S$^2$-MAS is a society‑scale multi‑agent simulation framework that models 500 million synthetic household personas based on real‑survey data. Its Genese engine separates decision making from state updates, while FinL‑mlx, a compact decision model trained on 2.93 million examples, achieves high accuracy in consumer financial‑choice scenarios. The framework evaluates eight financial task families and simulates 12 million distinct agents, revealing how purchase goals, credit limits, and demographic factors influence consumption, cash flow, debt repayment, and arrears over a 24‑month panel.
By Yang Li, Di Wan, Yike Wang, Chujun He, Zongsi Xu, Xiyu Chen, Sheng Zhong, Deng Binjie, Zhenwei Guo, Hou Jun, Kunhan Guo, Weihao Lin, Tuo Zhou, Hai Liu, Ye Luo
FraudSociety is a novel LLM‑agent‑based simulator that jointly models informational and financial dynamics in the financial‑fraud economy. It features a dynamic social environment, persistent financial ledgers, and a Need‑Intent‑Action architecture, allowing fraudsters to adapt their actions based on interaction histories and ledger states. Validation shows macro‑level outcomes match real‑world patterns, micro‑level loss concentration aligns with Canadian statistics, and personalized interventions can dramatically reduce repeat‑payment rates.
By Keyang Chen, Mingxuan Jiang, Zeping Li, Yiwen Zhao, Yifan Wang, Qiuyang Zhang, Shuaicheng Qi, Yicun Liu, Zhixin Li, Sen Liu, Yinan Jing, Guangnan Ye, Hongfeng Chai
arXiv:2510. 20748v2 Announce Type: replace-cross Abstract: This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during economic downturns.
By Brandon Kaplowitz
arXiv:2610.07563v1 Announce Type: new
Abstract: Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of...
By Jin Huang, Diego Ferreras Garrucho, Yutong Xie, Walter M. Yuan, Qiaozhu Mei, Chen Lian, Jonathon Hazell
Decision Models (DMs) are a new class of AI models that provide fast responses and output a probability distribution over a fixed set of options. In a first evaluation, 10 DMs were compared with 8 Large Language Models (LLMs) across three economic settings—a household spending survey, classic economic games, and a labour‑market field experiment. Frontier DMs, those scoring highest on general decision‑making benchmarks, reproduced human response variation more closely than LLMs in all settings, matched LLMs on modal responses in the spending survey, and were roughly ten times faster and over a hundred times cheaper than the fastest LLM.
By Maxime Saxena, Fabio Caccioli, R. Maria del Rio-Chanona
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
By Jie Wu, Ming Gong, Feixiang Cheng, Qinqin Zhao
arXiv:2507. 22758v2 Announce Type: replace-cross Abstract: Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling.
By Gautam Jajoo, Atharva Pandey, Pranjal A Chitale, Saksham Agarwal
StoreBench is a live‑commerce environment where an autonomous agent runs a mid‑size online apparel store on a production‑grade backend, facing continuous customer orders, supplier price changes, and market shocks. The agent uses the same 29 merchant tools as a human operator, with a windowed operation budget that ties simulated time to actions, ensuring that model latency does not affect the simulation. Seven large language models were evaluated across 11 scenarios of 30–45 days and a full simulated year, but none matched the scripted smart‑triage policy, and human experts outperformed all models.
By Daksh Raghuvanshi, Ved Vedere, Yifan Wang
arXiv:2610.28409v1 Announce Type: cross
Abstract: Large language model agents are often given an inference budget and asked to maximise success within it. A multi-turn agent has to allocate this budg...
By Tom A. Lamb, Adam Golinski, Eleonora Gualdoni, Sinead Williamson, Michael Kirchhof
arXiv:2607. 24341v1 Announce Type: new Abstract: Recent studies use Large language models (LLMs) to simulate human opinions and decisions by prompting models with demographic, attitudinal, or persona-based descriptions.
By Weijie Xia, Stefanie Horian, Hanyue Huang, Queena K. Qian, Jie Yang, Pedro P. Vergara Barrios
arXiv:2606. 29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots.
By Salavat Ishbulatov
The study evaluates whether multi‑agent systems (MAS) outperform single‑agent systems (SAS) when agents interact with users under a written policy on the dual‑control benchmark. Across 7,950 simulated conversations in airline, retail, and telecom domains, the MAS performed worse in 12 of 13 valid cells and tied only once, with a negative average gain of 28 points. Even in scenarios where prior theory predicted a benefit, the MAS lost performance and incurred higher token costs.
By Ethan Yang, Xiangcheng Shi