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

TL;DR

This paper introduces Collective Predictive Coding as a formal, probabilistic model of scientific activity, viewing science as a decentralized Bayesian inference process among researchers, with implications for understanding scientific progress and AI's role.

Contribution

It formalizes scientific activities within a collective predictive coding framework, offering a unified probabilistic model of science as a social, cognitive process.

Findings

Models scientific activities as Bayesian inference among agents

Provides insights into social objectivity and scientific progress

Suggests potential for automating scientific processes

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.