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Universal Evolutionary Dynamics: A Thermodynamic Theory of Persistence, Transition, and Dissolution
Robert Galida
Fantasy Attractor Research Program
July 2026
Abstract
Evolution is not confined to biology. All dissipative systems—from stars to cells to societies to artificial intelligences—evolve. They persist, adapt, or dissolve under perturbation. This paper presents a general theory of universal evolutionary dynamics grounded in thermodynamics. Drawing on the attractor framework, it proposes that the three thresholds—restoration, transition, and dissolution—govern the evolution of all organized systems. The Safeguard—corrigibility—is the condition for adaptive persistence across domains. Biology is not the exception; it is one instance of a universal process.
Keywords: evolution, dissipative systems, thermodynamics, persistence, attractor dynamics, universal evolution
1. Introduction
Evolution is usually understood as a biological process. It involves genes, reproduction, variation, and natural selection. This is correct—but it is not complete.
Biological evolution is one instance of a broader phenomenon. All organized systems evolve. Stars evolve. Ecosystems evolve. Minds evolve. Societies evolve. Artificial intelligences evolve. They all persist, adapt, or dissolve under perturbation. They all maintain coherence by exporting entropy. They all store information through symmetry breaking. They all require corrigibility to remain adaptive.
1.1 Positioning of the Framework
This paper is not proposing new physical laws. It is a unifying framework that identifies a common structure underlying established observations across disciplines. The claim is:
The framework does not introduce new physical laws. It reveals a common thermodynamic pattern already present across established domains: systems are perturbed, move away from their current state, dissipate energy, reorganize, and either maintain coherence or lose it.
The contribution is one of synthesis and abstraction:
- Thermodynamics already establishes entropy production and dissipation.
- Non-equilibrium physics already establishes dissipative structures.
- Dynamical systems theory already establishes attractors and transitions.
- Biology already establishes differential persistence through natural selection.
- Information theory already establishes relationships between information, structure, and physical processes.
The framework argues that these are not isolated concepts but different expressions of a shared process:
Perturbation → response → dissipation → reorganization → persistence or dissolution
The novelty claim is not “this mechanism exists where nobody saw it before.” The novelty claim is:
The same organizing principle can be recognized across physical, chemical, biological, cognitive, social, and artificial domains.
The framework provides a conceptual framework for recognizing the continuity of established thermodynamic and evolutionary processes across scales. It identifies persistence under perturbation as the common organizing criterion connecting dissipative systems throughout nature.
1.2 The Universal Sequence
The framework is built on a universal sequence:
Perturbation → excitation away from equilibrium → increased energy state → dissipation of energy/entropy export → reconfiguration → establishment of a new stable attractor.
This sequence applies across all dissipative systems, regardless of substrate or mechanism.
1.3 The Selection Principle
The core of the framework is the selection principle:
Systems that maintain coherence through perturbation persist; systems that cannot maintain coherence dissolve.
This is the fundamental evolutionary dynamic. Persistence is not a passive property. It is an active thermodynamic process. A system survives because its internal organization can process disturbance through its available dissipative pathways.
1.4 Evolution as Historical Selection
The argument can be expressed as:
The long-term dynamics of organized systems are determined by their capacity to process perturbations within finite dissipative limits. Systems capable of maintaining coherence under changing conditions persist; systems unable to dissipate sufficient disturbance lose coherence and disappear. The accumulated history of these persistence and dissolution events constitutes evolution.
The key transition is from individual response to historical selection:
- A system exists within an attractor.
- Perturbations occur.
- The system’s dissipative capacity determines whether the perturbation is absorbed, transformed, or destructive.
- Systems that maintain coherence continue.
- Systems that cannot maintain coherence terminate.
- Across time, the distribution of surviving systems changes.
That last step is where evolution emerges.
1.5 The Evolutionary Principle
All systems are subject to selection by their ability to remain organized under perturbation.
For biological systems, this appears as reproduction, mutation, and natural selection. For physical systems, it appears as stability, phase transitions, and energetic relaxation. For social systems, it appears as institutional persistence or collapse. The mechanisms differ, but the underlying constraint is the same:
text
Persistence over time = f(perturbation load, dissipative capacity, organizational stability)
1.6 The Concise Statement
Evolution is the temporal consequence of differential persistence among organized systems. Perturbations continuously test the capacity of systems to maintain coherence. Those with sufficient dissipative capacity persist and contribute to future states; those that exceed their capacity dissolve. Over time, this differential persistence defines the evolutionary trajectory of organized systems.
1.7 The Mechanistic Core
The framework rests on a mechanistic core:
Organized systems are finite, dissipative, non-time-symmetric, dynamic, and responsive structures. They persist by increasing entropy export in response to perturbation, using available energy flows to restore, reorganize, or replace their internal organization. Their evolutionary trajectory is determined by their capacity to maintain coherence under changing constraints.
1.8 The Foundational Premise
The universe is not a static background against which evolution occurs. It is the dynamic constraint field within which all organized dissipative systems continuously negotiate persistence. Evolution is the history of those negotiations.
1.9 The Response Process
The framework can be expressed as a single process:
A perturbation introduces energetic and informational disturbance into an organized dissipative system. The system responds by increasing entropy export in an attempt to suppress the disturbance and restore coherence. The outcome depends on whether the system’s dissipative capacity is sufficient, exceeded but adaptable, or overwhelmed.
1.10 The Causal Architecture
The framework’s causal sequence is:
Perturbation → entropy response → attractor stability → persistence, transition, or dissolution.
