Prediction Assumption Standard (PAS)
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: PGA™ — Prediction Governance Architecture
Operational Layer: Prediction Assumption Governance Layer
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Type: Parent Standard
Governed Space:
Prediction Assumptions
Origin ID: OOF-PGA-PAS
Version: 1.0
Status: Canonical · Open Standard
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
Prediction Assumptions
Canonical Definition
The Prediction Assumption Standard (PAS) defines the governance conditions under which assumptions supporting a prediction are identified, expressed, bounded, associated with their dependencies, assessed for sensitivity and materiality, monitored for change, and preserved as part of the governed predictive state.
PAS governs the assumptions that must hold, remain sufficiently plausible, or remain within defined conditions for a prediction and its stated probability, confidence, feasibility, interpretation, or expected outcome to remain meaningful.
It transforms implicit predictive dependencies into explicit governed assumptions.
PAS establishes that a prediction cannot be fully understood merely by knowing:
what is being predicted; which methodology is used; what information supports it.
It must also be possible to determine:
what the prediction assumes about the world in which it is expected to hold.
Operational Role
PAS governs the fourth major space in the PGA™ Prediction Lifecycle.
It receives:
Governed Prediction Object & Purpose
Governed Prediction Methodology
Governed Prediction Basis
and establishes the assumptions under which those elements may legitimately proceed toward Prediction Formation.
The standard asks:
What assumptions must hold for the prediction and its stated probability, confidence, feasibility, or expected outcome to remain meaningful?
PAS creates the transition:
Governed Prediction Basis → Governed Prediction Assumption Structure → Prediction Formation
Its primary governed output is:
Governed Prediction Assumption Set
Core Question
What assumptions must hold for the prediction and its stated probability, confidence, feasibility, or expected outcome to remain meaningful?
PGA™ Governance Flow
Prediction Object & Purpose → Prediction Methodology → Prediction Basis → → Prediction Assumptions → Prediction Formation → Probability & Uncertainty → Prediction Feasibility → Collective Prediction where applicable → Communication & Reliance → Monitoring & Revision → Outcome & Performance → Prediction Record & Continuity
Current Position: Prediction Assumptions
Prediction Assumption
A Prediction Assumption is a proposition, condition, relationship, continuity expectation, state expectation, dependency, simplification, or other predictive premise that is treated as holding sufficiently for the prediction or its interpretation to remain meaningful.
An assumption may concern:
future conditions; continuing conditions; system behavior; human behavior; market behavior; environmental conditions; policy conditions; operational continuity; causal relationships; model applicability; data continuity; resource availability; organizational behavior; decision behavior; external events; boundary stability; other material predictive conditions.
An assumption is not automatically a fact.
Prediction Assumption ≠ Established Fact
Why Prediction Assumptions Require Governance
Predictions frequently depend upon conditions that are neither directly observed nor guaranteed.
For example:
A demand forecast may assume:
no major competitor enters the market.
A financial forecast may assume:
interest rates remain within a defined range.
A supply-chain prediction may assume:
the supplier remains operational.
An industrial prediction may assume:
the machine continues operating under comparable load conditions.
An election prediction may assume:
turnout behavior remains sufficiently comparable to the modeled population.
An AI forecast may assume:
the relationship represented in historical data remains sufficiently applicable to future conditions.
If such assumptions remain implicit, a prediction may appear more robust than its actual predictive conditions justify.
PAS makes those dependencies governable.
Explicit Assumptions
An Explicit Assumption is deliberately identified and represented as part of the governed predictive structure.
Explicit assumptions allow users and systems to understand the conditions supporting the prediction.
PAS favors explicit representation of material assumptions.
Implicit Assumptions
An Implicit Assumption is a condition relied upon by the prediction without sufficient explicit representation.
Implicit assumptions may arise from:
methodology design; historical practice; model structure; organizational convention; unstated expectations; environmental continuity; system configuration; human judgment.
PAS requires materially relevant implicit assumptions to be surfaced where they can reasonably be identified.
