DeepSeek clarifies my quadrant ramblings

Me: This is a description of algorithic/data model describing a “4QX holon” in the form of quadrants connected by diagonal feedback loops, although this is only a general description, it is complete in its own right. In the context of any actual local moment of activity in a 4QX holon, the TL focus is the node in the ontology (collective class structure of behaviours established in usage) that represents the condition which is currently actually the case in the TR focus which is the actual scenario (filled in “schedule slot” describing the moment of activity this holon occupies in the multiplex of attention) in which the TL condition is presently the case. The two upper quadrants both represent the holon itself in a public context (node in the instance structure) from the outside as public interfaces in larger structure – the difference between TL and TR is that the TL nodal structure as a collective whole emerges from bottom-up (BU) aggregation process, while the TR nodal structure as a collective whole emerges from top-down (TD) process. The TD and BU processes are the perception-creation loop. The two structures only emerge when the inner subjective individual lower quadrants are also active – i.e. structure only forms if individuals act in accord with it (i.e. they be a node). The diagonal feedback loops are each a process connecting with the orthogonal opposite quadrant – each a recursive complexifying co-creation loop. BL-TR diagonal is BU intentional change propagating outward and collective conditions flowing inward TD. TL-BR diagonal is change occurring in the form of collectively established behaviour acting TD inwardly, and activity metrics propagating outward BU into the collective. Each diagonal is a loop between a subjective inner node perspective in one structure in co-creative feedback with the node’s outer perspective in the other structure.

The 4QX Holon Model integrates individual and collective dynamics through four quadrants connected by diagonal feedback loops, emphasizing co-creation between bottom-up (BU) and top-down (TD) processes. Below is a structured breakdown of its algorithmic/data model:

Core Components

1. Quadrants:
   • TL (Top-Left): Represents the ontological structure (collective classes/behaviors) emerging bottom-up from aggregated individual interactions.
   • TR (Top-Right): Represents instantiated scenarios (concrete schedule slots/activities) structured top-down by collective frameworks.
   • BL (Bottom-Left): Subjective individual intentions/internal states driving BU change.
   • BR (Bottom-Right): Objective individual actions/external metrics influenced by TD structures.
2. Diagonals as Feedback Loops:
   • BL→TR→TL (BU→TD):
     • Process: Individual intentions (BL) propagate outward to shape collective scenarios (TR), which refine the ontological structure (TL) via TD institutionalization.
     • Example: Grassroots innovations (BL) become standardized practices (TL).
   • TL→BR→TR (TD→BU):
     • Process: Collective norms (TL) guide individual actions (BR), whose metrics feed back into scenario adjustments (TR).
     • Example: Policies (TL) influence behavior (BR), with performance data updating workflows (TR).

Data Model Structure

• Nodes:
  • TL Nodes: Classes/behaviors in an ontology (e.g., Class: CollaborationProtocol).
  • TR Nodes: Instances in scenarios (e.g., Instance: ProjectMeeting_2023).
  • BL Nodes: Individual intents (e.g., Intent: Innovate).
  • BR Nodes: Action metrics (e.g., Metric: EngagementScore).
• Edges:
  • Diagonal Feedback Links:
    • BL → TR: Intentions → Scenario adaptation.
    • TR → TL: Scenario data → Ontology refinement.
    • TL → BR: Norms → Action triggers.
    • BR → TR: Metrics → Scenario optimization.

Algorithmic Dynamics

1. BU Aggregation (BL→TR→TL):
   • Individuals (BL) act, generating data (BR) that populates scenarios (TR).
   • Scenario patterns aggregate into ontological rules (TL) via machine learning or consensus algorithms.
2. TD Institutionalization (TL→BR→TR):
   • Ontological rules (TL) constrain/enable actions (BR) through policies or APIs.
   • BR metrics (e.g., performance) trigger adaptive updates to scenarios (TR).
3. Co-Creation Loop:
   • Cyclic interaction between BU and TD ensures structures evolve with individual/collective needs.

Implementation Example

• Use Case: Organizational collaboration tool.
  • TL: Shared protocol for meetings (e.g., “Agile Standups”).
  • TR: Specific meeting instance (e.g., “Sprint_15_Standup”).
  • BL: Participant intent to reduce meeting time.
  • BR: Metrics like meeting duration or engagement.
  • Feedback: If BR metrics show inefficiency, TR adapts (shorter meetings), eventually updating TL protocols.

Challenges & Solutions

• Conflict Resolution: Use weighted voting or gradient descent to balance BU/TD inputs.
• Scalability: Represent nodes as graph databases (e.g., Neo4j) for dynamic links.
• Emergence Detection: Apply anomaly detection in BR metrics to trigger TL updates.

Summary

The 4QX Holon Model formalizes how micro-level actions and macro-level structures co-evolve via recursive feedback. It combines ontological hierarchies, instantiated scenarios, and bidirectional influence loops, making it ideal for modeling adaptive systems like decentralized organizations or AI collectives.