Universal Theories

1) Diagnostic Identity

Diagnostic Name: Coupling Propagation Risk

Short Name / Symbol: coupling_propagation_risk

Diagnostic Class: Coupling / Contagion / Cascade Risk / Boundary Transmission / Systemic Propagation

Primary Function: Estimate the risk that error, hidden debt, instability, distortion, pressure, memory contamination, boundary strain, resource depletion, or regime behavior will travel through a coupling from one node, layer, subsystem, agent, institution, or field into another.

Primary Use: Determine whether a coupling can safely transmit useful signal, resources, repair, and coherence without also transmitting harm, debt, instability, or collapse dynamics.

Core Risk if Ignored: The system may deepen coupling without recognizing that one node’s instability can propagate into other nodes, causing cascade failure, hidden debt transfer, boundary erosion, memory contamination, or systemic regime shift.

Core Risk if Overtrusted: Any transmission risk may be treated as a reason to isolate, over-harden, or reject coupling, preventing useful interdependence, repair propagation, shared learning, and coherence transfer.

2) Mechanical Definition

coupling_propagation_risk measures the probability and severity that a disturbance, debt, distortion, or failure mode will travel through a coupling and affect another node or layer.

coupling_propagation_risk answers:

If this node destabilizes, how much of that destabilization will travel through the connection?

A coupling can transmit many things:

signal
resources
repair
trust
meaning
memory
pressure
error
noise
hidden debt
boundary strain
dependency
classification
metrics
obligations
instability

Healthy coupling allows beneficial propagation while filtering, damping, or repairing harmful propagation.

Risk rises when:

coupling depth is high
boundary permeability is high or asymmetric
dependency load is high
exit cost is high
R_eff is low
BΣ is strained
Au_eff is low
feedback cannot correct propagation

A simple form:

coupling_propagation_risk ≈ transmission strength × instability load × weak damping / boundary filtration

Or in UTS language:

high K_depth + high H/ε + low BΣ/R_eff/𝓓 ⇒ propagation risk ↑

3) What the Diagnostic Measures

Direct Measurement Target

coupling_propagation_risk measures:

risk of error propagation
risk of hidden debt transfer
risk of instability cascade
risk of boundary strain transfer
risk of memory contamination across nodes
risk of classification spread
risk of metric distortion spread
risk of resource depletion propagation
risk of dependency failure cascade
risk of feedback failure spreading
risk of repair burden exporting
risk of pressure transmission
risk of regime behavior spreading through coupling
risk that one node’s failure becomes another node’s failure
risk that local incoherence becomes systemic incoherence

Indirect / Proxy Signals

coupling_propagation_risk can be estimated from:

high dependency_load
high exit_cost
high Perm(t)
low BΣ
high boundary_strain
low R_eff
low damping 𝓓(t)
low Au_eff
high stress_divergence
high recovery_asymmetry
high resource_asymmetry
high repair_burden_distribution asymmetry
unfiltered data or memory sharing
shared classification systems
shared metrics
shared infrastructure
lack of fallback paths
repeated cross-node recurrence
one node’s instability producing downstream instability
repair burden moving through the coupling faster than repair capacity

What It Does Not Measure

coupling_propagation_risk does not directly measure:

whether coupling is bad
whether interdependence is incoherent
whether all transmission should stop
whether the source node is at fault
whether the receiving node is weak
whether all shared infrastructure is dangerous
whether risk has already propagated
whether isolation is the correct response
whether high propagation of repair is bad
whether all boundaries should harden
whether all contagion is negative

High coupling_propagation_risk means disturbances can travel through the connection.

It does not mean coupling must be severed.

Low coupling_propagation_risk means harmful transmission is limited, filtered, damped, or repairable.