Implicit Assumption ≠ Governed Assumption
Hidden Assumptions
A Hidden Assumption is a materially relevant assumption that influences the prediction while remaining insufficiently identifiable within its governed predictive structure.
Hidden assumptions create governance risk because the prediction may fail when the assumption fails without users understanding the dependency.
Foundational principle:
A hidden assumption cannot remain a governed assumption.
Assumption Identification
Material assumptions must be identifiable before they can be governed.
Identification may arise through:
methodology review; Prediction Basis review; dependency analysis; expert analysis; model inspection; scenario examination; operational analysis; historical failure analysis; system documentation; predictive testing.
PAS does not require every conceivable assumption to be documented.
It requires assumptions material to predictive meaning to become governable.
Assumption Scope
An assumption should be bounded sufficiently to establish what it actually asserts.
For example:
“The market remains stable.”
may be too vague.
A governed assumption may instead specify:
“No market-wide disruption materially altering customer demand occurs during the 30-day Prediction Horizon.”
Assumption governance requires sufficient semantic clarity.
Assumption Condition
An assumption may represent a condition expected to hold.
Examples include:
system remains operational; regulation remains unchanged; supplier capacity remains available; population behavior remains sufficiently stable; no extraordinary event occurs; required infrastructure remains accessible.
Such conditions must be identifiable where their failure materially affects the prediction.
Assumption State
A governed assumption may possess a state.
Potential states include:
Established for Predictive Use
Holding
Conditionally Holding
Uncertain
Under Challenge
Breached
No Longer Applicable
Unable to Determine
The applicable state model may depend upon the predictive methodology and implementation.
Assumption ≠ Fact
An assumption may be reasonable.
It may be supported by historical information.
It may have strong empirical support.
It may even be highly probable.
It remains conceptually distinct from an established fact.
Strongly Supported Assumption ≠ Established Fact
This distinction prevents predictive systems from silently converting uncertainty into certainty.
Assumption ≠ Prediction Input
Prediction Inputs belong to Prediction Basis.
Prediction Assumptions represent conditions or premises under which predictive reasoning proceeds.
An input may support an assumption.
An assumption may influence how inputs are interpreted.
They remain distinct governed objects.
Prediction Input ≠ Prediction Assumption
Assumption ≠ Prediction
An assumption is not itself necessarily the Prediction Object.
For example:
Prediction:
Factory output will exceed 10,000 units next month.
Assumption:
The primary production line remains operational.
The assumption supports the prediction.
It is not the prediction itself.
Assumption Origin
Assumptions may originate from:
Prediction Methodology; human judgment; model structure; operational conditions; external constraints; Prediction Basis; scenario definition; system configuration; institutional expectations; other documented predictive premises.
Where material, assumption origin should remain identifiable.
Methodological Assumptions
Prediction Methodology may contain assumptions necessary for legitimate application.
Examples may concern:
distributional structure; variable relationships; stationarity; independence; model applicability; sampling conditions; behavioral continuity.
PMES governs the methodology itself.
PAS governs assumptions as they participate in the specific prediction.
Operational Assumptions
Operational assumptions concern the continuation or state of real-world processes relevant to the prediction.
Examples include:
production continues; infrastructure remains available; system configuration remains unchanged; staffing remains sufficient; logistics remain functional.
PAS governs the predictive dependency upon these conditions.
ORA™ retains governance of Operational Reality itself.
Behavioral Assumptions
Predictions may depend upon expected human, organizational, market, agent, or system behavior.
Examples include assumptions that:
consumers respond similarly to prior conditions; market participants behave within expected patterns; operators follow established procedures; organizations continue current strategy.
Behavioral expectation must not silently become established fact.
Continuity Assumptions
Many predictions depend upon some degree of continuity between:
past → present → future.
Continuity assumptions may concern:
market structure; behavioral patterns; system relationships; environmental conditions; institutional rules; technological conditions; data-generating processes.
Historical continuity does not guarantee future continuity.
Historical Stability ≠ Future Stability
Boundary Assumptions
A prediction may assume that its defined Predictive Boundary remains meaningful.
