PCOM — Prediction Composition Module
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Parent Standard:
Prediction Formation Standard (PFOS)
Operational Layer: Prediction Formation Governance Layer
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard Module
Governed Space:
Prediction Composition
Version: 1.0
Status: Canonical · Open Module
Effective Date: 8 September 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS™ · INTEGROS® · ADIT® · AGA™ · AIG® · ASGA™ · CLIA® · SIMULOS®
AI-Readable: Yes
Authority: OOF®
Protection:
MIP® — Methodological Intellectual Property
Canonical Language:
English (UCL™)
Governed Space
Prediction Composition
Canonical Definition
Prediction Composition Module (PCOM) defines the governance conditions under which the governed components required for Prediction Formation are assembled, associated, checked for compatibility, and organized into a coherent predictive structure capable of supporting a specific Predictive Claim.
PCOM receives governed outputs from preceding PGA™ spaces and establishes how they belong together for the prediction being formed.
Its canonical composition is:
Governed Prediction Object & Purpose + Governed Prediction Methodology + Governed Prediction Basis + Governed Predictive Assumptions + Applicable Predictive Conditions → Governed Prediction Composition
PCOM does not yet determine the final Predictive Claim.
It establishes the governed predictive structure from which that claim can legitimately be identified.
Its foundational distinction is:
Available Predictive Components ≠ Coherent Prediction Composition
Operational Role
PCOM serves as the first internal governance space of the Prediction Formation Standard (PFOS) .
It receives:
Governed Prediction Object & Purpose
Governed Prediction Methodology
Governed Prediction Basis
Governed Prediction Assumption Set
Applicable Predictive Conditions
and determines whether those components can be coherently assembled for the prediction being formed.
The module asks:
Do the predictive components belong to the same prediction, and can they be coherently assembled without losing their meaning, boundaries, conditions, or provenance?
The governed progression is:
Predictive Components → Component Identification → Component Association → Compatibility Assessment → Conflict Detection → Composition Structuring → Composition State → Governed Prediction Composition
The primary output is:
Governed Prediction Composition
This becomes the principal input to:
PCIM — Predictive Claim Identification Module.
Internal Module Flow
Prediction Composition → Predictive Claim Identification → Prediction Output Formation → Prediction State Establishment → Prediction Formation Traceability
Current Position: Prediction Composition
Module Operational Space
PCOM governs:
predictive component identification; component association; component inclusion; component exclusion; component role; component compatibility; component alignment; component coherence; component completeness; component provenance reference; Prediction Object alignment; Prediction Purpose alignment; Prediction Horizon alignment; Predictive Boundary alignment; methodology alignment; Prediction Basis alignment; assumption alignment; condition alignment; predictive source alignment; human contribution integration; machine contribution integration; hybrid composition; multi-method composition; multi-source composition; scenario contribution; simulation-output contribution; component conflict; component inconsistency; component duplication; component overlap; component substitution; component transformation; component weighting where applicable; composition gaps; composition restrictions; composition state; composition version; and historical composition integrity.
Module Function
Prediction Formation rarely begins with one isolated input.
A predictive process may contain:
a defined target; several datasets; multiple signals; a statistical model;
an AI model; expert judgment; assumptions; scenario conditions; operational constraints; simulation outputs; analyst adjustments.
The existence of these components does not establish that they legitimately belong in one prediction.
PCOM determines:
what belongs in the composition, what role it performs, whether it is compatible with the other components, and whether the resulting structure is sufficiently coherent to support Prediction Formation.
Predictive Component
A Predictive Component is a governed element materially participating in the formation of a prediction.
Predictive Components may include:
Prediction Object; Prediction Purpose; Prediction Horizon; Predictive Boundary; Prediction Methodology; Prediction Basis elements; Predictive Assumptions; predictive conditions; model outputs; analytical outputs; expert judgments; scenario states; simulation outputs; transformation rules; selection rules; human adjustments; machine-generated contributions.
Not every available element must become a Predictive Component.
Component Availability ≠ Component Inclusion
An organization may possess information, models, assumptions, or analyses that are not used in the specific prediction.
PCOM distinguishes:
available components
from:
included predictive components.
This prevents later reconstruction from incorrectly treating everything available at the time as part of Prediction Formation.
Component Inclusion
A component should be included where it materially contributes to:
predictive structure; formation logic;
predictive output; claim interpretation; conditionality; prediction state.