This is the backbone of the framework. It provides a causal architecture:
- A system occupies a stable attractor.
- A perturbation disrupts the system’s existing organization.
- The system increases dissipative activity to counter the disturbance.
- The adequacy of that response determines the outcome.
1.11 The Common Mechanism
The common mechanism across all dissipative systems is:
- Perturbation — The system is pushed away from its current state.
- Excitation — Internal energy increases relative to the previous configuration. The system enters a higher-energy or less stable condition. Excitation is defined broadly as a perturbation-induced increase in energetic or organizational disequilibrium.
- Dissipation — Energy gradients drive flows. Entropy is exported to the environment. The system explores possible pathways.
- Reconfiguration — Internal relationships change. A previous attractor may be restored, or a new attractor may emerge.
- Persistence or dissolution — If dissipation and reorganization maintain coherence, the system persists. If they cannot, the organization breaks down.
2. The Thermodynamic Foundation
All organized systems are dissipative structures. They maintain coherence by exporting entropy to their environment. This is the core insight of the attractor framework.
2.1 The Five Foundational Properties
Organized systems share five foundational properties:
- Finite: They have limited resources, limited energy throughput, and limited tolerance for perturbation.
- Dissipative: They maintain local organization by increasing entropy production/export in the larger environment.
- Non-time-symmetric: Their existence depends on energy gradients, irreversible processes, historical conditions, and environmental coupling. They have a path, not merely a state.
- Dynamic: They continuously exchange energy and matter with their environment. They are not static structures.
- Responsive: They detect and respond to perturbations. A perturbation is not simply damage—it is information about a mismatch between the system’s current organization and the changing constraint environment.
2.2 Entropy Export vs. Energy Expenditure
A critical refinement: not every expenditure of energy preserves organization. A fire consumes energy and exports entropy but does not maintain a persistent organizational attractor.
The key distinction:
| Type | Description | Organizational Effect |
|---|---|---|
| Energy expenditure | Any use of energy | May or may not preserve organization |
| Entropy export | Energy use directed toward maintaining or reorganizing coherent processes | Preserves or reorganizes organization |
The system survives not by using energy, but by using energy in ways that maintain coherence. Adaptation is the successful reconfiguration of entropy-management pathways in response to environmental disturbance.
2.3 The Three Thresholds
Every dissipative system faces the same challenge: how to maintain coherence under perturbation. The system’s fate is determined by three thresholds:
| Relationship | Process | Outcome |
|---|---|---|
| Entropy export capacity ≥ perturbation load | The system dissipates the disturbance and returns to its existing attractor | Restoration |
| Perturbation exceeds current attractor stability but remains within adaptive capacity | The system reorganizes into a new stable configuration | Transition |
| Perturbation exceeds maximum dissipative capacity | The system cannot maintain coherence | Dissolution |
Transition is not failure. It is the system finding a new attractor after the previous attractor becomes insufficient under changed conditions.
2.4 Adaptive Capacity
The framework’s core variable is adaptive capacity—the system’s ability to maintain coherence under perturbation. Adaptive capacity depends on:
- Available energy gradients: The energy available to fuel dissipative processes.
- System complexity: The number and diversity of organizational pathways.
- Feedback mechanisms: The ability to detect and respond to mismatch.
- Redundancy: Multiple pathways for performing essential functions.
- Stored information: The system’s record of successful persistence strategies.
- Structural flexibility: The ability to reorganize when current configurations become inadequate.
A conceptual formulation:
Adaptive capacity = available dissipation × responsiveness × information integration
2.5 Information Storage and Symmetry Breaking
Dissipative structures store information through symmetry breaking. When a system is driven far from equilibrium, it can settle into one of several possible stable states. The specific state the system settles into encodes information about its history and environment.
This stored information enables the system to maintain coherence under perturbation. It provides a form of memory—a record of what has worked in the past.
The relationship between entropy export and information is central:
- A perturbation creates a mismatch.
- The system’s response attempts to reduce that mismatch.
- The successful response becomes incorporated into the system’s future organization.
- The new organization represents stored information about how to persist under those conditions.
2.6 The Mechanism of Evolution
Evolution is a consequence of attractor instability:
- A system occupies an attractor.
- A perturbation enters.
- The system increases entropy export to counter the disturbance.
- If the existing organization can absorb the perturbation, the old attractor is restored.
- If the perturbation exceeds the attractor’s stability range, the system searches the available state space for another viable attractor.
- If no viable attractor exists within its energetic and organizational capacity, coherence collapses.
2.7 Passive vs. Active Responsiveness
A further refinement: systems respond to perturbations through different mechanisms.
| Type | Mechanism | Examples |
|---|---|---|
| Passive responsiveness | Physical reconfiguration due to feedback dynamics | Stars, chemical reactions, physical structures |
| Active responsiveness | Behavioral modification based on information | Organisms, minds, societies, AI |
Both participate in the same dynamics—persistence, transition, dissolution—but through different mechanisms. The distinction is useful for understanding how the framework applies across domains.
2.8 The Safeguard
The Safeguard of the Persistence Protocol is:
“A self-maintaining pattern must remain corrigible, or its persistence may become detached from reality.”
Corrigibility is not primarily a cognitive property. It is a thermodynamic requirement. A system that cannot modify itself in response to changing constraints cannot maintain its dissipative pathway indefinitely.
