Distributed Science — The Scientific Process as Multi-Scale Active Inference

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Citation KeyFriedman2023DistributedScienceScientificProcess053

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Overview

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This paper presents a multi-scale description of the scientific process as a distributed system of evidence-seeking activities, applying the free energy principle to understand science as Bayesian belief updating. It argues that scientific practice is shaped by both human and non-human agents, emphasizing the need for an integrated view that combines modern and non-modern perspectives on knowledge production.

distributed sciencemulti-scale Active Inferencescientific processFree Energy Principlemeta-sciencecollective intelligencecultural evolutiondistributed cognition

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts

Introduces a multi-scale framework for understanding the scientific process as distributed active inference.
Integrates modern and non-modern conceptions of science to provide a comprehensive account of knowledge production.
Proposes a simulation approach for scientific practice that can inform the development of augmented intelligence systems.
Addresses foundational questions regarding the nature of science and its societal implications.

Methods / Techniques

Applies the free energy principle to model the scientific process as evidence-seeking.
Utilizes Bayesian belief updating to describe the dynamics of scientific cognition.
Explores the interaction between individual cognitive functions and collective scientific practices.
Employs a meta-theoretical approach to reconcile different epistemological views on science.

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2023. Distributed Science — The Scientific Process as Multi-Scale Active Inference. OSF.

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