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The methodology

Impact first, data last.

SMILE — Sustainable Methodology for Impact Lifecycle Enablement — is a design-science methodology that turns any project into a continuously learning digital twin. Its founding move is a simple inversion: the Outcome-to-Data chain (Outcome → Action → Insight → Information → Data) designs downward from intended outcomes to the observations actually needed. Data without an outcome relationship is a candidate for elimination.

SMILE — connecting outcomes, people and AI through reality.

Inspired by NASA JPL's Extreme Collaboration practice — humans, agents and experts in one loop, around a shared, live model of reality, instead of documents thrown over a wall.

DOI 10.5281/zenodo.21757691CC-BY-4.0v6.4.3 · Aug 2026
The six phases

Six concentric capabilities — each one keeps revalidating the ones before it.

Every phase is worked from four perspectives: From People · From Systems · From Planet · From AI.

What's new in v6.4.3

What changed since v5.1.

Acronym shift

"Interoperable" (v5.1) becomes "Impact" (v6.x) Lifecycle Enablement.

Reality Branch Lifecycle

Governed reality forks with a Reality Consensus Protocol for reconciling them.

Standards alignment

Canonical metamodel aligned to PROV-O, SOSA/SSN, OWL-Time and GeoSPARQL.

14 falsifiable propositions

Testable research propositions, not just descriptive claims — the paper can be wrong, on the record.

153 verified references

Every citation checked for the v6.4.3 record.

Applied

Where the phase logic has been applied.

The current corpus applies the phase logic across eight domains. The record is honest that this demonstrates transferability across contexts — not yet comparative superiority over alternative methods.

Buildings and energyRF and constructionEquine systemsPersonal healthGenomics infrastructureMunicipal systemsContributor coordinationPost-conflict reconstruction

Appendix D documents an unmodified 24 Jan 2022 WINNIIO building-data-collector screenshot — persistent GUID, spatial anchor, live CO₂/temperature/humidity/occupancy with provenance — showing the metamodel's core pattern running in production four years before the spec.

Maturity

Five maturity levels.

Later-phase advancement is gated by integrity in earlier phases — a draft agent while immature in Reality Emulation boundaries shouldn't be described as mature Continuous Intelligence.

FoundationSystem of RecordAugmented IntelligenceNetworked ValueAdaptive Ecosystem

Where are you on this ladder?

Your Twin Score on /readiness is this maturity model, operationalised — depth across phases, not isolated AI.

Score yourself against SMILE — Twin Score, 5 minutes →
Cite & download

Citation

Waern, N. (2026). SMILE — Sustainable Methodology for Impact Lifecycle Enablement (v6.4.3): Operational Grammar for Auditable, Shared and Forkable Reality. Zenodo. https://doi.org/10.5281/zenodo.21757691

Waern, N. (2026). SMILE v5.1: The Universal Methodology That Turns Any Project Into a Continuously Learning Digital Twin. Zenodo. https://doi.org/10.5281/zenodo.21268264

ORCID: 0009-0001-4011-8201 · WINNIIO AB · CC-BY-4.0