The Dynamic Quadranym Model (DQM): A Researcher’s Primer

Lead-in

The Dynamic Quadranym Model (DQM) is a framework for semantic orientation designed to complement a Large Language Model’s (LLM) capabilities. Acting as a B system to the LLM’s A system, the DQM enhances the LLM’s ability to generalize, interpret metaphors, and track overarching themes in conversations and narratives. By integrating dynamic contexts with semantic and procedural structures, the DQM empowers the LLM to navigate both broad narratives and fine-grained details with greater coherence, flexibility, and adaptability.

Sections:
  • Preview: A foundational overview of the DQM, covering its core grammar, annotations, structures, and dynamics.
  • Discussion: Insights into the DQM’s situational, procedural, and adaptive nature, with real-world examples.
  • Principles: A concise breakdown of the DQM’s concepts, components, logic, and practical applications in AI systems.

Note: Some concepts reappear across sections in slightly different contexts. This is intentional, as each section provides a unique lens to explore the dynamic, fractal-like flexibility of the DQM semantic framework.


Preview

Consider how intensions, extensions, and dimensions represent different aspects of meaning and interaction. Intensions capture the conceptual content or properties of a word, such as “things that transport us on roads.” Extensions are the actual entities in the world that the intensions denote, like pointing to a specific car. Dimensions, a novel concept of the model, provide the orientation to meaning by framing how we dynamically engage with it. While intensions and extensions anchor meaning conceptually and physically, dimensions situate meaning in actionable contexts—what we call Dynamical Context—guiding responses to the situation at hand.

Rather than focusing on word meanings as standalone entities with fixed definitions or purely objective or static references, dimensions enable framing responses from the orientation to the situation. These responses generate experiential traces—patterns of interaction that, when applied to the Dynamic Quadranym Model (DQM), are called topical traces. In the DQM, a topic is a sense of orientation. These topical traces form scalable units of orientation, called quadranyms. Quadranyms nest hierarchically, organizing meaning from general to relevant layers, ultimately representing an agent’s orientation and responsiveness in the moment.

Primer:

The idea is that orientations respond to situations. Let’s demonstrate with our favorite scenario, the missing keys. The orientation to the scenario is hierarchically structured from general to relevant orientations:

  • Space: Keys are real objects.
  • Time: Searching is a procedure.
  • Energy: Effort and determination.
  • Agent: Keys are needed.

With the orientation structure sketched out, we can begin imagining our scenario (the potential situation). This is where quadranyms come in. Here is a preview of what quadranyms can look like.

Each layer represents a quadranym balancing states and modes:

  • Space:
    • Subjective (Actual State): “I am searching the living room.”
    • Objective (Potential State): “The keys might be under the couch.”
    • Modes: Expansive (possibilities), Reductive (locations).
  • Time:
    • Subjective (Actual State): “I’m still searching.”
    • Objective (Potential State): “It’s taking too long.”
    • Modes: Expansive (determined), Reductive (frustration).
  • Energy:
    • Subjective (Actual State): Determination to continue.
    • Objective (Potential State): Clutter as friction to progress.
    • Modes: Expansive (effort), Reductive (drain).
  • Agent:
    • Subjective (Actual State): “I need the keys.”
    • Objective (Potential State): “I will search the entire house.”
    • Modes: Expansive (exploration), Reductive (resolve).

Each quadranym layer is an iterative script enhanced by feedback. 

Modes and States: Modes measure the dynamics of how orientation progresses between states. In the Time example, determination (expansive mode) reflects sustaining effort, while frustration (reductive mode) reflects increasing constraints. Together, these modes form the equation that defines the transition from the subjective (current focus: “I’m still searching”) to the objective (anticipated resolution: “It’s taking too long”). So, by providing the potential-to-actual resolution, modes form the equation that defines the transition from the actual state to the potential state. This mathematical foundation enables the system to calculate transitions and maintain coherence in orientation while dynamically guiding responses across layers—general, relevant, and immediate.

