标题： LLM能否帮助提高战略决策中的类比推理？人类和GPT-4的实验证据 显示英文标题

标题： Can LLMs Help Improve Analogical Reasoning For Strategic Decisions? Experimental Evidence from Humans and GPT-4

摘要： 本研究调查了大型语言模型，特别是GPT4，在战略决策背景下的类比推理能力是否能与人类相匹配。通过采用一种新颖的实验设计，涉及源到目标的匹配，我们发现GPT4通过检索所有可能的类比实现了高召回率，但因低精度而受到影响，经常根据表面相似性应用错误的类比。相比之下，人类参与者表现出高精度但低召回率，选择较少的类比却具有更强的因果对齐。这些发现通过识别匹配（类比推理的评价阶段）作为需要准确因果映射的独立步骤，从而推进了理论发展。尽管当前的大规模语言模型在生成候选类比方面很熟练，但在识别跨领域的深层结构相似性方面，人类仍具有比较优势。错误分析显示，人工智能错误源于表面匹配，而人类错误则源于因果结构的误解。综合来看，结果表明在人工智能辅助的组织决策过程中存在一种有效的劳动分工，其中大规模语言模型可以充当广泛的类比生成器，而人类则作为关键评估者，应用最符合情境的类比来解决战略问题。 显示英文摘要

摘要： This study investigates whether large language models, specifically GPT4, can match human capabilities in analogical reasoning within strategic decision making contexts. Using a novel experimental design involving source to target matching, we find that GPT4 achieves high recall by retrieving all plausible analogies but suffers from low precision, frequently applying incorrect analogies based on superficial similarities. In contrast, human participants exhibit high precision but low recall, selecting fewer analogies yet with stronger causal alignment. These findings advance theory by identifying matching, the evaluative phase of analogical reasoning, as a distinct step that requires accurate causal mapping beyond simple retrieval. While current LLMs are proficient in generating candidate analogies, humans maintain a comparative advantage in recognizing deep structural similarities across domains. Error analysis reveals that AI errors arise from surface level matching, whereas human errors stem from misinterpretations of causal structure. Taken together, the results suggest a productive division of labor in AI assisted organizational decision making where LLMs may serve as broad analogy generators, while humans act as critical evaluators, applying the most contextually appropriate analogies to strategic problems.