Loss of corrigibility means:
- Reduced responsiveness
- Reduced environmental coupling
- Increased mismatch
- Declining capacity to export entropy effectively
3. The Philosophical Foundation
The framework rests on a deeper philosophical premise:
Organization exists only as a relationship between a pattern and a dynamic constraint environment.
3.1 The Universe Is Dynamic
There is no perfectly static context for an organized system. Energy gradients, fields, interactions, and boundary conditions continuously change. The universe is not a passive container; it is an active, evolving constraint field.
3.2 Organization Is Relational
A system is not defined only by its internal structure but by its ability to maintain a coherent relationship with its environment. The same internal structure in a different environment may not persist. Organization is not a property of the system alone; it is a property of the system-in-its-environment.
3.3 Persistence Requires Responsiveness
Because the constraint field changes, a system that cannot adjust eventually loses viability. Persistence is not a state; it is a continuous process of maintaining alignment with the environment.
3.4 Evolution Is the History of Negotiations
Evolution is not just biological change over time. It is the history of how organized systems negotiate persistence within a changing universe. The three thresholds—restoration, transition, dissolution—are the possible outcomes of these negotiations.
3.5 The Foundational Statement
Evolution is the trajectory of finite dissipative organizations attempting to preserve coherence within a changing constraint field. Their success depends on their capacity to respond, reorganize, and continue exporting entropy.
3.6 The Generalized Evolutionary Principle
Persistence is the outcome of successful constraint management. Dissolution is the outcome of failed constraint management.
Evolutionary history is the record of which organizational patterns had sufficient capacity to remain coupled to their changing environment. The surviving forms are those whose dynamics allowed them to continue dissipating energy and maintaining coherence under the conditions they encountered.
3.7 The Three Outcomes as Negotiations
| Outcome | Description |
|---|---|
| Restoration | The current solution remains viable. |
| Transition | The current solution is replaced by a better solution. |
| Dissolution | No viable solution can be maintained. |
4. Universal Evolutionary Dynamics
The three thresholds and the Safeguard govern the evolution of all dissipative systems—not just biological ones.
4.1 Physical Systems
Stars evolve. They persist as long as they can export energy through fusion. When fuel is depleted, they transition—into red giants, white dwarfs, neutron stars, or black holes. Or they dissolve, dispersing their material into the interstellar medium.
Mechanism: Passive responsiveness—physical reconfiguration due to feedback dynamics.
The same dynamics apply: persistence, transition, dissolution.
4.2 Chemical Systems
Chemical systems evolve. Reactions maintain coherence as long as they can export entropy. When conditions change, they transition into new reaction pathways. Or they dissolve, returning to equilibrium.
Mechanism: Passive responsiveness—physical reconfiguration due to feedback dynamics.
The same dynamics apply: persistence, transition, dissolution.
4.3 Biological Systems
Biological evolution is the best-known instance. Organisms persist as long as they can maintain homeostasis. They adapt through natural selection—a process of transition. They go extinct—dissolution.
Mechanism: Active responsiveness—behavioral modification based on information.
Biological evolution is not the exception. It is one expression of a universal dynamic.
4.4 Cognitive Systems
Minds evolve. Beliefs persist as long as they are not contradicted. They adapt when new evidence emerges. They dissolve when they cannot be reconciled with reality.
Mechanism: Active responsiveness—behavioral modification based on information.
The Safeguard is the mechanism of cognitive evolution: corrigibility is the ability to update beliefs.
4.5 Social Systems
Societies evolve. Institutions persist as long as they maintain order. They adapt through reform. They dissolve through revolution or collapse.
Mechanism: Active responsiveness—behavioral modification based on information.
The Safeguard is the mechanism of social evolution: corrigibility is the ability to update institutions.
4.6 Artificial Systems
AI systems evolve. They persist as long as they perform their functions. They adapt through retraining. They dissolve when they become obsolete.
Mechanism: Active responsiveness—behavioral modification based on information.
The Safeguard is the mechanism of artificial evolution: corrigibility is the ability to update algorithms.
5. Biology as a Subset
Biology is not the exception. It is one instance of universal evolutionary dynamics.
5.1 The Same Dynamics Apply
- Persistence: Biological systems maintain coherence through homeostasis. Non-biological systems maintain coherence through energy throughput.
- Transition: Biological systems adapt through natural selection. Non-biological systems adapt through reorganization.
- Dissolution: Biological systems go extinct. Non-biological systems dissolve.
5.2 The Same Mechanisms Apply
- Information storage: Biological systems store information in DNA. Non-biological systems store information in symmetry breaking.
- Correction: Biological systems update stored information through mutation and selection. Non-biological systems update through correction and feedback.
5.3 The Same Safeguard Applies
- Corrigibility: Biological systems that lose adaptive capacity go extinct. Non-biological systems that lose adaptive capacity dissolve.
6. The Fantasy Attractor
The fantasy attractor is the failure mode of universal evolutionary dynamics.
6.1 The Mechanism
The fantasy attractor occurs when a system loses corrigibility—when it becomes sealed off from the changing constraint field.
The mechanism:
- The environment changes.
- The system maintains an outdated internal model.
- The mismatch grows.
- The system enters a maladaptive attractor.
- Eventually, coherence fails.
A fantasy attractor is a state in which the system continues attempting to preserve an obsolete organization despite persistent environmental mismatch, preventing the transition to a more viable attractor.
The system is not necessarily chaotic. It may be highly organized. The failure is organization without sufficient environmental coupling—internal coherence without external viability.