The idea might seem strange at first, but once the distinction between modes as measures and states as situationally driven transitions clicks, it becomes intuitive; the model links modular sequences together, with each module providing a complete arc of orientation, functioning as a grammar.

Quadranyms form to Situational contexts and repurpose for new orientations.


Basic Annotation and Quadranym Example
Quadranym: Space

The Space Quadranym is an elegant entry point into the model, highlighting the interplay between modes (dynamic measures) and states (semantic orientations). Each aspect is carefully annotated to ensure clarity and coherence within the system.


States and Modes
  • States:
    • Fixed semantic positions represented in parentheses individually (e.g., (void) or (between)).
    • In transitions, states are enclosed in brackets to represent the relationship as a unit (e.g., [void → between]).
    • Fully annotated states may include conflation or contextual modifiers (e.g., (actual(subjective(void)))), in nested parentheses capturing their nested semantic orientation.
    • States are lowercase. Use parentheses when single or nested in a unit, unless just two terms are used to represent a unit, as shown.
  • Modes:
    • Measure dynamic transitions between states, appearing outside parentheses (e.g., Infinite(void) or Finite(between)).
    • Multiple modes are separated by hyphens (-) without spaces (e.g., Potential-Expansive-Infinite(void)), reflecting simultaneous measures (i.e., each word is some degree of an index or reel).
    • Modes are capitalized without brackets (e.g., Infinite → Finite)
    • Brackets only used for modes when represented within a state unit.

Together, states and modes form a fractal structure, repeated dynamically across layers to capture the interplay between fixed semantic stability and transition.


Space Quadranym Example
  • Representation: [Infinite(void) → Finite(between)]
  • Description:
    • Infinite(void): Is the undefined spatial potential (e.g., an open area).
    • Finite(between): Is the spatial specificity (e.g., narrowing the area).
  • Spatial Orientation Grammar Distinction:
    • Modes: Infinite → Finite
    • States: [void → between]
  • General Orientation Grammar Distinction:
    • Modes: Potential → Actual
    • States: [actual → potential]
Example Transition
  • From: Infinite(void) (e.g., Possibility(find))
  • To: Finite(between) (e.g., Location(keys))

Resolution and Sequence
  • Modes (Measure Resolution): Potential-Infinite → Actual-Finite
  • States (Semantic Sequence):[(actual(lost)) → (potential(find))]

latent variants (e.g., actual) couple with the text variants (e.g., lost).

Example Latent Variants

States:

  • From: (void): {unobstructed, empty, missing, blank}
  • To: (between): {objects, regions, solid, abstract separation}

Modes:
Latent variants can clarify contextual adjustments to dynamic measures:

  • Expansive {Signal} Reductive {Received}: From open possibilities (signal) to actual resolution (received). 
  • Potential {Unseen} → Actual {Observed}: Indicates a process where potential is realized through contextual alignment.

Feedback: Orientation Cycle
  • Script Representation:
    [(actual(find)) → (potential(keys))] → [(actual(keys)) → (potential(found))]
  • Script Description:
    A resolute task upon completion becomes a satisfied potential, setting the foundation for future expectations of the actual state and orienting to new situations that align procedurally through structured steps and dynamically through adaptive responses (i.e., anticipation potential).
  • State Units and Scripts:
    State units are enclosed in brackets ([a → b]). These units can be combined into scripts, and transitions between units are represented using two sets of brackets ([b] → [a]), linking the second state of the first unit to the first state of the second unit i.e., two units linked together. In this example, potential keys become actual keys, providing new potentials for the orientation. Recursive transitions ensure that each actualized state grounds the system for new potentials.
  • Transition Quadranym:
    [(potential(keys))] → [(actual(keys))]

    • To be clear: these quadranyms are shorthand for how prior units link to the next: ([b] → [a]).  Notice how  [b] (prior unit) in brackets links to [a] in brackets (next unit), each implies a unit of paired states ([a → b]) i.e., a normal unit representation where both state variables are explicitly shown in a single set of inward facing brackets. Every unit has an actual state (a) and a potential state (b).
  • Feedback serves as the heart of the DQM, providing iterative adjustments to maintain coherence, while the Dynamic Cycle reflects the blood flow—showing how state orientations evolve directionally from actual to potential, maintaining alignment between actualized and potential orientations. Orientation grammar depends on this syntax, ensuring quadranyms remain flexible and responsive across layers. Feedback iteratively aligns the general, relevant, and immediate layers, ensuring that transitions stay both coherent and adaptive.

