Covolution.org - Recent changes [en]

Gerotype

Jongbhak — Mon, 25 May 2026 14:52:56 GMT

Gerotype

Jongbhak — Mon, 25 May 2026 14:27:09 GMT

Created page with " === The Gerotype hierarchy === ==== Top level: 4 Gerotypes ==== {| border="1" |- ! scope="col" | Code ! scope="col" | Name ! scope="col" | CAD interpretation |- | '''G-I'''..."

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=== The Gerotype hierarchy ===

==== Top level: 4 Gerotypes ====

{| border="1"
|-
! scope="col" | Code
! scope="col" | Name
! scope="col" | CAD interpretation
|-
| '''G-I'''
| Substrate Gerotype (基質老型, 기질노형)
| Failure to maintain the physical-informational carrier of the attractor
|-
| '''G-II'''
| Regulatory Gerotype (調節老型, 조절노형)
| Failure to maintain the control loops that sustain the attractor
|-
| '''G-III'''
| Coupling Gerotype (共調老型, 공조노형)
| Failure to maintain the BiO-symvironment exchange that defines the attractor
|-
| '''G-IV'''
| Lineage Gerotype (繼承老型, 계승노형)
| Failure to maintain the generative renewal of the attractor across cell and tissue generations
|}

Cybernetic Attractor Decay

Jongbhak — Mon, 25 May 2026 14:26:48 GMT

Created page with "CAD "

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CAD

CAD

Jongbhak — Mon, 25 May 2026 14:22:56 GMT

CAD

Jongbhak — Mon, 25 May 2026 14:22:27 GMT

Created page with "Cybernetic Attracgor Decay Gerotype *Gerotype = 노형(老型) , 노상(老相) *GeroType vector = 노형벡터 *Cybernetic Attractor D..."

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[[Cybernetic Attracgor Decay]] [[Gerotype]]
*Gerotype = [[노형(老型)]] , [[노상(老相)]]
*GeroType vector = [[노형벡터]]
*Cybernetic Attractor Decay = [[자율수렴붕괴]]

CovOmics

Jongbhak — Mon, 25 May 2026 10:47:13 GMT

Created page with "'''CovOmics''' CovOmics is an omics to generate biologically plausible artificial/experimental multi-omics data by simulating how cells move through regulatory..."

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'''CovOmics''' CovOmics is an omics to generate biologically plausible artificial/experimental multi-omics data by simulating how cells move through regulatory, transcriptional, epigenetic, proteomic, metabolic, and aging attractor landscapes.

A universal stochastic mechanism of aging from single-cell DNA methylation patterns.

Jongbhak — Mon, 25 May 2026 06:11:01 GMT

Created page with " '''Tarkhov et al. 2024.''' ''Nature Aging,'' December 2024. Andrei E. Tarkhov and Vadim Gladyshev (Harvard Medical School, Brigham and Women's). '..."

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'''Tarkhov et al. 2024.''' ''Nature Aging,''   December 2024. Andrei E. Tarkhov and Vadim Gladyshev (Harvard Medical School, Brigham and Women's). '''What they did.''' Single-cell DNA methylation data from multiple tissues across age '''in mouse and human.''' They decomposed methylation changes into two components: a ''coordinated'' component (CpG sites whose methylation shifts in correlated patterns across cells, suggesting shared regulatory drivers) and a ''stochastic'' component (CpG sites drifting independently, suggesting noise-like accumulation).

'''Key findings.'''

#Both components rise with age. Stochastic drift is the larger numerical contributor; coordinated drift is the smaller but biologically more interpretable component.
#The coordinated component localises preferentially to PRC2-bound, developmentally regulated, bivalent chromatin regions. These are the same regions Horvath-clock CpGs draw from.
#The stochastic component is distributed more uniformly across the genome.
#A model in which methylation is driven by a stochastic process plus a smaller deterministic drift recapitulates the empirical age-methylation relationship across tissues and species.

'''Relevance to CAD and Covolution.''' An important empirical anchor for covolution/CAD. The reason: CAD requires exactly the decomposition Tarkhov provides. 1) A ''structured scaffold'' (coordinated CpGs at regulatory boundaries) plus 2) a ''noise field'' (stochastic CpGs in the bulk). The decomposition is empirical. CAD claims the coordinated component dominates the boundary regions of the attractor landscape. The stochastic component dominates the bulk is what they found, post hoc.

'''The complication.''' Tarkhov and Gladyshev interpret their data within a ''stochastic-process-with-drift'' framework, not an attractor-decay framework. Their preferred interpretation is closer to [[Meyer/Maklakov/Schumacher: aging clocks measure stochastic drift through a structured landscape, and the structure is the developmental regulatory architecture]]. This is compatible with CAD but not identical to it. The disambiguation is whether the structure is ''passive'' (a fixed landscape through which stochastic drift accumulates) or ''active'' ([[an attractor maintained by feedback whose maintenance fails with age]]).

Aging increases cell-to-cell transcriptional variability upon immune stimulation

Jongbhak — Mon, 25 May 2026 06:03:02 GMT

Aging increases cell-to-cell transcriptional variability upon immune stimulation

Jongbhak — Mon, 25 May 2026 06:01:27 GMT

Created page with "'''Martinez-Jimenez et al. 2017.''' ''Aging increases cell-to-cell transcriptional variability upon immune stimulation.'' Science 355, 1433–1436. With..."

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'''Martinez-Jimenez et al. 2017.''' ''Aging increases cell-to-cell transcriptional variability upon immune stimulation.'' Science 355, 1433–1436. With proper single-cell RNA-seq. Martinez-Jimenez (from Marioni and Odom's groups, EMBL-EBI and CRUK Cambridge) profiled naive and activated CD4+ T cells from young and old mice. Key findings: (1) at baseline, young and old T cells looked similar; (2) under LPS or anti-CD3 stimulation, old cells showed markedly increased cell-to-cell transcriptional heterogeneity, especially in immune-response genes; (3) the increased variability was not random across the genome but concentrated in genes with cell-type-specific regulatory architecture. This shows that computational drift is ''revealed by perturbation'' and ''localised to regulated loci'', which is exactly what cybernetic attractor decay predicts (attractor erosion shows up under stress, not at rest, and concentrates at boundary regions).

Increased cell-to-cell variation in gene expression in ageing mouse heart

Jongbhak — Mon, 25 May 2026 05:59:29 GMT

Created page with "'''Bahar et al. 2006.''' ''Increased cell-to-cell variation in gene expression in ageing mouse heart.'' Nature 441, 1011–1014. Bahar and colleagues isolated..."

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'''Bahar et al. 2006.''' ''Increased cell-to-cell variation in gene expression in ageing mouse heart.'' Nature 441, 1011–1014. Bahar and colleagues isolated single cardiomyocytes from young and old mice and measured expression of a small panel of genes by single-cell qPCR (single-cell RNA-seq did not yet exist at scale). The finding: '''cell-to-cell variation in expression''' was '''substantially higher in old hearts,''' even when mean expression was similar. They interpreted this as '''stochastic transcriptional noise accumulating with age''', possibly from somatic DNA damage. It was the first clean demonstration that aging is a variance phenomenon, not just a mean-shift phenomenon. For [[cybernetic attractor decay]], it is the original empirical anchor for computational drift.

Civilization

Jongbhak — Sat, 23 May 2026 13:11:53 GMT

Civilization

Jongbhak — Sat, 23 May 2026 13:10:43 GMT

Created page with " Civilization is not just genetic evolution. It is a higher-order developmental platform. "

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Civilization is not just genetic evolution. It is a higher-order developmental platform.

 

Entelenomic Search Theorem

Jongbhak — Sat, 23 May 2026 13:08:19 GMT

Entelenomic Search Theorem

Jongbhak — Sat, 23 May 2026 13:07:54 GMT

Created page with "'''Entelenomic Search Theorem.''' Let an adaptive system occupy state sts_tst in an environment ete_tet. Suppose the environment has temporal struc..."

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'''Entelenomic Search Theorem.''' Let an adaptive system occupy state sts_tst in an environment ete_tet. Suppose the environment has temporal structure such that I(et;et+Δ)>0I(e_t;e_{t+\Delta})>0I(et;et+Δ)>0, and suppose the system contains an internal state hth_tht satisfying I(ht;et+Δ)>0I(h_t;e_{t+\Delta})>0I(ht;et+Δ)>0. If state transitions are biased by hth_tht so that expected future viability increases, then the expected hitting time to a future-adaptive basin is lower than under an otherwise identical blind mutation-selection process. Thus, predictive developmental architecture can generate directional adaptive search without invoking external teleology.

Covolution theory theorem

Jongbhak — Sat, 23 May 2026 13:07:17 GMT

Covolution theory theorem

Jongbhak — Sat, 23 May 2026 13:06:48 GMT

Created page with "<img style="null" src=https://Covolution.org/upload/20260523220646_image.png>"

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Darwin cannot explain evolution?

Jongbhak — Sat, 23 May 2026 13:06:43 GMT

Darwin cannot explain evolution?

Jongbhak — Sat, 23 May 2026 13:05:56 GMT

Created page with "Darwinian selection is a valid filtering process, but it is not always the full generator of adaptive direction. In systems with internal memory, developmental architectu..."

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Darwinian selection is a valid filtering process, but it is not always the full generator of adaptive direction. In systems with internal memory, developmental architecture, and predictive organism-symvironment coupling, adaptive search can become internally biased before external selection completes its filtering role.

Covolution simulations

Jongbhak — Sat, 23 May 2026 06:25:51 GMT

Created page with " https://claude.ai/public/artifacts/0846af93-3842-4571-926a-9b40f439773b"

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https://claude.ai/public/artifacts/0846af93-3842-4571-926a-9b40f439773b

How to read covolution.org

Jongbhak — Sat, 23 May 2026 05:53:13 GMT

Prediction

Jongbhak — Sat, 23 May 2026 05:48:31 GMT

Created page with "In covolution, prediction as non-conscious statistical anticipation. "

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In covolution, prediction as non-conscious statistical anticipation.

The defintion of sale-recursive organization

Jongbhak — Sat, 23 May 2026 05:19:02 GMT

The defintion of sale-recursive organization

Jongbhak — Sat, 23 May 2026 05:14:56 GMT

Created page with " = Scale-recursive organization in covolution = {| border="1" data-end="1202" data-start="312" |- data-end="343" data-start="312" ! data-col-size="sm" data-end="319" data-sta..."