For example:
geographic boundaries remain relevant; system boundaries remain stable; target population remains comparable; organizational scope remains unchanged.
Material boundary changes may invalidate or alter the assumption.
Temporal Assumptions
Some assumptions hold only during a defined period.
An assumption legitimate for a seven-day prediction may not remain legitimate for a five-year prediction.
Assumption Validity at Horizon A ≠ Assumption Validity at Horizon B
External-Condition Assumptions
Predictions may depend upon external conditions outside the predictor's control.
Examples include:
macroeconomic conditions; weather; regulation; political conditions; infrastructure availability; competitor behavior; supply conditions.
PAS makes these dependencies visible without claiming control over them.
No-Event Assumptions
Some predictions implicitly depend upon an event not occurring.
Examples:
No major regulatory intervention occurs.
No catastrophic equipment failure occurs.
No geopolitical disruption interrupts supply.
The absence of an event may therefore be a material predictive assumption.
Relationship Assumptions
A prediction may assume that a relationship between variables or conditions continues to hold.
For example:
higher mobility continues to correlate with higher store demand.
SRVM governs whether the signal is relevant.
PAS governs the assumption that the relevant relationship remains sufficiently applicable during the Prediction Horizon.
Assumption Dependency
Assumptions may depend upon:
other assumptions; inputs; conditions; system states; external events; methodology requirements; organizational actions; decisions.
PAS requires material dependencies to remain identifiable.
Assumption Independence ≠ Default State
Assumption Chains
A prediction may rely upon chains such as:
Assumption A → Assumption B → Predictive Condition C → Prediction
Failure of an upstream assumption may affect multiple downstream assumptions.
Such chains become especially important in complex predictive systems.
Assumption Clusters
Several assumptions may collectively support one predictive condition.
Where their combined behavior matters more than any individual assumption, they may be governed as an Assumption Cluster while retaining individual identities where material.
Dependent Assumption Failure
Failure of one assumption may cause another assumption to cease holding.
PAS therefore requires dependency relationships to become visible where material.
This prevents assumptions from being assessed only in isolation.
Assumption Sensitivity
Not all assumptions affect the prediction equally.
Assumption Sensitivity concerns how materially the prediction may change when an assumption changes, weakens, or fails.
A prediction may be:
highly sensitive; moderately sensitive; weakly sensitive; conditionally sensitive; insensitive within defined bounds
to a particular assumption.
Sensitivity does not itself establish assumption materiality.
Sensitivity Direction
Where meaningful, sensitivity may indicate whether assumption change tends to:
increase predicted probability; decrease predicted probability; widen uncertainty; change expected outcome; alter feasibility; change permitted interpretation; invalidate methodology applicability.
Sensitivity Threshold
An assumption may remain within a range where prediction meaning changes little.
Beyond a threshold, predictive consequences may become material.
PAS permits such thresholds to be governed where methodology supports them.
Assumption Materiality
Assumption Materiality represents the significance of an assumption to the legitimacy, meaning, probability, uncertainty, feasibility, or reliance conditions of the prediction.
An assumption may be material because its failure could:
materially change the predicted outcome; materially change probability; materially increase uncertainty; alter feasibility;
invalidate methodology applicability; change predictive boundaries; change permitted reliance; require prediction revision.
Material Assumption
A Material Assumption is an assumption whose change, failure, or invalidity could materially affect the governed prediction.
Material assumptions require explicit governance.
Material Assumption ≠ Most Probable Assumption
Materiality concerns consequence, not probability.
Critical Assumption
A Critical Assumption is a material assumption whose failure may invalidate the prediction or fundamentally alter its legitimate interpretation.
Criticality may justify stronger:
monitoring; disclosure; change triggers; escalation; revision requirements.
Low-Probability Assumption Failure
An assumption may have a low probability of failure while carrying severe predictive consequences.
PAS therefore prevents low likelihood from automatically becoming low materiality.
Low Probability of Failure ≠ Low Assumption Materiality
Assumption Uncertainty
The state of an assumption may itself be uncertain.
