Methodological Structure Definition Module
Module: MSDM — Methodological Structure Definition Module
Parent Standard: PMES — Prediction Methodology Standard
Architecture: PGA™ — Prediction Governance Architecture
Category: Governance & Enforcement
Subcategory: Prediction Governance
Governed Space: Methodological Structure Definition
Primary Governed Object: Prediction Methodology Structure
Module Type: Core Module
Canonical Language: UCL — Universal Canonical Language
Protection: MIP® — Methodological Intellectual Property
1. Canonical Definition
The Methodological Structure Definition Module (MSDM) governs the definition, organization, differentiation, relationship, sequencing, and structural coherence of the material components through which a selected predictive method operates as a Prediction Methodology.
MSDM establishes how the selected predictive method is structurally organized before its predictive logic, applicability, and methodology version are subsequently governed.
A selected method does not become a sufficiently governed methodology merely because its name, model, technology, or general methodological class is known.
Selected Predictive Method ≠ Defined Prediction Methodology Structure
2. Operational Role
MSDM is the second Core Module within PMES.
It receives the Selected Predictive Method from PMSM and establishes the structural organization through which that method participates in prediction construction.
Its principal transition is:
Selected Predictive Method → Defined Methodological Structure
MSDM governs what the methodology consists of and how its material components relate.
It does not yet provide the complete documentation of the predictive logic connecting those components to predictive output.
3. Internal Module Flow
Predictive Method Selection → → Methodological Structure Definition → Predictive Logic Documentation → Method Applicability → Methodology Version Governance
Current Position: Methodological Structure Definition
The defined structure proceeds into:
PLDM — Predictive Logic Documentation Module
4. Operational Space
MSDM operates within the Methodological Structure Definition space.
It governs:
methodological component identification; component differentiation; component relationships; methodological sequencing; structural dependencies; methodological stages; human and machine roles; multi-method structure; structural completeness; structural coherence; structural change; structural traceability.
MSDM governs the architecture of the Prediction Methodology.
5. Operational Scope
Methodological structures may contain:
analytical procedures; statistical mechanisms; mathematical models; AI models; machine-learning systems; human judgment; expert review; aggregation mechanisms; transformation stages; simulation-derived components; market-derived mechanisms; decision rules; model ensembles; computational processes; other material predictive components.
MSDM does not prescribe a universal methodology structure.
It requires the actual structure used to remain sufficiently identifiable.
6. Methodological Component Identification
The material components forming the Prediction Methodology must be identifiable.
A component is material where its presence, absence, configuration, relationship, or function can materially affect how the methodology operates or what predictive output it can produce.
Not every technical implementation detail must become a separate governed component.
The governance requirement is material structural visibility.
Technical Detail ≠ Methodological Component by Default
7. Component Differentiation
Methodological components performing materially different functions must remain distinguishable.
A model, human reviewer, aggregation mechanism, simulation process, transformation stage, or market mechanism must not be represented as methodologically interchangeable where their functions differ.
Shared Methodology ≠ Same Methodological Function
This distinction becomes especially important in hybrid and multi-stage predictive systems.
8. Component Relationships
MSDM must establish how material methodological components relate to one another.
Relationships may include:
input relationship; dependency; sequence; transformation; aggregation; comparison; filtering; weighting; review; escalation; selection; combination; feedback; another structurally material relationship.
Identifying components without identifying their material relationships may leave the methodology structurally undefined.
Known Components ≠ Defined Methodological Structure
9. Methodological Sequencing
Where sequence materially affects predictive operation, MSDM must establish the order in which methodological components act.
For example:
Source Processing → Model Analysis → Expert Review → Aggregation
is methodologically different from:
Expert Judgment → Model Adjustment → Final Aggregation
even where the same components are present.
Same Components + Different Sequence ≠ Same Methodological Structure
10. Structural Stages
A methodology may operate through multiple stages.
MSDM must distinguish stages where they represent materially different methodological functions.
Stages may include:
preparation; transformation; analysis; modeling; comparison; aggregation; review; synthesis; output preparation.
Stage names themselves are not canonical requirements.
What matters is that materially distinct methodological functions remain structurally identifiable.