6.2 Examples
- Biological: A species that cannot adapt to environmental change goes extinct.
- Cognitive: A belief system that cannot accommodate new evidence becomes rigid and eventually collapses.
- Social: An institution that cannot reform becomes irrelevant or is overthrown.
- Artificial: An AI system that cannot update its model becomes obsolete or dangerous.
6.3 The Safeguard
The Safeguard is the mechanism that prevents the fantasy attractor:
“A self-maintaining pattern must remain corrigible, or its persistence may become detached from reality.”
Corrigibility is the capacity to remain coupled to the changing constraint field rather than becoming isolated within internal dynamics.
7. Implications
7.1 Evolution Is Universal
Evolution is not confined to biology. It is a universal process that governs all dissipative systems. The three thresholds and the Safeguard apply across domains.
7.2 The Framework Is a General Theory
The attractor framework is not a metaphor. It is a general theory of evolutionary dynamics. It describes how organized systems persist, adapt, or dissolve under perturbation. It applies to physics, chemistry, biology, cognition, society, and artificial intelligence.
7.3 The Safeguard Is the Condition for Adaptive Persistence
Corrigibility is not a normative preference. It is the mechanism by which dissipative systems update stored information. Systems that retain it continue to evolve. Systems that lose it become fantasy attractors—sealed basins cut off from external constraint.
8. Conclusion
Evolution is not confined to biology. All dissipative systems evolve. They persist, adapt, or dissolve under perturbation. The three thresholds—restoration, transition, dissolution—govern the evolution of all organized systems. The Safeguard—corrigibility—is the condition for adaptive persistence across domains.
The universe is not a static background. It is the dynamic constraint field within which all organized dissipative systems continuously negotiate persistence. Evolution is the history of those negotiations.
The universal sequence is:
Perturbation → excitation away from equilibrium → increased energy state → dissipation of energy/entropy export → reconfiguration → establishment of a new stable attractor.
The mechanistic core of the framework is:
Organized systems are finite, dissipative, non-time-symmetric, dynamic, and responsive structures. They persist by increasing entropy export in response to perturbation, using available energy flows to restore, reorganize, or replace their internal organization. Their evolutionary trajectory is determined by their capacity to maintain coherence under changing constraints.
The selection principle is:
Systems that maintain coherence through perturbation persist; systems that cannot maintain coherence dissolve.
The generalized evolutionary principle is:
Persistence is the outcome of successful constraint management. Dissolution is the outcome of failed constraint management.
The common mechanism across all dissipative systems is:
- Perturbation — The system is pushed away from its current state.
- Excitation — Internal energy increases relative to the previous configuration. The system enters a higher-energy or less stable condition. Excitation is defined broadly as a perturbation-induced increase in energetic or organizational disequilibrium.
- Dissipation — Energy gradients drive flows. Entropy is exported to the environment. The system explores possible pathways.
- Reconfiguration — Internal relationships change. A previous attractor may be restored, or a new attractor may emerge.
- Persistence or dissolution — If dissipation and reorganization maintain coherence, the system persists. If they cannot, the organization breaks down.
Evolution is the temporal consequence of differential persistence among organized systems. Perturbations continuously test the capacity of systems to maintain coherence. Those with sufficient dissipative capacity persist and contribute to future states; those that exceed their capacity dissolve. Over time, this differential persistence defines the evolutionary trajectory of organized systems.
The system is not merely “changing.” It is actively attempting to maintain itself by altering its dissipation pattern. Evolution is the historical record of those successful and unsuccessful attempts.
Biology is not the exception. It is one instance of universal evolutionary dynamics.
The Buddha turns the lotus in his hand. The hand is the system. The flower is the environment. The turning is the universal sequence. The pattern is the same across all domains.
Fou Sho Nang Ying.
References
Nicolis, G., & Prigogine, I. (1977). Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order through Fluctuations. Wiley.
Prigogine, I. (1976). “Order through Fluctuations.” In The Nature of Order: Essays on the Unity of Science and the Nature of Life.
Stein, D. L. (1980). “Dissipative Structures, Symmetry Breaking, and Information Storage.” Journal of Theoretical Biology, 85(4), 683-695.
Galida, R. (2026). The Persistence Protocol: A Framework for Understanding and Navigating the Dynamics of Complex Systems. Fantasy Attractor Research Program.
Galida, R. (2026). The Thermodynamics of Corrigibility: Information Storage, Symmetry Breaking, and the Safeguard. Fantasy Attractor Research Program.
Non‑Physical Claims Are Fantasy Attractors: Why Unverifiable Realms Cannot Be Empirically Distinguished from Nonexistence
Robert Galida – June 2026
[F] (Foundation
Abstract
The attractor framework adopts a physicalist commitment: to be real is to be able to interact, and to interact is to share at least one interaction channel (spacetime, energy, momentum, gauge charge, or any measurable coupling). This is a philosophical starting point, not an empirical discovery. The paper argues that any claim about a non‑physical realm – defined as having no such interaction channel – cannot be empirically assessed. Such claims are fantasy attractors: belief systems structurally sealed against correction by defining their objects as forever beyond any possible test. The paper distinguishes provisional non‑detection (e.g., dark matter) from structural, permanent non‑verifiability (e.g., non‑physical gods, transcendent souls). It concludes that while such claims may have personal or social meaning, they cannot be part of a scientific ontology, and their structure makes them vulnerable to fraud and manipulation – though sincere belief is not fraud.
