Computational Care Ecology: Synthetic Experiments and Internal Verification of CCE-MVM v0.1
Scope and statusThis record preserves an unreviewed English research manuscript describing CCE-MVM v0.1. It reports synthetic experiments and author-reported internal verification, not empirical, clinical, ethical, or safety validation. No human participant or patient data were used. External validity and independent reproduction by a third party have not been established. AbstractBackground: Computational Care Ecology (CCE), defined in this study, separates information, proposals, responses, decision rights, and outcomes. Objective: To specify and internally verify CCE-MVM v0.1. Methods: No human data were used; inputs were synthetic. It has person, AI, family, and professional components. M0 records one decision event with observations, person models, proposals, an exogenous person response, six CCE rights gates, and actor-specific outcomes; M1 examines person model updating; M2 examines synthetic AI proposal-response coupling under rights conditions. Analytical verification targets were H1a, H2a, and H1b-1; their synthetic runs were implementation checks, not independent experimental evidence for those algebraic consequences. H2b was a recovery check limited to the stated conditions; H1b-2 was a scoped check of the decision rule and rights cases. Results: The H1a and H2a implementation checks reproduced more persistent error at lower learning rates after permanent change and greater tracking of temporary observations at higher rates. The H1b-1 implementation check matched the specified coupling-response relation. H2b showed faster recovery ordering at higher rates only for shock length 2 and tolerance 0.10; recovery was not measured under noise. The scoped H1b-2 checks matched the specified outcomes for response, option, and six rights gates. In author-reported internal verification, 96 core model tests passed; 41 future extension tests brought the development suite to 137 tests; CSV and JSON outputs matched during internal regeneration. Conclusions: Author-reported internal verification found CCE-MVM v0.1 internally consistent with its Specification under the tested conditions. This is verification, not empirical, clinical, ethical, or safety validation. It does not model endogenous interaction among four actors; external validity remains unestablished. Version and AI useThe scientific content is unchanged from the manuscript dated 16 September 2026. The model remains CCE-MVM v0.1. The manuscript includes an AI Use Disclosure describing AI-assisted drafting, analysis, and checking, and the human author's responsibility. Repository availability is not academic peer review or evidence that the model is suitable for clinical decisions. Related resourcesCode and fixed reproducibility release: https://github.com/Usa-Cyclist/cce-mvm-v0.1/releases/tag/v0.1CCE overview in Japanese: https://unknownlab.pages.dev/cce/CCE short summary in Japanese: https://unknownlab.pages.dev/cce/summary/Manuscript and Japanese educational article: https://unknownlab.pages.dev/cce/pdf/Author ORCID: https://orcid.org/0009-0009-6444-750X The Japanese article is an educational explanation, not an independently validated study or a complete literal translation of this manuscript.
Authors
- 大樹 森元 (ORCID: https://orcid.org/0009-0009-6444-750X)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-19
- DOI
- https://doi.org/10.5281/zenodo.22845650
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- preprint