Physical Response Is Boundary-Invariant, Internal Attribution Is Not.

Reduced descriptions of hidden Markov environments require choices about how unresolved or irreversible transitions are completed. This article separates two questions that are often conflated: whether a measured linear response is fixed by the physical boundary description, and whether its internal mechanistic attribution is fixed as well. For finite irreducible Markov dynamics, we classify source visibility through a projected Poisson response and distinguish it from an independent propagator axis. We then define boundary-equivalent families of hidden-state completions and prove that the response and its control derivative converge to common boundary values. The internal source–propagator attribution at finite completion is nevertheless non-identifiable. A constructive family of 24 admissible lifts and 576 exact decompositions spans more than an order of magnitude in the source-to-propagator norm ratio and includes both relative orientations, although all members share the same physical boundary operators. Along the canonical completion, the qualitative diagnosis reverses from source-dominated and counter-oriented to propagator-dominated and co-oriented at the boundary. The theory is supported by an exactly solvable hidden-environment benchmark and an independent continuous campaign of 128 backgrounds comprising more than four hundred million stochastic events. Complete proofs, numerical contracts, and robustness analyses are included in the article; the complete decomposition register is supplied as machine-readable supplementary data. The results delimit which response statements can be transferred across hidden completions and which mechanistic explanations must remain model-qualified.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-28
DOI
https://doi.org/10.5281/zenodo.21632918
Primary Topic
Neural dynamics and brain function
Type
preprint
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preprint

Physical Response Is Boundary-Invariant, Internal Attribution Is Not.

Antoine Druilhe
Zenodo (CERN European Organization for Nuclear Research)
Neural dynamics and brain function
preprint

Physical Response Is Boundary-Invariant, Internal Attribution Is Not.

Antoine Druilhe
preprint en

Abstract

Reduced descriptions of hidden Markov environments require choices about how unresolved or irreversible transitions are completed. This article separates two questions that are often conflated: whether a measured linear response is fixed by the physical boundary description, and whether its internal mechanistic attribution is fixed as well. For finite irreducible Markov dynamics, we classify source visibility through a projected Poisson response and distinguish it from an independent propagator axis. We then define boundary-equivalent families of hidden-state completions and prove that the response and its control derivative converge to common boundary values. The internal source–propagator attribution at finite completion is nevertheless non-identifiable. A constructive family of 24 admissible lifts and 576 exact decompositions spans more than an order of magnitude in the source-to-propagator norm ratio and includes both relative orientations, although all members share the same physical boundary operators. Along the canonical completion, the qualitative diagnosis reverses from source-dominated and counter-oriented to propagator-dominated and co-oriented at the boundary. The theory is supported by an exactly solvable hidden-environment benchmark and an independent continuous campaign of 128 backgrounds comprising more than four hundred million stochastic events. Complete proofs, numerical contracts, and robustness analyses are included in the article; the complete decomposition register is supplied as machine-readable supplementary data. The results delimit which response statements can be transferred across hidden completions and which mechanistic explanations must remain model-qualified.

Zenodo (CERN European Organization for Nuclear Research)
Électricité de France (France) (FR)
Neural dynamics and brain function
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