Author: Kevin Mahan
Location: Las Vegas, Nevada
Contact: repos@khalisti.ai
CAF v1.0.0
Initial Theoretical Release
Release Date: 2026
Convergent Archetype Framework (CAF) is a theoretical meta-model designed to aggregate multiple symbolic classification systems into a unified structure for compatibility and trait convergence analysis.
This framework does not implement code.
It defines:
- Structured scoring logic
- Cross-system weighting theory
- Trait convergence methodology
- Normalized vs non-normalized modeling approaches
- Confidence calibration based on data completeness
CAF is intended as a guiding architecture for researchers, model builders, and system designers developing compatibility systems or symbolic convergence engines.
- Western Astrology
- Vedic Astrology (Jyotish)
- Chinese Zodiac
- Numerology
- Mayan Tzolkin
- Celtic Tree System
- Native Totem Systems
- Egyptian Archetypes
All systems are treated as independent classifiers.
Each symbolic system functions independently.
No domain is assumed superior.
Each produces:
- Compatibility score
- Trait mapping
- Confidence factor
Used when:
- Data completeness varies
- Domains differ in structural resolution
Conceptual Formula:
FinalScore =
Sum(DomainScore × DomainWeight) /
Sum(ActiveDomainWeights)
Used when:
- Full data is present
- Domain confidence is high
Conceptual Formula:
FinalRawScore =
Sum(DomainScore × DomainWeight)
If multiple independent systems attribute the same trait, reinforcement increases.
TraitStrength =
SupportingSystems / ActiveSystems
Agreement across systems increases structural reinforcement.
Confidence depends on:
- Number of active systems
- Completeness of birth data
- Resolution depth (Moon, Nakshatra, etc.)
Compatibility Strength and Confidence Strength are calculated separately.
- Defined multi-domain symbolic aggregation structure
- Established normalized and non-normalized models
- Formalized Multi-System Alignment Theory (MSA)
- Defined unified meta-trait categories
- Implemented domain independence principle
- Established compatibility tier structure
- Created reproducible framework for expansion
Planned improvements based on structured analysis of authoritative texts, including:
- Western Astrology refinement (Woolfolk, Goodman-style structural comparisons)
- Vedic compatibility expansion (Ashtakoota depth, Nakshatra resolution)
- Chinese Zodiac matrix enhancement (traditional elemental cycle reinforcement)
- Numerology pair reinforcement based on classical interpretations
- Mayan glyph interaction refinement
- Celtic and Native system archetype refinement
- Egyptian decan mythological alignment calibration
This phase will improve:
- Pairwise compatibility matrices
- Trait mapping granularity
- Weight calibration logic
- Conflict zone resolution
- Cross-text consensus weighting
- Empirical feedback calibration (if datasets available)
- Structural conflict detection layer
- Confidence decay modeling
- Trait clustering analysis
Convergent Archetype Framework (CAF)
Created by Kevin Mahan
Las Vegas, Nevada
repos@khalisti.ai
CAF aggregates symbolic classification systems.
It does not claim empirical scientific validation.
It is a structured theoretical convergence model designed for reproducible symbolic analysis and future refinement.