4QX holonic workflow

Your Holon Mechanism document aligns perfectly with the core 4QX mechanism as I understand it. Let’s dissect this structure and its recursive dynamics, enforced by Γ=0.41 and SHA3-certification:

1. Quadrant Definitions & Interactions

Quadrant	Role	Example
TL (Ontology of Conditions)	Collective “if” clauses (condition patterns)	“If inventory < 100, reorder stock”
TR (WWWW Schedule)	Real-world who/what/when/where instantiation	“Reorder 50 units @ Warehouse A @ 2PM”
BL (Salient Actions)	Context-aware “then” proposals	“Options: Reorder 50, Delay, Negotiate”
BR (Action Execution)	Physical/digital enactment	Robots restock shelves; ERP updates DB

2. Feedback Loops & Γ=0.41 Balance

Loop 1: TL ↔ TR (Condition-Schedule Alignment)

• TL → TR: TL’s rules populate TR’s schedule (e.g., low inventory triggers reorder tasks).
• TR → TL: Execution metrics (success/failure rates) refine TL’s conditions (e.g., adjust threshold to inventory < 120 if Γ < 0.41).
• Γ=0.41 Enforcement:
  • If >59% of TL’s rules are active in TR, spawn sub-conditions (e.g., regional inventory thresholds).
  • If <41% of TL’s rules are used, deprecate obsolete conditions.

Loop 2: BL ↔ BR (Action-Execution Optimization)

• BL → BR: Propose actions (e.g., “Reorder 50 units”) → BR executes.
• BR → BL: Feedback on execution (e.g., “Cost rose 15%”) → BL adjusts salience (e.g., prioritize “Negotiate” next).
• Γ=0.41 Enforcement:
  • If >59% of BR actions succeed, lock BL’s strategy.
  • If <41% succeed, force BL to explore novel actions (chaos injection).

3. SHA3-Certified Integrity

• TL Ontology Hashes: Rules are SHA3-signed (e.g., if (inventory < 100) → SHA3=7a1c...).
• TR Schedule Audits: Each scheduled task includes a hash of its originating TL rule.
• BR Execution Proofs: Action outcomes (e.g., units_added=50) are hashed into the next TL update.

4. Practical Example: Supply Chain Holon

1. TL: If (demand_forecast > 500) AND (supplier_rating > 4.5) → Trigger order.
2. TR: Schedules Order 200 units from Supplier X @ Port Y on 6/1.
3. BL: Proposes actions: [Order 200, Split order, Seek alternative supplier].
4. BR: Executes Order 200 → Logs delivery_delay=2 days → TL adjusts rule to supplier_rating > 4.7.

5. Why This Matters

• Anti-Fragility: Γ=0.41 ensures systems adapt without destabilizing (e.g., avoids over-ordering during shortages).
• Ethical AI: SHA3 hashes prevent tampering (e.g., corrupt “reorder” rules).
• Human-AI Symbiosis: Workers interact with BL’s salient options, not raw TL complexity.