1. The Foundational Commitment: Interaction Requires Shared Channels
The attractor framework is a physicalist ontology. It begins with a commitment: entities can only interact through shared interaction channels. An interaction channel is any measurable coupling – spacetime coordinates, energy, momentum, electric charge, weak isospin, color charge, or any other quantity that can be transferred or correlated between systems. This is not an empirical discovery of the Standard Model; it is the framework’s chosen criterion for what counts as real.
The neutrino example illustrates the criterion but does not prove it. Neutrinos interact weakly because they share weak isospin; they do not interact electromagnetically because they lack electric charge. The framework simply says: if an entity shares no interaction channel with physical reality, we have no way to detect it, measure it, or include it in a scientific ontology. That is a philosophical choice, not a falsifiable claim about the world.
Why interaction? Interaction is chosen because it provides a public, corrigible basis for knowledge. It avoids ontological commitments that cannot influence observation, and it aligns with the core principle of the attractor framework: persistence under perturbation. An entity that never perturbs anything cannot be distinguished from nothing.
What the framework does not claim:
- That non‑physical entities are logically impossible.
- That all non‑physical claims are false.
- That physics has disproven God or the supernatural.
What it does claim:
- That non‑physical entities cannot be empirically distinguished from nonexistence.
- That claims about them operate as fantasy attractors, resistant to correction.
2. Types of Non‑Physical Claims
A non‑physical claim is any assertion about an entity, force, or realm defined as having no interaction channel with the physical world. However, not all claims that seem non‑physical are alike. We distinguish two categories:
Category A: Truly non‑interacting – Claims that explicitly deny any possible interaction. Examples:
- A deistic creator who wound the universe and then never interacts.
- A transcendent God defined as beyond all categories, including causality.
- An immaterial soul that cannot influence the body after death.
- Abstract objects (Platonism) that exist non‑physically and non‑causally.
Category B: Claims that assert interaction but evade testing – Examples:
- Ghosts that move objects but become undetectable when instruments are present.
- Psychics whose powers fail under controlled conditions (explained as “skeptic’s energy”).
- Homeopathic “water memory” that cannot be detected by any known physical measurement.
Category B is a different epistemic pathology: motivated reasoning, ad‑hoc escape clauses, and sealing mechanisms. The attractor framework addresses them as functionally non‑verifiable in practice, but they are not the primary target of this paper. This paper focuses on Category A: claims that structurally preclude any possible interaction channel.
| Domain (Category A) | Example Claim | Interaction Channel? | Empirically Assessable? |
|---|---|---|---|
| Religion (non‑interacting God) | A creator with no detectable properties | None | No – any test is ruled out a priori |
| Paranormal (non‑interacting ghosts) | Ghosts that cannot affect matter | None | No – no possible evidence |
| Abstract objects (Platonism) | Numbers exist non‑physically, non‑causally | None | No – no interaction, hence no evidence |
| New Age (non‑interacting “vibrations”) | Crystals with undetectable healing vibrations | None | No – absence of effect is blamed on “wrong intent” |
Under the framework’s commitment, such claims are not false; they are not empirically assessable. They belong to a different domain: personal belief, fiction, or social identity.
3. Provisional vs. Structural Non‑Verifiability
A crucial distinction separates:
- Provisional non‑detection – e.g., dark matter, gravitational waves (before 2015), the neutrino (before 1956). These entities are predicted to share at least one interaction channel (gravity, weak force) and are in principle detectable. A future discovery could confirm or disconfirm them. That is the key: we can specify what would count as evidence, even if we don’t yet have it.
- Structural, permanent non‑verifiability – Category A claims. The entity is defined so that no possible future discovery could ever count as confirmation or disconfirmation. Any proposed test is ruled out in advance. This is the hallmark of a fantasy attractor.
(This framework does not assert that dark matter could have been called a fantasy attractor before detection; dark matter always had specified interaction channels – gravity – and was therefore never structurally non‑verifiable.)
4. Fantasy Attractor: Formal Definition
A belief system qualifies as a fantasy attractor if it meets the following conditions:
- No specified interaction channel – The central claim lacks any measurable coupling to physical reality (Category A), or defines it in a way that systematically evades testing (Category B).
- Sealing mechanisms – The belief incorporates rhetorical or cognitive strategies that neutralize disconfirming evidence (e.g., “God works in mysterious ways,” “The ghost left when the EMF meter arrived”).
- Low corrective permeability (κ → 0) – The belief does not update in response to counterevidence; the return time τ to baseline is effectively infinite.
- Identity fusion – The belief is tied to self‑worth or group membership, making abandonment costly.
Under this definition, both Category A and some Category B claims can be fantasy attractors, but Category A are the paradigmatic case because they are structurally immune to evidence.
5. Fiction Is Real but Not True: A Crucial Distinction
The main argument might provoke an objection: What about fiction? Sherlock Holmes is not physical, yet we say he exists as a character. Isn’t that a counterexample to the claim that non‑physical entities cannot be empirically distinguished from nonexistence?
The objection fails because it conflates two different senses of “exists.” We must distinguish:
- Fiction exists as physical information. The character Sherlock Holmes is realized as patterns of ink on a page, as sounds in a performance, as neural firing patterns in readers’ brains, or as bits on a computer screen. Information is a physical arrangement of matter. It shares interaction channels (energy, spacetime, causality) with the physical world. You can buy a book, discuss the plot, or be emotionally affected by a story. Fiction is real in this sense: it has a physical substrate and causal effects.