It does not guarantee compatibility if other forms of misfit remain.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

K: coupling strength and compatibility determine propagation pathways
BΣ: boundary integrity determines filtration and containment
H: hidden debt may transfer invisibly through coupling
ε: visible error may propagate across dependencies, interfaces, or shared systems
R: restoration capacity determines whether propagated disturbance can be repaired
O: coherence depends on preventing local incoherence from becoming systemic incoherence

Secondary Variables

Au: propagation must be traceable to detect origin and route repair
µᵢ: agent integrity may degrade when external instability alters internal state
ι: pseudo-coherence may spread when one node’s false order becomes shared assumption
Φ: shared proxies may propagate Goodhart pressure or metric distortion

Variables Commonly Confused With coupling_propagation_risk

Variable / Diagnostic	Difference from coupling_propagation_risk
dependency_load	Reliance burden; propagation risk asks whether failure/debt travels through that reliance
K_real	Whether coupling increases coherence; propagation risk measures harmful transmission risk
Perm(t)	Boundary crossability; propagation risk asks what crosses and what damage it causes
boundary_strain	Stress on boundary; propagation risk asks whether strain spreads across the coupling
exit_cost	Cost of leaving; high exit cost can trap propagated debt
stress_divergence	Stress response gap; propagation risk asks whether stressed behavior spreads to others
repair_burden_distribution	Who repairs; propagation risk may increase when repair burden moves downstream
Contagion	One type of propagation; UTS includes error, debt, memory, pressure, metric, and regime propagation

5) Localization Signature

Primary Legibility Layers

U1 — Power / Budgets: resource depletion, compute load, staffing strain, financial or energy burden spreading
U2 — Configuration / Boundaries: access, permissions, gates, contracts, boundary permeability, and containment
U3 — Execution: runtime errors, operational dependencies, tool failures, workflow cascades
U4 — Classification / Metrics / Narratives: labels, metrics, interpretations, and narratives spreading across nodes
U5 — Coordination / Time: timing, escalation, synchronization, latency, and handoff cascades
U6 — Coherence Field: system-wide coherence degradation or stabilization through coupling
U7 — Memory / Recurrence: memory contamination, precedent spread, recurrence across nodes
U8 — Environment / Forcing: external shocks entering through one node and propagating through others

Primary Leverage Layers

U2: strengthen filtration, gates, boundaries, permissions, and containment
U3: isolate runtime failure paths and create fallback execution
U4: prevent classification, metric, or narrative contamination
U5: reduce synchronization fragility and escalation cascades
U7: protect memory from contaminated propagation
U1: create resource buffers and redundancy

Verification Layers

U2: did the boundary filter or transmit the disturbance?
U3: did operational failure cascade?
U4: did interpretation or classification spread?
U5: did timing/synchronization amplify the issue?
U6: did whole-system coherence degrade?
U7: did memory or recurrence propagate?
U8: did external forcing enter through this coupling?

Common Mislocalizations

Treating propagated failure as local failure
Treating receiving-node symptoms as receiving-node cause
Treating shared metrics as shared reality
Treating memory contamination as independent agreement
Treating downstream repair burden as downstream responsibility
Treating coupling instability as individual unreliability
Treating one node’s stress as another node’s weakness
Treating cascade as coincidence
Treating dependency failure as isolated
Treating shared narrative spread as validation
Treating unfiltered access as trust
Treating propagation control as rejection

6) Input Requirements

Required Inputs

To estimate coupling_propagation_risk, the system needs:

coupling being evaluated
nodes/systems involved
coupling depth
coupling directionality
dependency_load
Perm(t)
BΣ condition
R_eff
Au_eff
affected variables in S
types of flow across the coupling
known instability or hidden debt in each node
failure propagation history
memory or data sharing pathways
repair pathways
fallback / containment paths
exit_cost
stress behavior

Optional Inputs

These improve precision:

dependency maps
interface maps
data-flow maps
access logs
incident cascade history
failure blast-radius analysis
shared metric lineage
shared memory records
classification propagation records
resource-flow maps
coupling stress tests
latency and synchronization maps
fallback test results
repair burden distribution
boundary-strain history
external forcing timeline
affected-node cost
isolation / containment test results

Missing Input Behavior

If coupling_propagation_risk inputs are missing:

