ADMM — Assumption Dependency Mapping Module
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Parent Standard:
Prediction Assumption Standard (PAS)
Operational Layer: Prediction Assumption Governance Layer
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard Module
Governed Space:
Assumption Dependency Mapping
Version: 1.0
Status: Canonical · Open Module
Effective Date: 7 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
Assumption Dependency Mapping
Canonical Definition
Assumption Dependency Mapping Module (ADMM) defines the governance conditions under which identified Predictive Assumptions are mapped to the assumptions, Prediction Inputs, methodology requirements, operating conditions, system states, external conditions, decisions, and other predictive dependencies upon which they rely or which rely upon them.
ADMM establishes the governed dependency structure of Prediction Assumptions.
It transforms a list of assumptions into a relational structure showing:
what depends on what
and:
where assumption change may propagate through the predictive system.
ADMM does not determine how sensitive the prediction is to an assumption, how material the assumption is, or what governance action must follow when it changes.
Those functions belong to later PAS modules.
Its function is to make dependency visible.
Assumption Identification ≠ Assumption Dependency Governance
Operational Role
ADMM serves as the second internal governance space of the Prediction Assumption Standard (PAS) .
It receives:
Identified Predictive Assumptions
from:
PAIM — Predictive Assumption Identification Module
and establishes their material dependency relationships.
The module asks:
What does each assumption depend upon, what depends upon it, and how could a change in one assumption affect other parts of the predictive structure?
The governed progression is:
Identified Predictive Assumptions → Dependency Identification → Dependency Classification → Assumption Chain Mapping → Dependency Concentration → Dependency State → Governed Assumption Dependency Map
The resulting dependency map becomes an input to:
ASNM — Assumption Sensitivity Module.
Internal Module Flow
Predictive Assumption Identification → → Assumption Dependency Mapping → Assumption Sensitivity → Assumption Materiality → Assumption Change Governance
Current Position: Assumption Dependency Mapping
Module Operational Space
ADMM governs:
assumption-to-assumption dependency; assumption-to-input dependency; assumption-to-methodology dependency; assumption-to-operational-condition dependency; assumption-to-system-state dependency; assumption-to-external-condition dependency; assumption-to-decision dependency; assumption-to-resource dependency; direct dependency; indirect dependency; upstream dependency; downstream dependency; conditional dependency; shared dependency; common dependency; dependency chain; dependency cluster; dependency network; dependency concentration; dependency depth;
dependency direction; dependency state; dependency uncertainty; dependency conflict; circular dependency; dependency bottlenecks; dependency propagation paths; dependency change; and historical dependency preservation.
Module Function
A prediction may contain many assumptions that appear independent while actually relying on the same underlying condition.
For example:
Supplier remains operational
may support:
Components remain available
which supports:
Production remains uninterrupted
which supports:
Delivery targets remain achievable.
If the supplier fails, several downstream assumptions may fail together.
Without dependency mapping, the prediction may appear to rely on several independent assumptions when in reality all of them depend upon one upstream condition.
ADMM makes that structure visible.
Multiple Assumptions ≠ Independent Assumptions
Assumption Dependency
An Assumption Dependency exists where the applicability, plausibility, meaning, state, or continued relevance of one assumption materially depends upon another governed element.
That element may be:
another assumption; Prediction Input; methodology requirement; operational condition; system state; external condition; decision; resource; boundary; time condition.
Direct Dependency
A Direct Dependency exists where one assumption materially relies upon another element without a materially relevant intermediary.
Example:
Assumption A: Supplier remains operational
↓
Assumption B: Supplier continues deliveries
Assumption B directly depends upon Assumption A.
Indirect Dependency
An Indirect Dependency exists where a relationship operates through one or more intermediary assumptions or conditions.
Example:
Supplier Solvency
↓
Supplier Operations
↓
Component Availability
↓
Production Continuity
The relationship between supplier solvency and production continuity may be indirect but highly important.