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= Scale-recursive organization in covolution =

{| border="1" data-end="1202" data-start="312"
|- data-end="343" data-start="312"
! data-col-size="sm" data-end="319" data-start="312" | Term
! data-col-size="md" data-end="329" data-start="319" | Meaning
! data-col-size="sm" data-end="343" data-start="329" | My opinion
|- data-end="449" data-start="358"
| data-col-size="sm" data-end="380" data-start="358" | '''Scale-recursive'''
| data-col-size="md" data-end="425" data-start="380" | Similar logic repeats across nested levels
| data-col-size="sm" data-end="449" data-start="425" | Best for your theory
|- data-end="533" data-start="450"
| data-col-size="sm" data-end="479" data-start="450" | '''Cross-scale recurrence'''
| data-col-size="md" data-end="515" data-start="479" | A pattern reappears across scales
| data-col-size="sm" data-end="533" data-start="515" | Clear and safe
|- data-end="634" data-start="534"
| data-col-size="sm" data-end="562" data-start="534" | '''Multiscale recurrence'''
| data-col-size="md" data-end="609" data-start="562" | Recurrence across multiple biological levels
| data-col-size="sm" data-end="634" data-start="609" | Scientific and modest
|- data-end="738" data-start="635"
| data-col-size="sm" data-end="665" data-start="635" | '''Recursive composability'''
| data-col-size="md" data-end="717" data-start="665" | Lower-level units combine into higher-level units
| data-col-size="sm" data-end="738" data-start="717" | Good for switches
|- data-end="826" data-start="739"
| data-col-size="sm" data-end="759" data-start="739" | '''Scale-similar'''
| data-col-size="md" data-end="801" data-start="759" | Similar but not identical across scales
| data-col-size="sm" data-end="826" data-start="801" | Safe, but less strong
|- data-end="905" data-start="827"
| data-col-size="sm" data-end="846" data-start="827" | '''Fractal-like'''
| data-col-size="md" data-end="875" data-start="846" | Self-similar across scales
| data-col-size="sm" data-end="905" data-start="875" | Useful metaphor, but risky
|- data-end="1005" data-start="906"
| data-col-size="sm" data-end="931" data-start="906" | '''Scale-transposable'''
| data-col-size="md" data-end="971" data-start="931" | Logic can be translated across levels
| data-col-size="sm" data-end="1005" data-start="971" | Interesting, but less standard
|- data-end="1112" data-start="1006"
| data-col-size="sm" data-end="1045" data-start="1006" | '''Substrate-independent recurrence'''
| data-col-size="md" data-end="1093" data-start="1045" | Same logic, different material implementation
| data-col-size="sm" data-end="1112" data-start="1093" | Good but longer
|- data-end="1202" data-start="1113"
| data-col-size="sm" data-end="1144" data-start="1113" | '''Hierarchically recurrent'''
| data-col-size="md" data-end="1182" data-start="1144" | Recurs through biological hierarchy
| data-col-size="sm" data-end="1202" data-start="1182" | Good for biology
|}

Stability in cybernetic attractor

Jongbhak — Fri, 22 May 2026 15:19:38 GMT

Stability in cybernetic attractor

Jongbhak — Fri, 22 May 2026 14:44:07 GMT

Created page with "''Stability'', in Covolution, denotes the basin-relative resilience of an informational pattern, specified at three parameters: a description level '''L''' (the resolution at..."

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''Stability'', in Covolution, denotes the basin-relative resilience of an informational pattern, specified at three parameters: a description level '''L''' (the resolution at which identity is defined), a timescale '''τ''' (the horizon over which persistence is asserted), and a noise scale '''σ''' (the amplitude of perturbations the pattern must absorb). A pattern is stable when perturbed trajectories recur within a neighborhood of an attractor over τ, with basin depth ΔV > σ at level L. Stability is therefore a triple — ''stable(L, τ, σ)'' — not an absolute property.
''[[Co-stability]]'' is stability that obtains only under sustained organism↔symvironment coupling: severing the coupling collapses the basin even when its internal regulatory loops remain intact. Co-stability has two failure modes. The first is ''acute uncoupling'', in which the basin disappears as soon as the recursion is broken. The second is ''Cybernetic Attractor Decay (CAD)'': a slow structural deformation of the landscape itself under sustained coupling, in which basins flatten, separatrices erode, adjacent attractors merge, and ΔV progressively falls toward σ. CAD is loss of ''structural'' stability manifesting as eventual loss of ''basin'' stability — the geometry of viability decays while the organism is still trying to live within it.

'''Dimensions of stability and their CAD failure modes'''

{| border="1"
|-
! scope="col" | Dimension
! scope="col" | What it specifies
! scope="col" | CAD failure mode
|-
| Description level (L)
| Resolution at which identity is defined (molecular, network, cellular, phenotypic)
| Fine-grained patterns dissolve into coarse-grained noise; identity blurs across levels
|-
| Timescale (τ)
| Horizon over which recurrence is asserted
| Characteristic return time lengthens, then exceeds biological tolerance
|-
| Noise scale (σ)
| Amplitude of perturbations absorbed without leaving the basin
| Effective σ rises (damage accumulation) while ΔV falls; eventually ΔV < σ
|-
| Basin geometry
| Bounded neighborhood toward which trajectories converge
| Basin flattens, narrows, merges with neighbors; separatrices lower
|-
| Recursive coupling
| Stability sustained by organism↔symvironment loop
| Coupling weakens; co-stability decomposes into unstable component parts
|-
| Path dependence
| Memory and prior trajectory bias current persistence
| Memory degrades — epigenetic drift, immunological forgetting, mitochondrial heteroplasmy
|-
| Self-maintenance
| Internal feedback regenerates the pattern
| Loop gain drops; correction lags behind perturbation rate
|-
| Structural robustness
| The landscape itself is robust to parameter drift
| Bifurcations occur: old attractors vanish, pathological ones appear (senescence, fibrosis, tumor states)
|}

Homeostasis vs cybernetic attractor

Jongbhak — Fri, 22 May 2026 14:29:40 GMT

Homeostasis vs cybernetic attractor

Jongbhak — Fri, 22 May 2026 14:22:06 GMT

Created page with " '''Homeostasis is a control mechanism. A cybernetic attractor is a state-space structure.''' Homeostasis asks: <blockquote> How does a system correct deviation from a s..."

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'''Homeostasis is a control mechanism. A cybernetic attractor is a state-space structure.'''

Homeostasis asks:
<blockquote>
How does a system correct deviation from a set point?
</blockquote>
Cybernetic attractor theory asks:
<blockquote>
Why do many system trajectories converge into a stable basin of behavior or identity?
</blockquote>
So they are related, but not equivalent.

{| border="1"
|-
! Aspect
! Homeostasis
! Cybernetic attractor
|-
| Core idea
| Maintain a variable
| Stabilize a whole system-state
|-
| Logic
| Error correction
| State-space convergence
|-
| Reference
| Set point is specified
| Attractor emerges from dynamics
|-
| Stability
| Usually one preferred range
| Often multiple possible basins
|-
| Noise
| Disturbance to suppress
| Can trigger transitions between basins
|-
| Aging interpretation
| Regulators fail
| Attractor basins deform, flatten, merge, or collapse
|}

So a formula:
<blockquote>
'''cybernetic attractor = homeostasis + hierarchical switches'''
</blockquote>
is useful, but incomplete.

 A better Covolution formulation is:
<blockquote>
'''Homeostatic loops operate inside cybernetic attractors. Hierarchical switches can move systems between attractors. The attractor itself is the global geometry of stable informational-control states.'''
</blockquote>
For CAD ([[Cybernetic Attractor Decay]]), the key point is:
<blockquote>
'''Aging is not only failure of homeostasis. Aging is deformation of the attractor landscape.'''
</blockquote>
That means the organism does not merely lose regulation. Its viable-state space becomes shallower, noisier, less distinct, and more collapse-prone.

A concise definition:
<blockquote>
'''A cybernetic attractor is a stable or recurrent basin in informational state space produced by feedback, network topology, memory, constraints, and symvironmental coupling. Homeostasis is one local control process that helps maintain a system within such a basin.'''
</blockquote>
So the hierarchy should be:
<blockquote>
'''Feedback loops → Homeostasis/Allostasis → Switches → Cybernetic attractors → Attractor landscape → Covolutionary dynamics'''
</blockquote>
 '''Theoretical leverage of Covolution: ''' not just “life maintains balance,” but '''life explores, stabilizes, switches between, and loses cybernetic attractor basins across scales.'''

Why do we exist?

Jongbhak — Thu, 21 May 2026 08:29:43 GMT

Created page with " = Why Do We Exist? = The question "Why do we exist?" is the question a philosophy engine asks when its information-processing capacity reaches the depth required to model it..."

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= Why Do We Exist? =

The question "Why do we exist?" is the question a philosophy engine asks when its information-processing capacity reaches the depth required to model its own existence. Biosophy treats the asking as biological: it is one of biology's highest covolutionary expressions, not something added to biology from outside.

The question carries two readings, and biosophy answers both.

----

== Reading 1: Why does anything exist at all? ==

The cosmological question. Given the [https://biosophy.org/Existence distinction-cosmology of existence], this becomes: why did the primordial undifferentiated state differentiate?

The biosophical answer dissolves the apparent depth of the question. "Why is there something rather than nothing?" assumes that "nothing" is a stable state from which "something" must be derived. The framework rejects this assumption. The perfect undifferentiated state is not nothing; it is the state in which everything is contained in indistinguishable form. Distinction is how potentiality becomes actuality. Differentiation is what existence ''is'', not something added to it.

Asking why the universe differentiates is asking why existence happens. Existence is happening. The question is observational rather than mysterious. It does not require a transcendent answer because it does not posit a prior state from which the universe must be derived.

----

== Reading 2: Why do we, specifically, exist? ==

The biological-anthropological question. Given that the universe differentiates, why are there entities like us within it?

The biosophical answer has three layers.

'''Entelenomic structure.''' The three structural preconditions, thermodynamic disequilibrium, hierarchical self-organization, and computational irreducibility, make the emergence of persistence-biased, purpose-bearing systems statistically favored in the universe we inhabit. [https://biosophy.org/Entelenomy Entelenomy] is not an accident. It is what physical law produces when it has these features. Philosophy engines are the high-density expression of a structural feature that has been latent since the cosmological initial conditions.

'''Covolutionary emergence.''' Once entelenomic systems exist, [https://biosophy.org/Covolution covolution] elaborates them. The universe does not select philosophy engines through external Darwinian competition. It computes them through the internal constructive dynamics that have characterized organized matter from the first distinction. Philosophy engines are what the universe does at this level of complexity.

'''Self-recognition.''' A philosophy engine is a BiO that has become capable of distinguishing itself from the fabric and modeling that distinction. We exist as the particular kind of pattern that participates in its own pattern-recognition. The asking of the question "Why do we exist?" is not separate from our existence. It is the existence performing one of the things existence can do at this level.

----

== What "human" specifically adds ==

Under [https://biosophy.org/Covolution covolution theory], human beings are energy-consuming, self-reflective information systems. We exist by processing energy and information. We become ''human'' by doing something more specific:
<blockquote>
''converting information into questions, questions into meaning, and meaning into actions that reshape ourselves and our symvironment.''
</blockquote>
Each step is a covolutionary operation. Information becomes question when a philosophy engine notices that it does not know. Question becomes meaning when the engine integrates the unknown into its model of itself and its surround. Meaning becomes action when the engine modifies, even slightly, the conditions of its own existence and the existence of the BiOs it interacts with.

This chain, information to question to meaning to action, is what biology does when it reaches the level of human philosophy engines. It is not added to biology. It is biology operating at its current highest known scale on Earth.

----

== Three things this answer is not ==

'''It is not religious.''' No transcendent intention is invoked. No designer, no plan, no purpose given from outside. The structural account is sufficient.

'''It is not nihilist.''' The question is not dismissed as confused or as a misfiring of evolved cognition. The asking is a legitimate biological-philosophical event with structural significance.

'''It is not anthropocentric.''' Other BiOs at lower densities of self-reflection also exist for the same structural reasons. The cell, the bacterium, the forest, and the ecosystem all exist because the universe is structured to produce them. Humans differ in the depth of their self-recognition, not in the underlying reason for their existence.

----

== The short version ==

We exist because the universe differentiates, because differentiation produces persistence-biased systems, because persistence-biased systems elaborate into philosophy engines, and because philosophy engines are what biology does at this level of complexity.

The asking of "Why do we exist?" is the universe, through us, performing one of the operations that defines what we are.

Meaning is not added to the universe by humans. Meaning is what the universe does when it produces entities like us. The question and its asking are part of the answer.

