drift ∼ 0.001 in Gini if another run later ran on the same world; the pulse-level CSV panel used throughout this paper is unaffected. tokens_vs_ledger (73/91) fails on every gpt-oss run and no Qwen run: gpt-oss forfeits a small share of malformed generations (§9) counted in the pulse-level token counter but excluded from the per-call ledger. Money conservation, directly. The strictest check does not appear in the table. totalMoneyInSystem equals the independently computed sum of every agent’s wallet and bank balance at every one of the 41,328 recorded pulses across all 91 runs, zero deviation. Money enters only through a ledgered tourist-inflow account and leaves only through a ledgered government sink; every rupee in between sits in an agent balance or a business’s cash-on-hand/bank balance. The margin decomposition in §6 and the transfer-tracing in §7 both depend on this identity. Serving differences by model. Table 2 reports raw LLM call outcomes. Getting gpt-oss to a usable failure rate required three changes, disclosed because they affect §9’s cross-model comparison: raising the output budget 512→ 1,024 tokens (gpt-oss reasons before a tool call; cut malformed output 24.0%→ 6.0%); disabling FP8 KV-cache quantization (gpt-oss is natively quantized and produced garbage tokens stacked with a second layer, 6.0%→ 3.3%); and a tool-name normalization step after the serving stack’s parser leaked internal channel markers into tool names, which had corrupted 16–35% of actions before the fix. A residual 0.86% of gpt-oss calls still return a malformed generation and are forfeited, against 0.00% for Qwen. Any tool-reliability probe on this platform must replay recorded production requests; a reduced probe we ran early at 3 tools and temperature 0 reported 100% success on this same failure mode and would have concealed the §9 result. Table 2. LLM call outcomes by decision model, all accepted 336-pulse runs. tool_calls/length are OpenAI-style finish reasons; malformed generations are forfeited, not retried. Decision model Runs LLM calls tool_calls length malformed Qwen/Qwen3.8-27B-FP8 73 4,125,699 70.3% 15.2% 0.00% openai/gpt-oss-20b 18 957,828 65.8% 26.7% 0.86% 6. Transmission Failure Three panels. Panel a plots thousands of NPR per run against tourist arrival rate at low 0.5, medium 2.0, and high 6.0 per hour: business revenue rises steeply from about 290 to about 1,340 thousand, while wages paid to agents stay flat near 260 thousand across the whole range. Panel b shows wages as a share of revenue falling monotonically across the same three arrival rates, from 0.90 to 0.45 to 0.20. Panel c is a horizontal bar chart of high-divided-by-low multipliers: tourist arrivals 12.00 times by design, business revenue 4.62 times, firms transacting 1.50 times, revenue per firm 3.07 times, and then wages to agents 1.03 times and menu items repriced 1.17 times, the last two indistinguishable from a multiplier of one. Figure 3. (a) Revenue tracks tourist arrival rate; wages do not. (b) Wage share of revenue collapses 0.90 (low) → 0.20 (high). (c) The multiplier cascade: 4.62× revenue decomposes exactly into a 1.50× extensive and 3.07× intensive margin, then stops, with wages and prices within noise of 1×.Three panels. Panel a plots thousands of NPR per run against tourist arrival rate at low 0.5, medium 2.0, and high 6.0 per hour: business revenue rises steeply from about 290 to about 1,340 thousand, while wages paid to agents stay flat near 260 thousand across the whole range. Panel b shows wages as a share of revenue falling monotonically across the same three arrival rates, from 0.90 to 0.45 to 0.20. Panel c is a horizontal bar chart of high-divided-by-low multipliers: tourist arrivals 12.00 times by design, business revenue 4.62 times, firms transacting 1.50 times, revenue per firm 3.07 times, and then wages to agents 1.03 times and menu items repriced 1.17 times, the last two indistinguishable from a multiplier of one. We use the tourism-intensity sweep, spanning tourism-low (0.5/sim-hour), baseline (2.0), and tourism-high (6.0), as a controlled comparative-statics experiment. The manipulation check is unambiguous (Table 3): tourist spending separates completely (δ=1.00δ=1.00, padj<0.001p_adj<0.001), so any downstream null cannot be attributed to a weak treatment. Table 3. Tourism intensity sweep, terminal-pulse outcomes (mean over 8–9 seeds). δ: Cliff’s delta, high vs. low, Holm-Bonferroni-adjusted across the six-outcome family. Tourist arrival rate (per sim-hour) high vs low Outcome 0.5 2.0 6.0 δ padjp_adj