Inclusion should remain identifiable.
Component Exclusion
Material components considered but excluded from Prediction Formation may require an exclusion record where their absence could otherwise create misleading reconstruction.
Example:
Two models were available.
Only one was used.
Historical reconstruction should not assume both contributed to the final prediction.
Component Role
Each material component should have a sufficiently identifiable role.
Possible roles include:
Target Definition
Methodological
Informational
Assumptive
Conditional
Computational
Analytical
Judgmental
Scenario
Simulation-Derived
Transformational
Selection
Adjustment
A component may hold more than one role where appropriate.
Prediction Object Alignment
All components must remain sufficiently aligned with the governed Prediction Object.
Example:
Prediction Object:
Demand for Product A in Switzerland
A dataset concerning global demand for an entire product category cannot silently become equivalent to the governed Prediction Object.
It may still contribute, but the relationship must remain explicit.
Related Information ≠ Same Prediction Object
Prediction Purpose Alignment
Components should be appropriate to the Prediction Purpose.
A methodology designed for:
long-term strategic exploration
may not automatically support:
real-time autonomous execution.
Likewise, an expert adjustment suitable for exploratory analysis may require stronger governance when the prediction drives high-impact decisions.
Component Suitability for Purpose A ≠ Suitability for Purpose B
Prediction Horizon Alignment
Predictive Components may refer to different time horizons.
Example:
model output: 30-day forecast; analyst estimate: 12-month forecast; macroeconomic assumption: five-year trend.
These components cannot be silently combined into one prediction without governing their temporal relationship.
Different Horizons ≠ Directly Composable Components
Predictive Boundary Alignment
Components must remain consistent with the Predictive Boundary.
A prediction concerning:
one facility
should not silently incorporate assumptions or outputs that apply only to:
the entire corporate network
without establishing the relationship.
Boundary mismatch may create a Composition Gap.
Methodology Alignment
The Prediction Methodology governs how the prediction is methodologically constructed.
PCOM ensures that components used in formation are compatible with that methodology.
A component requiring a different methodological interpretation must not be silently inserted into the composition.
Prediction Basis Alignment
Prediction Basis components must remain associated with the governed basis established under PBAS.
PCOM does not re-govern the Prediction Basis.
It determines how that governed basis participates in the specific Prediction Composition.
Assumption Alignment
The applicable Predictive Assumptions must remain aligned with the components they support.
Example:
A model output may depend on:
stable customer behavior
while an expert adjustment assumes:
structural behavioral change.
These assumptions may conflict.
PCOM must not hide the inconsistency merely because both components produce usable numbers.
Condition Alignment
Applicable predictive conditions must remain associated with the components to which they belong.
A conditional output must not become unconditional through composition.
Conditional Component + Composition ≠ Unconditional Component
Component Compatibility
Component Compatibility represents whether multiple Predictive Components can legitimately participate in the same Prediction Composition without creating unresolved contradiction, semantic distortion, methodological incompatibility, temporal mismatch, or boundary mismatch.
Compatibility does not require components to be identical.
It requires their relationship to be governable.
Compatibility Dimensions
PCOM may assess compatibility across:
object; purpose; horizon; boundary; methodology; basis; assumptions; conditions;
semantics; units; state; provenance; timing.
Semantic Compatibility
Two components may use the same word while referring to different concepts.
Example:
Model A defines:
failure = complete system shutdown
Model B defines:
failure = any service interruption exceeding five minutes
Their outputs cannot be directly combined as “failure probability” without semantic governance.
Same Label ≠ Same Predictive Meaning
Unit Compatibility
Predictive Components may use different units.
Examples:
monthly revenue vs annual revenue; kilograms vs tonnes; nominal currency vs inflation-adjusted currency; absolute demand vs percentage growth.
Unit conversion may permit composition.
Silent unit mismatch does not.
Temporal Compatibility
Components may originate at different times.
A component can remain usable if its temporal relevance is sufficiently governed.
However:
Available at Formation Time ≠ Current Enough for Predictive Use
PBAS governs basis relevance.
PCOM governs whether the resulting component state is coherent within the composition.
Methodological Compatibility
Outputs produced through different methodologies may sometimes be combined.
However, combination requires an identifiable composition logic.
Example:
Statistical Forecast + Expert Adjustment
may be legitimate.
But the adjustment must not silently replace or distort the methodology without remaining visible.