Indirect Dependency ≠ Weak Dependency by Default
Upstream Dependency
An Upstream Dependency is a condition or assumption upon which another governed assumption depends.
Upstream dependencies may become critical because failure can propagate across multiple downstream assumptions.
Downstream Dependency
A Downstream Dependency is an assumption, predictive condition, or predictive state affected by an upstream dependency.
Mapping downstream relationships helps determine where an assumption change could propagate later in the Prediction Lifecycle.
Dependency Direction
Dependency should remain directionally identifiable where material.
For example:
A → B
does not automatically imply:
B → A
Dependency A→B ≠ Dependency B→A
This distinction prevents false assumptions of mutual dependence.
Conditional Dependency
Some dependencies exist only under defined conditions.
Example:
Demand Forecast Depends on Weather
only where:
Outdoor Event Condition = Active
Conditional dependency must not be represented as universal dependency.
Conditional Dependency ≠ Universal Dependency
Shared Dependency
Two or more assumptions may depend upon the same underlying condition.
Example:
Assumption A
and:
Assumption B
both depend upon:
System X remains operational.
This Shared Dependency can create concentrated predictive vulnerability.
Common Dependency
Common Dependency represents a materially shared underlying requirement across multiple assumptions.
It is especially important where apparently separate assumptions are only conditionally independent.
Separate Assumption Statements ≠ Separate Dependency Foundations
Assumption Chain
An Assumption Chain is a sequence of materially linked assumptions or conditions.
Example:
Regulation Remains Stable
↓
Product Remains Legally Marketable
↓
Distribution Continues
↓
Projected Sales Remain Achievable
ADMM preserves the chain so that later sensitivity and materiality assessment can examine the consequences of change.
Assumption Dependency Network
Complex predictions may involve many interconnected assumptions.
The resulting structure may be represented as an Assumption Dependency Network .
A network may contain:
upstream dependencies; downstream dependencies; shared dependencies; branching dependencies; converging dependencies; conditional dependencies; circular relationships.
ADMM does not require excessive modeling complexity.
It requires enough structure to identify material dependency relationships.
Dependency Cluster
A Dependency Cluster is a group of assumptions materially linked through one or more shared dependencies.
Clusters may be useful where predictive vulnerability is concentrated around:
one supplier; one market condition; one infrastructure system; one policy state; one external actor; one methodology condition.
Dependency Concentration
Dependency Concentration occurs where many assumptions depend upon a limited number of upstream conditions.
High dependency concentration can make a prediction structurally fragile.
It does not itself establish materiality.
That is governed later by AMAM.
Dependency Concentration ≠ Assumption Materiality by Default
Dependency Depth
Dependency Depth represents the number or complexity of dependency layers between an assumption and its underlying conditions.
Deep dependency chains may make predictive structure harder to understand and monitor.
Depth alone does not establish risk.
Dependency Bottleneck
A Dependency Bottleneck exists where one upstream condition supports many downstream assumptions.
For example:
Primary Data Feed Remains Available
may support:
market-state assumption; price-stability assumption; liquidity assumption; demand assumption; execution-condition assumption.
A bottleneck may become a critical structural point in later sensitivity and materiality analysis.
Assumption-to-Input Dependency
An assumption may materially depend upon Prediction Basis.
Example:
Demand remains stable
may be supported by recent transaction data.
ADMM maps the relationship:
Prediction Input → Assumption
without converting the assumption into fact.
Input Support ≠ Assumption Truth
Assumption-to-Methodology Dependency
A methodology may require certain assumptions to hold.
Example:
historical relationship remains applicable
may be necessary for a forecasting methodology to remain meaningful.
ADMM maps this relationship.
PMES retains ownership of Prediction Methodology.
Assumption-to-Operational-Condition Dependency
An assumption may depend upon an operational condition.
Example:
Delivery capacity remains sufficient
may depend upon:
Warehouse automation remains operational.
ADMM maps the predictive dependency.
ORA™ governs Operational Reality itself.
