Taniguchi Tadahiro, Takagi Shiro, Otsuka Jun, Hayashi Yusuke, Hamada Hiro Taiyo
Graduate School of Informatics, Kyoto University, Kyoto, Japan.
Research Organization of Science and Technology, Ritsumeikan University, Kyoto, Japan.
R Soc Open Sci. 2025 Jun 4;12(6):241678. doi: 10.1098/rsos.241678. eCollection 2025 Jun.
This article proposes a new conceptual framework called ) to formalize and understand scientific activities. Building on the idea of CPC originally developed to explain symbol emergence, CPC-MS models science as a decentralized Bayesian inference process carried out by a community of agents. The framework describes how individual scientists' partial observations and internal representations are integrated through communication and peer review to produce shared external scientific knowledge. Key aspects of scientific practice like experimentation, hypothesis formation, theory development and paradigm shifts are mapped onto components of the probabilistic graphical model. This article discusses how CPC-MS provides insights into issues like social objectivity in science, scientific progress and the potential impacts of artificial intelligence on research. The generative view of science offers a unified way to analyse scientific activities and could inform efforts to automate aspects of the scientific process. Overall, CPC-MS aims to provide an intuitive yet formal model of science as a collective cognitive activity.
本文提出了一个名为)的新概念框架，以将科学活动形式化并加以理解。基于最初为解释符号出现而开发的CPC理念，CPC-MS将科学建模为一个由智能体群体进行的去中心化贝叶斯推理过程。该框架描述了个体科学家的部分观察结果和内部表征如何通过交流和同行评审进行整合，以产生共享的外部科学知识。科学实践的关键方面，如实验、假设形成、理论发展和范式转变，都被映射到概率图形模型的组件上。本文讨论了CPC-MS如何为科学中的社会客观性、科学进步以及人工智能对研究的潜在影响等问题提供见解。科学的生成性观点提供了一种统一的方式来分析科学活动，并可为科学过程自动化方面的努力提供参考。总体而言，CPC-MS旨在提供一个直观而形式化的科学模型，将其作为一种集体认知活动。
