Decoherence and Probability

Abstract One cannot justifiably presuppose the physical salience of structures derived via decoherence theory based upon an entirely uninterpreted use of the quantum formalism. Non-probabilistic accounts of the emergence of probability via decoherence are unconvincing. An alternative account of the emergence of probability involves the combination of a partially interpreted decoherence model and an averaging of observables with respect to a positive-definite quasi-probability function and neglect of terms of $O\left( \hbar \right)$ . Our analysis delimits the context in which the combination of decoherence and a semi-classical averaging allows us to recover a classical probability model within an emergent coarse-grained description.

Authors

Institutions

Publication Details

Journal
Philosophy of Science
Published
2025-08-22
DOI
https://doi.org/10.1017/psa.2025.10110
Citations
1
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoherence and Probability

Richard Dawid, Karim P. Y. Thébault
1 citations
Philosophy of Science
Adversarial Robustness in Machine Learning
2.48
article

Decoherence and Probability

Richard Dawid, Karim P. Y. Thébault
article en
1 citations

Abstract

Abstract One cannot justifiably presuppose the physical salience of structures derived via decoherence theory based upon an entirely uninterpreted use of the quantum formalism. Non-probabilistic accounts of the emergence of probability via decoherence are unconvincing. An alternative account of the emergence of probability involves the combination of a partially interpreted decoherence model and an averaging of observables with respect to a positive-definite quasi-probability function and neglect of terms of $O\left( \hbar \right)$ . Our analysis delimits the context in which the combination of decoherence and a semi-classical averaging allows us to recover a classical probability model within an emergent coarse-grained description.

Philosophy of Science
Stockholm University (SE), University of Bristol (GB)
John Templeton Foundation, Vetenskapsrådet
Openalex Percentile: Top 9%
Adversarial Robustness in Machine Learning
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.