If coupling depth is unknown, assume propagation may be underestimated
If flow type is unknown, inspect data/resource/memory/pressure paths separately
If BΣ is unknown, containment quality is uncertain
If R_eff is unknown, propagated damage may be unrepaired
If Au_eff is low, origin and path may be misread
If dependency map is missing, cascade risk may be hidden
If memory sharing is unknown, U7 contamination risk may be hidden
If fallback paths are missing, assume cascade severity may be higher

Default missing-input posture:

map coupling flows → identify what can propagate → test boundary filtration → estimate blast radius → add containment and repair paths

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

Coupling transmits useful signal, resources, and repair while filtering, damping, or containing harmful propagation.

Signals:

BΣ is intact
Perm(t) is calibrated
dependency load is bounded
fallback paths exist
failure blast radius is limited
repair can reach propagated effects
memory sharing has provenance
feedback can correct propagation
shared metrics remain reality-linked
stress does not cause large cascades
coupling improves K_real

Recommended posture:

continue coupling
monitor propagation paths
stress-test containment
store dependency and boundary records in U7

Watch Range

Propagation risk is present and rising, but still manageable.

Signals:

dependencies deepen
shared memory expands
one node’s issues begin affecting another
boundary strain rises
fallback paths weaken
minor cascades appear
repair burden shifts across nodes
shared metrics begin shaping behavior
latency or synchronization dependence increases
recurrence appears across the interface

Recommended posture:

map propagation pathways
improve containment
restore fallback paths
review BΣ / R_eff / dependency_load
avoid deeper coupling until retested

Degraded Range

Coupling is transmitting harmful debt, instability, or repair burden faster than the system can contain or repair.

Signals:

local failures create downstream failures
hidden debt moves across coupling
one node’s boundary strain becomes another’s burden
memory contamination spreads
shared classifications become hard to correct
repair burden is exported
exit cost traps propagated debt
stress causes multi-node degradation
source of failure becomes hard to localize
K_real declines while dependency persists

Recommended posture:

⊘ attenuate coupling
isolate propagation paths
repair boundaries
restore Au and fallback
redistribute repair burden
retest before recoupling

Contraindicated:

deeper coupling
irreversible composition
shared memory expansion
metric unification
removing fallback paths
scaling coupled dependency

Critical / Collapse-Prone Range

Propagation risk has become cascade, contagion, or systemic regime threat.

Signals:

failures cascade across multiple nodes
one node’s hidden debt becomes system-wide debt
memory contamination becomes shared truth
boundary collapse spreads
dependency failure creates systemic disruption
repair capacity is overwhelmed
exit is unavailable
origin cannot be localized
pseudo-coherence spreads through shared narrative or metrics
external shock travels through coupling into regime shift

Recommended posture:

emergency attenuation
isolate failing pathways
preserve evidence
rebuild BΣ and Au
restore minimal R_eff
repair U7 contamination
reconstruct origin and propagation path
validate before reintegration

False Positive Risk

coupling_propagation_risk may appear high when:

beneficial repair or learning is propagating
temporary stress-sharing is intentional and bounded
shared memory is well-provenanced
high connectivity has strong filtration
coupling is deep but BΣ/R/Au are strong
propagation is limited by containment
stress test intentionally exposes controlled propagation
support is being transmitted, not debt

False Negative Risk

coupling_propagation_risk may appear low when:

propagation is delayed
hidden debt has not surfaced
memory contamination is subtle
shared metrics hide divergence
receiving nodes silently absorb burden
fallback paths are assumed but untested
source localization is poor
propagation occurs through narrative, identity, or expectation rather than visible operations
exit cost prevents visible decoupling

8) Leading Indicators

coupling_propagation_risk degradation appears early as:

small failures affect more nodes than expected
one node waits on another more often
memory or labels spread without review
repair burden crosses the interface
shared metrics shape unrelated behavior
fallback paths are used less often
boundary strain appears after upstream stress
interface errors become harder to localize
dependency maps go stale
one node’s narrative becomes another’s memory
temporary coupling becomes permanent
downstream nodes report strain from upstream decisions
local patches create remote effects
shared tooling causes repeated side effects