----

== See also ==

*[https://biosophy.org/Existence Existence]
*[https://biosophy.org/Biosophy Biosophy]
*[https://biosophy.org/Covolution Covolution]
*[https://biosophy.org/Entelenomy Entelenomy]
*[https://biosophy.org/Biological_information_processing_objects Biological information processing objects]
*[https://biosophy.org/Philosophy_engine Philosophy engine]
*[https://biosophy.org/The_purpose_of_life The purpose of life]
*[https://biosophy.org/Purposism Purposism]
*[https://biosophy.org/Symvironment Symvironment]
*[https://biosophy.org/Biological_systems_seek_information Biological systems seek information]

Horon definition

Jongbhak — Wed, 20 May 2026 14:20:56 GMT

Created page with " A '''horon''' is a bounded information object that maintains its identity, has internal states, computes through switches, and couples predictively with its '''symvironment''..."

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A '''horon''' is a bounded information object that maintains its identity, has internal states, computes through switches, and couples predictively with its '''symvironment'''.

In one sentence:

'''A horon is an encapsulated information object that can distinguish itself from its surroundings, maintain internal state, process information, and act in relation to possible future states of its symvironment.'''

== Four conditions of a horon ==

{| border="1"
|-
! Condition
! Meaning
|-
| '''Distinguishability'''
| It can be differentiated from its symvironment.
|-
| '''Internal state-space'''
| It has multiple possible internal states.
|-
| '''Computation'''
| It transforms inputs and internal states into outputs, actions, or further states.
|-
| '''Predictive coupling'''
| Its internal states are organized in relation to future-relevant states of its symvironment.
|}

So, a horon is not just any object. A rock is distinguishable and has physical states, but it does not meaningfully compute or predictively couple. A cell, mind, institution, or sufficiently autonomous technological system may qualify.

== Examples ==

{| border="1"
|-
! Type
! Example
|-
| '''Biological horon'''
| cell, organism, immune system, ecosystem
|-
| '''Cognitive horon'''
| mind, belief, attention-state, intention, self-model
|-
| '''Social horon'''
| family, company, university, research community, civilization
|-
| '''Technological horon'''
| AI system, database, self-driving system, power grid
|-
| '''Cultural-symbolic horon'''
| language, scientific theory, religion, legal system
|}

== Relation to covolution theory ==

In covolution theory, living and thinking systems are not passive objects. They are '''horons''': self-maintaining, information-processing entities that interact with and partly construct their symvironments.

A horon is therefore the basic unit of covolutionary organization.

== Very short version ==

'''A horon is a self-bounded, information-processing unit that maintains itself and predictively interacts with its world.'''

Or even shorter:

'''A horon is an information object with boundary, state, computation, and future-directed coupling.'''

Horon

Jongbhak — Wed, 20 May 2026 14:19:38 GMT

Show changes

Conscious and unconscious minds

Jongbhak — Wed, 20 May 2026 14:08:14 GMT

Show changes

Conscious and unconscious minds

Jongbhak — Wed, 20 May 2026 14:03:23 GMT

Created page with " = Conscious and unconscious minds = '''Status: Working.''' The framework offers a structural account of why a covolving information object would have two cognitive modes. Th..."

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= Conscious and unconscious minds =

'''Status: Working.''' The framework offers a structural account of why a covolving information object would have two cognitive modes. The framework does not solve the hard problem of consciousness (why there is something it is like to be a system); it explains why two modes are structurally expected and what each is for.

== Core idea ==

A living information object engaged in continuous predictive coupling with its symvironment has more switching to do than its integrated self-model can handle. Most switching therefore runs below the threshold at which it is reportable, narratable, or self-attributed. This is what we call the unconscious mind. A smaller subset of switching is integrated into the horon's self-model and becomes reportable and deliberately controllable. This is what we call the conscious mind.

Concisely: the unconscious is switching that does not pass through the self-modeling integration layer. Consciousness is switching that does.

The two are not separate substances. They are operational modes of a single switching architecture, distinguished by whether or not the switching is integrated into the horon's self-model.

== Why two modes are structurally expected ==

Three framework commitments make the two-mode architecture predictable.

'''Computational economy.''' A horon must regulate heartbeat, breathing, balance, perception, immune state, threat detection, motor sequences, social interpretation, language production, and dozens of other ongoing processes simultaneously. Routing all this through an integrated self-model would overwhelm the self-modeling capacity and slow processes that need to be fast. Most switching has to happen below the integration layer because the integration layer is bandwidth-limited.

'''Predictive coupling requires self-modeling, but only sometimes.''' The framework's commitment to predictive coupling means the horon maintains models of its symvironment and biases action toward predicted futures. For routine prediction (where will the ball land, what is the next word in this sentence, what is the texture of this surface), self-modeling is unnecessary; the prediction can be made and acted on without self-attribution. For deliberate prediction (should I take this job, what does this person think of me, what kind of future should I pursue), self-modeling is required because the horon's own decisions are part of what shapes the future. The two modes correspond to these two prediction regimes.

'''Layered architecture is more robust.''' A single integrated layer that handles everything has no fallback when the integration fails. A two-layer architecture in which most switching can continue without self-model integration means that failures of the integration layer (sleep, anesthesia, dissociation) do not stop the organism from functioning. The two modes provide redundancy.

== What the unconscious does ==

The unconscious mind is the distributed, non-reportable switching system that maintains the organism and prepares conscious possibilities. It includes:

{|
|-
! scope="col" | Layer
! scope="col" | Unconscious function
|-
| Autonomic
| Heart rate, digestion, arousal
|-
| Immune
| Self/non-self discrimination
|-
| Metabolic
| Energy allocation
|-
| Perceptual
| Pattern detection, sensory integration
|-
| Emotional
| Valuation and action preparation
|-
| Memory
| Implicit associations, priming
|-
| Motor
| Posture, gestures, learned skills
|-
| Social
| Rapid reading of faces, tone, status
|-
| Linguistic
| Word priming, grammatical preparation
|-
| Motivational
| Desire and goal biasing
|-
| Defensive
| Stored trauma-related priors
|}

The unconscious is not pre-rational or sub-rational. Some unconscious processing is highly rational: rapid arithmetic in expert calculators, perceptual judgments by trained radiologists, social inference by experienced clinicians. These are not deliberate, but they are not irrational either. The unconscious is ''non-self-modeled'' switching, which is a different distinction than the rational/irrational distinction.

== What the conscious mind does ==

The conscious mind is the globally integrated, reportable, self-referential layer of switching. It allows the horon to:

{|
|-
! scope="col" | Function
! scope="col" | Conscious role
|-
| Attention
| Select what matters now
|-
| Language
| Name, explain, and share states
|-
| Planning
| Simulate possible futures
|-
| Self-modeling
| Represent "I am the one experiencing this"
|-
| Social coordination
| Communicate intentions and reasons
|-
| Moral reasoning
| Evaluate actions beyond immediate impulse
|-
| Long-term goals
| Override short-term drives
|-
| Explicit modeling
| Build articulated models of reality
|}

Consciousness is not merely awareness. It is a person-level switching interface where the horon becomes an object to itself, capable of being named, narrated, and deliberately modified.

== The transition: from unconscious to conscious ==

A workable mechanism for how unconscious switching becomes conscious can be sketched in four stages.

''Distributed unconscious computation.'' Many systems compute in parallel: sensory networks, emotional valuation, memory retrieval, social perception, motor preparation, language priming, reward systems.

''Salience competition.'' These distributed signals compete for selection. The system asks which signal should control the next global integration. Pain, danger, novelty, opportunity, social threat, insight, or moral conflict may win.

''Global integration into the self-model.'' The winning information becomes integrated across attention, memory, language, emotion, and self-representation. It becomes reportable as "I am perceiving / feeling / thinking X."

''Deliberate switching.'' The integrated, self-modeled state can now be acted on in ways that bypass automatic response: inhibit impulse, choose a strategy, reinterpret meaning, communicate, plan.

Consciousness, in this reading, emerges when unconscious computation becomes globally integrated into a self-referential switch.

This is closely related to the Global Workspace theory of consciousness (Baars, Dehaene) and to predictive processing accounts of self-modeling (Friston, Hohwy). The framework's distinctive contribution is to position these accounts within its broader vocabulary of switching architectures and information objects.

== Unconscious is larger than conscious ==

The unconscious is larger because it must maintain the whole organism. Consciousness is narrow because integration is expensive.

{|
|-
! scope="col" | Feature
! scope="col" | Unconscious
! scope="col" | Conscious
|-
| Speed
| Fast
| Slow
|-
| Bandwidth
| Massive
| Narrow
|-
| Energy per item
| Low
| High
|-
| Reportability
| No
| Yes
|-
| Time scale
| Milliseconds to lifetime
| Moment to future planning
|-
| Function
| Maintain and prepare
| Integrate and choose
|-
| Typical error
| Bias, habit, blind spot
| Overthinking, narrative distortion
|-
| Evolutionary depth
| Ancient
| Later-emerging
|}

The mind is not divided because of a flaw. It is divided because layering is efficient. An operating system analogy is useful: the user interface does not show every machine-level process, not because the machine-level processes are mysterious but because exposing them would overload the interface.

== Mental illness as failures of coupling between modes ==

Several mental illnesses can be read as failures in the coupling between unconscious and conscious switching. This complements the four-function mapping in the Covolution and Disease page rather than replacing it.

{|
|-
! scope="col" | Condition
! scope="col" | Conscious-unconscious coupling pattern
|-
| Anxiety
| Unconscious threat prediction floods conscious interpretation
|-
| Depression
| Unconscious negative priors restrict conscious future models
|-
| PTSD
| Unconscious trauma computation intrudes on present awareness
|-
| OCD
| Conscious uncertainty becomes trapped in compulsive unconscious loops
|-
| Addiction
| Unconscious reward switching overrides conscious goals
|-
| Psychosis
| Unconscious salience misclassified as externally perceived
|-
| Dissociation
| Conscious self-model disconnects from unconscious body and memory
|}

The framework's claim is that mental health requires regulated permeability between unconscious and conscious layers. Too little coupling produces dissociation, numbness, or loss of self-understanding. Too much uncontrolled coupling produces panic, intrusive thoughts, flashbacks, craving, or psychosis. Healthy cognition involves the right amount of coupling at the right times.

== Consciousness and cultural covolution ==

Human consciousness is distinctive because it is symbolic. It does not merely register internal states; it names them, and the naming makes them transmissible. Conscious states can be turned into words, theories, laws, rituals, science, philosophy, technology, institutions, and moral systems.

This means consciousness allows biological computation to leave the individual organism and enter cultural switching networks. A cell's regulatory state does not leave the cell. A human's conscious state can be expressed in language and become input to other humans' switching architectures, to written records, to institutional decisions, and to technological systems. Consciousness is the operational bridge by which organism-level covolution becomes cultural and technological covolution.

This connects consciousness to the framework's broader account of recursive hierarchical switching: cultural switching is not separate from biological switching but extends it through symbolic transmission. Without consciousness, biological information objects can still covolve, but they cannot covolve into the cultural and technological forms that humans exhibit.

== Concrete examples ==

To make the abstract account concrete, consider what each mode does in specific situations.

{|
|-
! scope="col" | Situation
! scope="col" | Unconscious computation
! scope="col" | Conscious computation
|-
| Meeting a stranger
| Reads face, tone, posture, danger, familiarity
| "Can I trust this person?"
|-
| Feeling anxious
| Body arousal, threat prediction, memory activation
| "Why am I anxious?"
|-
| Writing a theory
| Associations, intuitions, metaphors arise
| Deliberate structure, wording, argument
|-
| Deciding to invest
| Reward signals, fear signals, status cues
| Explicit risk reasoning
|-
| Facing illness
| Pain, fear, immune signals
| Meaning, treatment plan, decision
|-
| Driving in routine traffic
| Lane keeping, speed regulation, hazard scanning
| Conversation, navigation choice
|}

The conscious mind in each case is selecting from and integrating across the unconscious mind's outputs. The unconscious is doing most of the work; consciousness is choosing what to do with the work.