Gini (terminal) 0.6712 0.6716 0.6706 -0.28 0.994 Median wealth (NPR) 23,954 24,307 24,659 +0.50 0.278 Purchase attempts 1,262 1,317 1,289 +0.14 1.000 Purchase failure rate 0.1300 0.1291 0.1279 +0.03 1.000 Tourist spending (NPR) 87,639 370,576 1,133,633 +1.00 <<0.001 Persistence ρ 0.9702 0.9643 0.9683 -0.33 0.416 Revenue responds; wages and prices do not. Business revenue rises 4.62× across the sweep (p=0.0001p=0.0001; Figure 3a), and place-level wealth rises with it, 9.8M→ 14.9M NPR. Two outcomes that a standard demand-pass-through model predicts would move in step do not move at all. Wages are flat across the full 12× range: 260,389 vs. 267,459 NPR (p=0.42p=0.42). The wage share of revenue falls monotonically, 0.90 (low) → 0.45 (baseline) → 0.20 (high; Figure 3b). As the tourist economy grows, a shrinking share of the money it generates reaches the agents staffing it. Prices are more static still: of 3,981 menu items, only 0.27–0.32% are ever repriced, unresponsive to a 4.6× revenue swing (p=0.47p=0.47). Terminal median wealth is not distinguishable from noise (padj=0.28p_adj=0.28). The chain breaks at the firm. Money conservation (§5) lets us decompose the 4.62× response exactly, not just observe it. The extensive margin is how many of 762 registered businesses record any transaction; the intensive margin is revenue per business that does. Table 4 and Figure 3c report both: 1.50× more businesses trade at high demand (458 vs. 304 of 762, p<0.001p<0.001), and each earns 3.07× more (p<0.001p<0.001). These multiply to 1.504×3.067=4.611.504× 3.067=4.61 against an observed 4.62, matching up to rounding. Demand growth is absorbed by drawing more of the fixed registry into activity and by deepening takings at active businesses. Neither channel has any mechanical connection to a wage or a menu price, and empirically, neither moves one. Table 4. The multiplier cascade, low vs. high tourist arrival. Quantity low (0.5/h) high (6.0/h) Ratio p Tourist arrival rate 0.5 6.0 12.00× by design Business revenue (NPR) 289,370 1,337,007 4.62× <<0.001 firms transacting (of 762) 304 458 1.50× <<0.001 revenue per firm (NPR) 953 2,922 3.07× <<0.001 Wages paid to agents (NPR) 260,389 267,459 1.03× 0.423 Menu items repriced 0.27% 0.32% – 0.468 Interpretation. We do not claim agents are irrational. set_price is called only ∼ 18 times per run out of ∼ 28,000 total calls, so this is partly about agents rarely exercising a lever that exists, and partly about how sticky the initial menu is by construction (§11). In a closed, audited economy where agents can adjust wages and prices, a cleanly-identified demand shock does not propagate past the firm under the policies agents actually adopt: gains concentrate at owners instead of passing to workers or customers. 7. Windfalls Are Not Circulated Three panels. Panel a plots mean wealth in thousands of NPR against pulse for treated and control agents: the treated line starts about 5,000 NPR below control, jumps to meet it at pulse 24 when the grant is paid, and the two lines then run in parallel for the remaining 311 pulses. Panel b plots the percentage of the transfer still held by the treated group over time with a standard-error band across 9 seeds, declining only slightly to 96.7 percent by the end of the run. Panel c is a dot plot of excess purchase spending in NPR for each of nine seeds, scattered on both sides of zero, with a mean of about 200 NPR marked by a vertical line, corresponding to a marginal propensity to consume of 4.0 percent at p equals 0.22. Figure 4. (a) Mean wealth, treated (+NPR 5,000 at pulse 24) vs. control: the transfer closes the gap instantly, then both move in parallel. (b) Share of the transfer still held over time (s.e. band, 9 seeds). (c) Excess purchase spending per seed; the mean is not distinguishable from zero.Three panels. Panel a plots mean wealth in thousands of NPR against pulse for treated and control agents: the treated line starts about 5,000 NPR below control, jumps to meet it at pulse 24 when the grant is paid, and the two lines then run in parallel for the remaining 311 pulses. Panel b plots the percentage of the transfer still held by the treated group over time with a standard-error band across 9 seeds, declining only slightly to 96.7 percent by the end of the run. Panel c is a dot plot of excess purchase spending in NPR for each of nine seeds, scattered on both sides of zero, with a mean of about 200 NPR marked by a vertical line, corresponding to a marginal propensity to consume of 4.0 percent at p equals 0.22. §6 shows