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Record W4413334575 · doi:10.1080/00048402.2025.2520529

Evil and the Quantum Multiverse

2025· article· en· W4413334575 on OpenAlexaff
Eddy Keming Chen, Daniel Rubio

Bibliographic record

VenueAustralasian Journal of Philosophy · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Mechanics and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhilosophyQuantumEpistemologyTheoretical physicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Problems in moral philosophy and philosophy of religion can take on new forms in light of contemporary physical theories. Here we discuss how the problem of evil is transformed by the Everettian ‘Many-Worlds’ theory of quantum mechanics. We first present an Everettian version of the problem and contrast it to the problem in single-universe physical theories such as Newtonian mechanics and Bohmian mechanics. We argue that, pace Turner and Zimmerman, the Everettian problem of evil is no more extreme than the Bohmian one. The existence and multiplicity of (morally) terrible branches in the Everettian multiverse in contrast to the mere possibility of them in the Bohmian universe does not entail there is ‘more evil’ in the former than in the latter. Low probability in the Bohmian case and low branch weight in the Everettian case should modulate how we respond to them in exactly the same way. We suggest that the same applies to the divine decision of creating an Everettian multiverse. For an empirically adequate Everettian quantum mechanics that justifies the Born rule, there is no special problem of evil. In order for there to be a special Everettian problem of evil, the Everettian interpretation must already have been exposed to decisive refutation. In the process, we hope to show how attention to the details of physical and metaphysical theories can and should impact the way we think about problems in moral philosophy and philosophy of religion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.265
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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