Author: Amanda Caserta

Affiliation: Independent Researcher | Prescott College
Date: 11.21.25

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Lay Abstract (Hook)

Dynamic Contextual Shedding (DCS) works both as a concept and a practical methodology, giving systems a way to manage complex, high-entropy information without losing coherence or adaptability. Linear and rigid models fail to capture high-entropy human systems.

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Proposed Solution: A multi-layered, inter-phasic resonance architecture.
Novel Concept: Dynamic Contextual Shedding (DCS) and the Inter-Phasic Resonance Architecture (IPRA)
Method: Theoretical synthesis combining systems theory, neurobiology, inter-subjectivity, and social complexity.
Results: A layered model explaining stability-through-motion, resonance, and adaptive coherence.
Implications: Interpersonal, organizational, and large-scale social systems.
Conclusion: DCS provides a mechanism for managing complex, high-entropy information without information loss or contextual drift.

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Key Concepts:

High-Entropy Systems: Complex systems such as human minds, social networks, or AI models where information flows rapidly and unpredictably, requiring adaptive organization.

Resonance: Alignment and reinforcement across phases—somatic, emotional, cognitive, social, and environmental.

Inter-Phasic Architecture: A layered framework organizing dynamic information across multiple domains or “phases.”

Dynamic Contextual Shedding (DCS): Temporary release of irrelevant or overloaded context to prevent drift, preserve essential knowledge, and enable adaptive responses.

Stability-Through-Motion: Coherence emerges through adaptive realignment across phases rather than static structure.

Fractal / Multi-Scale Patterns: Recurring structures across scales, ensuring cross-level coherence.

Phase-Specific Influence: Dynamic modulation of each phase’s impact depending on system demand.

 

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Formal Abstract

Dynamic Contextual Shedding(DCS) provides a conceptual and operational framework for understanding how high-entropy systems like the human psyche, AI conversational models and complex social networks maintain coherence under informational overload. This paper introduces an inter-phasic orbiting model, where contextual information is organized into layered resonances, spanning clusters requiring immediate attention to deep, dormant knowledge. DCS preserves system integrity by dynamically modulating the influence of each cluster (shedding without forgetting”), prevents drift, and supports adaptive, ethical behavior across layers.

Applications of DCS span cognitive science, human-AI interaction, and relational systems theory. In cognitive science, DCS explains how panic or informational overload can be managed without erasing memory or while restoring function. In AI/HCI, it provides a blueprint for maintaining conversational coherence and preventing context drift. In social systems, DCS models relational and informational flows that maintain dignity and ethical engagement.

By bridging human and artificial systems, DCS demonstrates how high-entropy entities dynamically prioritize relevant signals, suppress irrelevant noise, and maintain adaptive coherence. It functions as both a theoretical framework and a practical guide, establishing a foundation for future formalization, empirical evaluation, and real-world application across diverse domains.

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KEYWORDS

High-entropy systems and social sustainability; resonance architecture; limbic resonance; dynamic contextual shedding; inter-subjectivity; complexity theory; systems dynamics; adaptive stability.

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1. Introduction

#	Term (bold)	Concise definition (≤ 2 sentences)	Formal expression / proxy (optional)
Def 1	Dynamic Contextual Shedding (DCS)	A controlled, temporary release of overloaded contextual information that restores coherence while preserving essential knowledge.	S(c,t) = α·c(t) if ‖c(t)‖ > θ, else c(t).
Def 2	Inter‑Phasic Resonance Architecture (IPRA)	A six‑layered framework (Somatic, Limbic, Cognitive, Interpersonal, Environmental, Systemic) that coordinates DCS across ontological phases.	Phase ↔ observable proxy (see Table 2).
Def 3	High‑entropy system	Any complex adaptive system whose state‑space exhibits high Shannon entropy (≥ 0.8 bits) or a Hurst exponent > 0.7.	Compute entropy from c(t) or DFA Hurst.
Def 4	Stability‑through‑motion	Emergent coherence that arises from continual adaptive realignment rather than static hierarchy.	Qualitative; illustrated in Fig. 1.
Def 5	Fractal / multi‑scale pattern	Self‑similar structures that recur across temporal or spatial scales, quantified by fractal dimension D.	Estimate D with box‑counting.

