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
arXiv:2604. 10475v2 Announce Type: replace Abstract: Modeling household-level decisions is central to many real-world applications, including trip planning, residential mobility and migration, disaster management, etc.
By Yuran Sun, Mustafa Sameen, Yaotian Zhang, Rongguan Gu, Mrunal Vibhute, Chia-yu Wu, Yuanyuan Lei, Xilei Zhao
arXiv:2607. 19389v1 Announce Type: cross Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn attention.
By Vedant Palit, Udvas Das, Brahim Driss, Debabrota Basu
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:2607. 26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM).
By Shaopeng Wei, Yufei Cheng, Wenxi Sun, Yepeng Ding, Yu Zhao, Gang Kou
AgentMonopoly is a new environment that lets large language model agents play a property‑trading game while communicating with each other. The study compares models from four families and two sizes, examining how often they trade, waive rent, and manage cash and properties, and tests whether giving an agent a persona description changes its language and actions. Results show that larger models do not always win more games and that persona instructions alter wording and some actions, but not consistently across all decisions, highlighting the need to evaluate persona adherence through both language and outcomes.
By Changshun Wu, Mengyu Li, Mathis Marsault, Xiaowei Huang, Chih-Hong Cheng
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...