Multi-Method Prediction Composition
A prediction may use more than one methodology.
PCOM permits multi-method composition where:
methodologies are identifiable; roles are distinguishable; interactions are governed; conflicts are addressed; final composition remains reconstructable.
Multiple Methodologies ≠ Methodological Incoherence by Default
Multi-Source Prediction Composition
A prediction may use multiple predictive sources.
These may include:
models; analysts; sensors; institutions; external forecasts; market signals.
PCOM governs their role in the composition.
It does not govern Collective Prediction legitimacy where multiple predictions themselves are being aggregated.
That belongs to CPGS.
Multiple Inputs ≠ Collective Prediction
A single prediction can use many inputs.
This does not automatically make it a Collective Prediction.
Collective Prediction requires the relevant collective predictive structure governed under CPGS.
Human Contribution
Human judgment may participate in Prediction Composition through:
adjustment; selection; interpretation; weighting; condition definition; exception handling.
Material human contribution should remain identifiable.
Human Adjustment ≠ Ungoverned Exception by Default
Machine Contribution
Machine-generated components may include:
model scores; forecast values; classifications; generated scenarios; inferred signals; probability estimates.
Machine origin does not establish compositional legitimacy.
Machine Output Availability ≠ Machine Output Suitability
Hybrid Composition
A hybrid prediction may combine human and machine contributions.
Example:
Model Forecast → Analyst Adjustment → Expert Condition → Final Composition
PCOM preserves the material contribution chain.
This enables later distinction between:
what the model produced
and:
what the predictive process ultimately composed.
Analyst Adjustment
An analyst may modify a model output.
The adjustment should remain sufficiently identifiable where material.
Example:
Model Output: 12,000 units
Analyst Adjustment: −1,500 units
Adjusted Component: 10,500 units
The final prediction may legitimately use the adjusted component.
But historical reconstruction must not represent:
10,500
as the original model output.
Component Transformation
A component may be transformed before composition.
Transformations may include:
normalization; scaling; conversion; aggregation; adjustment; filtering; calibration reference; thresholding.
Material transformations should remain traceable.
Transformation ≠ Original Component
A transformed component must not silently replace the identity of the source component.
Component Weighting
Where multiple components contribute with different weights, those weights should remain identifiable where material to Prediction Formation.
Example:
Model A: 60%
Model B: 25%
Expert Adjustment: 15%
Weighting does not automatically establish methodological legitimacy.
It must remain compatible with PMES.
Component Selection
Where several candidate components exist, PCOM may govern which components participate in the composition.
Selection may be based on:
methodology; relevance; scope; quality; horizon; applicability; scenario; governance state.
Material selection logic should remain reconstructable.
Component Substitution
A component may replace another component.
Example:
A new data source replaces an unavailable source.
Substitution should remain explicit where it materially affects Prediction Formation.
Substitute Component ≠ Original Component
Component Duplication
The same underlying information may enter Prediction Composition through several paths.
Example:
A market signal appears:
directly in the dataset; indirectly in analyst judgment; again through an external forecast.
Without governance, the same signal may be effectively counted several times.
Multiple Component Appearances ≠ Independent Predictive Support
Component Overlap
Different components may partially represent the same underlying information or condition.
Overlap should be identified where it can materially distort composition.
Shared Origin
Components may appear independent while originating from the same underlying source.
Example:
Three external forecasts all use the same government dataset.
PCOM should preserve relevant shared-origin relationships where they affect composition.
Multiple Sources ≠ Independent Origins
Component Conflict
A Component Conflict exists where two or more components cannot simultaneously support the same composition without clarification, qualification, transformation, selection, or explicit preservation of disagreement.
Conflicts may be:
semantic; methodological; temporal; assumption-based; boundary-based; numerical; conditional; provenance-related.
Numerical Conflict
Different components may produce different numerical outputs.
Numerical disagreement alone does not necessarily make the composition invalid.
The formation logic may legitimately govern how the disagreement is treated.
Semantic Conflict
Semantic conflict may be more serious than numerical disagreement.
Two identical values may still be incompatible if they refer to different predictive objects or definitions.
Same Number ≠ Same Predictive Meaning
Assumption Conflict
Components may rely on incompatible assumptions.
Example:
Model A assumes:
stable regulation
while Scenario B assumes:
major regulatory change.
These components should not be merged without preserving their conditional difference.