11. Structural Dependencies
A methodological component may depend upon another component, system, process, capability, or condition.
Material structural dependencies must remain identifiable where failure, replacement, or alteration of the dependency could materially change the methodology.
Component Presence ≠ Component Independence
MSDM governs structural dependency.
Prediction Basis source relationships remain governed by PBAS.
12. Mandatory and Conditional Components
A methodology may contain components that are:
Mandatory
Conditional
Optional
Alternative
Fallback
Where these distinctions materially affect how the methodology operates, they must remain identifiable.
A conditional component must not retrospectively be represented as though it had participated in every predictive instance.
Available Component ≠ Applied Component
13. Human Methodological Components
Human participation may form a structural component of Prediction Methodology.
Roles may include expert judgment, interpretation, review, adjustment, selection, escalation, or synthesis.
Where human intervention can materially alter the predictive process, its structural position must remain identifiable.
Human Involvement ≠ Undefined Methodology
Likewise:
Human Review ≠ Automatic Methodological Validation
14. AI and Machine Components
AI models, algorithms, machine-learning systems, and other computational mechanisms may form methodological components.
MSDM must establish their structural role rather than treating the presence of AI as a sufficient description of the methodology.
A methodology containing an AI model may also contain preprocessing, multiple models, retrieval systems, external tools, aggregation, human review, or other components.
AI Model Presence ≠ Defined AI Prediction Methodology
15. Multi-Model Structures
A Prediction Methodology may contain multiple models.
MSDM must establish their structural relationship where material.
Models may operate:
independently; sequentially; competitively; hierarchically; through voting; through weighting; through ensemble aggregation; through fallback logic; through another defined structure.
Multiple Models ≠ Ensemble by Default
16. Hybrid Methodological Structures
A methodology may combine human, computational, market-based, simulation-derived, statistical, or other predictive mechanisms.
MSDM governs how those mechanisms are structurally connected.
A hybrid methodology cannot be sufficiently described merely by listing its components.
Component List ≠ Hybrid Methodological Structure
The relationship among the components is part of the methodology.
17. Prediction Market Structures
Where prediction-market mechanisms form part of Prediction Methodology, MSDM identifies their structural position within the methodology.
A market-derived signal may function as:
primary predictive mechanism; one input into another method; comparison signal; aggregation component; adjustment mechanism; another defined methodological component.
MSDM does not determine whether market participants are independent, representative, information-advantaged, or concentrated.
Those governance questions belong principally to PBAS and CPGS.
18. Simulation-Supported Structures
Where simulation contributes to Prediction Methodology, MSDM establishes where simulation-derived outputs enter the predictive structure and how they relate to other methodological components.
Simulation itself remains governed by SIMULOS®.
Simulation Component ≠ Prediction Methodology in Its Entirety
The structural use of simulation within prediction does not transfer simulation governance to PGA™.
19. Structural Inputs and Outputs
Each material methodological component should have sufficiently identifiable relationships to what it receives and what it produces where those relationships are necessary to understand the methodology.
MSDM governs structural input-output relationships.
It does not determine the evidentiary quality or sufficiency of the information entering the methodology.
Methodological Input Position ≠ Prediction Basis Sufficiency
20. Structural Transformation
Where one methodological component transforms the output of another, the existence and role of that transformation must remain identifiable.
A transformation may materially affect the predictive meaning of downstream outputs even where the original source remains unchanged.
MSDM identifies the transformation stage.
PLDM subsequently governs the predictive logic explaining how that transformation contributes to prediction construction.
21. Structural Aggregation
Where multiple methodological components produce outputs that are subsequently combined, the aggregation point must remain structurally identifiable.
MSDM establishes where and between what components aggregation occurs .
It does not necessarily determine the detailed aggregation logic.
Structural Aggregation Point ≠ Aggregation Logic
Detailed predictive logic belongs to PLDM, while collective prediction governance may additionally belong to CPGS where multiple predictors are involved.
22. Structural Feedback
Some methodologies may contain feedback relationships in which outputs from one stage affect an earlier or parallel stage.
Where such feedback materially changes methodology operation, the feedback structure must remain identifiable.