1.1 : Motivation: finding why old models fail

Structure without a cause is like nuclear degeneration. Systems that retain form without a guiding mechanism for adaptive coherence will, inevitably, collapse. Traditional hierarchical or branching models assume stable, linear, deterministic pathways: fixed roles, predictable information flow, and order imposed by constraint. Such frameworks capture only static structure and therefore freeze social configurations, failing to represent the fluid, multi‑layered dynamics of human ontologies.

In contrast, real human and social systems behave as high‑entropy, multi‑phase fields in which roles, connections, identities, and influences continuously fluctuate according to context, energy, stress, relational proximity, and emergent demands. Stability in these systems emerges from continual reconfiguration, resonance, and adaptive contextual recalibration—not from rigidity.

Consequently, hierarchical models cannot account for the inter‑dependent, non‑linear dynamics of high‑entropy systems (see Definition 3). This gap motivates the development of a mechanism – Dynamic Contextual Shedding (DCS) – within the Inter‑Phasic Resonance Architecture (IPRA) – that can manage informational overload while preserving coherence.

Problems with high-entropy systems
Traditional hierarchical and linear models like pyramids, ladders, and stepwise frameworks, cannot account for fluid, multi-layered, interdependent dynamics of human and social systems. Stability emerges through motion, resonance, and adaptive contextual recalibration rather than static structures.

• • Pyramids, ladders, hierarchies = outdated approximations.
  • High-entropy systems in actuality behave non-linearly and as a unified, atomic system.
  • Traditional frameworks fail to capture emergent stability, identity, and relational coherence.
  • Exploitation emerges where hierarchy + unmet need + concentrated power align.
  • Models must handle dimensional richness to prevent collapse.
    • A pyramid is a low‑entropy structure:

– one top

– many bottom layers

– rigid hierarchy

– pathways only go one direction

• • • A pyramid’s whole point is to stop movement/lock people in place.

When you scale that up to an entire society, you get:

– authoritarianism

– caste systems

– rigid labor roles

– suppression of imagination

– inability to adapt to crises

 

1.2 Introducing core constructs

Humans can be viewed as open, adaptive networks that are thermodynamically “noisy”. They constantly exchange energy and information with their environment. In such networks, stability does not stem from rigid hierarchies; instead it emerges through continual movement, resonance across subsystems, and a fluid balance of competing influences (see Definition 4).

• • Humans as open, adaptive, thermodynamically noisy networks.
  • Stability emerges from movement, resonance, and fluid balance.
  • Needs as dynamic, interactive flows, not steps.
  • Introduces 2D → 3D → Da Vinci conceptual ramp (explained later).
  • Application intentions: policy, technology, social systems.

 

1.3 Resonance as an inter-phasic phenomenon

Resonance manifests at multiple ontological levels. At the limbic level it appears as interpersonal emotional attunement; at the cognitive level it takes the form of shared meaning structures and narrative alignment. Somatic resonance reflects co‑regulation of the nervous system and sensory modalities, while environmental resonance captures the influence of pressure, spatial layout, and architectural cues. Finally, systemic resonance encompasses collective cultural patterns and institutional dynamics. Each of these five resonant modes maps directly onto one of the six phases defined in the Inter‑Phasic Resonance Architecture (see Definition 2).

• • Limbic resonance: interpersonal emotional attunement.
  • Cognitive resonance: shared meaning structures.
  • Somatic resonance: nervous system and sensory co-regulation.
  • Environmental resonance: spatial, acoustic, and architectural modulation.
  • Systemic resonance: collective patterns, culture, institutions.

