标题： 机器能进行哲学思考吗？ 显示英文标题

标题： Can Machines Philosophize?

摘要： 受图灵测试的启发，我们提出了一种新的方法论框架，以评估机器群体在多大程度上反映了人类群体的哲学观点。 该框架包括三个步骤：(i) 指示机器模仿群体中的每个个体，反映他们的背景和信念，(ii) 向人类和机器分别发放涵盖各种哲学立场的问卷，(iii) 对产生的回答进行统计分析。 我们将这种方法应用于科学实在论的争论，这是一个长期存在的哲学探究，探讨科学与现实之间的关系。 通过考虑超过500名人类参与者（包括物理学家和科学哲学家）的调查结果，我们使用基于大型语言生成模型的人工智能引擎生成了他们的机器人格。 我们发现，无论这些机器是物理学家还是科学哲学家，其群体的哲学观点平均而言与人类群体所支持的观点相似。 然而，与人类相比，机器对科学实在论的倾向较弱，而其哲学观点的一致性更强。 鉴于人类和机器群体之间的观察到的相似性，这一方法论框架可能为实验哲学的研究提供前所未有的机会，通过用其机器模仿的对应物替换人类参与者，可能缓解基于调查的实证研究中影响效率和可重复性的问题。 显示英文摘要

摘要： Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting responses. We apply this methodology to the debate on scientific realism, a long-standing philosophical inquiry exploring the relationship between science and reality. By considering the outcome of a survey of over 500 human participants, including both physicists and philosophers of science, we generate their machine personas using an artificial intelligence engine based on a large-language generative model. We reveal that the philosophical views of a population of machines are, on average, similar to those endorsed by a population of humans, irrespective of whether they are physicists or philosophers of science. As compared to humans, however, machines exhibit a weaker inclination toward scientific realism and a stronger coherence in their philosophical positions. Given the observed similarities between the populations of humans and machines, this methodological framework may offer unprecedented opportunities for advancing research in experimental philosophy by replacing human participants with their machine-impersonated counterparts, possibly mitigating the efficiency and reproducibility issues that affect survey-based empirical studies.