A methodology with feedback is structurally different from an otherwise similar one-directional process.
Feedback Structure ≠ Linear Structure
23. Structural Completeness
A methodological structure is sufficiently complete when all components and relationships necessary to understand the material organization of the predictive method are represented.
Completeness does not require documentation of every software function, mathematical operation, internal parameter, or implementation detail.
Structural Completeness ≠ Exhaustive Technical Documentation
The appropriate level is the level necessary for methodological governance.
24. Structural Coherence
Components must form a coherent methodological structure.
A collection of individually legitimate methods does not automatically constitute a coherent Prediction Methodology.
MSDM must identify whether components can legitimately operate together within the selected predictive method.
Valid Components ≠ Coherent Methodology
Where material structural incompatibility exists, the methodology requires clarification or redesign before proceeding.
25. Structural Complexity
Methodologies may range from a single defined predictive mechanism to highly distributed human-machine systems.
MSDM does not reward complexity.
A structure should contain the components necessary to govern the methodology and no implied requirement exists to add methodological stages merely to create sophistication.
Structural Complexity ≠ Methodological Quality
26. Structural Change
A material change to methodological components, relationships, sequencing, dependencies, aggregation, or human- machine roles may constitute a methodological structure change.
Such change must remain identifiable.
MSDM establishes that the structure changed.
MVGM — Methodology Version Governance Module subsequently governs whether that change requires a new methodology version and how continuity is preserved.
Structural Change ≠ Same Methodology Structure by Default
27. Structural Definition Sufficiency
Methodological Structure is sufficiently defined when:
material components are identifiable; materially different functions are distinguishable; component relationships are identifiable; sequence is defined where material; dependencies are identifiable; mandatory and conditional components are distinguishable where relevant; multi-method relationships are defined; human and machine roles are visible where material; aggregation and feedback structures are identifiable where applicable; the overall structure is coherent; material structural gaps are not concealed.
Structural sufficiency does not establish Method Applicability or predictive correctness.
28. Governance Requirements
MSDM requires that:
1. material methodological components be identifiable; 2. components performing materially different functions remain distinguishable; 3. material component relationships be established; 4. methodological sequence be defined where relevant; 5. material structural dependencies remain identifiable; 6. mandatory, conditional, optional, and alternative components remain distinguishable where material; 7. human and machine roles remain structurally visible where they can materially affect prediction; 8. multi-model and hybrid structures define component relationships; 9. aggregation and feedback structures remain identifiable where applicable; 10. structural completeness be sufficient for methodological governance; 11. structural coherence be established; 12. material structural changes remain traceable.
29. Governance Logic
MSDM applies the following operational logic:
Selected Predictive Method
↓
Material Components Identified
↓
Component Functions Differentiated
↓
Component Relationships Established
↓
Sequence / Stages Established where applicable
↓
Dependencies Identified
↓
Conditional / Alternative Components Identified
↓
Multi-Method / Human-Machine Structure Established where applicable
↓
Aggregation / Feedback Structure Established where applicable
↓
Structural Completeness Assessment
↓
Structural Coherence Assessment
↓
Defined Methodological Structure
↓
PLDM — Predictive Logic Documentation Module
Where structural sufficiency is not established:
Structural Governance Gap → Clarification / Restructuring → Reassessment
30. Minimum Implementation Framework
A minimum implementation of MSDM must establish:
A. Selected Method Reference
The predictive method selected under PMSM.
B. Material Components
The components necessary to represent the methodology's material structure.
C. Component Functions
The methodological function performed by each material component.
D. Component Relationships
Material relationships connecting the components.
E. Methodological Sequence
The applicable order of operation where sequence matters.
F. Structural Dependencies
Dependencies capable of materially affecting methodology operation.
G. Component Status
Where applicable:
Mandatory / Conditional / Optional / Alternative / Fallback
H. Human–Machine Structure
The relationship between human and machine components where applicable.
I. Aggregation / Feedback Structure
Material aggregation or feedback relationships where present.
J. Structural Coherence State
Whether the components form a coherent Prediction Methodology structure.