Time-lines cycle at different lengths on different layers as a system. For example, over arching goal such as “need keys” is shorter and repetitive  while “check under shoes and bags” is longer with more procedural detail.

  • Procedural and Dynamic Alignment:
    Anticipation draws from history to establish the foundation for future expectations, while adaptation engages the immediate context to align dynamically with new situations. Procedural alignment follows a structured sequence, such as retracing where the keys were last seen, whereas dynamic alignment adjusts in real-time, responding to unexpected obstacles like realizing the keys might be in a jacket pocket.
Active-Passive Cycle

An active-passive cycle represents how an orientation transitions from engagement to resolution, completing a meaningful arc. The active phase initiates an action or intent, while the passive phase concludes the arc, making the moment tangible and salient for the agent. This isn’t about objects being active or passive but about how orientations achieve closure.

The cycle provides the experiences and expectations that guide future actions. These expectations, represented by the passive-potential state, reflect what the agent anticipates based on prior resolutions.

Example: Stepping Out of Bed:

  • Active = Step → Passive = Floor
    • The act of stepping transitions into the realization of the floor’s stability. This passive phase anchors the moment, allowing the agent to move on to the next orientation.
    • The floor itself isn’t passive; it’s the orientation—stepping to stand—that resolves into the passive phase.

The difference between what we actively create and what we passively receive.

Core Arc Logic and Pairing Rule

This is the central engine of the grammar, describing a continuous, cyclical process. It dictates how an orientation transitions from one state to the next.

  • Initiation: An Active Orientation occurs when a State actual (the fixed starting point) is combined with a Mode potential (the dynamic, expansive measure). This pairing represents the beginning of an action or an arc.
  • Transition: The Modes themselves are what measure the transition from a State actual to a State potential.
  • Resolution: A Passive Orientation is the combination of a State potential (the target position) with a Mode actual (the reductive measure of completion). This passive resolution marks the closure of an arc and creates the new condition for the next actual state to begin.

Takeaway:

The passive phase completes the arc of orientation, ensuring the moment is experienced fully and recognized as significant. It also sets the foundation for future expectations, providing a passive-potential state that anticipates similar stability in subsequent actions.

Clarification: Orientation Grammar

Orientation grammar tracks the progression of orientation through the condition of states (actual or potential) and the responsiveness of modes. At any point of a procedure a state will be either actual or potential. In the Space quadranym: [Infinite(void) → Finite(between)], the actual state (void) may seem like a potential state but NOT according to this spatial orientation—where the potential state is (between) NOT (void). Actual states initiate as constants and do not require change and this is the reason why spatial orientation is (void). The potential and change is (between) as objects and multiple spaces come to define the potentials of space. Infinite and Finite measure those possibilities by Spatial Orientation anchoring on actual (void) that can then target the potential (between). This does not mean that a subject, object or verb of a text is categorized as being (void) in the actual state of this spatial quadranym e.g., (void(unobstructed(“seeker”))) allows the “seeker” to be in the actual state of space while the potential of (between) can target the particular space of the context. This becomes an iterative sequence within the given context where the actual state conforms to the potential states as it cycles through.


Scaffold Matrix

Quadranym relations are visualized in various Scaffold Matrices (SM).