== What the framework does not claim ==

The framework does not solve the hard problem of consciousness. Why integration into the self-model produces phenomenal experience rather than just functional integration is a question the framework does not answer. The structural account explains why a covolving information object would have two cognitive modes and what each is for; it does not explain why there is something it is like to be in either mode.

The framework does not claim that all unconscious processing is the same kind of thing. The unconscious of routinized motor skills is structurally different from the unconscious of dream content, which is different from the unconscious of implicit emotional learning. The framework's reading is that they are all examples of switching that bypasses the self-model, but the substantive content of each differs.

The framework does not adopt a specific adaptationist story for why consciousness evolved. Adaptationist explanations of consciousness are notoriously hard to defend. The framework can say that complex social-symbolic organisms benefit from an integrated self-modeling layer, but it does not claim to know why consciousness specifically evolved or whether it is a single evolutionary innovation or several.

The framework's account is broadly consistent with Global Workspace theory, with predictive processing accounts of self-modeling, and with the higher-order theories of consciousness. It does not adjudicate among these competing accounts of how consciousness works at the neural level.

== Honest limits ==

The framework's account of consciousness is structural and high-level. It does not specify which neural mechanisms implement the self-modeling integration layer, how the salience competition is actually adjudicated in real brains, or what determines which unconscious content reaches consciousness in any given moment. These are empirical questions for neuroscience and computational psychiatry, not questions the framework answers.

The boundary between unconscious and conscious is not as sharp as the page may suggest. There are intermediate states (peripheral awareness, fringe consciousness, marginal attention) that are neither fully unconscious nor fully integrated. The two-mode account is a simplification of what may be a graded phenomenon.

The framework's treatment of consciousness in non-human animals is undeveloped. Many species exhibit some of the functions the framework attributes to consciousness (self-recognition, planning, social inference, communication), but the framework does not yet say whether these constitute consciousness in the framework's sense or something more limited. This is a substantial gap.

== Short version ==

We have an unconscious mind because life requires massive hidden computation that would overwhelm any integration layer. We have a conscious mind because complex social-symbolic life benefits from a layer in which selected information becomes globally integrated, reportable, and deliberately switchable. Mental illness frequently involves failures in the coupling between the two layers.

== See also ==

Switch Information object Horon Symvironment Covolution Covolution and disease Cybernetic attractor Recursive hierarchical switching

External traditions the framework engages with:

*Baars, B. (1988). ''A Cognitive Theory of Consciousness''. Cambridge University Press.
*Dehaene, S. (2014). ''Consciousness and the Brain''. Viking.
*Friston, K. (2010). The free-energy principle: a unified brain theory? ''Nature Reviews Neuroscience''.
*Hohwy, J. (2013). ''The Predictive Mind''. Oxford University Press.
*Tononi, G. (2008). Consciousness as integrated information: a provisional manifesto. ''Biological Bulletin''.
*Chalmers, D. (1995). Facing up to the problem of consciousness. ''Journal of Consciousness Studies''.

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Covolution and diseases

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Covolution and diseases

Jongbhak — Wed, 20 May 2026 13:44:11 GMT

Created page with " = Covolution and disease = '''Status: Working.''' The core concepts are stable. The framework still requires mathematical formalization, molecular mapping, and empirical val..."

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= Covolution and disease =

'''Status: Working.''' The core concepts are stable. The framework still requires mathematical formalization, molecular mapping, and empirical validation before it can do clinical work.

== Core claim ==

In covolution theory, disease is a breakdown of switching coherence across the levels of a biological information object.

An organism remains alive because its switching architecture coordinates computing units across molecular, cellular, tissue, and organismal levels. Disease occurs when this coordination breaks down. This may happen when some computing units begin to act according to local functional priorities rather than organism-level coherence; when regulatory switches fail to register relevant inputs; when distinct biological states become confused; when stable states cannot be maintained; or when couplings between levels become miswired, weakened, or pathological.

Damage and disease are not the same thing. Many diseases involve molecular damage, but damage becomes disease when it disrupts the organism's capacity to maintain coherent switching across levels. The framework's distinctive claim is not that damage is irrelevant but that the conversion of damage into disease is itself a structural phenomenon: it depends on which switching functions are affected, at which levels, and whether coherence can be restored.

Disease, in this framing, is misaligned, degraded, or runaway biological computation within a living information object.

== Disease and the four switching functions ==

Covolution theory describes biological regulation through four switching functions: respond, distinguish, hold, and couple. Disease can emerge when any of these fails.

{|
|-
! scope="col" | Switching function
! scope="col" | Disease failure
! scope="col" | Example
|-
| Respond
| A system fails to register or react to relevant inputs
| Nutrient-sensing pathways fail to respond in metabolic disease
|-
| Distinguish
| A system loses the ability to separate biologically meaningful states
| Immune self/non-self distinction fails in autoimmunity
|-
| Hold
| A system cannot maintain or return to an appropriate state
| Chronic inflammatory activation fails to return to baseline
|-
| Couple
| Regulatory links between switches break, weaken, or rewire
| Cancer cells decouple from organism-level growth control
|}

These four failure modes are also central to the gerostasis framework, so aging and disease share a common structural vocabulary. Aging is cumulative switching-coherence decline; disease is localized, acute, chronic, or systemic switching-coherence failure. They differ mainly in timescale, reversibility, localization, and severity.

== Typology of diseases as switching failures ==

{|
|-
! scope="col" | Disease type
! scope="col" | Main switching failure
! scope="col" | Covolutionary interpretation
|-
| Cancer
| Coupling failure
| Cells compute for local proliferation rather than organismal coherence
|-
| Autoimmunity
| Distinguish failure
| Immune systems misclassify self, non-self, or altered-self
|-
| Chronic inflammation
| Hold failure
| Inflammatory switches fail to return to baseline
|-
| Neurodegeneration
| Couple and hold failure
| Neural networks lose connectivity, memory stability, and state maintenance
|-
| Metabolic disease
| Respond and couple failure
| Nutrient, energy, insulin, and mitochondrial signals become misread or miscoordinated
|-
| Infection
| Information-object conflict
| Pathogen information objects invade and consume host switching capacity
|-
| Fibrosis
| Hold and couple failure
| Repair programs fail to terminate and become tissue-distorting attractors
|-
| Mental illness
| Cognitive switching failure
| Respond, distinguish, hold, or couple failure in neural and behavioral switching
|-
| Aging-related decline
| Multi-function failure
| Respond, distinguish, hold, and couple functions degrade together
|}

This typology is not meant to replace medical classification. Most real diseases involve multiple failure modes simultaneously. The purpose is to provide a structural language that can connect diseases normally treated as separate.

== What covolution theory adds ==

The framework offers three things beyond conventional disease accounts.

A unified structural vocabulary. Conventional medicine classifies disease by organ, symptom, pathogen, mutation, or pathway. Covolution theory asks instead: which switching functions have failed, at which biological level, and how has coherence across levels been lost? This allows cancer, autoimmunity, inflammaging, metabolic disease, and neurodegeneration to be compared using the same structural vocabulary.

A bridge between disease and aging. If aging is cumulative gerostatic decline, and gerostasis is the maintenance of switching coherence, then chronic disease and aging are related expressions of the same deeper process: loss of biological information coherence over time. This fits the geroscience view that aging is the major risk factor for many chronic diseases, but covolution theory adds a mechanistic interpretation. Aging increases disease risk because it weakens the organism's ability to respond, distinguish, hold, and couple correctly.

A reframing of treatment. Treatment is not only killing pathogens, removing damaged cells, blocking pathways, or repairing lesions. It is also the restoration of switching coherence.

{|
|-
! scope="col" | Therapeutic question
! scope="col" | Meaning
|-
| Can the system respond correctly again?
| Restore sensitivity to relevant inputs
|-
| Can it distinguish states correctly?
| Restore classification of self/non-self, healthy/damaged, repair/growth
|-
| Can it hold proper states?
| Restore stable homeostasis, quiescence, tolerance, or repair
|-
| Can it couple correctly across levels?
| Restore communication among cells, tissues, organs, immunity, and organism-level regulation
|}

This explains why interventions that improve systemic regulation may affect many diseases at once, while narrowly targeted interventions may fail when the disease is fundamentally architectural.

== Cancer as illustrative case ==

Cancer is one of the clearest examples of covolutionary disease.

A cancer cell is not simply damaged. In many cases it is metabolically active, proliferative, adaptive, and resistant. Its problem is not lack of biological activity but misaligned activity. The cancer cell's local switching architecture continues to operate, often hyperactively, but its coupling to the higher-level organism has failed. It no longer obeys organism-level constraints on proliferation, apoptosis, tissue architecture, immune visibility, metabolic cooperation, spatial boundaries, or developmental identity.

In covolutionary terms, cancer is the emergence of a locally self-serving information object inside a higher-level organismal information object. The cancer cell lineage has partially defected from the organism and created a local attractor of proliferation, immune evasion, angiogenesis, invasion, and metabolic rewiring.

Cancer treatment can be interpreted along several axes within this framing.

{|
|-
! scope="col" | Strategy
! scope="col" | Covolutionary interpretation
|-
| Kill cancer cells
| Remove decoupled information objects
|-
| Restore immune recognition
| Recouple immune surveillance to abnormal cells
|-
| Modify tumor microenvironment
| Restructure the local symvironment that sustains malignancy
|-
| Systemic therapy
| Restore organism-level control over local cellular computation
|}

Immunotherapy is especially compatible with this view, since it attempts to restore immune coupling to cells that had escaped recognition.

== Disease as attractor failure ==

Many diseases can be interpreted as pathological attractors. A healthy organism maintains dynamic stability by moving among viable states. Disease appears when the system falls into a state that is stable but harmful, a configuration sustained by feedback loops that no longer serve organism-level coherence.

{|
|-
! scope="col" | Pathological attractor
! scope="col" | Disease example
|-
| Proliferative attractor
| Cancer
|-
| Inflammatory attractor
| Chronic inflammation, inflammaging
|-
| Fibrotic attractor
| Organ fibrosis
|-
| Auto-reactive attractor
| Autoimmune disease
|-
| Degenerative attractor
| Neurodegeneration
|-
| Insulin-resistant attractor
| Type 2 diabetes and metabolic syndrome
|-
| Senescent-cell attractor
| Aging-related tissue decline
|}

A therapeutic intervention should therefore not only suppress symptoms. It should help move the system from a pathological attractor back into a viable regulatory attractor. This is consistent with the framework's broader treatment of cybernetic attractors and with the geometric language of Waddington landscapes in developmental biology.

== Disease, symvironment, and context ==

Disease is not located only inside the organism. It often emerges from a disturbed relationship between the organism and its symvironment, the biologically meaningful environment the organism senses, modifies, and interprets.

{|
|-
! scope="col" | Symvironmental factor
! scope="col" | Disease relevance
|-
| Diet
| Metabolic switching, microbiome, inflammation
|-
| Pathogens
| Immune activation and host-pathogen conflict
|-
| Toxins
| Damage, repair load, stress responses
|-
| Social stress
| Neuroendocrine and immune regulation
|-
| Microbiome
| Immune education, metabolism, inflammation
|-
| Physical activity
| Energy-information coupling, repair, resilience
|-
| Technology and lifestyle
| Sleep, cognition, metabolism, social regulation
|}

A disease state is often a failure of organism-symvironment covolution, not just an internal defect.

== Infection as information-object competition ==

Infection deserves separate treatment because the framework's reading is genuinely distinctive here.