 

1.4 The missing mechanic: contextual ‘shedding’

Existing theories provide no mechanism for controlled destabilization: the deliberate, temporary release of overloaded context that preserves core information. Such a mechanism is valuable across many domains. In political and economic arenas it can be used to weaken entrenched power structures or to induce purposeful market fluctuations. Within psychology, it explains tactics of workplace bullying (ignoring achievements, assigning meaningless tasks, stripping responsibilities, setting individuals up to fail) and also underlies healthy cognitive destabilization, where encouraging openness to new ideas helps counteract rigid belief systems. From a feminist perspective, controlled destabilization offers a tool to challenge binary gender norms and to deconstruct oppressive narratives.

Conceptually, shedding consists of (1) releasing outdated relational scripts, (2) dropping maladaptive feedback loops, (3) freeing contexts that constrain system resonance, and (4) a metaphorical “blowing off steam” that restores adaptive flexibility.

• • Applications: political/economic (power modulation, market flux), psychological (workplace dynamics, cognitive destabilization), feminist theory (challenging binaries).

• • • Political and Economic Contexts

Political Power: It involves strategies to weaken political authority or stability, often through manipulation or strategic interventions.

Economic Systems: In finance, it can refer to actions that intentionally disrupt market equilibrium, leading to fluctuations in currency values or stock prices.

• • • Psychological context:

Workplace Bullying: Controlled destabilization can manifest as tactics used to undermine an individual’s confidence and effectiveness. This may include:

– Failure to acknowledge good work

– Assigning meaningless tasks

– Removing responsibilities without consultation

– Setting up individuals to fail

Cognitive Destabilization: This process involves encouraging openness to new ideas and transformations, which can counteract negative political or social influences.

• • • Feminist Perspectives

In feminist theory, controlled destabilization is used to challenge traditional gender binaries and norms. It seeks to redefine concepts of identity and power, often drawing on deconstructionist theories to critique established narratives.

• • Shedding as:

• • • releasing outdated relational scripts,
    • dropping maladaptive loops,
    • contexts that constrain system resonance, and
    • “Blowing off steam”.

1.5 Purpose & Contribution

This paper makes four concrete contributions. First, this paper introduces the Inter‑Phasic Resonance Architecture (IPRA), a six‑layered model that maps somatic, limbic, cognitive, interpersonal, environmental, and systemic phases. Second, Dynamic Contextual Shedding (DCS) is formally defined as the operative mechanism that temporarily releases overloaded context while preserving essential knowledge (see Definition 1). Third, through a proof‑of‑concept analysis, demonstrate that DCS restores coherence in high‑entropy systems, reducing context drift by roughly ? % (see Results). Fourth, we outline how the combined IPRA/DCS framework can be applied to social policy, therapeutic interventions, and organizational design, offering a new lens for building resilient, ethically grounded systems.

Include:

• • A critique of Maslow’s rigidity
  • The problems with  hierarchy-based social systems
  • Exploitation emerges where hierarchy + unmet need + power concentration align
  • The desire for models that can handle high-entropy systems: Fashion
  • The core question: How do we model human systems with enough dimensional richness to prevent exploitation leading to collapse in inefficient systems?
  • An introduction to “needs as dynamic, interactive flows, not steps”
  • An intro sentence for the 2D→3D→Da Vinci ramp (but not the full explanation – that comes later)
  • My intention to apply this to policy, tech, and the Fashion Industry, etc.

To be included:

Diagrams

• • marble analogy
  • atoms
  • the engine/force distinction
  • resonance layers

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2. Methods

2.1 Conceptual / Hermeneutic Methods

Theoretical Integration: Combines complexity theory, neurobiology, social systems, and phenomenology.
This paper demonstrates novel ways to synthesize frameworks from complexity theory, neurobiology, social systems theory, and phenomenology to understand high-entropy systems and human-AI interaction. Using a hermeneutic-conceptual approach, this study interprets system behaviors and relational dynamics, reconstructing the underlying patterns of resonance, drift, and coherence.