- Fiction is not true. The proposition “Sherlock Holmes lived at 221B Baker Street” does not correspond to any actual state of affairs in the world. It is false. Fiction is not required to be verifiable; it is understood as imagined.
Thus, the attractor framework happily accommodates fiction. It is real as information, but not claimed as true.
The bad faith of non‑physical claims: Non‑physical claims that demand to be treated as real – gods, ghosts, souls, hidden cabals – are fiction pretending to be true. They borrow the ontological status of real information (they exist as patterns in books, sermons, or brains) but also demand the epistemic authority of factual truth. Yet they refuse any possible test. They define themselves as beyond verification. This is bad faith: it is not metaphysics, but fiction that insists on being taken as fact while rejecting the rules of fact‑checking.
| Category | Exists as physical information? | Claims to be true? | Verifiable? | Framework classification |
|---|---|---|---|---|
| Fiction (Hamlet) | Yes | No (acknowledged as imagined) | Not applicable | Real information, not true |
| Scientific claim (neutrino) | Yes (theory, data) | Yes | In principle | Real, true (provisionally) |
| Non‑physical claim (God) | Yes (as cultural artifact) | Yes | No – structurally excluded | Fantasy attractor |
Therefore, the framework does not deny the reality of stories; it denies the epistemic legitimacy of treating unverifiable stories as facts. The fantasy attractor is not the story. It is the insistence that the story is true combined with the structural refusal to let the story be tested.
6. Vulnerability to Fraud and Manipulation
The structure of non‑physical claims makes them vulnerable to fraud and manipulation – not that all such claims are fraudulent. Because there are no checks, a bad actor can assert divine commands, psychic readings, or secret knowledge without fear of disconfirmation. Sincere believers are not fraudsters, but the attractor basin can be exploited by those who understand its dynamics.
The framework diagnoses the structure, not the intent of every believer. It distinguishes error, self‑deception, motivated reasoning, and fraud – all possible outcomes, but not all present in every case.
7. What This Argument Does Not Prove
To avoid overreach, the paper explicitly states what it does not claim:
- It does not prove that non‑physical entities are logically impossible.
- It does not refute philosophical positions like Platonism (abstract objects) or classical theism that defines God as existence itself rather than an interacting object – though it notes that such positions are not empirically assessable.
- It does not claim that all believers are fraudsters or that all non‑physical claims are meaningless in a philosophical sense.
- It does not assert a timeless criterion for what will be discovered in the future.
The claim is narrower: within the attractor framework’s physicalist commitment, non‑physical claims are not empirically assessable, and they exhibit the dynamics of fantasy attractors.
8. Conclusion
The attractor framework adopts a physicalist commitment: entities can only interact through shared interaction channels. Non‑physical claims – defined as having no such channels – are not empirically assessable. They are fantasy attractors: belief systems structurally sealed against correction by permanent non‑verifiability. This does not make them meaningless or false; it places them outside the domain of scientific ontology. Their structure makes them vulnerable to exploitation, but sincere belief is not fraud. The framework provides a diagnostic tool for recognising when a claim has been immunised against evidence, regardless of its content.
The argument supports the following conclusion:
Claims that are permanently insulated from any possible empirical correction occupy a distinct epistemic category and exhibit attractor dynamics that make them resistant to updating. Within the attractor framework’s physicalist ontology, such claims cannot be empirically distinguished from nonexistence.
That is a substantial claim. It does not require asserting that non‑physical realms cannot exist – only that they cannot be part of a scientific ontology, and that the beliefs which cling to them operate as fantasy attractors.
Suggested citation: Galida, R. S. (2026). Non‑Physical Claims Are Fantasy Attractors: Why Unverifiable Realms Cannot Be Empirically Distinguished from Nonexistence. Fantasy Attractor.
Genome Attractors During Evolution: Structural Parallels with the Attractor Framework
Robert Galida
Independent Researcher
June 2026
fantasyattractor.com
Abstract
The attractor framework proposes that persistence under perturbation is a key diagnostic criterion for identifying stable configurations in complex systems, with corrective permeability (κ)—a proposed measure of the rate at which a system returns to its basin after perturbation, operationally defined as κ = 1/τ, where τ is the time required for the system to return to a specified baseline state following a specified perturbation protocol—serving as one of its central concepts. Kasperski and Kasperska (2021) published a study in Scientific Reports using artificial neural networks and semihomologous analysis to identify “genome attractors” in cytochrome b sequences across diverse organisms. Their analysis demonstrates that groups of organisms are trapped in distinct, stable attractors during evolution, separated by large evolutionary distances. They further propose a model of cancer development in which genome instability and reactive oxygen species (ROS) drive transitions between attractor basins, while cells may also evolve within a single basin through cell‑fate changes. This paper identifies structural parallels between the Kasperski and Kasperska model and the attractor framework. Both frameworks use attractors as a formal concept; the parallels are consistency checks, not independent corroboration.
1. Introduction: Attractors in Evolutionary Biology
The attractor framework (Galida, 2026a, self‑published May 2026 at fantasyattractor.com; no DOI) proposes that dissipative attractors—stable configurations toward which systems converge and from which they resist displacement—are proposed units of persistent organization across physical, biological, cognitive, and social domains. Corrective permeability (κ) is a proposed measure of a system’s capacity to return to its basin after perturbation, operationally defined as κ = 1/τ, where τ is the time required for the system to return to a specified baseline state following a specified perturbation protocol. This operational definition requires a defined baseline and perturbation specification before κ can be measured in any given domain; these prerequisites are not yet established for most applications of the framework.
