Notes Page

See home page for a succinct overview.

Some of the Ideas are organized below. These are mostly just notes. Word-sensibility is not formally defended. This is for preliminary assessment only.

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Introduction

PROJECT B

Note: A List of Concepts and Strategies

Scaffold Matrix Strategies

Nesting Strategies

Purpose: Organize layers hierarchically to represent contexts within contexts (e.g., General → Nest → Sub-Nest).
Explanation: Each layer encapsulates a particular scope or level of detail, allowing for a structured progression from broad to specific. This hierarchical nesting supports context-sensitive orientations without losing track of overarching goals.

Latent Variant Strategies:

Purpose: Highlight hidden or latent dynamics within layers (e.g., potential states within “the void”).
Explanation: By examining unseen or unactivated facets—“latent” states—systems can discover unused capacities or future paths. This approach identifies opportunities for adaptation that aren’t immediately apparent in a single active layer.

Sticky-Slick Strategies:

Purpose: Balance fixed (“sticky”) anchors with adaptable (“slick”) transitions to maintain both stability and fluidity.
Explanation: “Sticky” elements provide a consistent reference point, while “slick” elements ensure the system remains flexible. This balance helps preserve essential identity or meaning while allowing smooth transitions when contexts shift.

Continuum Strategies:

Purpose: Use continuous transitions between quadranym states to model gradual shifts (e.g., Infinite ↔ Finite).
Explanation: Rather than abrupt changes, continuum strategies smoothly morph states along a gradient, reflecting how real-world changes often evolve incrementally. This approach enriches the nuance in orientation and adaptation.

Bifurcation Strategies:

Purpose: Handle the moment when meaning splits along two principal axes (e.g., literal vs. abstract), generating multiple potential interpretations or actions.
Explanation: This fork in orientation leverages two primary axes (X and Y). As contexts or internal states shift, the system bifurcates, enabling it to explore separate paths without losing coherence.

Context-Free Spectral Strategies:

Purpose: Adjust positioning on the X or Y axis (or both) to modify semantic nuclei independently of a specific context.
Explanation: By detaching from immediate situational cues, the system can maneuver along a “spectral” scale of meaning (more/less, open/closed, etc.), retaining adaptability and readiness for future, as-yet-unknown contexts.

Spectral Semantic Strategies:

Purpose: Delve into the range of possible meanings within a quadranym, emphasizing depth and subtlety.
Explanation: This approach looks at the spectrum of relationships—how concepts move from one semantic pole to another. It highlights intermediate states that might be overlooked by purely binary models.

Feedback Loop Strategies:

Purpose: Integrate recursive feedback to refine quadranyms and identify cross-layer interactions dynamically.
Explanation: After each step or orientation shift, the system reevaluates and updates its internal states. These iterative loops ensure continual refinement, weaving insights from multiple layers or quadranyms into a coherent whole.

Temporal Layer Strategies:

Purpose: Introduce time-based dynamics for evolving states (e.g., Future → Present → Past).
Explanation: In many contexts, orientation isn’t static—things change over time. Temporal layer strategies track how states progress or degrade, capturing the sequential aspect of processes and experiences.

Nested Loop Strategies:

Purpose: Allow recursive nesting within layers, creating complex, fractal-like orientations.
Explanation: By embedding loops within loops, the system can handle multifaceted scenarios where one layer’s output becomes another layer’s input, mirroring real-world complexities.

Boundary Mapping Strategies:

Purpose: Focus on transitions between quadranym states to analyze thresholds and boundaries (e.g., Void ↔ Between).
Explanation: Critical shifts often occur at boundaries, where orientation flips or meaning drastically alters. Mapping these points reveals how small contextual changes can provoke significant reorientations.

Causal Flow Strategies:

Purpose: Track cause-effect relationships within and across layers, showing how orientations prompt or inhibit outcomes.
Explanation: Orientations are not just states but drivers of action or interpretation. Linking these states to their consequences offers a blueprint of how changes in orientation produce tangible effects.

Hybrid Quadranym Strategies:

Purpose: Combine multiple quadranyms across layers to explore multidimensional intersections (e.g., Space + Energy).
Explanation: Real-life scenarios often involve overlapping domains—spatial, temporal, energetic, etc. By merging quadranyms, the system addresses complex situations with greater completeness and contextual synergy.

Hyper Q Strategies

Hyper Quadranyms focus on multi-layered, recursive, or interconnected dynamics that go beyond single-layer analysis. These strategies emphasize complex, high-dimensional interactions.

Layer Integration Strategies:
Combine multiple layers into a Hyper Q to explore cross-layer dynamics.
Example: General Layer (Space) + Relevant Layer (Agent) → Hyper Q: Space-Agent Interaction.
Recursive Mapping Strategies:
Apply Hyper Qs to model repeated patterns or self-similar dynamics across scales.
Example: Space:[Infinite(void) → Finite(between)] ↔ Energy:[Active(motion) → Passive(matter)].
Cross-Domain Strategies:
Use Hyper Qs to analyze concepts spanning domains (e.g., time, space, and energy combined).
Example: Time-Space Continuum Quadranym.
Meta-Quadranym Strategies:
Construct a Hyper Q that reflects the relationships between quadranyms themselves.
Example: Space Quadranym ↔ Perception Quadranym relationship analysis.

Standard Q Strategies

Standard Quadranyms focus on layer-specific, context-driven orientations, emphasizing clarity and simplicity.

Single-Layer Strategies:
Apply a Quadranym to a specific layer for focused analysis.
Example: Immediate Layer: Perception:[Stimuli(interpret) → Select(organize)].
Contextual Refinement Strategies:
Tailor the Quadranym to fit specific contextual dynamics.
Example: Nest Context: Space:[Above(ocean) → Beneath(reef)].
Transition Strategies:
Emphasize smooth transitions between states within a single Quadranym.
Example: Space:[Infinite(void) → Finite(between)] exploring gradual narrowing of focus.
Simplified Framework Strategies:
Use Standard Qs for straightforward, high-level overviews without added complexity.
Example: General Layer with basic quadranym definitions.
Bifurcation Strategies:
By folding a continuum (e.g., Expansive ↔ Reductive), a bifurcation is created between polarities where neutral points can diverge. Highlight dual pathways or splits in orientation, emphasizing branching possibilities.
Example: Expansive-Stimuli(wt. 6) → Reductive-Focus(wt. 4)

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A Theoretical Look at the Role of Words for AI: Commonsense Ontology & Reference Frames