Assumption-to-System-State Dependency
AI, software, industrial, and autonomous systems may contain assumptions dependent upon system states such as:
model version; operating mode; system configuration; network availability; permission state; infrastructure state.
A change in system state may therefore affect multiple assumptions.
Assumption-to-External-Condition Dependency
External dependencies may include:
regulation; interest rates; weather; geopolitical conditions; supplier state; infrastructure; commodity availability.
Externality does not reduce governance importance.
Assumption-to-Decision Dependency
A predictive assumption may depend upon a decision made by:
management; regulator; operator; customer; supplier; government; algorithmic system; autonomous agent.
The decision may not yet be known.
The assumption must still preserve the dependency where material.
Assumption-to-Resource Dependency
Some assumptions rely upon continuing availability of:
capital; personnel; energy; materials; computing capacity; logistics; infrastructure.
These dependencies can become critical in operational predictions.
Dependency Independence
An assumption should not be treated as independent merely because no explicit dependency has yet been documented.
No Documented Dependency ≠ Demonstrated Independence
Where independence materially affects predictive interpretation, it should be sufficiently supported.
Dependency Uncertainty
Dependency relationships may themselves be uncertain.
Potential states may include:
Established
Probable
Possible
Uncertain
Not Materially Established
ADMM preserves uncertainty rather than forcing a deterministic dependency map.
Dependency Strength
Where methodology supports it, dependency may be represented as:
Weak
Moderate
Strong
Critical
or other governed states.
Dependency strength should not be confused with assumption materiality.
Strong Dependency ≠ High Materiality by Default
Circular Dependency
Circular Dependency exists where assumptions materially depend upon one another in a loop.
Example:
Market Confidence Remains Strong
supports:
Investment Continues
which supports:
Market Confidence Remains Strong.
Circular structures require explicit visibility because they can create misleading predictive reinforcement.
Mutual Support ≠ Independent Confirmation
Feedback Dependency
Some predictive environments contain dynamic feedback relationships.
A prediction may depend upon assumptions that are themselves affected by predictive outputs, decisions, or actions.
Where material, ADMM identifies the dependency relationship.
Prediction-level reflexivity remains governed through the broader PGA™ lifecycle.
Dependency Conflict
A Dependency Conflict may occur where assumptions depend upon conditions that cannot simultaneously hold or where one dependency undermines another.
For example:
Assumption A requires low prices
while:
Assumption B requires high supplier margins.
Such structural tension must remain identifiable.
Dependency Change
Dependencies may change over time.
Examples include:
supplier replacement; methodology change; system redesign; new regulation; organizational restructuring; change in decision authority; new information pathway.
A historical dependency map must not be silently overwritten.
Dependency State
A governed dependency may possess a state such as:
Active
Conditional
Uncertain
Weakened
Broken
No Longer Applicable
The applicable state depends upon implementation context.
Dependency Breach
A dependency may cease to support the assumption as expected.
This does not itself determine that the downstream assumption has failed.
It creates a governance condition requiring later sensitivity and change analysis.
Dependency Breach ≠ Automatic Assumption Failure
Dependency Propagation Path
A Dependency Propagation Path identifies how a change in one assumption or condition may affect other assumptions.
Example:
Fuel Supply Disruption
↓
Transport Capacity Assumption Weakens
↓
Delivery Continuity Assumption Weakens
↓
Revenue Forecast Assumption Weakens
ADMM maps the pathway.
ASNM later assesses sensitivity.
ACGM later governs actual change propagation.
Dependency Isolation
An assumption may be relatively isolated from other assumptions.
Isolation should not be assumed without examining material dependencies.
An isolated assumption may still be highly material.
Historical Dependency State
The dependency structure applicable at prediction formation must remain distinguishable from later dependency relationships.
For example, a supplier may later become vertically integrated into the organization.
That later relationship must not rewrite the historical dependency state.