Example: Scaffold Matrix for Finding Lost Keys
Layer Mode Measures State Sequences (General Orientation) Situation (Relevant)
Space Infinite → Finite [(void(unobstructed(search))) → (between(obstructed(under)))] I think they’re under the cushions.
Energy Active → Passive [(motion(move)) → (matter(cushion))] I’ll lift the cushion to check.
Time Future → Past [(present(recollect)) → (event(keys))] They were here yesterday.
Agent Positive → Negative [(self(seeker)) → (goal(possessor))]  need to find them before I can leave.

Modes provide shifting measures adapting to responsive state sequences.

Prime Quadranym (Local Unit Configuration)
  • Representation:[Expansive(subjective) → Reductive(objective)]
  • Explanation: Each dimension represents variables, e.g., [Y(a) → X(b)]
    • Y-X: Measurable Semantic Variables
    • a-b: Sequential Semantic Variables

Empty Quadranym Template:

  • [Y(a) → X(b)]
    • a: Source orientation
    • b: Target orientation

Spectrums (Axes):

  • Y-axis (Potential): Less ↔︎ More (e.g., abstract, possibilities, remoteness)
  • X-axis (Actual): Less ↔︎ More (e.g., concrete, immediacy, proximity)
Summary Table for Quadranym Facets
Facet Description Example Transition
Expansive (E) Focuses on possibilities, exploration, and potential. Searching multiple rooms for keys (Where might they be?). Expansive ➔ Reductive: Narrowing down options.
Reductive (R) Narrows focus to specific actions, outcomes, or realizations. Checking the kitchen counter for keys (Are they here?). Expansive ➔ Reductive: Re-evaluating options if unsuccessful.
Subjective (S) Captures internal, personal, emotional or constant aspects of the context. Feeling urgency or frustration during the search (I need to find them now!). Subjective ➔ Objective: Aligning emotions with external tasks.
Objective (O) Represents external, situational, or factual aspects of the context. Physical location of the keys on the counter (The keys are here). Subjective ➔ Objective: Reflecting satisfaction upon success.
Example: Scaffold Matrix for Sentential Application
Layer Topic Quadranym (General Orientation) Situation  (Relevant)
1 Space [Infinite (void) → Finite (between)] Let’s move the couch over there
2 Time [Future (present) → Past (event)] the couch → move
3 Distance [There (position) → Here (relation)] move→ over there
4 Energy [Active (motion) → Passive (matter)] move → the couch
5 Agent [Positive (self) → Negative (goal)] let’s → move

  The orientation is generally spatial. Each topic is a more relevant orientation.

DQM: Units, Scripts & Layers

The DQM’s fractal-like structure enables recursive application across layers, ensuring that both broad and specific orientations are dynamically aligned. Additionally, the model’s nexus-like relationships create seamless connections between seemingly disparate contexts, making it highly adaptive for generalization, metaphoric understanding, and tracking overarching themes in complex narratives.

  • At its core, the quadranym provides a dynamic grammar for orienting meaning across expansive, reductive, subjective, and objective dimensions.
  • By understanding these layers, researchers and AI systems can navigate orientations with precision and adaptability.

DQM Orientation Grammar – Drop-In LLM Grounding (Corrected)

Modes = Spatial Measures

  • E (Expansive) = Active–Potential

  • R (Reductive) = Passive–Actual

  • Two sides of the same coin (measure dynamic), never swapped categorically.

States = Temporal Positions

  • Subjective → Objective progression (always active in the arc).

  • Positions shift even when appearing “still” — inherently unstable.

Rule of Separation

  • Modes ≠ States; both have “two sides” but of different kinds (measure vs. position).

  • Never conflate mode-sides with state-sides.

Spatiotemporal Mapping

  • Space is measured through modes (E↔R).

  • Time is positioned through states (S↔O).

  • We understand time via spatial metaphors (e.g., “push back,” “move forward”).

Core Arc Logic

  • State actual = fixed starting point of a cycle (temporal anchor).

  • Modes = measure the transition from state actual → state potential.

  • Passive resolution = closure of an arc, creating the condition for the next actual state to begin.