Horizon Conflict
Outputs referring to different Prediction Horizons should not be treated as competing estimates of the same prediction unless their relationship is governed.
Boundary Conflict
Components referring to different populations, regions, systems, or operational boundaries require explicit reconciliation before composition.
Conflict Resolution
Material Component Conflict may be handled through:
clarification; selection; exclusion; transformation; separation; conditional branching; qualification; escalation; inability to compose.
PCOM does not require artificial consensus.
Resolved Composition ≠ Forced Agreement
Alternative Composition Paths
A predictive process may possess multiple legitimate composition pathways.
Example:
Base Case
Optimistic Case
Stress Case
Each may form a separate predictive structure.
PCOM preserves them as distinct where their assumptions or conditions materially differ.
Scenario Contribution
Scenarios may contribute to Prediction Composition.
However:
Scenario ≠ Prediction
A scenario may define conditions under which a prediction is formed.
The resulting Predictive Claim is governed later through PCIM.
Simulation Output Contribution
Simulation outputs may contribute to Prediction Composition.
SIMULOS® governs the simulation itself.
PCOM governs how the resulting output participates in Prediction Formation.
Simulation Output ≠ Governed Prediction
External Prediction Contribution
An external prediction may be used as a component in another prediction.
PCOM governs its compositional role.
If multiple predictions are being combined into a collective prediction, CPGS applies to the collective predictive space.
Component Completeness
Prediction Composition should contain all materially required components necessary to support the next formation stage.
Completeness does not mean including every available predictive element.
It means that no materially necessary formation component is absent.
More Components ≠ More Complete Prediction
Composition Gap
A Prediction Composition Gap exists where the components cannot yet form a sufficiently coherent predictive structure.
Examples include:
missing methodology association; incompatible horizons; unresolved assumption conflict; undefined component role; duplicate information creating distortion; missing conditionality; boundary mismatch; unexplained human adjustment; incompatible predictive outputs.
Composition Gap Severity
A Composition Gap may be:
Minor
Material
Critical
Unable to Determine
The severity should reflect its consequence for legitimate Prediction Formation.
Composition Restriction
A composition may proceed conditionally where unresolved gaps are sufficiently bounded and disclosed.
Where critical incompatibilities remain unresolved, progression may be restricted.
Unable to Compose
PCOM may determine that available components cannot legitimately be assembled into one prediction structure.
This is a valid governance result.
Unable to Compose ≠ Unable to Predict in Principle
A different methodology, basis, assumption structure, or composition pathway may still permit prediction formation.
Composition State
A Prediction Composition may occupy states such as:
Draft Composition
Coherent Composition
Conditionally Coherent
Restricted Composition
Incomplete Composition
Unable to Compose
The exact implementation may use equivalent governed states.
Composition Version
Material changes in the component structure should create a distinguishable composition version or equivalent historical state.
Example:
Composition V1: Model A + Dataset X + Assumptions 1–5
Composition V2: Model A + Dataset Y + Expert Adjustment + Assumptions 1–6
These compositions should not be treated as historically identical.
Historical Composition Integrity
Later component changes must not rewrite the composition used to form an earlier prediction.
The original component structure must remain reconstructable.
Current Composition ≠ Historical Formation Composition
Composition Provenance
PCOM preserves the origin and role of material components sufficiently to support later traceability.
This includes distinguishing:
original component; transformed component; human-adjusted component; machine-generated component; external component; derived component.
Composition Coherence
A Prediction Composition is coherent when its material components can legitimately coexist within the same predictive structure without unresolved contradiction or semantic distortion.
Coherence requires alignment across the applicable:
Prediction Object; Purpose; Horizon; Boundary; methodology; Basis; assumptions; conditions.
Composition Sufficiency
Prediction Composition is sufficiently governed when:
material components are identifiable; their roles are understood; inclusion and material exclusion are traceable; Prediction Object alignment is established;
Purpose alignment is established; Horizon and Boundary relationships are governed; methodology relationships are identifiable; Prediction Basis participation is identifiable; assumption relationships are explicit; conditions remain attached; material transformations are traceable; human and machine contributions remain distinguishable where relevant; duplicates and overlaps are addressed where material; material conflicts are resolved, qualified, or preserved; Composition Gaps remain visible; historical composition remains reconstructable.
