Collective Predictive Coding as Model of Science: Formalizing Scientific Activities Towards Generative Science

Tadahiro Taniguchi, Shiro Takagi, Jun Otsuka, Yusuke Hayashi, Hiro Taiyo Hamada
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Abstract

This paper proposes a new conceptual framework called Collective Predictive Coding as a Model of Science (CPC-MS) to formalize and understand scientific activities. Building on the idea of collective predictive coding 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 paper discusses how CPC-MS provides insights into issues like social objectivity in science, scientific progress, and the potential impacts of AI on research. The generative view of science offers a unified way to analyze 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.
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作为科学模式的集体预测编码:将科学活动形式化,迈向生成科学
本文提出了一个名为 "作为科学模型的集体预测编码(CPC-MS)"的新概念框架,以正规化和理解科学活动。集体预测编码最初是为了解释符号的出现而提出的,在此基础上,CPC-MS 将科学建模为由代理群体执行的分散式贝叶斯推理过程。该框架描述了科学家个人的部分观察结果和内部陈述如何通过交流和同行评审进行整合,从而产生共享的外部科学知识。科学实践的关键环节,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各个组成部分上。本文讨论了 CPC-MS 如何为科学的社会客观性、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。科学的生成观为分析科学活动提供了一种统一的方法,可以为科学过程的自动化提供参考。总之,CPC-MS 旨在提供一个直观而正式的科学模型,将科学视为一种集体认知活动。
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