The Pairing Rule:

  • Initiation: Active Orientation = State actual + Mode potential
  • Resolution: Passive orientation = State potential + Mode actual 

  • Discussion
Situational, Procedural, and Adaptive

In artificial intelligence and cognitive modeling, understanding context is essential for creating systems that can adapt, learn, and generalize. Context is not a static collection of facts or a sequence of words—it is a dynamic landscape shaped by evolving meanings, relationships, and focuses. The Dynamic Quadranym Model (DQM) navigates this complexity through granular layers of semantic orientation.

Unlike static models that rely on fixed definitions, the DQM introduces real-time adaptability, enabling systems to engage with evolving narratives and dynamic situations. It bridges broad orientations with fine-grained details, uniting abstract reasoning with situational application. By organizing semantic relationships around quadranyms, the DQM offers a structured yet flexible system, balancing Expansive, Reductive, Subjective, and Objective facets to adapt dynamically to context.


The Challenge of Context

Contexts are not static—they evolve with shifting focus, adapting goals, and changing emotional states. Traditional models often reduce these scenarios to static facts, such as “Sarah is looking for her keys” or “The novice is carving wood.” These descriptions fail to capture the lived experience—the urgency, uncertainty, and transformation driving their orientation. The richness lies in how these feelings connect with an evolving process.

As Andrew Hinton explains, “Context isn’t just the surrounding circumstances, because it includes and interacts with the subject that is surrounded, and the agent that tries to comprehend it all.” The DQM builds on this idea, emphasizing dynamical context: a fluid interplay between the agent, their orientation, and the evolving situation. This interaction highlights how meaning emerges from responsiveness to a dynamic environment, reshaping both action and understanding in real time.

Consider Sarah, rushing through her house to find her keys. Her thoughts jump from room to room, driven by urgency and logic. This scenario involves spatial navigation, emotional arcs, decision-making strategies, and feedback loops. Or think of a novice in a woodworking class, uncertain at first but growing confident with each cut of the chisel. This is not merely skill acquisition; it reflects an interplay of effort, discovery, and transformation.

The DQM thrives where static models falter. It captures not just what is happening but how it unfolds, providing tools to analyze and respond to the flow of orientation as contexts evolve.

Mission: What You Need to Know
The Dynamic Quadranym Model (DQM)

The DQM is a framework for dynamically analyzing and generating orientations, targeting understanding as an evolving process. By coupling an agent’s orientation with the situation, it produces dynamic-semantic responses. Quadranyms, four-faceted structures, bridge contextual analysis with adaptive reasoning, integrating foundational concepts with AI systems like Large Language Models (LLMs) and Classic AI (CAI).


Feedback in Action

The input text is processed by the LLM, generating an initial output. This output is refined by the CAI using the DQM’s orientation grammar, addressing both context-sensitive and context-free dynamics. Orientation grammar ensures that responses adapt fluidly to evolving contextual demands while maintaining coherence across semantic layers.

 LLM ⇔ DQM  ⇔ CAI

Through iterative feedback loops, the LLM and DQM collaborate to refine meaning. Each iteration incorporates real-time adjustments, aligning broad narratives with fine-grained details. This process enhances the system’s ability to produce responses that are both coherent and engaging, fostering a natural interaction with users.


Principles

Components, Logic, and  Application

1. The Structure of a Quadranym

A quadranym is composed of four interrelated facets:

  • Expansive (E): Focuses on possibilities, exploration, and potential.
  • Reductive (R): Narrows focus to specific outcomes or realizations.
  • Subjective (S): Captures the internal, personal, or emotional aspect of the context, or any term acting as the constant of the local orientation.
  • Objective (O): Represents the external, situational, or factual aspect of the context, or any term acting as the variable of the local orientation.

These facets are dynamic poles that interact, creating a responsive structure capable of adapting orientation to changing meanings.