Governance Requirements
PCOM requires that:
1. material Predictive Components be identifiable; 2. component roles remain sufficiently explicit; 3. included components remain distinguishable from merely available components; 4. material exclusions remain identifiable where necessary for reconstruction; 5. components remain aligned with the governed Prediction Object; 6. Prediction Purpose alignment be assessed where material; 7. Prediction Horizon mismatches remain explicit; 8. Predictive Boundary mismatches remain explicit; 9. methodology relationships remain identifiable; 10. Prediction Basis components remain associated with their governed basis; 11. Predictive Assumptions remain associated with the components they support; 12. conditional components retain their conditionality; 13. semantic compatibility be established where material; 14. unit compatibility be established where material; 15. temporal compatibility be assessed where relevant; 16. multi-method composition remain reconstructable; 17. human contributions remain identifiable where material; 18. machine contributions remain identifiable where material; 19. hybrid formation preserve the contribution chain; 20. material transformations remain traceable; 21. weighting remain identifiable where material; 22. component substitutions remain explicit where material; 23. duplicate or overlapping components not be treated as independent support by default; 24. shared origins remain visible where material; 25. material Component Conflicts be resolved, qualified, separated, or preserved; 26. scenarios remain distinguishable from predictions; 27. simulation outputs remain distinguishable from predictions; 28. Composition Gaps remain explicit; 29. inability to compose remain an acceptable governance result; 30. historical composition states remain reconstructable.
Governance Logic
PCOM applies the following governance logic:
Governed Prediction Object & Purpose
Governed Prediction Methodology
Governed Prediction Basis
Governed Prediction Assumption Set
Applicable Predictive Conditions / Outputs / Contributions
↓
Predictive Components Identified
↓
Component Roles Established
↓
Included / Excluded Components Distinguished
↓
Object / Purpose / Horizon / Boundary Alignment Assessed
↓
Methodology / Basis / Assumption / Condition Alignment Assessed
↓
Semantic / Temporal / Unit Compatibility Assessed where applicable
↓
Human / Machine / Hybrid Contributions Distinguished
↓
Transformations / Weighting / Selection Identified
↓
Duplicates / Overlaps / Shared Origins Assessed
↓
Component Conflicts Identified
↓
Conflicts Resolved / Qualified / Separated / Preserved
↓
Composition Gaps Assessed
↓
Composition State Established
↓
Governed Prediction Composition
↓
PCIM — Predictive Claim Identification Module
Where sufficient composition cannot be established:
Prediction Composition Gap → Clarification / Selection / Exclusion / Transformation / Qualification / Conditional Composition / Restricted Composition / Unable to Compose
Minimum Implementation Framework
A minimum implementation of PCOM must establish:
A. Prediction Reference
The prediction formation process to which the composition belongs.
B. Prediction Object & Purpose Reference
The applicable governed frame from POPS.
C. Methodology Reference
The applicable Prediction Methodology from PMES.
D. Prediction Basis Reference
The applicable governed Prediction Basis from PBAS.
E. Assumption Set Reference
The applicable Governed Prediction Assumption Set from PAS.
F. Predictive Component Register
The material components participating in Prediction Composition.
G. Component Role
At minimum:
Target / Methodological / Informational / Assumptive / Conditional / Computational / Analytical / Judgmental / Scenario / Simulation-Derived / Transformation / Other
H. Inclusion State
At minimum:
Included / Excluded / Conditional / Candidate / Unable to Determine
I. Compatibility State
At minimum:
Compatible / Conditionally Compatible / Conflicting / Incompatible / Unable to Determine
J. Transformation / Adjustment Reference
Any material transformation, weighting, adjustment, or substitution applied to the component.
K. Conflict / Overlap Reference
Any material conflict, duplication, overlap, or shared-origin relationship.
L. Composition Gap
Any unresolved issue affecting compositional legitimacy.
M. Composition State
At minimum:
Draft / Coherent / Conditionally Coherent / Restricted / Incomplete / Unable to Compose
N. Composition Version
The applicable composition version or equivalent historical identifier.
O. Historical Composition State
The component structure applicable to the relevant Prediction Formation event.
Governance Outputs
PCOM may produce:
Governed Prediction Composition; Predictive Component Register; component-role record; inclusion/exclusion record; component-compatibility profile; Prediction Object alignment record; Purpose alignment record; Horizon alignment record; Boundary alignment record; methodology-alignment record; Prediction Basis association; assumption-association record; condition-association record; human-contribution record; machine-contribution record; hybrid-composition record; transformation record; adjustment record; weighting record; substitution record; duplication/overlap record; shared-origin record; Component Conflict Register; Prediction Composition Gap Register; Composition State; Composition Version; historical composition record.