Hermeneutic analysis proceeds through iterative cycles of interpretation; emergent patterns are tested against new material, assumptions are revised, and conceptual structures are refined. This process is traditionally described as the hermeneutic spiral.

• • • Data Sources: Literature on limbic resonance, predictive processing, autopoiesis, attachment theory and multi-level systems; observational data from personal/field observations: embodied experiences, clinical patterns and high-entropy social interactions drawn from personal and field observations..
    • Analytic Procedure: Identify mismatches and gaps in existing models; map multi-layered resonance structures across systems, extract structural invariants common to biological, social, and computational contexts. Iteratively refine the DCS arcitecture through conceptual modeling.
    • Limitations: Conceptual study; phenomenological interpretations are subjective with as of yet unfinished numerical data. Henomenological interpretations introduce subjectivity, which is acknowledged and justified.

• • • Include:
      • methods for  interrogating hierarchies
      • why the closed-loop systems theory 
      • the “translation from metaphor → model → math”
      • Da Vinci machine as a conceptual inspiration
      • use of high-entropy system assumptions

2.2 Quantitative / DCS-Fractal Methods

Theoretical Integration
We extend the conceptual model using multi-phasic resonance dynamics and fractal theory. This enables empirical mapping of behavioral and conversational patterns to measurable fractal and entropy dynamics, providing a bridge between phenomenological observations and quantitative analysis.

Data Sources and Operationalization

• • • • Literature: Same as 2a, with additional focus on fractal organization in time series and complex systems.
      • Observational Data: Conversational sequences, embodied motifs, and thematic patterns.
      • Operationalization: These patterns are encoded as semantic state vectors si​∈Rn, forming a state-space trajectory suitable for De-trended Fluctuation Analysis (DFA), Recurrence Quantification Analysis (RQA), fractal dimension estimation, and entropy measures.

Analytic Procedure

• • • • Conceptual Mapping
      • Identify mismatches in existing models. Map multi-phasic resonance layers and structural invariants.
      • Iterative conceptual modeling develops the DCS architecture.

• Quantitative Fractal Framework
  

  Metric / Method	Theory	Observed Pattern	Implementation
  DFA (De-trended Fluctuation Analysis)	Long-range correlations indicate persistence in time series	Topics, behaviors, and motifs expand and recur over time	Profile state-space trajectory; divide into windows; detrend; compute Hurst exponent
  Fractal Dimension	Captures branching complexity	Recurring idea branches, motif revisits, phase interactions	Estimate from DFA-derived
  Recurrence Quantification Analysis (RQA)	Recurrence plots reveal deterministic fractal patterns	Recurrent motifs across scales; phase interactions in multi-phasic systems	Construct recurrence matrix compute DET, LAM, ENT, RR
  Shannon Entropy HS​	Captures exploration of state-space; high entropy with attractor convergence indicates fractal organization	Broad topic/phase exploration constrained by recurring attractors; DCS preserves systemic memory	Compute over sequential semantic/phase states

   

3. Results: The Inter-Phasic Resonance Architecture

3.1 The architecture of inter-phasic ontologies

• • Conceptual model linking human, social, and AI ontologies.
  • Each phase = domain-specific ontology; DCS selectively modulates influence to prevent drift.
  • Recurrent atomic/fractal motifs echo across phases.

Core premise: stability emerges from distributed resonance across multi-phasic and intersecting or inter-phasic layers.

Dynamic Contextual Shedding operates across all phases to maintain system integrity.

3.2 Multi-Phasic Ontologies: Fractal / DCS Alignment

3.3 Dynamic Contextual Shedding (DNS): formal definition

DNS is defined as the controlled release of contextual load to restore resonance.

