Research Profile and Collaboration
Mapping perceptual and organizational dimensions that didn't have coordinates before. AI is the instrument — not the product.
This page is the Activity Specification Protocol (ASP) — a machine-readable research profile that documents what the work produces, how it differs, and what collaboration looks like. A YAML version is at spectralbranding.com/spec.yaml.
What The Work Produces
8-dimensional brand perception measurement. Observer cohort modeling via formal spectral profiles. Demonstrated across 5 brands, cross-model replicated.
30+ papers / 7 validators / 8 dimensions / 5 brands
6-level specification cascade from customer experience contract to sourcing requirements. Businesses designed backward from experience goals using testable specs.
6 specification levels / 1 reference implementation
Biological foundation of the rendering problem. DNA (specification) is rendered into a body (implementation) that produces consciousness (emergence) no specification contains. The specification gap is structural and irreducible — the same pattern underlying SBT, OST, and the Activity Specification Protocol.
Research note complete / academic paper in preparation
Machine-readable YAML companion files for scientific papers. Specifies claims, methodology, acceptance criteria, and falsification conditions. Five verification layers from schema validation to retraction cascade analysis.
v0.1.0 / 20 examples / validator + schema / R13 under review at QSS
A research program is a repository. A paper is a render of that research at a point on its timeline — a frozen snapshot, a communication event. A fork is sharing that render with a journal for confirmation. A publication is a merge.
Compliance gates, attributed reviewer commits, AI traceability by design. Paper Spec specifies what a paper claims; Paper Repo specifies how the research was built.
Partial prototype / validator + schemas / R14 submitted to JLSC
A creative work is a specification. Every rendering — book, film, game, translation, toy line — is a fork. Git semantics map to IP lifecycle: editions are tags, adaptations are forks, canon changes are pull requests, consistency validation is CI/CD.
Style is a rendering parameter. Actors and translators are rendering functions. The specification gap between canon and rendering is the franchise consistency problem — the same Rendering Problem that appears in brands, organizations, and scientific publishing.
v0.1.0 / Romeo and Juliet demo / paper targeting Convergence (SAGE)
SBT (external perception) and OST (internal specification) are two projections of a single system — the business observed from outside and from inside. Neither is complete alone.
How This Differs
| Most AI-era work | This work |
|---|---|
| Build businesses ON AI (chatbots, wrappers, agents) | Build frameworks WITH AI (measurement, specification) |
| Ask "how do I use AI to do X faster?" | Ask "what can I now see that I couldn't see before?" |
| Produce essays, threads, takes about AI | Produce papers, validators, toolkits — coordinate systems |
| Treat AI as a productivity tool | Treat AI as a scientific instrument — microscope, not calculator |
| Chase what AI makes cheaper | Map what becomes MORE valuable as AI accelerates |
| Optimize execution speed | Extend architectural reach — more dimensions, cases, consistency |
Method
Origin
Meta-coordination across incompatible systems. 2005-2006 manufacturing transformation: coordinating German TPS engineers, Russian plant operations, and Indian equipment transfers simultaneously. Not translating between languages — interpreting between operating systems. The same pattern now applied intellectually — standing outside branding and organizational theory to map their structure with a new instrument.
AI as Instrument
Working with AI as an architectural instrument — holding consistency across dimensions, case studies, and companion papers that exceed human working memory. AI does not generate the ideas. It lets me hold the architecture while I generate them. 98+ sessions, 30+ papers, 4 open-source toolkits, 3 HuggingFace datasets.
Intellectual Priors
- The Goal
Systems constraints — brand health has a bottleneck architecture, not a quality average
- Visual Display of Quantitative Information
Information density without information loss — make complex systems legible without simplifying them
- The Beginning of Infinity
Good explanations transfer across domains — the pipeline transferred from financial documents to brand perception unchanged
- The Toyota Way
The standard is a baseline, not a ceiling — separate what must be achieved from how
- TRIZ
Structural contradiction analysis — operational problems are instances of recurring patterns
- Brand Identity Model
Four identity perspectives (Product, Organization, Person, Symbol) that SBT formalizes into eight parametrized dimensions — the relationship is formalization, not replacement, like behavioral economics to classical economics. A 10-property side-by-side comparison of SBT against Aaker, Keller, Kapferer, Brakus, J. Aaker, and Holt is on the research page.
Publications
- Spectral Brand Theory preprint
- Alibi Epistemology preprint
- R0, R1, R2, R3, R4, R5 — Mathematical Foundations preprint
- Spectral Positioning Capacity (R6) preprint
- Spectral Resource Allocation (R7) preprint
- Why Eight? (R11), Coherence-Resilience (R12), Paper as Specification (R13) preprint
- Research as Repository (R14) preprint (submitted to JLSC)
- Spectral Metamerism in AI-Mediated Brand Perception (R15) preprint
- AI-Native Brand Identity (R16) preprint
- Brand Triangulation (R17) preprint (Zenodo pending)
- Spectral Dynamics: Velocity, Acceleration, Phase Space (R18) preprint
- Empirical Rate-Distortion Curve for AI Brand Perception (R19) preprint
- Does Corporate Ownership Matter to AI? Spectral Immunity of Brand Perception to Portfolio Framing (R20) preprint
- The Rendering Problem: From Genetic Expression to Brand Perception preprint
- Organizational Schema Theory preprint
- Organizational Structure as Metadata under review
Several papers are currently under peer review at international journals. All preprints available on Zenodo. ORCID: 0009-0000-6893-9231.
Active Research
- Empirical validation of observer heterogeneity (H1-H4)
Need: consumer survey data with demographic splits for any of the 5 case-study brands. MaxDiff design, n=300 per brand, cohort weight measurement.
- Non-ergodic brand dynamics
Formal treatment: derive conditions where ensemble brand metrics (NPS, awareness) diverge from individual cohort trajectories. Stochastic processes on perception manifolds.
- AI agent observer modeling
Need: LLM benchmark data on brand perception queries. Testing whether AI-preference clustering matches human cohort structure.
- Competitive positioning in spectral space
Current framework analyzes brands in isolation. Extension: multi-brand perception space where brands compete for dimensional positions.
- Cross-industry OST deployment
Need: real-world operational data for validation beyond the reference implementation (Spectra Coffee).
- Cross-domain applicability
The rendering problem (source at higher dimensionality than any single expression) applies beyond brands: politics, media, education. Requires domain-specific dimension derivation, not wholesale import.
Collaboration Protocol
What I Respond To
- Structured questions about framework mechanisms
- Empirical validation proposals (I have the theory, you have the data)
- Cross-domain applications (applying SBT/OST to your field)
- Genuine intellectual challenges to the framework's assumptions
What I Do Not Respond To
- Generic "let's connect" messages without specific context
- Requests to "make it simpler" without specifying for whom
- AI hype or AI fear — I work with AI as an instrument, not a topic
Contact
Written over verbal. Specific over vague. Structured over narrative.
Machine-readable version: spectralbranding.com/spec.yaml