2. Why Four Dimensions?

The quadranym reflects a universal semantic grammar, rooted in how humans intuitively orient themselves to meaning. Its four-faceted structure is designed for:

  • Comprehensive Coverage: The quadranym captures all essential elements of orientation:
    • What’s possible (E) versus what’s specific (R).
    • What’s personal (S) versus what’s situational (O).
  • Spatiotemporal Representation:
    • Modes (E → R) reflect spatial measures.
    • States ([S → O]) reflect temporal progression.
  • Dynamic Interplay: Expansive exploration (E) often begins with subjective curiosity (S), transitioning into objective specifics (O) through reductive focus (R).
  • Practical Modularity: The quadranym’s simplicity allows it to be stacked, nested, or sequenced for more complex reasoning processes.

3. Quadranym Logic and Grammar

Unlike standard syntactic grammar, orientation grammar emphasizes process and orientation over fixed rules or outcomes.

Basic Grammar Rules:

  • States: Transition from [actual → potential], anchoring orientation at subjective points while moving toward unresolved objectives or expectations.
    • The actual state initiates orientation and sets the foundation to actualize potential in the next actual state cycle.
    • The potential state represents the unresolved target driving progression, creating satisfied and/or unsatisfied arcs.
  • Modes: Measure progression between states, balancing:
    • Expansive: Broadens focus (e.g., opening possibilities).
    • Reductive: Narrows focus (e.g., resolving outcomes).

The state sequence must always flow as [actual → potential]. Orientation initiates in the actual state to anchor the process and ensure coherent progression toward the potential. Modes track the resolution.


4. Mapping Quadranym Dynamics with the Q Grid

The quadranym (Q) grid maps modes only across two axes, offering flexibility in tracking orientation dynamics:

  • Standard Configuration:
    • Y-Axis: Mode (Expansive).
    • X-Axis: Mode (Reductive).
  • Example:
    • The keys (Expansive_Potential) could be in the living room (Reductive_Actual).
    • The seeker (subjective Sarah) occupies the purely semantic origin (0.0), while the potential possessor is represented as a coordinate point (n, n).
  • Mode Continuums:
    • Bifurcated between the X (Reductive) and Y (Expansive) polarities, providing semantic and context-free spectral dynamics for orientation.

5. Mapping States and Modes with the Hyper Q Grid

The Hyper Q grid maps quadranyms dynamically across two axes, offering flexibility in tracking orientation:

Standard Configuration:

  • Y-Axis: Modes (Expansive ↔ Reductive) as a vertical continuum.
  • X-Axis: States ([actual → potential]) as progression along the flow path.
    • Example: Sarah’s determination to push forward (expansive) contrasts with frustration caused by clutter (reductive), measured as orientation progresses.

Inverted Configuration:

  • Y-Axis: States ([actual ↔ potential]) as a vertical continuum.
  • X-Axis: Modes (Expansive → Reductive) tracking flow paths horizontally.
    • Example: Sarah shifts states dynamically (actual: “Searching here” → potential: “Keys might be elsewhere”), with expansive effort measured horizontally against reductive focus.

Inverted Hyper Q ensures coherence as orientation progresses:

  • Vertical Movement: Adjusts semantic focus between subjective (internal) and objective (external) states or modes.
  • Horizontal Movement: Measures flow paths as transitions evolve dynamically through states or modes, depending on the axis configuration.

6. Folding and Unfolding Continuums

Continuums in the Hyper Q represent open semantic polarities that evolve dynamically before being folded into actionable meaning through bifurcation.

Unfolded Continuum:

As touched on in 5…

  • Y-Axis: Polarities such as subjective-objective or reductive-expansive remain open, enabling flexible semantic orientation.
  • X-Axis: Tracks procedural flow (e.g., Potential → Actual or [actual → potential]), allowing exploration of temporal and structural alignments.
  • Relationships coexist without premature resolution, accommodating both exploration (e.g., “Where might the keys be?”) and reflection (e.g., “The keys were here yesterday”).