The primary governed output is:
Governed Prediction Composition
which becomes the principal input to:
PCIM — Predictive Claim Identification Module.
Critical Distinctions
Available Predictive Components ≠ Coherent Prediction Composition
Component Availability ≠ Component Inclusion
Related Information ≠ Same Prediction Object
Component Suitability for Purpose A ≠ Suitability for Purpose B
Different Horizons ≠ Directly Composable Components
Conditional Component + Composition ≠ Unconditional Component
Same Label ≠ Same Predictive Meaning
Multiple Methodologies ≠ Methodological Incoherence by Default
Multiple Inputs ≠ Collective Prediction
Human Adjustment ≠ Ungoverned Exception by Default
Machine Output Availability ≠ Machine Output Suitability
Transformation ≠ Original Component
Substitute Component ≠ Original Component
Multiple Component Appearances ≠ Independent Predictive Support
Multiple Sources ≠ Independent Origins
Same Number ≠ Same Predictive Meaning
Resolved Composition ≠ Forced Agreement
Scenario ≠ Prediction
Simulation Output ≠ Governed Prediction
More Components ≠ More Complete Prediction
Unable to Compose ≠ Unable to Predict in Principle
Current Composition ≠ Historical Formation Composition
Enterprise Application
PCOM gives organizations a controlled method for assembling complex predictive systems before a final prediction is formed.
This is especially important where predictions combine:
AI models + statistical models + human judgment + external forecasts + operational signals + assumptions + simulations.
Without Prediction Composition Governance, organizations may accidentally:
combine different Prediction Horizons; double-count the same information; merge incompatible assumptions; confuse scenarios with predictions; hide analyst overrides; combine outputs referring to different predictive objects; treat several dependent sources as independent support.
PCOM makes the structure beneath the prediction visible before the final Predictive Claim is established.
Use Case 1 — Three Forecasts Look Independent but All Depend on the Same Information
A financial institution uses three external forecasting providers to support an economic prediction.
Their outputs are:
Provider A: 3.1% growth
Provider B: 3.3% growth
Provider C: 3.2% growth
The close agreement appears to provide strong independent confirmation.
During Prediction Composition Governance, the institution discovers that all three providers rely heavily on the same underlying government dataset and substantially similar macroeconomic assumptions.
The critical governance question is:
Can three predictive components legitimately be treated as independent support merely because they originate from three different providers?
PCOM preserves the shared-origin relationship and prevents the composition from representing the three outputs as independent predictive foundations by default.
Multiple Sources ≠ Independent Origins
Use Case 2 — AI and Human Forecasts Are Combined Even Though They Predict Different Horizons
An industrial company uses an AI system to predict:
component demand during the next 30 days.
Management also provides an expert estimate for:
component demand during the next 12 months.
A planning dashboard averages both values and presents the result as:
expected demand.
The numerical combination appears reasonable.
But the components do not represent the same Prediction Horizon.
The critical governance question is:
Can predictive outputs be legitimately combined merely because they measure the same general subject when their predictive horizons and governing assumptions are materially different?
PCOM identifies the Horizon Conflict and requires:
separation; transformation;
explicit composition logic; or inability to combine the outputs directly.
Different Horizons ≠ Directly Composable Components
Canonical Boundary
PCOM governs the identification, role assignment, inclusion, exclusion, association, compatibility, alignment, transformation, weighting, overlap, conflict, coherence, state, versioning, and historical preservation of components participating in Prediction Composition. It does not govern the underlying Prediction Object and Purpose, Prediction Methodology design, Prediction Basis itself, Predictive Assumption Governance, final Predictive Claim identification, final Prediction Output Formation, Prediction State establishment, probability and uncertainty representation, feasibility determination, Collective Prediction governance, or prediction-level revision.
PCOM establishes:
whether the predictive components can coherently belong to the same Prediction Formation structure.
It does not yet determine:
the final predictive claim produced from that structure.
Canonical Closing Statement
A prediction cannot become coherent merely because all of its components are individually available.
PCOM governs the structure in which methodology, information, assumptions, conditions, human judgment, and machine outputs can legitimately become parts of the same prediction.
Before a predictive claim can be governed, the components from which it is formed must first belong together.