标题： 人工智能金融：人工智能如何思考金钱 显示英文标题

标题： Artificial Finance: How AI Thinks About Money

摘要： 在本文中，我们通过系统比较全球人类参与者的回答，探讨大型语言模型（LLMs）如何处理金融决策。 我们将一组常用的金融决策问题提供给七种领先的LLMs，包括五个来自GPT系列的模型（GPT-4o、GPT-4.5、o1、o3-mini）、Gemini 2.0 Flash和DeepSeek R1。 然后，我们将它们的输出与涵盖53个国家的数据集中的用户回答进行比较。 我们的分析揭示了三个主要结果。 首先，LLMs通常表现出风险中性的决策模式，在面对彩票类问题时倾向于选择与期望值计算一致的选项。 其次，当评估现在与未来之间的权衡时，LLMs有时会产生似乎与规范推理不一致的回答。 第三，当我们考察跨国相似性时，发现LLMs的总体回答最类似于坦桑尼亚参与者的结果。 这些发现有助于理解LLMs如何模仿人类决策行为，并突出了其输出中可能嵌入的文化和训练影响。 显示英文摘要

摘要： In this paper, we explore how large language models (LLMs) approach financial decision-making by systematically comparing their responses to those of human participants across the globe. We posed a set of commonly used financial decision-making questions to seven leading LLMs, including five models from the GPT series(GPT-4o, GPT-4.5, o1, o3-mini), Gemini 2.0 Flash, and DeepSeek R1. We then compared their outputs to human responses drawn from a dataset covering 53 nations. Our analysis reveals three main results. First, LLMs generally exhibit a risk-neutral decision-making pattern, favoring choices aligned with expected value calculations when faced with lottery-type questions. Second, when evaluating trade-offs between present and future, LLMs occasionally produce responses that appear inconsistent with normative reasoning. Third, when we examine cross-national similarities, we find that the LLMs' aggregate responses most closely resemble those of participants from Tanzania. These findings contribute to the understanding of how LLMs emulate human-like decision behaviors and highlight potential cultural and training influences embedded within their outputs.