In conventional language, a pathogen "hijacks" the host. In covolutionary language, an infection is a competing information object that has invaded the host's switching network and is now consuming the host's computational resources to support its own covolution. The pathogen has its own switching architecture, its own predictive models, and its own covolutionary lineage. When it enters a host, two information objects are now sharing the same substrate, and the host's switching coherence is disrupted by the pathogen's competing computation.

This reading has several implications.

The host's immune response is the host's attempt to restore information-object boundaries by eliminating the invading information object. Successful immunity returns the substrate to the host's switching network.

Chronic infections are cases where the competing information object has established stable coupling within the host without being eliminated. The two information objects covolve, often pathologically.

Symbiotic relationships are cases where competing information objects have negotiated mutually compatible switching, with the boundaries between host and partner becoming functionally permeable in specific channels. The microbiome occupies this category.

Antibiotic resistance and viral evolution can be read as the pathogen's covolutionary response to selective pressure from the host's defenses. Pathogens are themselves engaged in covolution; the host-pathogen relationship is a covolutionary interaction between competing information objects, not a one-way attack.

This reading does not change the medical biology of infection but does change the conceptual framing in ways that connect infection to the framework's broader treatment of information objects.

== Mental illness ==

Mental illness fits the framework's account but operates at the level of cognitive horons rather than primarily molecular ones. Cognitive switching networks (attention states, recognition states, decision points, belief states, memory states) can exhibit the same four-function failures as molecular switching networks.

{|
|-
! scope="col" | Mental illness
! scope="col" | Cognitive switching failure
|-
| Depression
| Hold failure: negative cognitive states cannot be returned to baseline
|-
| Anxiety disorders
| Respond failure: threat-detection switches fire on inputs outside their proper class
|-
| Psychosis
| Distinguish failure: state-space of perceptual and conceptual categories collapses
|-
| Addiction
| Couple failure: reward switches decouple from organismal regulation
|-
| PTSD
| Hold failure: trauma-activated states cannot return to baseline, intrude into present cognition
|}

These mappings are suggestive rather than diagnostic. The framework's claim is not that mental illnesses reduce to single failure modes but that the four-function vocabulary applies to cognitive horons as it applies to molecular ones. The substrate (neural and behavioral switching) differs from molecular substrates, but the functional logic is the same.

Mental illness has been historically separated from physical illness by both medical practice and ordinary discourse. The covolution framework offers no support for this separation. Both are switching failures in information objects, differing in the substrates where failures occur but not in their structural character.

== Iatrogenic disease ==

A framework that claims to address disease structurally should be able to say what makes iatrogenic harm distinctive. Iatrogenic disease is, in the framework's reading, medicine inducing switching failures rather than correcting them.

This can occur in several ways. A drug intended to restore one switching function may degrade another. Surgical intervention to remove a decoupled information object (a tumor) may also damage healthy switching coherence. Treatments that suppress symptoms by blocking pathological switching may also block adaptive switching in the same pathway. Polypharmacy in aging patients can produce switching incoherence as multiple interventions interact in ways that no single intervention was designed to handle.

The framework's distinctive observation is that effective medicine requires attention not only to the targeted switching failure but to the broader switching coherence that the intervention will affect. Narrowly conceived interventions that correct one failure while inducing others are not making the patient more covolutionary coherent; they are trading one form of incoherence for another. This is consistent with established medical wisdom about side effects but provides a structural reason for treating side effects as central to therapeutic evaluation rather than peripheral to it.

== Gerostasis and disease ==

Gerostasis is the organism's capacity to maintain switching coherence across time. During youth and maturity, the organism usually maintains high coherence: damage is sensed, repair is activated, inflammation resolves, stem cells remain appropriately poised, cells preserve identity, tissues communicate, and immune recognition remains calibrated.

With aging, these functions degrade.

{|
|-
! scope="col" | Gerostatic decline
! scope="col" | Disease consequence
|-
| Poor damage sensing
| Cancer, degeneration
|-
| Failed repair termination
| Fibrosis, chronic inflammation
|-
| Identity drift
| Cancer, tissue dysfunction
|-
| Immune misclassification
| Autoimmunity, inflammaging
|-
| Loss of stem-cell quiescence
| Regenerative failure
|-
| Network uncoupling
| Frailty, multimorbidity
|}

Aging increases disease risk because it weakens the organism's ability to maintain coherent multi-level computation.

== Honest limits ==

The framework is currently conceptual.

Saying that cancer is a coupling failure does not yet identify which molecular couplings matter most in a given patient. It does not tell a clinician which drug to use, which biomarker to measure, or which intervention will restore coherence. Existing disease classifications are supported by large bodies of medical evidence that covolution theory should not displace prematurely. The framework's value is to provide an integrative layer above existing classifications, not to replace them.

The four-function decomposition requires empirical validation. It must be mapped onto measurable biological variables: gene-regulatory networks, immune states, epigenetic drift, cell-cell communication, tissue architecture, multi-omics signatures, and clinical phenotypes.

The mental illness mapping is preliminary. Translating cognitive switching failures into operational measures requires methods that bridge molecular psychiatry and clinical phenomenology. This work has not been done.

The framework becomes scientifically useful only when it can generate testable predictions, measurable indices, and intervention strategies that competing accounts do not.

== Testable directions ==

{|
|-
! scope="col" | Hypothesis
! scope="col" | Possible measurement
|-
| Diseases can be classified by dominant switching failure mode
| Multi-omics, imaging, immune profiling, clinical phenotypes
|-
| Aging increases disease risk by reducing switching coherence
| Longitudinal geromic and phenotypic data
|-
| Cancer progression reflects progressive decoupling from organismal control
| Tumor microenvironment, immune evasion, spatial transcriptomics
|-
| Chronic inflammation reflects hold failure
| Time-series cytokine and NF-κB pathway dynamics
|-
| Autoimmunity reflects distinguish failure
| T-cell/B-cell repertoire, autoantibody profiles, tolerance markers
|-
| Mental illness reflects cognitive switching failure
| Computational psychiatry measures of belief updating, attention, reward
|-
| Rejuvenation restores switching coherence
| GeroIndex/GeroActionScore before and after intervention
|}

== Concise definition ==

Disease is a failure of switching coherence within or between biological information objects, causing local computation, regulatory state, or symvironmental interaction to become misaligned with organism-level viability.

== Short version ==

Disease is misaligned biological computation. Aging is cumulative loss of switching coherence.

== See also ==

Switch Gerostasis Horon dissolution Cybernetic attractor Symvironment Information object Covolution What is life?

Informational jump of critical mass of computing units in species expansion

Jongbhak — Wed, 20 May 2026 13:36:42 GMT

Created page with " = Informational jump of critical mass of computing units in species expansion = '''Status: Brainstorm.''' This page articulates an exploratory hypothesis. Key terms (computi..."

New page

= Informational jump of critical mass of computing units in species expansion =

'''Status: Brainstorm.''' This page articulates an exploratory hypothesis. Key terms (computing-unit density, threshold, switching capacity at the lineage level) are not yet operationalized, and the historical examples have not been worked through against the published literature on major transitions. Statements here should be read as preliminary.

== Short version ==

Species expand when they cross informational thresholds. These thresholds are crossed when the lineage's distributed information-processing capacity becomes numerous, connected, switch-rich, and energetically supported enough to produce a new level of covolutionary organization. Darwinian selection filters the resulting possibilities; informational jumps expand the space of possibilities itself.

== The core hypothesis ==

A ''biological lineage'' gains expansion capacity through phase transitions in its distributed information-processing architecture. The hypothesis is that these transitions occur when the joint switching capacity of the lineage crosses a threshold, producing a discontinuous rather than gradual change in what the lineage can do.

Three claims are made jointly:

The relevant variable is the lineage's aggregate information-processing capacity, distributed across its molecular, cellular, organismal, and (where applicable) social and cultural levels.

There is a threshold in this aggregate capacity below which certain covolutionary dynamics cannot be sustained and above which they can.

Crossing the threshold produces an informational jump: a discontinuous shift to a new regime in which previously inaccessible covolutionary trajectories become available.

The hypothesis stands or falls on whether the aggregate capacity is a well-defined quantity, whether the threshold is real, and whether the transitions are actually discontinuous rather than gradual when examined carefully.

== What is a computing unit? ==

A ''computing unit'' is any biological or informational component capable of receiving input, transforming state, and contributing to the system's regulation, memory, or action. In the framework's vocabulary, computing units are operationally complete switches (in the four-function sense) embedded in encapsulated information objects.

Computing units exist at multiple levels of organization, with each level potentially contributing to the lineage's aggregate capacity.

{|
|-
! scope="col" | Level
! scope="col" | Computing unit
! scope="col" | What it computes
|-
| Molecular
| Gene, RNA, protein, metabolite
| Regulatory and biochemical states
|-
| Cellular
| Cell
| Survival, division, differentiation, repair
|-
| Tissue
| Cell network
| Coordination, signaling, structure
|-
| Neural
| Neuron, circuit
| Prediction, behavior, memory
|-
| Immune
| Immune cell, receptor clone
| Self/non-self distinction, threat response
|-
| Organismal
| Brain-body system
| Movement, choice, reproduction, learning
|-
| Social
| Individual organism
| Communication, cooperation, conflict
|-
| Cultural
| Language, symbol, institution
| Collective memory and coordination
|-
| Technological
| Tool, computer, AI agent
| Externalized computation and prediction
|}

A computing unit need not be conscious. It only needs to process information in a way that affects system behavior. The framework's claim is that the same four-function test applies at all these levels, with substrate-specific implementation but substrate-independent functional logic.

== Critical mass ==

''Critical mass'' is a metaphor borrowed from nuclear physics, where it names the amount of fissile material required to sustain a chain reaction. The framework's use is metaphorical: it names the structural feature that a process requires a threshold quantity of the relevant material before it can sustain itself. The framework does not claim informational jumps follow nuclear-reaction dynamics.

Before critical mass: units act locally, responses are limited, coordination is weak, switching options are few, expansion is constrained.

After critical mass: units become networked, feedback becomes recursive, memory becomes stabilizable, prediction improves, switching capacity increases, new ecological or evolutionary spaces become reachable.

Critical mass is therefore not just about having more parts. It is about having enough connected computing units to produce a new level of organization, with the connectivity being as important as the count.

== Informational jump ==

An ''informational jump'' is a discontinuous transition in the lineage's covolutionary dynamics following the crossing of critical mass. It is informational rather than purely demographic: what changes is not just the number of organisms but the species' capacity to accumulate and elaborate informational structure.

The jump is characterized by three concurrent changes.

A change in switching density. The lineage crosses from one regime to a regime in which switching architecture accumulates faster, supports more recursive hierarchical organization, and admits more elaborate predictive coupling with the symvironment.

A change in symvironmental coupling. The lineage's relationship with its symvironment shifts from occupying an existing niche toward actively constructing new niches, modifying its symvironment more pervasively, and engaging in covolutionary loops at wider scales.

A change in the rate of further expansion. The lineage becomes capable of further informational jumps that were not accessible from the prior regime. The first jump opens the path to subsequent ones.

These three changes are not independent. They are aspects of the same transition viewed from different angles.

== Candidate historical examples ==

The framework can offer candidates without claiming the hypothesis has been validated for any of them. Each candidate is a case the hypothesis would need to account for if it is correct, and where the hypothesis would face refutation if the transition turns out to be gradual rather than discontinuous, or to lack the structural threshold-crossing the framework predicts.

