arXiv cs.MA· Maxime Saxena, Fabio Caccioli, R. Maria del Rio-Chanona·· 11 小时前AI 评分38
决策模型作为可扩展的经济替代模型：比 LLM 更能捕捉人类行为差异
Decision Models as Scalable Economic Surrogates that Capture Human Variation
AI 导读
一项 arXiv 研究首次评估决策模型（DM）作为经济学人类替代模型的表现，在消费调查、经典经济博弈和劳动力市场田野实验三种场景中对比了 10 个 DM 与 8 个 LLM。
正文 · 原文
Abstract:Decision Models (DMs), a new class of AI models, give fast responses and return a probability distribution over a fixed set of options. These properties make them candidate human surrogates in economic simulations and surveys, a role increasingly filled by Large Language Models (LLMs), which are costly at scale and under-represent the variation in human behaviour. We present a first evaluation of DMs as human surrogates in economics, comparing 10 DMs with 8 LLMs in three economic settings: a household spending survey on consumption, several classic economic games and a labour-market field experiment. Frontier DMs, those that score highest on general decision-making benchmarks, reproduce the variation in human responses more closely than LLMs in all three settings. They capture the modal human response as well as LLMs in the spending survey, but less well in the economic games. Frontier DMs are also roughly ten times faster than the fastest LLM and can be over a hundred times cheaper. Together, these results suggest that DMs capture the heterogeneity of human behaviour better than LLMs and make large-scale economic simulations more feasible.
| Subjects: | General Economics (econ.GN); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.05553 [econ.GN] |
| (or arXiv:2611.05553v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05553 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Maxime Saxena [view email]
[v1]
Tue, 3 Nov 2026 19:47:00 UTC (281 KB)
来源：arXiv cs.MA · arxiv.org