Current Dependency Structure ≠ Historical Dependency Structure
Dependency Mapping Sufficiency
Assumption Dependency Mapping is sufficiently governed when:
material dependency relationships are identifiable; dependency direction is clear; direct and indirect dependencies are distinguishable where necessary; shared dependencies are visible; dependency chains can be reconstructed; dependency concentration is identifiable; circular dependencies remain visible; dependency uncertainty is preserved; material propagation paths can be traced; dependency changes remain historically reconstructable.
Governance Requirements
ADMM requires that:
1. identified Predictive Assumptions be assessed for material dependencies; 2. assumption-to-assumption dependencies be identifiable; 3. assumption-to-input dependencies remain distinguishable; 4. assumption-to-methodology dependencies be represented where material; 5. operational and system-state dependencies remain visible; 6. external-condition dependencies be identified where material; 7. dependency direction remain explicit; 8. direct and indirect dependencies remain distinguishable where necessary; 9. conditional dependencies not be represented as universal; 10. shared dependencies remain visible; 11. dependency chains be reconstructable; 12. dependency concentration be identifiable; 13. no documented dependency not be treated as demonstrated independence; 14. dependency uncertainty remain explicit; 15. circular dependencies remain visible; 16. mutual support not be mistaken for independent confirmation; 17. dependency conflicts remain governable; 18. dependency breach remain distinguishable from automatic assumption failure; 19. material propagation paths remain identifiable; 20. current dependency structure remain distinguishable from historical dependency structure.
Governance Logic
ADMM applies the following governance logic:
Identified Predictive Assumptions
↓
Potential Dependencies Identified
↓
Assumption-to-Assumption Relationships Mapped
↓
Input / Methodology / Operational / System / External Dependencies Mapped
↓
Dependency Direction Established
↓
Direct / Indirect / Conditional Relationships Classified
↓
Shared Dependencies Identified
↓
Assumption Chains and Clusters Mapped
↓
Dependency Concentration / Bottlenecks Identified
↓
Circular Relationships and Conflicts Identified
↓
Dependency State and Uncertainty Established
↓
Propagation Paths Preserved
↓
Governed Assumption Dependency Map
↓
ASNM — Assumption Sensitivity Module
Where dependency relationships remain materially unresolved:
Assumption Dependency Governance Gap → Clarification / Additional Mapping / Qualification / Explicit Uncertainty
Minimum Implementation Framework
A minimum implementation of ADMM must establish:
A. Assumption Reference
The Predictive Assumption governed through PAIM.
B. Dependency Identifier
A sufficiently unique identifier for the dependency relationship.
C. Dependency Target
The assumption, input, methodology requirement, condition, system state, decision, resource, or other governed element upon which the assumption depends.
D. Dependency Type
At minimum:
Assumption / Input / Methodology / Operational Condition / System State / External Condition / Decision / Resource / Other
E. Dependency Direction
At minimum:
Upstream → Downstream
F. Dependency Form
Where applicable:
Direct / Indirect / Conditional / Shared / Circular
G. Dependency State
At minimum:
Active / Conditional / Uncertain / Weakened / Broken / No Longer Applicable
H. Dependency Strength
Where used:
Weak / Moderate / Strong / Critical / Unable to Determine
I. Shared Dependency Reference
Where several assumptions rely upon the same underlying condition.
J. Dependency Chain Reference
Where the assumption participates in a wider dependency chain.
K. Propagation Path
The material downstream relationships potentially affected by dependency change.
L. Conflict / Circularity Flag
Any material structural conflict or circular relationship.
M. Historical Dependency State
The dependency relationship applicable at prediction formation and material later changes.
Governance Outputs
ADMM may produce:
Governed Assumption Dependency Map; Assumption Dependency Register; assumption-to-assumption map; assumption-to-input map;
methodology dependency record; operational dependency record; system-state dependency record; external-condition dependency record; decision dependency record; resource dependency record; Shared Dependency Map; Assumption Chain Map; Dependency Cluster Map; Dependency Concentration Profile; Dependency Bottleneck Record; Circular Dependency Record; Dependency Conflict Record; Dependency Propagation Map; dependency-state record; dependency-uncertainty record; historical dependency record; unresolved dependency requirements.