{|
|-
! scope="col" | Candidate transition
! scope="col" | Suggested new capacity
! scope="col" | Engagement with existing literature
|-
| Chemical network → cell
| Self-maintenance and boundary formation
| Origin-of-life research, autopoiesis
|-
| Prokaryote → eukaryote
| Internal compartmental computation
| Endosymbiotic theory (Margulis); major transitions (Maynard Smith and Szathmáry)
|-
| Single cell → multicellularity
| Division of labor among cells
| Multiple independent origins; major-transitions literature
|-
| Simple nervous system → integrated brain
| Fast integrated prediction
| Neural evolution; predictive processing
|-
| Individual → social learning
| Transmissible behavioral memory
| Cultural evolution (Tomasello, Boyd)
|-
| Vocal signals → language
| Symbolic coordination
| Language origins; major-transitions literature
|-
| Tool use → cumulative technology
| Externalized switching capacity
| Collective brain hypothesis (Henrich)
|}

The major-transitions framework of Maynard Smith and Szathmáry (1995) is the closest existing relative of this hypothesis. The framework's contribution would be to identify a common structural feature (threshold-crossing in distributed switching architecture) across transitions that Maynard Smith and Szathmáry treated case by case. Whether this common structure actually exists, or whether the transitions are too heterogeneous to unify, is the empirical question the framework would need to answer.

The hypothesis is more careful than the major-transitions framework in one respect (it specifies threshold-crossing dynamics rather than just listing transitions) and less developed in another (Maynard Smith and Szathmáry engaged the specific biology of each transition in detail; this framework currently does not).

== Computing-unit density ==

The central variable is the lineage's ''computing-unit density'' (CUD): not just the number of computing units but their connectivity, diversity, switching capacity, and memory stability.

The intuition can be stated qualitatively. A system with many poorly connected units may remain weak. A system with fewer but highly connected and switch-rich units may become powerful. The relevant quantity combines count, connectivity, and capacity.

The framework does not yet provide an operational definition of CUD that would allow it to be measured for real lineages. This is the most important gap in the hypothesis. Without an operational definition, the threshold cannot be located, and the central empirical claim cannot be tested. Moving the hypothesis toward Working status requires developing a defensible counting and measurement procedure, even an approximate one.

Note that this framework does ''not'' propose a multiplicative formula combining count, connectivity, switching, memory, and energy. Earlier formulations attempted this, but a multiplicative formula without specified units, functional form, or empirical anchor commits to nothing while appearing rigorous. The honest current position is that CUD captures an intuition that has not yet been formalized. The formalization is open work.

== Capacities that increase with informational jumps ==

A lineage that crosses an informational threshold acquires new operational capacities. These can be described informally.

{|
|-
! scope="col" | Capacity
! scope="col" | Meaning
|-
| Sense
| Detect more relevant differences
|-
| Interpret
| Assign meaning to signals
|-
| Predict
| Anticipate future states
|-
| Switch
| Choose among alternative responses
|-
| Coordinate
| Align many units into collective action
|-
| Remember
| Preserve useful states across time
|-
| Transmit
| Pass information across generations
|-
| Construct
| Modify its own symvironment
|}

These capacities allow the lineage to enter new environments, resist perturbations, exploit resources, form social systems, and create new ecological roles. In Darwinian language, expansion may look like increased fitness. In covolutionary language, expansion reflects increased ''informational reach'': the capacity of a lineage to detect, model, and act upon a larger portion of its possible symvironment.

== Relationship to Darwinism ==

Darwinian evolution explains how heritable variants are filtered by survival and reproduction. Covolutionary informational-jump theory asks a different question: how does a lineage become capable of generating, coordinating, and stabilizing more possible variants, behaviors, and futures?

Darwinism emphasizes filtering. Informational-jump theory emphasizes capacity generation. Both are compatible.

{|
|-
! scope="col" | Darwinian view
! scope="col" | Covolutionary extension
|-
| Variation appears
| Information systems generate structured variation
|-
| Selection filters
| Symvironmental feedback stabilizes or rejects states
|-
| Fitness increases
| Informational reach and switching capacity expand
|-
| Adaptation accumulates
| Computing-unit networks cross thresholds
|-
| Species diversify
| Lineages enter new phase spaces
|}

A fair formulation: ''Darwinian selection filters biological possibilities; informational jumps expand the space of possibilities available to be filtered.''

This is consistent with the covolution framework's broader position that selection is one phase of a closed loop rather than the entire dynamics of evolution. The informational jump is a feature of the loop's structural changes, not a denial of selection.

== Relationship to aging and disease ==

The framework treats aging as the reverse trajectory of a healthy informational jump. Development builds computing-unit critical mass; aging erodes its coherence. During development, computing units become organized, connected, and coordinated. During aging, switching precision declines, repair systems lose efficiency, cellular states become noisy, tissue communication degrades, immune computation distorts, stem-cell decisions impair, and systemic gerostasis advances.

Rejuvenation, in this framing, aims to restore informational organization and switching precision. This connects informational-jump theory to the framework's account of gerostasis and to applied frameworks like GeroIndex and GeroActionScore.

Disease can occur when computing units become misaligned with the higher-level information object they belong to. Examples include cancer (cells compute for local proliferation rather than organismal coherence), autoimmunity (immune units misclassify self as enemy), neurodegeneration (neural units lose network integrity), and metabolic disease (energy-information coupling becomes distorted). The framework's reading is that treatment means restoring the correct level of informational integration.

These connections to aging and disease are suggestive rather than worked out. The pages on gerostasis and horon dissolution develop them more carefully.

== Important distinction: complexity versus informational jump ==

An informational jump is not the same as a complexity increase. A system can become more complicated without becoming more effectively alive, adaptive, or expansive. For an informational jump to matter in the framework's sense, it must increase coordinated computation, viable switching, memory transmission, predictive control, symvironmental reach, and lineage-level stability.

Complexity alone is not enough. Functional, coordinated, transmissible computation is required. This distinction is what prevents the hypothesis from being trivially confirmed by the observation that organisms are complex.

== What the hypothesis predicts ==

If the hypothesis is correct, several predictions follow.

Major species expansions should show discontinuous rather than gradual signatures in the fossil and historical record, with relatively rapid acceleration after long stable phases rather than smooth trajectories. This is consistent with what is observed in some major transitions but requires careful disambiguation from sampling artifacts.

The threshold should be associated with structural changes in the lineage's switching architecture, not just demographic ones. Lineages that grow in numbers without crossing the structural threshold should not show the expansion dynamics; lineages that cross the threshold even at modest population size should.

Different lineages crossing analogous thresholds should show convergent dynamics. The shape of the transition should be similar across substrates, and substrate-specific details should be less important than the structural threshold-crossing.

Once a threshold is crossed, the lineage should become capable of subsequent jumps. The first jump opens the path; lineages that cross one threshold should be more likely to cross subsequent thresholds than lineages that have not crossed any.

These predictions are demanding and have not been tested directly. The hypothesis identifies them as the empirical work that would either support or refute the framework's claim.

== What is missing ==

The hypothesis has substantial gaps that must be acknowledged honestly.

''Operational definition of computing-unit density.'' Without a defensible counting and measurement procedure, CUD remains metaphorical and the threshold cannot be located. This is the most consequential gap.

''Engagement with the major-transitions literature.'' Maynard Smith and Szathmáry, Tomasello, Henrich, and others have developed sophisticated accounts of the candidate transitions. The framework should be in conversation with these accounts rather than freestanding.

''Demonstration with at least one worked case.'' For the hypothesis to mature, at least one candidate transition needs to be analyzed in enough detail to estimate the relevant switching architecture before and after, and to identify the threshold that was crossed. Without this, the framework lacks an exemplar that demonstrates the hypothesis can be made operational.

''Sharpening of predictions to distinguish from neighboring claims.'' The predictions above are consistent with several existing frameworks. The hypothesis needs to specify what it predicts that other accounts do not.

''Quantitative versus qualitative formulation.'' The earlier brainstorm attempted a formula (CUD = number × diversity × connectivity × switching capacity × memory stability) that does not survive scrutiny. The framework's honest current position is qualitative. A genuine quantitative formulation would require empirical work that has not been done.

These are real gaps. The Brainstorm status reflects them. Moving the page toward Working would require closing at least the first two of them.

== Open questions ==

Several questions remain open within the hypothesis itself.

What determines the threshold? Is it a property of the substrate, of the symvironment, or of the interaction between them?

Are jumps reversible? Once a lineage has crossed a threshold and entered a new regime, can it fall back below the threshold under sufficient pressure or disruption?

How does the hypothesis relate to gerostasis at the species level? Just as individual horons undergo gerostasis, lineages may undergo something analogous as their aggregate switching architecture loses integrity.

What is the relationship between species-level thresholds and individual-level cognitive or behavioral thresholds? Does individual capacity matter, or only aggregate species capacity?

How should the framework handle near-misses: lineages that approached but did not cross a threshold? If these can be identified, they provide empirical leverage for testing the framework's claims.

== Concise final definition ==

The informational jump of critical mass of computing units is the covolutionary phase transition by which a lineage gains enough connected information-processing components, at the relevant levels of organization, to create new levels of prediction, switching, coordination, memory, and symvironmental reach, thereby enabling species expansion. The transition is hypothesized to be discontinuous rather than gradual, with the discontinuity arising from threshold-crossing in the lineage's aggregate switching architecture.

== See also ==

Covolution Symvironment Recursive hierarchical switching Information object Horon Gerostasis What is life?

External literature the hypothesis engages with:

*Maynard Smith, J. and Szathmáry, E. (1995). ''The Major Transitions in Evolution''. Oxford University Press.
*Tomasello, M. (1999). ''The Cultural Origins of Human Cognition''. Harvard University Press.
*Henrich, J. (2015). ''The Secret of Our Success: How Culture Is Driving Human Evolution''. Princeton University Press.
*Scheffer, M. (2009). ''Critical Transitions in Nature and Society''. Princeton University Press.
*West, G. (2017). ''Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life''. Penguin Press.
*Margulis, L. (1970). ''Origin of Eukaryotic Cells''. Yale University Press.
*Lane, N. (2015). ''The Vital Question: Energy, Evolution, and the Origins of Complex Life''. W. W. Norton.

How to read covolution.org

Jongbhak — Wed, 20 May 2026 13:30:09 GMT

Created page with " = How to read covolution.org = Covolution.org is a working wiki. It contains material at very different stages of development: early brainstorming, ideas under active refine..."

New page

= How to read covolution.org =

Covolution.org is a working wiki. It contains material at very different stages of development: early brainstorming, ideas under active refinement, and pages that have reached a settled disciplined form. To make this transparent to readers, every page on the wiki carries a maturity label at the top.

The labels are not value judgments. Brainstorming is where ideas come from, and the wiki preserves the brainstorming alongside the refined material rather than discarding it. The labels exist so readers can tell what they are reading and adjust their expectations accordingly.

== The three labels ==

'''Brainstorm.''' The page is at an early exploratory stage. The author has put down ideas to think with, not finished positions. Statements on the page may be hasty, may overreach, may contradict other pages on the wiki, and may use language the disciplined framework has elsewhere disavowed. Brainstorm pages are valuable as records of where ideas come from and as raw material for later refinement. They should not be cited as the framework's settled position, and readers should treat their claims with caution.

'''Working.''' The page is under active development. Its content engages the disciplined framework and has been thought through more carefully than brainstorm material, but the formulation is not yet settled. Statements on the page are intended to be defensible but may change as the framework matures. Working pages are appropriate for substantive engagement, with the understanding that revisions are likely.

'''Stable.''' The page reflects the disciplined version of the framework. Its content has been tested against alternatives, criticism, and internal coherence with the rest of the wiki. Stable pages are intended to be defensible against serious scrutiny and are suitable for citation. They will still be revised when the framework develops further, but the revisions will be incremental rather than wholesale.