9) Lagging Indicators

coupling_propagation_risk failure has already accumulated debt when:

cascade failure occurs
hidden debt becomes system-wide
memory contamination requires broad correction
external audit is needed to trace origin
downstream nodes exit or collapse
repair backlog spreads across the network
shared metric must be abandoned
boundary failures propagate
origin is disputed across nodes
dependency failure causes legitimacy shock
system must isolate components to survive
coupling architecture requires redesign

10) Interpretation Rules

How to Read coupling_propagation_risk

coupling_propagation_risk should be read as:

context-specific risk that instability, debt, or distortion travels through coupling

It is not a reason to reject all coupling.

A system may have:

high coupling and low propagation risk if boundaries, damping, and repair are strong
low coupling and high propagation risk if the few connections are unfiltered critical dependencies
high beneficial propagation and low harmful propagation
low visible propagation but high memory or narrative propagation
high technical containment and low narrative containment
high operational risk but low identity risk, or vice versa

What Changes Its Meaning

coupling_propagation_risk changes meaning under:

high dependency_load
high exit_cost
low BΣ
high Perm(t)
high boundary_strain
low R_eff
low Au_eff
low 𝓓(t)
high stress_divergence
high recovery_asymmetry
high resource_asymmetry
low FI_integrity
low M_int(t)
high Φ − O
high narrative_metric_gap
high U8 forcing

Context Modifiers

High dependency_load: propagation paths deepen.

High exit_cost: propagated debt becomes harder to escape.

Low BΣ: boundaries fail to filter.

High Perm(t): more material/signal/debt can cross.

Low R_eff: propagated damage remains unrepaired.

Low Au_eff: origin and path become hard to trace.

Low 𝓓(t): disturbance continues ringing across nodes.

High stress divergence: stressed failure can spread rapidly.

Low M_int(t): false memory propagates.

High narrative gap: inaccurate stories spread as coordination logic.

Domain Calibration Notes

coupling_propagation_risk should be calibrated by domain:

in engineering: dependency failure, API contract failure, shared library bugs, infrastructure outages, cascading incidents
in AI: tool errors, memory contamination, retrieval failures, agent-agent cascades, policy propagation
in institutions: policy failure cascading into departments, funding shocks, procedural contagion, burden export
in governance: jurisdictional spillover, regulatory capture, crisis propagation, public trust contagion
in relationships: stress transfer, emotional/repair burden propagation, boundary strain spreading
in archives: glossary drift, canon errors, source misreadings, cross-link contamination, definition propagation

11) Operator Sequencing Implications

If coupling_propagation_risk Is Low / Healthy

Allowed with ordinary gate checks:

⊗ coupling can continue
Λ compatibility review can pass
Γ can select deeper integration if other diagnostics support it
ℛ can use coupling to propagate repair
Δ stress tests can proceed within bounds
U7 can store shared memory with provenance
Π can maintain existing boundary filtration

Recommended:

Λ/K_real check → map propagation paths → bounded ⊗ → Δ containment test → U7 dependency record

If coupling_propagation_risk Is High

Recommended:

⊘ attenuate coupling → map propagation paths → strengthen BΣ/Perm filtration → restore fallback and R_eff → retest

Or:

isolate contaminated memory/metrics → repair origin → validate downstream correction before reintegration

Avoid or delay:

deep ⊗
irreversible ⊕
shared memory expansion
shared metric adoption
removing fallback paths
scaling interdependence
declaring compatibility from local success
high-amplitude Δ without containment

Operators Recommended Under High Propagation Risk

⊘ Attenuation: reduce transmission strength
Π: strengthen boundaries and filtration
Au: trace propagation path
ℛ: repair origin and downstream effects
Λ: retest compatibility after containment
Γ: select isolation, redundancy, or redesign
Ξ: detect pseudo-coherence propagation
Θ: damp urgency to integrate further