K. Structure State
At minimum:
Undefined → Candidate → Clarification Required → Defined → Changed → Superseded
Only a sufficiently Defined methodological structure proceeds into predictive logic documentation.
31. Governance Outputs
MSDM may produce:
Defined Methodological Structure; methodological component map; component-function references; component relationships; methodological sequence; dependency structure; component-status references; human-machine structural relationship; aggregation structure; feedback structure; structural coherence state; methodology structure state; unresolved structural requirements.
The primary governed output is:
Defined Methodological Structure
This becomes the principal input into:
PLDM — Predictive Logic Documentation Module
32. Critical Distinctions
Selected Predictive Method ≠ Defined Prediction Methodology Structure
Technical Detail ≠ Methodological Component by Default
Known Components ≠ Defined Methodological Structure
Same Components + Different Sequence ≠ Same Methodological Structure
Component Presence ≠ Component Independence
Available Component ≠ Applied Component
Human Involvement ≠ Undefined Methodology
Human Review ≠ Automatic Methodological Validation
AI Model Presence ≠ Defined AI Prediction Methodology
Multiple Models ≠ Ensemble by Default
Component List ≠ Hybrid Methodological Structure
Simulation Component ≠ Prediction Methodology in Its Entirety
Methodological Input Position ≠ Prediction Basis Sufficiency
Structural Aggregation Point ≠ Aggregation Logic
Valid Components ≠ Coherent Methodology
Structural Completeness ≠ Exhaustive Technical Documentation
Structural Complexity ≠ Methodological Quality
33. Enterprise Application
MSDM allows organizations to move beyond labels such as “AI forecast,” “expert forecast,” “ensemble model,” or “market prediction” and establish what the predictive methodology actually consists of.
This is particularly important in complex predictive environments where a final prediction may pass through multiple models, external services, human interventions, transformations, simulations, and aggregation stages before becoming visible to a decision-maker.
Without structural governance, an organization may know which technologies are present while remaining unable to explain how they collectively form the Prediction Methodology.
34. Use Case 1 — AI Prediction Built from an Unknown Chain of Systems
An organization receives a high-impact prediction from an AI-based forecasting platform.
The final interface identifies the prediction as “AI-generated,” but the output actually passes through data transformation, retrieval systems, several specialized models, external analytical services, a simulation component, an aggregation layer, and human adjustment before publication.
The organization knows the final model name but does not possess a governed representation of how these components interact.
The governance question is:
Can an organization claim to know the methodology behind a prediction when it knows the technologies involved but cannot establish the structure through which they collectively produced the predictive result?
MSDM requires the material components, relationships, sequence, dependencies, and human-machine roles to become structurally identifiable.
Knowing the Components ≠ Knowing the Methodology Structure
35. Use Case 2 — Human Override Changes a Model Prediction
A predictive system produces a model-based probability estimate.
Before the prediction reaches decision-makers, a domain expert reviews the output and may increase, decrease, reject, or replace the model prediction based on professional judgment.
The published result continues to be described as a “model prediction.”
The governance question is:
Is the methodology still legitimately represented as model-based when an undocumented human intervention can materially determine the final predictive output?
MSDM requires the human intervention to be represented as part of the methodological structure where it can materially affect prediction formation.
The issue is not whether human intervention is good or bad.
The issue is whether the actual structure that produced the prediction is the structure being represented.
Presented Methodology ≠ Actual Methodology unless their material structures correspond.
36. Canonical Boundary
MSDM governs the identification, organization, differentiation, relationships, sequencing, dependencies, human- machine roles, aggregation structure, feedback structure, completeness, coherence, and change of the material components forming Prediction Methodology. It does not govern predictive method selection, detailed predictive logic documentation, final Method Applicability, methodology version governance, Prediction Basis sufficiency, assumptions, prediction formation, probability representation, feasibility, or predictive performance.
MSDM establishes what the Prediction Methodology consists of and how its material parts are structurally connected .
It does not yet establish the complete predictive logic by which those parts produce the prediction .
Canonical Closing Statement
A list of models, tools, experts, and systems does not define a methodology. Their governed relationships do.
MSDM makes the structure behind a prediction identifiable before its predictive logic can be legitimately documented and evaluated.