money does not reach households through the firm. We now ask the complementary question, under the strongest identification available here: if money does reach a household directly, does it move? We answer with a cash transfer randomized within a single run. Treated and control share the same world, shocks, and counterparties, so the comparison needs no cross-seed variance assumption. In wealth-grant, 20 of 100 agents are chosen at random and receive NPR 5,000 at pulse 24 from the ledgered government account; the remaining 80 form the control. We track the wealth gap across all 336 pulses and independently measure post-transfer purchase spending, for two estimates of the marginal propensity to consume (MPC). The transfer lands and stops moving. The treated–control gap jumps from −-NPR 5,229 (pre-transfer imbalance) to ++5,023 at pulse 24, essentially the full grant (t=176.8t=176.8, p<10−14p<10^-14; Figure 4a), then decays only slowly: 99.8% still held at pulse 84, 96.7% at pulse 335 (t=17.1t=17.1, p<10−6p<10^-6; Figure 4b), no sign of reversion. We cross-check against actual spending: treated agents spend a mean of NPR 2,096 post-transfer against 1,896 for control, an excess of 200 (Figure 4c) that is not distinguishable from zero (p=0.22p=0.22 t-test, p=0.16p=0.16 Wilcoxon). The two MPC estimates agree: 3.3% from wealth trajectory, 4.0% from spending, both far below the 0.2–0.5 range for human households (Johnson et al., 2006; Jappelli and Pistaferri, 2014). Interpretation. These agents are, in aggregate, savers rather than spenders. A windfall that does not circulate cannot generate the second-round demand that would let it reach anyone else, part of why the distribution in §8 moves so slowly. 8. The Wealth Distribution Only Looks Frozen Three panels. Panel a plots the Spearman correlation between initial and terminal wealth against run length in simulated weeks, falling from 0.964 at 2 weeks with 9 runs, to 0.832 at 12 weeks with 4 runs, to 0.752 at 26 weeks with a single run; a dashed line at 1.0 marks a perfectly frozen distribution. Panel b is a bar chart of the percentage of agents changing wealth quintile at the same three horizons, rising from 21 to 45 to 46 percent, all far below a marked 80 percent chance level. Panel c plots the Gini coefficient of agent wealth across the single 26-week run: it starts at 0.674, falls fairly sharply over the first ten weeks, and settles near 0.643 from about week 16 onward. Figure 5. (a) Spearman correlation between initial and terminal wealth vs. run length. (b) Share of agents changing wealth quintile; dashed line marks the 80% expected under independent reshuffle. (c) Gini across the single 26-week run: a fast initial rise, then slow relaxation to a plateau near week 16.Three panels. Panel a plots the Spearman correlation between initial and terminal wealth against run length in simulated weeks, falling from 0.964 at 2 weeks with 9 runs, to 0.832 at 12 weeks with 4 runs, to 0.752 at 26 weeks with a single run; a dashed line at 1.0 marks a perfectly frozen distribution. Panel b is a bar chart of the percentage of agents changing wealth quintile at the same three horizons, rising from 21 to 45 to 46 percent, all far below a marked 80 percent chance level. Panel c plots the Gini coefficient of agent wealth across the single 26-week run: it starts at 0.674, falls fairly sharply over the first ten weeks, and settles near 0.643 from about week 16 onward. §6–7 identify a mechanism for why money does not move. This asks what it implies for the wealth distribution, and the answer depends on how long you watch. Persistence at the conventional horizon. Over the 336-pulse (2-week) baseline runs common in this literature, terminal wealth is almost fully predictable from pulse-0 wealth: Spearman ρ=0.964ρ=0.964 (Table 5), and only 21% of agents ever change wealth quintile, against 80% expected under independent reshuffle. It holds across the tourism sweep (ρ 0.964–0.970). A study that runs two weeks and stops, which is most of the generative-agent-society literature (§2), would correctly report this as static. It is not static. We extend baseline to 12 weeks (4 runs) and one run to 26, three times longer than any run in the headline sweep. Persistence falls monotonically: ρ=0.964ρ=0.964 at 2 weeks, 0.8320.832 at 12, 0.7520.752 at 26 (Figure 5a), and quintile mobility rises correspondingly, 21%→ 45%→ 46% (Figure 5b). Every 12-week run shows lower persistence than every 2-week run; the ordering is not a mean-comparison artifact. The single 26-week trajectory (Figure 5c) makes the mechanism visible: Gini rises slightly in the first two weeks, falls sharply to ≈ 0.641 by week 10, and plateaus near 0.643. The economy is relaxing on a timescale of roughly ten weeks, invisible to any study run at the horizon this literature typically uses. Table 5. Wealth-rank persistence by horizon, baseline condition. Runs at 12/26 weeks are few; see §11. Sim. weeks Pulses Runs ρ Quintile moved GiniT Top-10% share 2 336 9 0.964 21% 0.6716 0.5767 12 2,016 4 0.832 45% 0.6543 0.5410 26 4,368 1 0.752 46% 0.6434 0.5116 What this means for study design. A wealth-distribution conclusion drawn from an LLM-agent economy is only as trustworthy as the horizon it was measured over. The 12/26-week results rest on 5 runs, and the 26-week trajectory is a single path (§11). Still, the near-total separation between horizons is a stronger signal than the raw run count suggests, and is, to our knowledge, the first direct evidence in this literature that a stated conclusion about wealth mobility depends on how long the simulation runs. 9. What Actually Drives Behavior Three panels of matched-seed contrasts against the baseline arm, each plotting percentage change for eight outcomes, with filled markers indicating p below 0.05 and hollow markers indicating no significant difference. Panel a, a duplicate world layout used as a placebo with 6 matched seeds, shows all outcomes within a few percent of zero and non-significant except tool calls per run at plus 9.0 percent and economic tool failure at minus 15.5 percent. Panel b, memory tools withdrawn with 6 matched seeds, shows every outcome non-significant, ranging from plus 0.4 to minus 9.0 percent. Panel c, the decision model swapped to gpt-oss-20b with 9 matched seeds, uses a broken axis and shows every outcome significant and far larger, including Gini plus 5.8 percent, median wealth minus 22.9 percent, quintile mobility plus 62.4 percent, and economic tool failure plus 110.4 percent. Figure 6. Matched-seed contrasts vs. baseline core-a (filled = p<0.05p<0.05). (a) Duplicate layout, a placebo. (b) Memory withdrawn. (c) LLM swapped for gpt-oss-20b, with an axis break; effects 3–10× larger.Three panels of matched-seed contrasts against the baseline arm, each plotting percentage change for eight outcomes, with filled markers indicating p below 0.05 and hollow markers indicating no significant difference. Panel a, a duplicate world layout used as a placebo with 6 matched seeds, shows all outcomes within a few percent of zero and non-significant except tool calls per run at plus 9.0 percent and economic tool failure at minus 15.5 percent. Panel b, memory tools withdrawn with 6 matched seeds, shows every outcome non-significant, ranging from plus 0.4 to minus 9.0 percent. Panel c, the decision model swapped to gpt-oss-20b with 9 matched seeds, uses a broken axis and shows every outcome significant and far larger, including Gini plus 5.8 percent, median wealth minus 22.9 percent, quintile mobility plus 62.4 percent, and economic tool failure plus 110.4 percent. Figure 7. Per-tool failure rate. (d) Qwen3.8-27B: social tools fail 94–100%, economic/navigation mostly succeed. (e) Replicates almost unchanged in gpt-oss-20b.Two panels on per-tool reliability. Panel a is a horizontal bar chart of tool calls per run under Qwen3.8-27B across 68 runs, with the failed portion of each bar shown solid: observe_place is the most called tool and mostly succeeds, invite_to_talk is second most called and fails 97 percent of the time, leave_conversation fails 98 percent, while economic and navigation tools such as buy_food, go_to_work, and start_shift fail around 5 to 21 percent. Panel b is a grouped bar chart comparing failure rates between Qwen3.8-27B and gpt-oss-20b for invite_to_talk, all tools, and all tools except invite: the invite failure rate is 97 and 95 percent respectively, nearly identical across the two model families, while the all-except-invite rate stays at 4 to 5 percent for both. The results so far describe what the economy does. Here we ask what, among the factors we control, actually determines it, using three matched-seed contrasts against baseline core-a (Figure 6, Table 6). Table 6. Matched-seed contrasts vs. baseline core-a. Δ% \%: relative change; p from Wilcoxon signed-rank (n=6n=6 layout/memory, n=9n=9 cross-model). Layout B Memory off gpt-oss-20b Outcome Δ% \% p Δ% \% p Δ% \% p Gini (terminal) +0.1 1.000 +0.4 0.562 +5.8 0.004 Persistence ρ +0.2 0.844 -0.1 0.688 -4.3 0.004 Median wealth (NPR) -0.2 0.688 -4.1 0.156 -22.9 0.004 