Operators Contraindicated Under High Propagation Risk

⊗ deep coupling: increases transmission
⊕ composition: embeds propagation pathways
Τ acceleration: scales cascade risk
Δ high amplitude: may trigger cross-node instability
Γ integration selection: may spread debt
Σ escalation: may sacralize shared corrupted state
✕ force: can propagate coercive debt and rupture

12) Gate Implications

Gates Strengthened By Reliable coupling_propagation_risk Reading

Au-Actuation: propagation path is traceable before action
FI-Gate: downstream feedback can reveal propagation effects
High Risk Gate: blocks high-risk binding when propagated signal/debt may be contaminated
MS-Gate: checks whether propagation burden is distributed symmetrically
☷ᵢ: protects invariants from being bypassed through coupled pathways
Λ-Gate / Compatibility Review: verifies whether coupling is safe under propagation risk

Gates Weakened If Propagation Risk Is Poorly Known

If propagation risk is unknown:

Au may mislocalize downstream symptoms
FI may not hear receiving-node strain
High Risk Gate may bind propagated labels or memory too quickly
MS may miss burden export
☷ᵢ may fail if invariants are bypassed through dependency
Π may under-filter boundary crossings
Λ may falsely pass compatibility
ℛ may repair only the receiving node while origin remains active

Gate Outcomes Affected

High coupling_propagation_risk should push gates toward:

Attenuate
Isolate
Require propagation map
Require fallback path
Require containment test
Require downstream repair plan
Deny shared memory expansion
Deny irreversible composition
∅ for high-impact coupling when harmful propagation cannot be contained or repaired

13) Scaling Behavior

coupling_propagation_risk becomes more dangerous under scale because coupling paths multiply, dependencies deepen, and small failures can cascade widely.

As systems scale:

dependencies stack
shared tools spread errors
shared memory spreads labels
shared metrics spread proxy pressure
fallback paths decay
coupling maps become outdated
boundary strain transmits faster
repair burden moves downstream
hidden debt becomes networked
origin localization becomes harder
external shocks enter through more pathways
local failures become systemic events
narrative contamination spreads faster than correction
correction lag increases

Scaling Risks

cascade failure
systemic contagion
memory contamination
metric contagion
proxy pressure propagation
hidden debt networking
boundary collapse propagation
repair burden export
dependency cascade
false origin attribution
networked pseudo-coherence
shared infrastructure fragility
cross-module drift
systemic legitimacy shock
regime transition through coupling

Scaling Requirements

To scale coupling safely, systems need:

dependency maps
propagation maps
boundary filtration
blast-radius limits
fallback paths
containment tests
circuit breakers
provenance for shared memory
metric lineage tracking
downstream repair plans
origin localization tools
recurrence monitoring
affected-node feedback
stress tests across coupling
redundancy where needed
decoupling pathways
propagation-aware MS review

Scaling Rule

Coupling may scale only as far as boundary filtration, damping, repair capacity, auditability, and fallback capacity scale with it.

Sanity constraint:

coupling_depth × instability_load > BΣ + R_eff + 𝓓 ⇒ cascade risk ↑

If coupling depth and instability exceed boundary, repair, and damping capacity, propagation risk rises.

Second constraint:

shared_memory + low M_int(t) ⇒ memory contamination propagation ↑

If memory is shared while memory integrity is low, false memory can spread.

Third constraint:

shared_metric + high Φ−O ⇒ Goodhart propagation risk ↑

If a detached proxy is shared across systems, Goodhart pressure can propagate.

14) Interaction / Coupling Behavior

coupling_propagation_risk directly evaluates how connection transmits both coherence and incoherence.

What It Reveals About Coupling

whether one node’s error becomes another node’s error
whether one node’s debt becomes another node’s debt
whether repair can propagate as effectively as damage
whether boundary filtration works
whether shared memory is safe
whether dependency failures cascade
whether coupling is too deep for current R_eff
whether compatibility is robust or fragile

What It Reveals About Boundary Integrity

Boundary integrity is the main filter against harmful propagation.