== How to use the labels ==

If you are looking for the framework's considered position on a topic, read the Stable and Working pages.

If you are interested in the framework's intellectual genealogy or want to see where ideas are forming, read the Brainstorm pages. These show the framework in motion rather than at rest.

If you encounter a contradiction between a Brainstorm page and a Stable page, the Stable page reflects the framework's current disciplined position. The Brainstorm page is preserved because the earlier formulation is part of the project's history, not because it is currently endorsed.

If you are a scientific reviewer evaluating the framework, the Stable pages and the disciplined Working pages are the material against which the framework should be assessed. Brainstorm pages are not yet ready for that kind of evaluation and should be read as such.

== What the labels do not mean ==

Brainstorm does not mean wrong. Some brainstorming ideas are correct and become the seeds of stable positions. Others turn out to be hasty and are eventually retired. The label only says that the page has not yet been through the refinement process.

Stable does not mean final. The framework will continue to develop, and pages currently labeled Stable will be revised. Stable means the page reflects the disciplined version of the framework at the present time, not that the page will never change.

The labels are not a strict hierarchy. A page can move from Brainstorm to Working to Stable as it matures, but a page can also move from Stable back to Working if a revision opens a question that was previously thought settled. The labels track the current state of each page, not its destiny.

== Why this wiki uses this system ==

Covolution Theory and biosophy are long-running intellectual projects that combine speculative exploration with careful scientific and philosophical work. The two activities feed each other: brainstorming generates the material that careful work refines, and careful work suggests new directions for brainstorming. Both activities need to be visible.

Most academic publishing collapses these stages into a single output (the finished paper) and hides the brainstorming. Most popular intellectual writing collapses them in the opposite direction (the speculative essay) and hides the careful work. This wiki tries to keep both visible without confusing readers about which is which.

The labels are the operational mechanism for this. They ask the reader to do a small amount of work in understanding what kind of material a given page contains, in exchange for access to a wider range of the project's intellectual life than either pure academic publishing or pure popular writing would provide.

== See also ==

[[Biosophy]] [[Covolution]] [[What is life?]]

Main Page

Jongbhak — Wed, 20 May 2026 13:29:28 GMT

@@ Line 1: / Line 1: @@
-[[Cybernetic Attracgor Decay]]<br/> <br/> <br/> [[Gerotype]]
-*Gerotype = [[노형(老型)]] , [[노상(老相)]]
+[[Cybernetic_Attractor_Decay|Cybernetic Attractor Decay]]<br/> <br/> <br/> [[Gerotype|Gerotype]]
 *GeroType vector = [[노형벡터]]
-*Cybernetic Attractor Decay = [[자율수렴붕괴]]
+*Gerotype = [[노형(老型)|노형(老型)]] , [[노상(老相)|노상(老相)]]

← Older revision		Revision as of 06:03, 25 May 2026
Line 1:		Line 1:
−	'''Martinez-Jimenez et al. 2017.'''<br/> <br/> ''Aging increases cell-to-cell transcriptional variability upon immune stimulation.'' Science 355, 1433–1436.<br/> <br/> With proper single-cell RNA-seq. Martinez-Jimenez (from Marioni and Odom's groups, EMBL-EBI and CRUK Cambridge) profiled naive and activated CD4+ T cells from young and old mice.<br/> <br/> Key findings:<br/> (1) at baseline, young and old T cells looked similar;<br/> (2) under LPS or anti-CD3 stimulation, old cells showed markedly increased cell-to-cell transcriptional heterogeneity, especially in immune-response genes;<br/> (3) the increased variability was not random across the genome but concentrated in genes with cell-type-specific regulatory architecture.<br/> <br/> This shows that computational drift is ''revealed by perturbation'' and ''localised to regulated loci'', which is exactly what cybernetic attractor decay predicts (attractor erosion shows up under stress, not at rest, and concentrates at boundary regions).<br/> <br/>	+
		+	'''Martinez-Jimenez et al. 2017.'''<br/> <br/> ''Aging increases cell-to-cell transcriptional variability upon immune stimulation.'' Science 355, 1433–1436.<br/> <br/> With proper single-cell RNA-seq. Martinez-Jimenez (from Marioni and Odom's groups, EMBL-EBI and CRUK Cambridge) profiled naive and activated CD4+ T cells from young and old mice.<br/> <br/> Key findings:<br/> (1) at baseline, young and old T cells looked similar;<br/> (2) under LPS or anti-CD3 stimulation, old cells showed markedly increased cell-to-cell transcriptional heterogeneity, especially in immune-response genes;<br/> (3) the increased variability was not random across the genome but concentrated in genes with cell-type-specific regulatory architecture.<br/> <br/> This shows that computational drift is ''revealed by perturbation'' and ''localised to regulated loci'', which is exactly what cybernetic attractor decay predicts (attractor erosion shows up under stress, not at rest, and concentrates at boundary regions).<br/> <br/> '''Worth noting.''' This paper interprets the variance increase as stochastic noise.<br/> <br/> [[Covolution framework]] reinterprets it as attractor flattening: the noise rises because the basin walls have eroded, not because the noise source has changed.<br/> <br/>

← Older revision		Revision as of 13:11, 23 May 2026
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−	Civilization is not just genetic evolution. It is a higher-order developmental platform.	+	Civilization is not just genetic evolution. It is a higher-order developmental platform.<br/> <br/>
		+
		+	The Moon landing, science, medicine, and aging intervention are not explained well by blind mutation-selection.<br/> They are better modeled as:
		+	<blockquote data-end="5903" data-start="5821">
		+	'''recursive prediction, memory, planning, error correction, and collective action.'''
		+	</blockquote>
		+	That is Covolution at a cultural-technological scale.

← Older revision		Revision as of 13:08, 23 May 2026
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−	'''Entelenomic Search Theorem.'''<br/> <br/> Let an adaptive system occupy state sts_tst in an environment ete_tet.<br/> <br/> Suppose the environment has temporal structure such that I(et;et+Δ)>0I(e_t;e_{t+\Delta})>0I(et;et+Δ)>0,<br/> and suppose the system contains an internal state hth_tht satisfying I(ht;et+Δ)>0I(h_t;e_{t+\Delta})>0I(ht;et+Δ)>0.<br/> <br/> If state transitions are biased by hth_tht so that expected future viability increases,<br/> then the expected hitting time to a future-adaptive basin is lower than under<br/> an otherwise identical blind mutation-selection process.<br/> Thus, predictive developmental architecture can generate directional adaptive<br/> search without invoking external teleology.	+
		+	<span style="font-size:18px;">'''Entelenomic Search Theorem.'''</span><br/> <br/> <img style="null" src=https://Covolution.org/upload/20260523220808_image.png><br/> <br/> Let an adaptive system occupy state sts_tst in an environment ete_tet.<br/> <br/> Suppose the environment has temporal structure such that I(et;et+Δ)>0I(e_t;e_{t+\Delta})>0I(et;et+Δ)>0,<br/> and suppose the system contains an internal state hth_tht satisfying I(ht;et+Δ)>0I(h_t;e_{t+\Delta})>0I(ht;et+Δ)>0.<br/> <br/> If state transitions are biased by hth_tht so that expected future viability increases,<br/> then the expected hitting time to a future-adaptive basin is lower than under<br/> an otherwise identical blind mutation-selection process.<br/> Thus, predictive developmental architecture can generate directional adaptive<br/> search without invoking external teleology.

← Older revision		Revision as of 13:07, 23 May 2026
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−	<img style="null" src=https://Covolution.org/upload/20260523220646_image.png>	+
		+	<img style="null" src=https://Covolution.org/upload/20260523220646_image.png><br/> <br/> [[Entelenomic Search Theorem]]

@@ Line 6: / Line 6: @@
 {| border="1"
 |-
-! scope="col" | Code
+! scope="col" style="width: 68px;" | Code
-! scope="col" | Name
+! scope="col" style="width: 310px;" | Name
-! scope="col" | CAD interpretation
+! scope="col" style="width: 640px;" | CAD interpretation
 |-
-| '''G-I'''
+| style="width: 68px;" | '''G-I'''
-| Substrate Gerotype (基質老型, 기질노형)
+| style="width: 310px;" | &nbsp;Substrate Gerotype (基質老型, 기질노형)
-| Failure to maintain the physical-informational carrier of the attractor
+| style="width: 640px;" | Failure to maintain the physical-informational carrier of the attractor
 |-
-| '''G-II'''
+| style="width: 68px;" | '''G-II'''
-| Regulatory Gerotype (調節老型, 조절노형)
+| style="width: 310px;" | &nbsp;Regulatory Gerotype (調節老型, 조절노형)
-| Failure to maintain the control loops that sustain the attractor
+| style="width: 640px;" | Failure to maintain the control loops that sustain the attractor
 |-
-| '''G-III'''
+| style="width: 68px;" | '''G-III'''
-| Coupling Gerotype (共調老型, 공조노형)
+| style="width: 310px;" | &nbsp;Coupling Gerotype (共調老型, 공조노형)
-| Failure to maintain the BiO-symvironment exchange that defines the attractor
+| style="width: 640px;" | Failure to maintain the BiO-symvironment exchange that defines the attractor
 |-
-| '''G-IV'''
+| style="width: 68px;" | '''G-IV'''
-| Lineage Gerotype (繼承老型, 계승노형)
+| style="width: 310px;" | &nbsp;Lineage Gerotype (繼承老型, 계승노형)
-| Failure to maintain the generative renewal of the attractor across cell and tissue generations
+| style="width: 640px;" | Failure to maintain the generative renewal of the attractor across cell and tissue generations
 |}

← Older revision		Revision as of 13:06, 23 May 2026
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−	Darwinian selection is a valid filtering process, but it is not always the full generator of adaptive direction.<br/> In systems with internal memory, developmental architecture, and predictive<br/> organism-symvironment coupling, adaptive search can become internally biased<br/> before external selection completes its filtering role.<br/> <br/>	+
		+	Darwinian selection is a valid filtering process, but it is not always the full generator of adaptive direction.<br/> In systems with internal memory, developmental architecture, and predictive<br/> organism-symvironment coupling, adaptive search can become internally biased<br/> before external selection completes its filtering role.<br/> <br/> [[Covolution theory theorem]]<br/> <br/>

@@ Line 2: / Line 2: @@
 = How to read covolution.org =
-Covolution.org is a working wiki. It contains material at very different stages of development: early brainstorming, ideas under active refinement, and pages that have reached a settled disciplined form. To make this transparent to readers, every page on the wiki carries a maturity label at the top.
+Covolution.org is a working wiki!<br/> <br/> It contains material at very different stages of development: early brainstorming, ideas under active refinement, and pages that have reached a settled disciplined form.<br/> To make this transparent to readers, every page on the wiki carries a maturity label at the top.
 The labels are not value judgments. Brainstorming is where ideas come from, and the wiki preserves the brainstorming alongside the refined material rather than discarding it. The labels exist so readers can tell what they are reading and adjust their expectations accordingly.
@@ Line 42: / Line 42: @@
 == See also ==
-[[Biosophy]]<br/> [[Covolution]]<br/> [[What is life?]]
+[[Biosophy|Biosophy]]<br/> [[Covolution|Covolution]]<br/> [[What_is_life?|What is life?]]<br/> [[BioLicense]]<br/> &nbsp;