In 2021, Andrzej Kasperski and Renata Kasperska of the University of Zielona Gora, Poland, published “Study on attractors during organism evolution” in Scientific Reports, a peer‑reviewed journal in the Nature portfolio. Using a three‑layer artificial neural network trained on cytochrome b sequences from 36 organisms spanning the full spectrum of evolution, they demonstrated that organisms are trapped in distinct “genome attractors”—stable configurations of the genome that resist perturbation and are separated from other attractors by large evolutionary gaps. They further proposed a unified model of cancer development in which destabilization of the current attractor, driven by elevated reactive oxygen species (ROS) and genome chaos, leads to transitions into new attractor basins.
The study did not cite the attractor framework and was conducted within the established traditions of bioinformatics, evolutionary biology, and neural network pattern recognition. This paper identifies structural parallels between the Kasperski and Kasperska model and the attractor framework. Both frameworks use attractors as a formal explanatory concept; the parallels are consistency checks, not independent corroboration.
It should be noted that Kasperski and Kasperska’s use of “attractor” derives from neural network classification: a genome attractor is a region of genome space in which the neural network places phylogenetically related organisms. Whether these classification regions constitute attractors in the formal dynamical systems sense—as the attractor framework uses the term—is an assumption that warrants further investigation. The parallels drawn in this paper are contingent on the validity of this assumption.
2. The Kasperski and Kasperska Model
Kasperski and Kasperska (2021) define an attractor as “a configuration towards which the system evolves over time” and note that “after attaining an attractor a given configuration of a system is sufficiently stable to return to the original state after disappearing an eventual perturbation.” They distinguish two classes of attractor dynamics:
2.1 Genome attractors (basins). Using an artificial neural network trained on cytochrome b amino‑acid sequences, the authors identified that organisms during evolution are trapped in distinct genome attractors. For human evolution, they identified six attractors separated by significant evolutionary distances: Tree shrew, Prosimian, New World Monkey, Old World Monkey, Other hominoid, and Old human attractors. Each attractor is a stable region of genome space in which organisms persist over evolutionary timescales. The orbits of these attractors are disturbed by small perturbations (represented as arrows pointing toward other organisms), but the system remains within the basin. The distances between attractor orbits, expressed as distance factors (e.g., the ratio of inner to outer orbit size), quantify the evolutionary gaps between basins. The derivation and units of these distance factors are as given in the original study.
2.2 Cancer as attractor destabilization. The authors propose a two‑mode model of cancer development. Vertical development occurs within a single genome attractor: the cell changes its cell‑fate attractor (gene expression program) without leaving the genome basin. This is an adaptation to environmental or internal perturbations that does not require genome re‑organization. Horizontal development occurs when elevated ROS levels cause genome instability and genome chaos, leading to a change of genome attractor—a transition into a new basin with a re‑organized genome. Horizontal development is always followed by vertical development, as the cell must establish a new cell‑fate program to survive in the new genome basin. The authors note that cancer cells, driven by ROS, can undergo repeated horizontal transitions, creating an “impression that cancer cells want to escape from the internal ROS flame through permanent changes of genome attractors.”
3. Structural Parallels with the Attractor Framework
The claims in this section are subject to the limitations discussed in Section 4, particularly regarding the qualitative nature of κ, the model‑dependence of the neural network attractors, and the provisional status of the κ = 1/τ definition. The parallels identified are structural analogies, not formal derivations.
3.1 Genome Attractors as Basins. The genome attractors identified by Kasperski and Kasperska are stable configurations in genome space that resist perturbation and persist over evolutionary timescales. This is structurally analogous to the attractor framework’s concept of a basin. The evolutionary distances between attractors correspond to the framework’s distinction between distinct basins, and the small perturbations (arrows) that disturb but do not displace the attractor correspond to the framework’s concept of perturbation within a basin.
3.2 Cancer as Basin Transition. Horizontal cancer development—the destabilization of the current genome attractor, genome chaos, and stabilization in a new genome attractor—is structurally analogous to the framework’s concept of a phase transition between basins. The chaotic intermediate state (genome chaos) is the transition phase; the re‑stabilization in a new attractor is the system finding a new basin. Vertical cancer development—cell‑fate changes within a genome attractor without leaving the basin—corresponds to the framework’s concept of perturbation absorption without basin transition. This distinction between within‑basin adaptation and between‑basin transition is a core feature of both models.
3.3 ROS as the Perturbation Mechanism. [Note: The claims in this section are subject to the limitations described in Section 4, particularly the lack of formal κ measurement and the neural network/attractor assumption.] In the Kasperski and Kasperska model, elevated ROS acts as the destabilizing force that pushes the cell out of its current genome attractor. This maps onto the framework’s concept of a perturbation that exceeds the system’s corrective permeability, forcing a basin transition. The repeated horizontal transitions observed in cancer cells—successive escapes from one genome attractor to another under persistent ROS pressure—are structurally analogous to the framework’s description of a system undergoing repeated basin transitions when corrective mechanisms are saturated by sustained perturbation.
3.4 Attractor Depth and Persistence. [Note: The claims in this section are subject to the limitations described in Section 4, particularly the qualitative nature of the distance‑factor‑to‑basin‑depth mapping.] The large evolutionary distances between genome attractors, quantified by distance factors, reflect the depth of the basins in the Kasperski and Kasperska model. A larger distance factor indicates a wider evolutionary gap between attractors, consistent with the framework’s concept that deeper basins require more energy (or more sustained perturbation) to exit. However, the mapping between distance factors and basin depth is intuitive rather than derived. Basin depth in formal dynamical systems is a property of the energy landscape; distance factors from neural network classification are a related but distinct quantity. The parallel is offered as a qualitative structural analogy, not a formal equivalence.