The primary governed output is:
Governed Assumption Dependency Map
This becomes a principal input into:
ASNM — Assumption Sensitivity Module.
Critical Distinctions
Assumption Identification ≠ Assumption Dependency Governance
Multiple Assumptions ≠ Independent Assumptions
Indirect Dependency ≠ Weak Dependency by Default
Dependency A→B ≠ Dependency B→A
Conditional Dependency ≠ Universal Dependency
Separate Assumption Statements ≠ Separate Dependency Foundations
Dependency Concentration ≠ Assumption Materiality by Default
Input Support ≠ Assumption Truth
No Documented Dependency ≠ Demonstrated Independence
Strong Dependency ≠ High Materiality by Default
Mutual Support ≠ Independent Confirmation
Dependency Breach ≠ Automatic Assumption Failure
Current Dependency Structure ≠ Historical Dependency Structure
Enterprise Application
ADMM enables organizations to see whether predictive assumptions are truly independent or merely different expressions of the same underlying dependency.
This becomes critical in:
AI forecasting + supply-chain prediction + financial modeling + industrial systems + strategic planning + insurance + healthcare + infrastructure + public policy + autonomous systems.
Without dependency mapping, a prediction may appear diversified while remaining structurally dependent upon:
one supplier; one data source; one infrastructure service; one regulatory state; one market condition; one human decision; one system assumption.
ADMM exposes these dependency structures before PAS evaluates sensitivity and materiality.
Use Case 1 — Five Assumptions Fail Together Because They All Depend on One Supplier
A manufacturer maintains five explicit predictive assumptions:
production capacity remains available; component supply remains stable; delivery schedules remain achievable; inventory remains above minimum threshold; customer orders can be fulfilled.
The assumptions appear separate.
Dependency mapping reveals that all five materially depend upon one overseas supplier providing a critical component.
The supplier represents a single upstream dependency supporting much of the prediction.
The critical governance question is:
Can five documented assumptions legitimately be treated as five independent predictive conditions when all of them materially depend upon one underlying supplier relationship?
ADMM maps the shared upstream dependency and the downstream assumption chain.
Later PAS modules can then determine how sensitive and material this concentration is.
Multiple Assumptions ≠ Independent Assumptions
Use Case 2 — An AI Forecast Contains a Circular Assumption That Reinforces Itself
An AI system predicts continued growth in an investment market.
Its reasoning depends partly upon:
investor confidence remains strong because investment continues.
At the same time, another assumption states:
investment continues because investor confidence remains strong.
Each assumption appears to support the other.
Without dependency mapping, the system may present the circular relationship as two reinforcing pieces of predictive support.
The critical governance question is:
Can mutually dependent assumptions legitimately be interpreted as independent support when each derives its plausibility from the other?
ADMM identifies the circular dependency.
The prediction can then preserve the relationship as a structural assumption condition rather than treating it as independent confirmation.
Mutual Support ≠ Independent Confirmation
Canonical Boundary
ADMM governs the identification, classification, direction, structure, concentration, state, uncertainty, historical preservation, and propagation pathways of material dependencies among Predictive Assumptions and between assumptions and relevant inputs, methodologies, conditions, systems, decisions, resources, and other predictive elements. It does not govern assumption identification itself, Assumption Sensitivity, Assumption Materiality, Assumption Change Governance, evidence validity, Operational Reality, Prediction Formation, probability representation, or prediction-level revision.
ADMM establishes:
what each assumption depends upon and what may depend upon it.
It does not determine:
how strongly the prediction will change when that dependency changes.
Canonical Closing Statement
Assumptions do not become independent merely because they are written on separate lines.
ADMM reveals the dependency structure beneath the Prediction Assumption Set so that a single hidden dependency cannot masquerade as multiple independent predictive conditions.