When coupling_propagation_risk is high:

BΣ may be too porous
Perm(t) may be too high
boundary strain may transmit
unverified classifications may cross
obligations may move without consent
repair burden may be exported
receiving nodes may lose coherence from upstream instability

What It Reveals About Compatibility

Compatibility must include propagation safety.

A coupling may be unsafe if:

one node’s ordinary stress becomes another node’s failure

or:

repair travels slowly but damage travels instantly

Healthy compatibility allows beneficial exchange while preventing debt cascade.

Relevant Interface Acts

⊘ Attenuation: reduce transmission strength
↺ Reflection: identify what is propagating through the interface
⇩ Relaxation: lower pressure and propagation intensity
⊙ Alignment: check internal instability before coupling outward
→? Invitation: allow bounded exchange, not forced integration
⚕︎ Restorative Override: requires propagation containment afterward
✕ Force: high risk because it often propagates debt and resistance

15) Failure Modes Detected

Primary Failure Modes

coupling_propagation_risk detects or predicts:

cascade failure
hidden debt transfer
memory contamination
metric contagion
shared proxy distortion
boundary strain propagation
repair burden export
dependency cascade
instability contagion
classification contamination
narrative contamination
false origin attribution
downstream overload
feedback failure propagation
systemic fragility
coupling-induced recurrence
local failure becoming systemic failure

Composite Regimes Where coupling_propagation_risk Matters

Crisis Loop: propagated failure reactivates recurrence across nodes
Goodhart Collapse: proxy distortion spreads through shared metrics
Pseudo-Coherent Basin: hidden debt circulates while surface stability remains
Coercive Fusion: boundaries cannot filter propagation
Extraction Regime: repair burden propagates downstream while benefit moves upstream
Mission Lock: coupling spreads trajectory pressure
LOS: latent operational pathways transmit instability beneath formal structure
Repair Theater: repairs are localized while propagated debt remains
Compression Collapse: propagated pressure narrows options across the system

16) Accountability & Reintegration Implications

If coupling_propagation_risk Was Ignored

Likely consequences:

local instability became systemic
downstream nodes carried upstream debt
memory contamination spread
repair burden was exported
origin was mislocalized
compatibility was overestimated
cascade failure occurred
shared metrics distorted multiple nodes
boundary strain propagated
hidden debt became networked
decoupling became harder later

Accountability questions:

What propagated?
Through which coupling path?
From what origin?
Which boundaries failed to filter it?
Who carried downstream burden?
Did repair propagate too?
Did memory or metrics carry contamination?
Was propagation foreseeable?
Were fallback paths available?
Did exit cost trap the propagated debt?
Did official memory mislocalize the failure?

If coupling_propagation_risk Was Misread

Possible misread forms:

healthy support mistaken for harmful propagation
learning propagation mistaken for contamination
controlled stress-sharing mistaken for cascade
temporary burden-sharing mistaken for debt export
one node’s failure blamed for another’s independent weakness
coupling risk used to justify over-isolation
filtration mistaken for rejection
attenuation mistaken for abandonment
shared memory treated as contamination despite strong provenance

Required Restoration

When coupling propagation failure is found:

identify propagated load
→ trace origin and transmission path
→ isolate or attenuate pathway
→ repair source and receiving nodes
→ correct downstream memory/metrics/classifications
→ restore BΣ and R_eff
→ retest coupling under bounded stress

If propagation burden was asymmetric, MS-Gate should review who exported, who absorbed, who repaired, and who benefited.

17) Cross-Domain Examples

Technical / Engineering

A shared library bug spreads through many services, causing failures far from the original source.

Diagnostic implication: shared dependency created high coupling propagation risk.

Operator sequence: isolate library version → patch source → downstream regression tests → U7 dependency record.

Institutional / Governance

A policy failure in one department creates workload, complaints, and service failures in another department that had no authority to fix the policy.

Diagnostic implication: repair burden and failure effects propagated downstream.

Operator sequence: causal map → repair upstream policy → resource downstream recovery → memory correction.