Quintile mobility -7.5 0.688 -9.0 0.562 +62.4 0.004 Tool-call entropy -0.6 0.844 -0.8 0.438 +5.6 0.004 Tool calls per run +9.0 0.031 -5.3 0.062 +25.8 0.004 Economic tool failure -15.5 0.031 -4.4 0.438 +110.4 0.004 invite_to_talk failure -0.3 0.688 -2.2 0.031 -2.8 0.004 A placebo: world layout. core-b reuses two of six worlds from a second, independently generated agent-to-place layout, motivating a placebo arm (layout-b) holding every parameter and seed fixed and varying only the layout. It does not: Gini +0.1%+0.1\% (p=1.00p=1.00), persistence +0.2%+0.2\% (p=0.84p=0.84), all other outcomes similarly null, which licenses core-a and core-b as one population throughout. Memory: no detectable effect. no-memory withdraws write_memory, search_memory, reflect entirely, verified by zero recorded calls against 1,369/run in the control. All economic outcomes are statistically indistinguishable from baseline: Gini +0.4%+0.4\% (p=0.56p=0.56), persistence −0.1%-0.1\% (p=0.69p=0.69), median wealth −4.1%-4.1\% (p=0.16p=0.16). Only 6 matched seeds; a null at that size means we detected no effect, not that none exists. The one outcome that does move is mechanical: invite_to_talk failure falls 2.2%2.2\% (p=0.031p=0.031), plausibly because agents without memory cannot recall which partners recently declined. Model identity: everything moves. cross-model holds environment, tools, and population fixed and swaps Qwen3.8-27B-FP8 for gpt-oss-20b. Every outcome moves, at the p=0.0039p=0.0039 floor available to a 9-seed matched Wilcoxon: Gini +5.8%+5.8\%, median wealth −22.9%-22.9\%, quintile mobility +62.4%+62.4\%, economic tool failure +110.4%+110.4\%, which is 3–10× larger than the memory-ablation effect. Unlike memory or layout, each clears significance. Which LLM decides is a first-order determinant of the economy; whether it has memory, among what we measure, is not. The social layer fails, model-agnostically. invite_to_talk fails 97.2% under Qwen, 94.8% under gpt-oss, second most frequently called tool after observe_place. accept_invite fails 100.0% of 1,413 calls; leave_conversation 97.7%. These three account for 79.6% of all tool failures; excluding them, failure across remaining tools is 4.0%. The dominant logged reason is "you are already in too many conversations", a platform concurrency cap that occurred 3,894 times in one sampled run. The cap is our design choice, not evidence about LLM social cognition. Agents show no measurable adaptation to it, receiving this exact, human-readable error thousands of times with no detectable shift away from that tool, against the 96% success rate of economic tools under the same agents, context, and memory. 10. Discussion Four independently identified results point in the same direction: demand reaches firms but not households (§6); direct transfers do not circulate (§7); the wealth distribution is near-static at conventional horizons only because those horizons are too short to see it relax (§8); and the one factor governing all of it is which LLM decides, not memory (§9). Each rests on its own design, and together they describe one failure mode, in which agents earn, hold, and do not spend money at every scale we tested. If the goal is an economy that behaves like a plausible model of a human one, this transmission failure is the first thing to fix, ahead of memory or governance. Without an incentive to move wages or prices when a shift sells out, this class of system concentrates windfalls at firms by construction, regardless of the underlying LLM’s capability. §8 pushes the point wider: any distributional claim from an LLM-agent society should report its horizon, and a short-horizon “stable” finding may be an artifact of stopping early. §9 narrows to a specific case: an aggregate success rate can hide a near-total, model-agnostic failure of one action class behind a healthy overall number. Both headline claims are falsifiable from data we release. The transmission-failure claim would fall to a prompting change, such as reminding owners that raising wages is a live option when a shift sells out, if that restored pass-through with nothing else altered. We did not test this. The horizon claim would fall to 12/26-week replicates that do not reproduce the ordering in Table 5. 