@@ Line 1: / Line 1: @@
 = Scale-recursive organization in covolution =
 {| border="1" data-end="1202" data-start="312"
@@ Line 10: / Line 14: @@
 | data-col-size="sm" data-end="380" data-start="358" | '''Scale-recursive'''
 | data-col-size="md" data-end="425" data-start="380" | Similar logic repeats across nested levels
-| data-col-size="sm" data-end="449" data-start="425" | Best for your theory
+| data-col-size="sm" data-end="449" data-start="425" | The most general in covolution theory
 |- data-end="533" data-start="450"
 | data-col-size="sm" data-end="479" data-start="450" | '''Cross-scale recurrence'''
 | data-col-size="md" data-end="515" data-start="479" | A pattern reappears across scales
-| data-col-size="sm" data-end="533" data-start="515" | Clear and safe
+| data-col-size="sm" data-end="533" data-start="515" | Emphasizing cross-scale organization
 |- data-end="634" data-start="534"
 | data-col-size="sm" data-end="562" data-start="534" | '''Multiscale recurrence'''
 | data-col-size="md" data-end="609" data-start="562" | Recurrence across multiple biological levels
-| data-col-size="sm" data-end="634" data-start="609" | Scientific and modest
+| data-col-size="sm" data-end="634" data-start="609" | Recurrence at multiscale biological layers
 |- data-end="738" data-start="635"
 | data-col-size="sm" data-end="665" data-start="635" | '''Recursive composability'''
 | data-col-size="md" data-end="717" data-start="665" | Lower-level units combine into higher-level units
-| data-col-size="sm" data-end="738" data-start="717" | Good for switches
+| data-col-size="sm" data-end="738" data-start="717" | Low to high level recursion
 |- data-end="826" data-start="739"
 | data-col-size="sm" data-end="759" data-start="739" | '''Scale-similar'''
 | data-col-size="md" data-end="801" data-start="759" | Similar but not identical across scales
-| data-col-size="sm" data-end="826" data-start="801" | Safe, but less strong
+| data-col-size="sm" data-end="826" data-start="801" | Like fractal
 |- data-end="905" data-start="827"
 | data-col-size="sm" data-end="846" data-start="827" | '''Fractal-like'''
 | data-col-size="md" data-end="875" data-start="846" | Self-similar across scales
-| data-col-size="sm" data-end="905" data-start="875" | Useful metaphor, but risky
+| data-col-size="sm" data-end="905" data-start="875" | Metaphor but needs formal definition
 |- data-end="1005" data-start="906"
 | data-col-size="sm" data-end="931" data-start="906" | '''Scale-transposable'''
 | data-col-size="md" data-end="971" data-start="931" | Logic can be translated across levels
-| data-col-size="sm" data-end="1005" data-start="971" | Interesting, but less standard
+| data-col-size="sm" data-end="1005" data-start="971" | Biological (akin to biological transposble elements)
 |- data-end="1112" data-start="1006"
 | data-col-size="sm" data-end="1045" data-start="1006" | '''Substrate-independent recurrence'''
 | data-col-size="md" data-end="1093" data-start="1045" | Same logic, different material implementation
-| data-col-size="sm" data-end="1112" data-start="1093" | Good but longer
+| data-col-size="sm" data-end="1112" data-start="1093" | &nbsp;
 |- data-end="1202" data-start="1113"
 | data-col-size="sm" data-end="1144" data-start="1113" | '''Hierarchically recurrent'''
 | data-col-size="md" data-end="1182" data-start="1144" | Recurs through biological hierarchy
-| data-col-size="sm" data-end="1202" data-start="1182" | Good for biology
+| data-col-size="sm" data-end="1202" data-start="1182" | Biological (Earth) interpretation of scale-recursive organization
 |}

@@ Line 1: / Line 1: @@
 ''Stability'', in Covolution, denotes the basin-relative resilience of an informational pattern, specified at three parameters: a description level '''L''' (the resolution at which identity is defined), a timescale '''τ''' (the horizon over which persistence is asserted), and a noise scale '''σ''' (the amplitude of perturbations the pattern must absorb). A pattern is stable when perturbed trajectories recur within a neighborhood of an attractor over τ, with basin depth ΔV > σ at level L. Stability is therefore a triple — ''stable(L, τ, σ)'' — not an absolute property.
-''[[Co-stability]]'' is stability that obtains only under sustained organism↔symvironment coupling: severing the coupling collapses the basin even when its internal regulatory loops remain intact. Co-stability has two failure modes. The first is ''acute uncoupling'', in which the basin disappears as soon as the recursion is broken. The second is ''Cybernetic Attractor Decay (CAD)'': a slow structural deformation of the landscape itself under sustained coupling, in which basins flatten, separatrices erode, adjacent attractors merge, and ΔV progressively falls toward σ. CAD is loss of ''structural'' stability manifesting as eventual loss of ''basin'' stability — the geometry of viability decays while the organism is still trying to live within it.
+''Stability'', in Covolution, denotes the basin-relative resilience of an informational pattern, specified at three parameters: a description level '''L''' (the resolution at which identity is defined), a timescale '''τ''' (the horizon over which persistence is asserted), and a noise scale '''σ''' (the amplitude of perturbations the pattern must absorb). A pattern is stable when perturbed trajectories recur within a neighborhood of an attractor over τ, with basin depth ΔV > σ at level L.<br/> <br/> Stability is therefore a triple — ''stable(L, τ, σ)'' — not an absolute property.
 '''Dimensions of stability and their CAD failure modes'''
@@ Line 42: / Line 44: @@
 | Bifurcations occur: old attractors vanish, pathological ones appear (senescence, fibrosis, tumor states)
 |}

@@ Line 53: / Line 53: @@
 '''Homeostatic loops operate inside cybernetic attractors.<br/> Hierarchical switches can move systems between attractors.<br/> The attractor itself is the global geometry of stable informational-control states.'''
 </blockquote>
-For CAD ([[Cybernetic Attractor Decay]]), the key point is:
+For CAD ([[Cybernetic_Attractor_Decay|Cybernetic Attractor Decay]]), the key point is:
 <blockquote>
 '''Aging is not only failure of homeostasis. Aging is deformation of the attractor landscape.'''
@@ Line 61: / Line 61: @@
 A concise definition:
 <blockquote>
-'''A cybernetic attractor is a stable or recurrent basin in informational state space produced by feedback, network topology, memory, constraints, and symvironmental coupling. Homeostasis is one local control process that helps maintain a system within such a basin.'''
+'''A cybernetic attractor is a [[stable or recurrent basin]] in informational state space produced by feedback, network topology, memory, constraints, and symvironmental coupling. Homeostasis is one local control process that helps maintain a system within such a basin.'''
 </blockquote>
 So the hierarchy should be:
@@ Line 67: / Line 67: @@
 '''Feedback loops → Homeostasis/Allostasis → Switches → Cybernetic attractors → Attractor landscape → Covolutionary dynamics'''
 </blockquote>
-<br/> '''Theoretical leverage of Covolution: '''<br/> not just “life maintains balance,” but '''life explores, stabilizes, switches between, and loses cybernetic attractor basins across scales.'''<br/> <br/> &nbsp;
+<br/> '''Theoretical leverage of Covolution: '''<br/> not just “life maintains balance,” but '''life explores, stabilizes, switches between, and loses cybernetic attractor basins across scales.'''<br/> <br/> ==> [[Stability in cybernetic attractor]]<br/> &nbsp;

@@ Line 2: / Line 2: @@
 '''<span style="font-size:24px;">Covolution:&nbsp;</span>'''The universe is covolving into a complex [[Information_network|information network]].
-Covolution describes evolution as the exploration and stabilization of [https://claude.ai/chat/Cybernetic%20attractors cybernetic attractors] in informational phase space, where feedback loops computationally bias life toward structured, recursive, and anticipatory architectures.<br/> <br/> Within this network, [https://claude.ai/chat/Biological%20information%20objects biological information objects] are connected by edges forming dynamic interaction networks.<br/> The underlying rules of this information dynamics are not arbitrary: the universe is [https://claude.ai/chat/Paradetermined paradetermined].
+Covolution describes evolution as the exploration and stabilization of [[Cybernetic_attractors|cybernetic attractors]] in informational phase space, where feedback loops computationally bias life toward structured, recursive, and anticipatory architectures.<br/> <br/> Within this network, biological information objects are connected by edges forming dynamic interaction networks.<br/> The underlying rules of this information dynamics are not arbitrary: the universe is paradetermined.
-Covolution Theory is the scientific component of [https://claude.ai/chat/Biosophy biosophy], the broader philosophical project of which it is the empirical and theoretical expression.
+Covolution Theory is the scientific component of biosophy, the broader philosophical project of which it is the empirical and theoretical expression.
 == Start here ==
-*[[How to read covolution.org]]
+*[[How_to_read_covolution.org|How to read covolution.org]]
 *[[What_is_covolution?|What is covolution?]] — the definition page
 *[[The_difference_between_evolution_and_covolution|The difference between evolution and covolution]]

@@ Line 1: / Line 1: @@
-<p><br />
+<span style="font-size:x-large">'''[http://biosophy.org Biosophy.org]'''</span>
-<p><span style="font-size:16px">The BioGrid is a dynamic network with information resonance.</span></p>
+----
-<p><span style="font-size:16px">Biosophy has a teleological view of the life propagation. Life has a well-controlled or regulated orientations in a fractally hiearchical information system.</span></p>
+&nbsp;
 <div class="boilerplate metadata plainlinks" id="stub">&nbsp;</div>

@@ Line 8: / Line 8: @@
 == Start here ==
 *[[What_is_covolution?|What is covolution?]] — the definition page
 *[[The_difference_between_evolution_and_covolution|The difference between evolution and covolution]]
@@ Line 15: / Line 16: @@
 == Core ideas ==
-*[[Evolution, covolution, switches, and complexity]]
+*[[Evolution,_covolution,_switches,_and_complexity|Evolution, covolution, switches, and complexity]]
-*[[Cybernetic attractors]]
+*[[Cybernetic_attractors|Cybernetic attractors]]
-*[[Biological information objects]]
+*[[Biological_information_objects|Biological information objects]]
-*[[Paradetermined]] — there is no fate, but there are structures to it
+*[[Paradetermined|Paradetermined]] — there is no fate, but there are structures to it
-*[[Stasome and Dynome]] (also: hard genome and soft genome)
+*[[Stasome_and_Dynome|Stasome and Dynome]] (also: hard genome and soft genome)
-*[[Informational jump of critical mass of computing units in species expansion]]
+*[[Informational_jump_of_critical_mass_of_computing_units_in_species_expansion|Informational jump of critical mass of computing units in species expansion]]
 == In contrast to other accounts ==
-*[[What is wrong with Darwinian evolution?]]
+*[[What_is_wrong_with_Darwinian_evolution?|What is wrong with Darwinian evolution?]]
-*[[Lamarckism]], [[Darwinism]] (see Examples of covolution)
+*[[Lamarckism|Lamarckism]], [[Darwinism|Darwinism]] (see Examples of covolution)
 == Applications ==
-*[[Covolution and diseases]]
+*[[Covolution_and_diseases|Covolution and diseases]]
-*[[Economy and covolution]]
+*[[Economy_and_covolution|Economy and covolution]]
-*[[Examples of covolution]]
+*[[Examples_of_covolution|Examples of covolution]]
 == Related questions ==
-*[[What is life?]]
+*[[What_is_life?|What is life?]]
-*[[Biosophy]] — the umbrella project
+*[[Biosophy|Biosophy]] — the umbrella project
-*[[There is no fate, but there are structures to it]]
+*[[There_is_no_fate,_but_there_are_structures_to_it|There is no fate, but there are structures to it]]
 == References and further reading ==
-*[[Covolution references]]
+*[[Covolution_references|Covolution references]]
 *External: [[The_Caterpillar_Defense|The Caterpillar Defense]] (National Geographic, 2014)