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arXiv AI 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

S$^2$-MAS: A Society-Scale Multi-Agent Simulation Framework with a 500-Million-Persona Resource

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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