决策模型可作为捕捉人类行为差异的经济替代模型
Decision Models as Scalable Economic Surrogates that Capture Human Variation
AI 导读
一项新评估比较了 10 个决策模型（DMs）与 8 个 LLM 在三类经济场景中作为人类代理的效果：家庭消费支出调查、经典经济博弈和劳动力市场田野实验。在通用决策基准上得分最高的前沿 DMs 在三个场景中都比 LLM 更好地还原人类反应的差异，在支出调查中捕捉众数反应与 LLM 相当，在博弈中略逊。前沿 DMs 速度约为最快 LLM 的 10 倍，成本可低至其百分之一，使大规模经济仿真更可行。
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.AI · arxiv.org