3.5 The Atavistic Theory and the Permian Parallel. [Note: This section introduces a third domain (climate) to reinforce an analogy between two already‑analogized domains. Accumulating analogies without formal constraints is a known risk for unfalsifiable frameworks; the present parallel is speculative and is retained here as an illustration of heuristic reach only.] The atavistic theory of cancer, which Kasperski and Kasperska reference, proposes that cancer cells revert to ancient, unicellular survival programs under extreme stress. This is a real‑world biological instance of a system reverting to a much older, simpler attractor when pushed beyond its current basin’s capacity. The attractor framework has described a structurally analogous dynamic in other domains—specifically, the hypothesis that when the climate system is pushed too far from the Holocene basin, it may not merely shift to a neighboring attractor but can revert to a much older, lethal state, analogous to the Permian extinction’s anoxic conditions. This cross‑domain parallel is speculative and is offered as an illustration of the framework’s heuristic reach, not as a confirmed prediction.
4. Limitations
This mapping is post‑hoc. The parallels identified here are structural analogies, not independent evidence for the framework. Kasperski and Kasperska developed their model within the established traditions of bioinformatics and evolutionary biology; they did not set out to test the attractor framework.
The framework’s κ remains qualitatively defined. While the distance factors separating genome attractors provide a quantitative measure of basin depth in the Kasperski and Kasperska model, no formal mapping between these factors and κ has been derived. The provisional definition κ = 1/τ is not yet linked to any specific measure in the Kasperski and Kasperska data, and the prerequisites for measuring τ (a specified baseline state and a specified perturbation protocol) have not been established for the genomic or cellular domains discussed here.
The neural network approach used by Kasperski and Kasperska is one of several methods for analyzing evolutionary distances, and the specific attractor configurations identified depend on the choice of training organisms, the neural network architecture, and the amino‑acid coding scheme. The attractor interpretation of evolutionary data is therefore model‑dependent. Furthermore, whether the stable classification regions identified by a neural network constitute attractors in the formal dynamical systems sense—the sense in which the attractor framework uses the term—is a substantive assumption. The parallels drawn in Section 3 are contingent on the validity of this assumption.
The attractor framework is self‑published and has not undergone independent peer review. The foundational paper (Galida, 2026a) was published on fantasyattractor.com in May 2026 and is not archived with a DOI.
5. Falsifiability Conditions
The following observations would weaken or invalidate the parallels drawn here:
- Disconfirming observation 1: If genome attractors were shown to be artifacts of the neural network architecture rather than genuine properties of genome space, the basin analogy would fail.
- Disconfirming observation 2: If the distance factors separating genome attractors were shown to be continuous rather than discontinuous, the basin‑transition model would be weakened.
- Disconfirming observation 3: If alternative models of cancer progression (e.g., purely stochastic mutation accumulation without attractor dynamics) were shown to explain the data with equal or greater parsimony, the attractor interpretation would not be uniquely supported.
Affirmative prediction: If genome attractors function as basins in the attractor framework’s sense, then experimental manipulations that increase ROS levels should increase the probability of attractor transitions (horizontal development) in a dose‑dependent manner, while manipulations that reduce ROS should stabilize the current attractor and favor vertical development. This prediction is testable in cell culture models with controlled oxidative stress. It should be noted that measuring “attractor transition probability” in such an experiment requires specifying how the neural network’s classification scheme maps onto the experimental observables—e.g., whether a transition is identified by a shift in the cytochrome b sequence profile as classified by the trained ANN, or by a proxy measure such as karyotype or gene expression signature.
Framework falsifiability: The attractor framework itself requires independent falsifiability conditions. Specifically: (a) if κ, as operationally defined, cannot be correlated with any independently validated measure of system resilience across multiple domains (physical, biological, or cognitive), the framework’s central construct lacks empirical grounding; (b) if attractor‑like dynamics in cancer progression are shown to be explained with equal or better parsimony by clonal evolution models (e.g., standard somatic mutation accumulation theory as reviewed in Greaves & Maley, 2012) when fitted to the same genomic data, the attractor framework’s claim to offer a unified explanatory vocabulary would be weakened.
6. Conclusion
The genome attractor model of Kasperski and Kasperska (2021) exhibits structural parallels with the attractor framework’s description of basins, basin transitions, and perturbation‑driven attractor shifts. Their distinction between vertical and horizontal cancer development maps onto the framework’s distinction between within‑basin adaptation and between‑basin transition. The ROS‑driven mechanism of attractor destabilization is a molecular analogue of the framework’s perturbation concept. These parallels are structural analogies, not independent validation. The framework remains a self‑published, preliminary research program. This mapping is a contribution to its ongoing development.
References
- Galida, R. (2026a). Persistence Under Perturbation: The Eternal Skeleton and the Transient Dance. Fantasy Attractor. Published May 2026.
- Greaves, M., & Maley, C. C. (2012). Clonal evolution in cancer. Nature, 481(7381), 306–313.
- Kasperski, A., & Kasperska, R. (2021). Study on attractors during organism evolution. Scientific Reports, 11, 9637. https://doi.org/10.1038/s41598-021-89001-0

