louspringer/decision-density.md

## decision-density.md

      
    Raw
  

              decision-density.md
            
          
    Let’s distill and formalize these two statements into clean, interoperable decision-theory primitives you can drop directly into your Fort / Beast stack. I’m keeping it straight, skeptical, and crisp—no hand-waving.

1. The Core Claims (Normalized)

Claim A — Decision Density → Solution Correctness


A solution whose architecture is derived from a high percentage of requirements-driven decisions (≈90%) has a materially higher probability of correctness than a solution derived from a sparse requirement base (≈20%).

Rewritten in decision-theory terms:
Decision Density (δ)
[
\delta = \frac{\text{Decisions justified by requirements}}{\text{Total architectural decisions}}
]
Hypothesis:
[
P(\text{Correctness}\mid \delta_{high}) \gg P(\text{Correctness}\mid \delta_{low})
]
Where correctness means:

fewer invalid states
tighter SCIG gap
fewer downstream retractions
higher model conformance
lower emergent defect rate
stronger alignment with constraints, stakeholders, and intent

This is a structural assertion: more validated decisions → fewer latent contradictions.

Claim B — Multi-Perspective Confidence Boost


Multiple perspectives increase decision confidence.

Decision theory translation:
If (e_1, e_2, …, e_n) are signals from independent or semi-independent perspectives (disciplines, agents, analysis lenses), then confidence grows as correlation decreases.
[
\text{Confidence} \propto \frac{n}{1 + \rho}
]
Where ρ = correlation of perspectives.
Low correlation → bigger free lunch (your diversity thesis).
High correlation → little to no gain.
This also plugs directly into the 21+1 dimensional risk model: each perspective spans a dimension or a subspace.

2. Combined Effect (Fort-style)

When Decision Density is high and the decisions are validated across multiple low-correlation perspectives, you get:
[
P(\text{Correctness}) \approx 1 - \text{SCIG}
]
Because SCIG (Systemic Continuous Invalid-state Gap) shrinks with both:

higher requirement saturation
higher perspective diversity

This is mechanically true in any complex, multi-constraint system.

3. Micro-Ontology Snippet (TTL)

Here is a minimal TTL you can drop into your requirements, decision-logs, or LIM42 alignment workflow:
@prefix fort: <https://nkllon/fort#> .
@prefix req:  <https://nkllon/requirements#> .
@prefix dec:  <https://nkllon/decision#> .
@prefix scig: <https://nkllon/scig#> .
@prefix xsd:  <http://www.w3.org/2001/XMLSchema#> .

### Core Classes
fort:Decision a rdfs:Class .
fort:Requirement a rdfs:Class .
fort:Perspective a rdfs:Class .
fort:Solution a rdfs:Class .

### Properties
dec:justifiedBy a rdf:Property ;
    rdfs:domain fort:Decision ;
    rdfs:range fort:Requirement .

dec:hasPerspectiveEvidence a rdf:Property ;
    rdfs:domain fort:Decision ;
    rdfs:range fort:Perspective .

fort:decisionDensity a rdf:Property ;
    rdfs:domain fort:Solution ;
    rdfs:range xsd:decimal .

fort:confidenceScore a rdf:Property ;
    rdfs:domain fort:Solution ;
    rdfs:range xsd:decimal .

### Inference-ready patterns
fort:HighDecisionDensitySolution a owl:Class ;
    owl:equivalentClass [
        a owl:Class ;
        owl:intersectionOf (
            fort:Solution
            [ a owl:Restriction ;
              owl:onProperty fort:decisionDensity ;
              owl:someValuesFrom [
                  a rdfs:Datatype ;
                  owl:onDatatype xsd:decimal ;
                  owl:withRestrictions ( [ xsd:minInclusive "0.9"^^xsd:decimal ] )
              ]
            ]
        )
    ] .

fort:MultiPerspectiveDecision a owl:Class ;
    owl:equivalentClass [
        a owl:Class ;
        owl:intersectionOf (
            fort:Decision
            [ a owl:Restriction ;
              owl:onProperty dec:hasPerspectiveEvidence ;
              owl:minCardinality "2"^^xsd:nonNegativeInteger
            ]
        )
    ] .

### SCIG relationship
scig:reducesInvalidStateGap a rdf:Property ;
    rdfs:domain fort:HighDecisionDensitySolution ;
    rdfs:range scig:SCIGReduction .

4. How This Plays in the Fort Workflow


LIM42 can treat decision density as a scalar energy parameter.
Ghostbusters agents can flag low-density decisions as potential invalid-state generators.
Elmo can run multi-perspective fusions and calculate estimated correlation (ρ).
BFG9K stores the decision traces, making them queryable and auditable.

This is directly compatible with your TaaS, NetBox, Snowflake, and multi-agent mesh workflows.

5. If You Want Next:

I can generate:
✅ SHACL for validating solutions with insufficient decision density
✅ A PDCA loop agent for automatically classifying and correcting low-density decisions
✅ A Fort diagram showing the causal chain between density → confidence → SCIG
✅ A GitHub-ready ZIP package containing all artifacts
Just say the word.
No results found