Collective predictive coding as model of science: formalizing scientific activities towards generative science
This article 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 CPC originally developed to explain symbol emergence, CPC-MS models science as a decentralized Bayesian infer...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2025-06-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241678 |
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| author | Tadahiro Taniguchi Shiro Takagi Jun Otsuka Yusuke Hayashi Hiro Taiyo Hamada |
| author_facet | Tadahiro Taniguchi Shiro Takagi Jun Otsuka Yusuke Hayashi Hiro Taiyo Hamada |
| author_sort | Tadahiro Taniguchi |
| collection | DOAJ |
| description | This article 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 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. |
| format | Article |
| id | doaj-art-55a4fbf23b0945e3b266a4b8775b0708 |
| institution | DOAJ |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-55a4fbf23b0945e3b266a4b8775b07082025-08-20T03:07:25ZengThe Royal SocietyRoyal Society Open Science2054-57032025-06-0112610.1098/rsos.241678Collective predictive coding as model of science: formalizing scientific activities towards generative scienceTadahiro Taniguchi0Shiro Takagi1Jun Otsuka2Yusuke Hayashi3Hiro Taiyo Hamada4Graduate School of Informatics, Kyoto University, Kyoto, JapanIndependent Researcher, Tokyo, JapanFaculty of Social Informatics, ZEN University, Kanagawa, JapanAI Alignment Network, Tokyo, JapanARAYA Inc., Chiyoda-ku, Tokyo, JapanThis article 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 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.https://royalsocietypublishing.org/doi/10.1098/rsos.241678collective predictive codingmodel of sciencemulti-agent systemBayesian inference |
| spellingShingle | Tadahiro Taniguchi Shiro Takagi Jun Otsuka Yusuke Hayashi Hiro Taiyo Hamada Collective predictive coding as model of science: formalizing scientific activities towards generative science Royal Society Open Science collective predictive coding model of science multi-agent system Bayesian inference |
| title | Collective predictive coding as model of science: formalizing scientific activities towards generative science |
| title_full | Collective predictive coding as model of science: formalizing scientific activities towards generative science |
| title_fullStr | Collective predictive coding as model of science: formalizing scientific activities towards generative science |
| title_full_unstemmed | Collective predictive coding as model of science: formalizing scientific activities towards generative science |
| title_short | Collective predictive coding as model of science: formalizing scientific activities towards generative science |
| title_sort | collective predictive coding as model of science formalizing scientific activities towards generative science |
| topic | collective predictive coding model of science multi-agent system Bayesian inference |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241678 |
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