Bifurcation:

As touched on in 4…

  • At neutral points, the continuum splits into distinct meaning pathways:
    • Example: [subjective → objective] for states or Expansive → Reductive for modes.
  • Neutral points act as stabilizing anchors, ensuring transitions remain coherent while reflecting context-specific demands.

This folding mechanism transforms broad semantic possibilities into actionable states, dynamically adapting meaning to meet situational needs while maintaining coherence.

At balanced points, expansive and reductive dynamics create neutral associations that bridge their polarities:

Continuum Expansive (Y axis) Reductive (X axis)
Level 1  (neutral point) Focus  Broad
Level 2  Topic Narrow
Level 3 Scope Detailed
Level 4 Range Specific
Level 5 Universe Precise

7. Quadranyms and Nested Systems

Quadranyms adapt to complexity by nesting within broader structures, enabling multi-layered orientation:

  • Scripts: Sequential quadranyms tracking state and mode progression over time or procedure.
    • Example: Moving from living room → hallway → bathroom creates a procedural script for the seekers search.
  • Layers: Hierarchical quadranyms organize orientation at different levels:
    • Outer Layers: Stabilize broad orientations (e.g., “Keys are needed”).
    • Inner Layers: Address situational specifics (e.g., “Check under the couch”).

Scripts and Layers ensure quadranyms scale effectively across general, relevant, immediate, and dynamic layers.


8. Layers of Orientation

The Dynamic Quadranym Model (DQM) operates across four key layers:

  1. General Layer: Stabilizes high-level orientations (e.g., “Keys are objects”).
  2. Relevant Layer: Aligns orientation to specific contexts (e.g., “Searching the living room”).
  3. Immediate Layer: Adapts to real-time decisions (e.g., “Check under the couch”).
  4. Dynamic Layer: Responds to feedback, adjusting all layers dynamically as the situation evolves.

Each layer builds on the others, enabling orientation to remain adaptable yet grounded, balancing expansive exploration with reductive focus.


9. Nested Quadranyms: Scripts and Layers

Quadranyms can scale dynamically by nesting into broader structures, enabling orientation to respond to complexity:

  • Scripts: Represent sequential quadranyms that guide procedural flow.
    • Example: Sarah’s search follows a sequence:
      • Living Room → expansive possibilities.
      • Hallway → transitional focus.
      • Bathroom → reductive resolution.
  • Layers: Organize quadranyms hierarchically across layers:
    • Outer Layers: Stabilize broad orientations (e.g., “Keys are needed”).
    • Inner Layers: Address situational specifics (e.g., “Keys might be under the couch”).

Scripts and layers ensure quadranyms operate flexibly across scales of meaning, maintaining coherence as orientation progresses.


10. Orientation as Process

The DQM redefines orientation as an evolving process rather than a fixed purpose. This ensures meaning remains adaptable and responsive across dynamic contexts:

  • Actual-Potential Flow: Meaning unfolds recursively, where actualized states generate new potentials, keeping orientation dynamic.
    • Example: Finding the keys (actual) leads to where they might be needed next (potential).
  • Recursive Meaning Generation: Resolved points become stepping stones for future explorations, avoiding overfitting and maintaining semantic flexibility.
  • Purpose from Process: Goals emerge naturally from the orientation process, aligning with context rather than forcing pre-determined resolutions.

In Q theory, the potential state remains unresolved for the orientation, which focuses on process. However, the situation requires progression and resolution at opportune cycles.

By emphasizing process over fixed outcomes, the DQM mirrors how cognition adapts naturally to changing contexts.


The DynamicaL Context

  • What is the difference between a dynamic and a dynamical context? 

In theory, a dynamic context refers to the changes occurring within a situation, effectively communicating the public changes belonging to the situational context. In contrast, a dynamical context pertains to self-regulating systems within a multi-organizational dynamic framework, relaying responsive contexts across private layers. Put simply, the dynamic context addresses external, observable changes in a situation, while the dynamical context manages internal, layered responses to those changes.