S$^2$-MAS is a multi‑agent simulation framework that uses 500 million synthetic household personas to model economic responses at society scale. It integrates trainable candidate‑scoring policies, reusable financial scenarios, and explicit state transitions, and has been trained on 2.93 million examples to achieve high accuracy on consumer financial‑choice tasks. Experiments demonstrate the framework’s ability to evaluate millions of households across different purchase‑funding conditions, revealing heterogeneous consumption and repayment patterns driven by policy decisions and accounting rules.
By Yang Li, Yike Wang, Di Wan, Chujun He, Zongsi Xu, Yitao Yu, Shuai Zhong, Minghao Pan, Quanfeng Lu, Xiyu Chen, Sheng Zhong, Deng Binjie, Yichen Guo, Zhenwei Guo, Hou Jun, Kunhan Guo, Weihao Lin, Tuo Zhou, Hai Liu, Chenwei Wu, Jianyuan Zhong, Xilin Dai, Ye Luo
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
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
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: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
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