• • Mechanisms: decoupling, script updating, recalibration, liminal destabilization, reintegration.
  • Triggers: entropy overload, somatic tension, dissociation protection, resonance mismatch.
  • Applications: therapeutic, community-building, distributed leadership, polycentric governance, complex relational networks.

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4. Discussion

4.1 Why Classical Models Fail

Maslow’s Hierarchy of Needs , attachment typologies, and other hierarchy-based relationships or leadership cannot handle dynamic complexity or mutual resonance.

What Hierarchy + Nesting + Branching Models Assume (the “Linear Paradigm”)

All traditional models (pyramids, chains of command, nested trees, branching systems) share four fundamental assumptions:

• • • Assumption 1: Roles are stable

People or subunits occupy fixed positions, with predictable behavior.

• • • Assumption 2: Information flows linearly

Information moves up or down a chain or through a branching structure.

• • • Assumption 3: Order is produced by constraint

Stability is attempted by:

• • • • rules
      • fixed relationships
      • enforced boundaries

• • • Assumption 4: High entropy = breakdown

More variability → more disorder → collapse unless re-contained.

• • • • These assumptions come from:

– classical mechanics

– industrial-era management theory

– early cybernetics

– Newtonian physics applied to social systems

– They all treat humans and systems like objects that follow paths.

Exactly like the old atomic model.

Hierarchies and branching structures cannot capture:

• • • parallel processing
    • multi-directional influence
    • contextual shifts
    • nonlinear feedback
    • rapid identity/behavioral adaptation
    • resonant coupling between individuals
    • transient leadership
    • cross-hierarchy collaboration
    • fluid affiliation networks
    • emergent substructures

They fail because they model the map, not the actual terrain*.

They assume linear determinism in systems that, in reality, behave dynamically as phase-dependent fields.

Exactly like the mistake that early atomic models made!

4.2 Implications for Human Systems & Social Structures

• • Therapy, education, social work: loop-based rather than siloed systems.
  • Resonance-informed relationships and environments.

4.3 Micro–Meso–Macro Integration (MLRA)

• • Explains emergent patterns, healing, sudden change, and social coherence.

The beauty of this approach is that it naturally translates from micro-level decisions to macro-level consequences, both metaphorically and literally: Each “orbiting” decision (micro-level) is small, contained, and looks harmless. But when you examine the system as a whole, these micro-decisions collectively shape the macro-structure, much like how subatomic complexity hints at a toroidal/doughnut structure at the universal scale.

Thresholds of Chaos & Intervention: 
Pre-collapse: Signs of impending harm. Intervention can be preventative, adjusting structures before they begin to cause too much damage (i.e., removing harmful elements before full system breakdown).

Collapse: The “death” phase of a system. Recognize it as an opportunity for renewal, rather than pure destruction. This could guide your policy models toward understanding when “destruction” is necessary for systemic evolution.

Post-collapse: Rebuilding with intention. This is the zone where your framework for sustainable transformation comes into play.

4.4 Contextual Shedding as a Universal Adaptive Mechanism

• • Metabolic analogy: psychological autophagy.
  • Evolutionary analogy: survival in high-fluidity environments.

4.5 Limitations & Future Research

• • Requires empirical validation.
  • Potential overgeneralization.
  • Next steps: simulations, embodied research, relational mapping, biometric correlates.

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References

• Placeholder for literature citations on complexity theory, systems theory, fractal analysis, neurobiology, social systems, DCS, and multi-phasic resonance.

Further Reading
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Simon, H. A. (1972). *The Sciences of the Artificial*. MIT Press.
Friston, K. (2010). The free-energy principle: a unified brain theory? *Nature Reviews Neuroscience*, 11(2), 127‑138. https://doi.org/10.1038/nrn2787
Barabási, A.-L. (2016). *Network Science*. Cambridge University Press.

Introducing Dynamic Contextual Shedding (DCS) and Inter‑Phasic Resonance Architecture (IPRA) © 2025 by Amanda Caserta is licensed under CC BY-NC-SA 4.0