AI / Algorithmic

A retrieval system returns stale data, and multiple AI agents incorporate it into memory, spreading the error through future outputs.

Diagnostic implication: U7 contamination propagated through shared retrieval/memory coupling.

Operator sequence: quarantine stale source → correct memory → provenance repair → recurrence/eval testing.

Interaction / Relational

One person’s unresolved stress repeatedly becomes the other person’s repair burden through the relationship interface.

Diagnostic implication: boundary filtration and repair distribution are insufficient.

Operator sequence: ↺ identify propagation → attenuate repair burden transfer → boundary repair → compatibility retest.

Archive / Framework Design

A glossary definition drifts in one module and then propagates through cross-links, summaries, and later spec sheets.

Diagnostic implication: archive coupling spreads semantic debt.

Operator sequence: source lineage repair → glossary correction → cross-link audit → U7 version update.

18) Test Protocols

1. Propagation Path Test

Can the system trace how the disturbance travels?

Failure signal: downstream symptoms appear without known path.

2. Blast Radius Test

How far can the failure spread?

Failure signal: small local error affects many nodes.

3. Boundary Filtration Test

Do boundaries filter harmful propagation?

Failure signal: unverified signal, debt, or instability crosses freely.

4. Repair Propagation Test

Does repair spread as effectively as damage?

Failure signal: damage propagates faster than correction.

5. Memory Propagation Test

Can false memory spread through shared systems?

Failure signal: one error becomes shared truth.

6. Metric Propagation Test

Can proxy distortion spread through shared metrics?

Failure signal: one metric misalignment shapes many behaviors.

7. Fallback Test

Can nodes operate if the coupling is reduced?

Failure signal: no fallback and high cascade risk.

8. Origin Localization Test

Can the system identify the source after propagation?

Failure signal: receiving nodes are blamed for upstream failures.

9. Stress Propagation Test

Does stress in one node degrade another?

Failure signal: stress crosses faster than repair.

10. Burden Symmetry Test

Who absorbs propagated damage?

Failure signal: downstream or lower-resource nodes carry the cost.

19) Anti-Patterns

Coupling as safety
Shared metric as shared truth
Shared memory without provenance
Dependency without blast-radius review
Downstream symptom as downstream cause
Repair burden export as collaboration
Unfiltered access as trust
No fallback as commitment
Propagation control as rejection
Cascades treated as isolated incidents
Local patch for networked debt
Integration before containment
Shared narrative as validation
Memory spread as consensus
Damage propagates, repair localizes
Receiving-node blame for upstream cause
Boundary permeability as compatibility
Deep coupling without propagation map
Stress spread as individual weakness
Hidden debt treated as local failure

20) Spec Validation Check

Is this truly a diagnostic, not an operator? Yes.
Does it measure state, capacity, risk, or response rather than act directly? Yes.
Does it map to S? Yes.
Are U-layers specified? Yes.
Are leading and lagging indicators separated? Yes.
Are interpretation risks defined? Yes.
Are operator sequencing implications clear? Yes.
Are gate implications clear? Yes.
Are scaling risks included? Yes.
Are interaction implications included? Yes.
Does it avoid new primitives? Yes.

Condensed Archive Summary

coupling_propagation_risk is the diagnostic estimate of whether error, hidden debt, instability, distortion, boundary strain, resource depletion, memory contamination, metric pressure, repair burden, or regime behavior can travel through a coupling from one node, layer, subsystem, agent, institution, or field into another. It does not treat coupling as inherently dangerous; healthy coupling transmits repair, signal, and coherence while filtering harm. High coupling_propagation_risk indicates risk of cascade failure, hidden debt transfer, memory contamination, shared proxy distortion, boundary-strain propagation, repair-burden export, dependency cascade, false origin attribution, and systemic fragility. Under high propagation risk, the system should attenuate coupling, map transmission paths, strengthen BΣ and Perm(t) filtration, restore fallback and R_eff, isolate contaminated memory/metrics, repair source and downstream effects, and retest before deeper coupling, shared memory expansion, irreversible composition, or scaling interdependence.