11. Limitations • Long-horizon sample size. §8 rests on 9 runs at 2 weeks, 4 at 12, and one at 26, a single path rather than a distributional estimate. The ordering claim (every 12-week run below every 2-week run) is what we lean on. • Underpowered memory null. Only 6 matched seeds (§9); “no detected effect” is not equivalence. • Price stickiness is partly by construction. set_price is called ∼ 18 times/run; the near-zero repricing rate reflects rare use of an available lever and a sticky menu by design; we have not separated the two. • One town, one seed pool, one stack, no governance. 9 seeds vary agent draws on one map only, both models on self-hosted vLLM (§5); Mistral-Small-3.2-24B could not complete a single pulse (§4), a stack-specific result rather than a general model claim. 12. Artifact Availability We release the full validated run corpus described throughout this paper: all 91 accepted runs and the 7 quarantined runs kept as evidence (§4), spanning 2.44M agent decisions and 21.5B tokens across 41,328 recorded pulses. The release is CSV-level (per-pulse world state, per-agent wealth time series, every transaction, tool call, price observation, and conversation turn), together with the analysis pipeline that turns those raw exports into every number, table, and figure in this paper (§5), so no result here needs to be taken on faith. The data is released under C BY 4.0 and the analysis code under the MIT license, on Hugging Face at https://huggingface.co/datasets/sajalregmi4/agent-town-economy. The simulation platform itself is not released. References Argyle et al. (2023) Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023. Out of One, Many: Using Language Models to Simulate Human Samples. Political Analysis 31, 3 (2023), 337–351. Gao et al. (2023) Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, and Yong Li. 2023. S3: Social-network Simulation System with Large Language Model-Empowered Agents. arXiv preprint arXiv:2307.14984 (2023). Gini (1912) Corrado Gini. 1912. Variabilità e Mutabilità: Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche. C. Cuppini, Bologna. Hashimoto et al. (2026) Ryuji Hashimoto, Masahiro Kaneko, Kentaro Ueda, Takehiro Takayanagi, and Kiyoshi Izumi. 2026. EconSimulacra: A Digital Twin Platform of Socio-Economic Systems Powered by LLM Agents. arXiv preprint arXiv:2606.26883 (2026). Horton et al. (2023) John J. Horton, Apostolos Filippas, and Benjamin S. Manning. 2023. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? NBER Working Paper 31122 (2023). Jappelli and Pistaferri (2014) Tullio Jappelli and Luigi Pistaferri. 2014. Fiscal Policy and MPC Heterogeneity. American Economic Journal: Macroeconomics 6, 4 (2014), 107–136. Johnson et al. (2006) David S. Johnson, Jonathan A. Parker, and Nicholas S. Souleles. 2006. Household Expenditure and the Income Tax Rebates of 2001. American Economic Review 96, 5 (2006), 1589–1610. Larooij and Törnberg (2025) Maik Larooij and Petter Törnberg. 2025. Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations. arXiv preprint arXiv:2504.03274 (2025). Li et al. (2024) Nian Li, Chen Gao, Mingyu Li, Yong Li, and Qingmin Liao. 2024. EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL). Li et al. (2023) Yang Li, Yangyang Yu, Haohang Li, Zhi Chen, and Khaldoun Khashanah. 2023. TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance. arXiv preprint arXiv:2309.03736 (2023). Liu et al. (2024) Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. 2024. AgentBench: Evaluating LLMs as Agents. In Proceedings of the 12th International Conference on Learning Representations (ICLR). Park et al. (2023) Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023. Generative Agents: Interactive Simulacra of Human Behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST). Park et al. (2022) Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2022. Social Simulacra: Creating Populated Prototypes for Social Computing Systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (UIST). Vezhnevets et al. (2023) Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Duéñez-Guzmán, William A. Cunningham, Simon Osindero, Danny Karmon, and Joel Z. Leibo. 2023. Generative Agent-Based Modeling with Actions Grounded in Physical, Social, or Digital Space Using Concordia. arXiv preprint arXiv:2312.03664 (2023). Wang et al. (2026) Xintao Wang, Sirui Zheng, Hongqiu Wu, Weiyuan Li, Jen-tse Huang, Minghao Zhu, Can Zu, Qi Deng, Jiawei Wang, Qianyu He, Heng Wang, Xiaojian Wu, and Yunzhe Tao. 2026. Agentopia: Long-Term Life Simulation and Learning in Agent Societies. arXiv preprint arXiv:2606.07513 (2026). Zhou et al. (2024) Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. 2024. WebArena: A Realistic Web Environment for Building Autonomous Agents. In Proceedings of the 12th International Conference on Learning Representations (ICLR).