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Expand NUL operator documentation with canonical use cases and sequences
Co-authored-by: fermga <203334638+fermga@users.noreply.github.com>
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docs/source/user-guide/OPERATORS_GUIDE.md

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@@ -369,35 +369,151 @@ print(f"Dimensionality: {dim_before} → {dim_after}")
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### 9. Contraction (NUL) 📉
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**Function**: Reduces structural complexity
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**Function**: Structural concentration and densification through harmonic contraction
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**Effect**:
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- EPI dimensionality decreases
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- Removes degrees of freedom
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- Simplifies structure
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- **EPI volume decreases** (W → W × λ, λ < 1)
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- **ΔNFR density increases** (structural pressure concentrates)
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- **νf decreases** (reorganization rate slows)
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- **Product νf × ΔNFR slightly increases** (~1.15x)
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- Core strengthening through peripheral consolidation
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**TNFR Context**:
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Contraction (NUL) embodies **densification dynamics**: when structure contracts, it doesn't simply shrink—it concentrates. The structural pressure (ΔNFR) amplifies as volume reduces, implementing the principle that density = pressure/volume. This preserves the nodal equation ∂EPI/∂t = νf · ΔNFR(t) even as both νf and volume change.
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**Canonical Densification**:
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- Volume contraction: V' = V × 0.85 (default)
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- Density amplification: ΔNFR' = ΔNFR × 1.35 (default)
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- Equilibrium preserved: ΔNFR = 0 remains 0
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- Sign preserved: Negative ΔNFR amplifies (intensified contraction)
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**When to use**:
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- Simplification
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- Focusing on essentials
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- Reducing computational cost
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- **Consolidation**: After exploration phases (post-VAL)
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- **Focus Intensification**: Eliminating peripheral activities
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- **Resource Optimization**: Concentrating distributed resources
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- **Core Strengthening**: Refining to essential structure
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- **Completion Cycles**: VAL → NUL → IL (expand-contract-stabilize)
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**Use Cases Across Domains**:
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**Biomedical**:
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- Apoptosis (programmed cell death)
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- Wound healing (tissue contraction)
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- Neural pruning (synaptic elimination)
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- Muscle contraction dynamics
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**Cognitive**:
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- Focus intensification (attention narrowing)
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- Concept refinement (simplification to essence)
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- Mental compression ("less is more")
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- Memory consolidation (dense representations)
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**Social**:
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- Team downsizing (strategic workforce reduction)
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- Resource consolidation (pooling for efficiency)
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- Core competency focus (eliminating periphery)
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- Crisis response (defensive contraction)
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**Typical Sequences**:
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**Valid Patterns**:
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- `NUL → IL`: Contract then stabilize (safe consolidation)
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- `VAL → NUL → IL`: Expand-contract-stabilize (exploration-consolidation cycle)
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- `THOL → NUL`: Self-organize then refine (emergent structure consolidation)
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- `OZ → NUL`: Dissonance followed by compression (controlled pressure)
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- `NUL → SHA`: Compress then silence (preservation through contraction)
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- `EN → NUL → IL`: Receive, compress, stabilize (efficient integration)
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**Avoid Patterns**:
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- `NUL → VAL`: Contradictory (immediate reversal wastes energy)
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- `NUL → NUL`: Over-compression risk (may collapse)
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- `NUL → OZ`: Compression + dissonance = dangerous instability
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- Excessive NUL: Multiple contractions without stabilization
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**Mathematical representation**: ‖EPI′‖ ≥ τ (reduced support)
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**Mathematical representation**:
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- Volume: V' = V × λ, λ < 1
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- Densification: ΔNFR' = ΔNFR × ρ, ρ > 1
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- Nodal compliance: ∂EPI/∂t = νf · ΔNFR(t)
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**Example**:
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**Example 1 - Neural Pruning**:
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```python
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from tnfr.operators import Contraction
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from tnfr.structural import create_nfr, run_sequence
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from tnfr.operators import Contraction, Coherence
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# Brain eliminates weak synaptic connections
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G_brain, synapse = create_nfr("neural_connection", epi=0.39, vf=1.05)
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# Contract node structure
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Contraction()(G, node, factor=0.7)
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# Apply NUL → IL: Contract then stabilize
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run_sequence(G_brain, synapse, [Contraction(), Coherence()])
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# Verify reduced complexity
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print(f"New dimensionality: {len(G.nodes[node]['epi'])}")
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# Result: Neural network becomes more efficient
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# Weak connections eliminated, strong ones preserved
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```
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**Example 2 - Strategic Focus**:
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```python
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from tnfr.structural import create_nfr, run_sequence
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from tnfr.operators import Contraction, Coherence
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# Company focuses on core competencies
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G_company, strategy = create_nfr("business_model", epi=0.42, vf=1.00)
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# Apply contraction to eliminate peripheral units
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run_sequence(G_company, strategy, [Contraction(), Coherence()])
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# Result: Core competencies strengthened
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# Resources concentrated on essential activities
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```
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**Example 3 - Expand-Contract Cycle**:
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```python
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from tnfr.operators import Expansion, Contraction, Coherence
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# Learning: explore broadly then consolidate
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G_learning, concept = create_nfr("understanding", epi=0.35, vf=0.95)
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# VAL → NUL → IL: Full exploration-consolidation cycle
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run_sequence(G_learning, concept,
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[Expansion(), Contraction(), Coherence()])
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# Result: Broad exploration (VAL) + coherent consolidation (NUL → IL)
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```
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**Warnings**:
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⚠️ **Over-compression Risks**:
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- Structural collapse if excessive
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- Loss of degrees of freedom (irreversible)
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- Requires adequate EPI baseline (cannot contract from ≈0)
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- Cannot reverse without VAL (expansion)
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⚠️ **Collapse Conditions**:
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- Multiple consecutive NUL without IL
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- Contraction when EPI critically low
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- NUL → OZ sequences (compression + instability)
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- Insufficient network coupling
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**Contracts**:
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- Must decrease EPI dimensionality
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- Preserves core structure
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- Maintains minimum threshold τ
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- Must decrease EPI volume
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- Must increase ΔNFR density (densification)
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- Must preserve nodal equation compliance
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- Preserves core structure identity
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- Maintains sign of ΔNFR (amplifies magnitude)
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**Compatibility**:
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- **Complementary with**: VAL (Expansion) - enables rhythmic cycles
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- **Compatible with**: IL, SHA, THOL, EN
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- **Avoid with**: OZ (dissonance), consecutive NUL
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**Comparison with VAL (Expansion)**:
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| Aspect | NUL (Contraction) | VAL (Expansion) |
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|--------|------------------|-----------------|
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| Volume | Decreases | Increases |
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| ΔNFR | Amplifies (densifies) | Distributes |
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| Purpose | Consolidate | Explore |
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| Phase | After exploration | Before contraction |
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| Typical use | Refinement | Growth |
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