Probability as Closure in General Probabilistic Theories: Uniqueness and Rigidity of State Assignment from Auditability Principles Paper 39 of the NEMS Suite

Probability is often introduced as a primitive axiom in operational theories. This paper proves a complementary, theorem-grade statement at the level of general probabilistic theories (GPTs): under closure-style constraints—context-independence of effects, additivity, and normalization—probability assignments are not free; they are forced (unique or nearly unique) by the requirement of internal semantic closure. We work in a finite-dimensional GPT framework: an ordered unit space (V, V_+, u), effects 0 \le e \le u, measurements as decompositions of the unit, and states as positive linear functionals \omega with \omega(u)=1. We assume V = \mathrmspan_\mathbbRe \mid 0\le e\le u. We state closure principles (context-independence, additivity, normalization, and convex mixing) and prove that any assignment satisfying these principles corresponds to a unique affine state functional on effects; if effects span V, this extends uniquely to a linear functional on V. States are therefore determined by agreement on all effects (extensionality) and by agreement on a spanning set of effects. The quantum Born rule is the instance for matrix-ordered spaces. The development is mechanized in Lean 4 as the GPTClosure library in nems-lean, with zero sorry and no custom axioms in the core framework. The abstract GPTClosure core is machine-checked; the finite-dimensional quantum bridge currently contains three explicitly documented formal gaps in the QuantumFinite module. A quantum finite-dimensional instance (GPTClosure.Instances.QuantumFinite) bridges the Paper 13 Born-rule formalization with the GPT framework, showing that the Born rule is an instance of closure-forced probability. This overview presents the core NEMS theorem engine and selected applications; stronger domain-specific derivation and ontological synthesis claims belong to separate release surfaces with their own premise bundles and formal artifacts. Trust boundary. The abstract GPTClosure core is machine-checked as claimed; the finite-dimensional quantum bridge (QuantumFinite) carries explicitly documented formal gaps separate from the main closure theorems. Mechanization is nems-lean . See .

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-13
DOI
https://doi.org/10.5281/zenodo.22733212
Primary Topic
Logic, Reasoning, and Knowledge
Type
preprint
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preprint

Probability as Closure in General Probabilistic Theories: Uniqueness and Rigidity of State Assignment from Auditability Principles Paper 39 of the NEMS Suite

Nova Spivack
Zenodo (CERN European Organization for Nuclear Research)
Logic, Reasoning, and Knowledge
preprint

Probability as Closure in General Probabilistic Theories: Uniqueness and Rigidity of State Assignment from Auditability Principles Paper 39 of the NEMS Suite

Nova Spivack
preprint en

Abstract

Probability is often introduced as a primitive axiom in operational theories. This paper proves a complementary, theorem-grade statement at the level of general probabilistic theories (GPTs): under closure-style constraints—context-independence of effects, additivity, and normalization—probability assignments are not free; they are forced (unique or nearly unique) by the requirement of internal semantic closure. We work in a finite-dimensional GPT framework: an ordered unit space (V, V_+, u), effects 0 \le e \le u, measurements as decompositions of the unit, and states as positive linear functionals \omega with \omega(u)=1. We assume V = \mathrmspan_\mathbbRe \mid 0\le e\le u. We state closure principles (context-independence, additivity, normalization, and convex mixing) and prove that any assignment satisfying these principles corresponds to a unique affine state functional on effects; if effects span V, this extends uniquely to a linear functional on V. States are therefore determined by agreement on all effects (extensionality) and by agreement on a spanning set of effects. The quantum Born rule is the instance for matrix-ordered spaces. The development is mechanized in Lean 4 as the GPTClosure library in nems-lean, with zero sorry and no custom axioms in the core framework. The abstract GPTClosure core is machine-checked; the finite-dimensional quantum bridge currently contains three explicitly documented formal gaps in the QuantumFinite module. A quantum finite-dimensional instance (GPTClosure.Instances.QuantumFinite) bridges the Paper 13 Born-rule formalization with the GPT framework, showing that the Born rule is an instance of closure-forced probability. This overview presents the core NEMS theorem engine and selected applications; stronger domain-specific derivation and ontological synthesis claims belong to separate release surfaces with their own premise bundles and formal artifacts. Trust boundary. The abstract GPTClosure core is machine-checked as claimed; the finite-dimensional quantum bridge (QuantumFinite) carries explicitly documented formal gaps separate from the main closure theorems. Mechanization is nems-lean . See .

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Logic, Reasoning, and Knowledge
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