For example, an organization may not know whether:
a supplier will remain available; a policy will change; a system condition will persist; a behavioral relationship remains stable.
Such uncertainty must not be hidden behind binary assumption states where the information does not support certainty.
Assumption Support
Assumptions may possess informational support from Prediction Basis.
Support may strengthen the reasonableness of an assumption.
However:
Supported Assumption ≠ Established Fact
PAS may reference Prediction Basis without absorbing evidence governance.
Unsupported Assumptions
An assumption may be materially important while possessing limited informational support.
This does not automatically prohibit the prediction.
It creates a governance condition that may require:
explicit disclosure; increased uncertainty; restricted reliance; sensitivity analysis; monitoring; revision triggers.
Assumption Plausibility
Assumption Plausibility concerns whether an assumption is reasonably supportable within the predictive context.
Plausibility must not be confused with certainty.
Plausible Assumption ≠ Guaranteed Condition
PAS does not establish a universal numerical plausibility threshold.
Contradicted Assumptions
New information may contradict an assumption.
Contradiction may result in:
challenge; reassessment; breach determination; prediction restriction; revision trigger.
A contradiction does not automatically determine the final prediction response.
PMRS later governs prediction-level monitoring and revision.
Assumption Change
Assumptions may change during the Prediction Lifecycle.
Change may result from:
new information; changed Operational Reality; changed source conditions; policy change; system change; market change; behavioral change; event occurrence;
methodology change; elapsed time.
PAS governs the assumption state transition.
Assumption Breach
An Assumption Breach occurs when a material condition represented by the assumption no longer holds within its governed bounds.
A breach does not necessarily mean the prediction is wrong.
It means the predictive conditions supporting the prediction have materially changed.
Assumption Breach ≠ Prediction Error
Assumption Change Trigger
Material assumptions may require predefined or governable triggers for reassessment.
Triggers may include:
threshold crossing; event occurrence; new information; state transition; source update; elapsed time; detected contradiction; Operational Reality change.
Assumption Monitoring
Where assumptions remain material after Prediction Formation, their state may require continuing observation.
PAS establishes what assumption conditions require governance.
PMRS later governs prediction-level monitoring, revision, supersession, and withdrawal.
Assumption Change Propagation
When a material assumption changes, its effect may propagate through:
Assumption → Dependent Assumptions → Prediction Formation Conditions → Probability / Uncertainty → Feasibility → Reliance
PAS preserves the dependency structure necessary to recognize such propagation.
Assumption Reassessment
A material assumption may require reassessment when:
supporting information changes; dependencies change; Prediction Basis changes; the Predictive Boundary changes; methodology changes;
relevant Operational Reality changes; its defined review point is reached.
Assumption Version
Where an assumption materially changes, historical and current assumption states must remain distinguishable.
Current Assumption ≠ Historical Assumption State
The original prediction must remain associated with the assumptions that applied when it was formed.
Historical Assumption Integrity
Later knowledge must not silently rewrite the assumptions under which an earlier prediction was formed.
For example, after an unexpected event occurs, it may appear obvious that an assumption should never have been made.
That later knowledge does not alter the historical fact that the assumption governed the earlier prediction.
Later Knowledge ≠ Historical Assumption State
Assumption Set
A Prediction Assumption Set represents the governed collection of assumptions materially supporting a prediction.
The set may include:
assumption identifiers; definitions; scope; states; dependencies; sensitivity; materiality; support; uncertainty; change triggers; historical states.
The Assumption Set becomes the primary governed output of PAS.
Assumption Set Coherence
Individual assumptions may appear reasonable while becoming contradictory when combined.
PAS therefore requires material contradictions among assumptions to remain identifiable.
Individually Plausible Assumptions ≠ Coherent Assumption Set
Assumption Conflict
Two assumptions may conflict.
Where both materially support the prediction, the conflict must be resolved, bounded, or explicitly preserved as uncertainty before legitimate Prediction Formation.
Assumption Coverage
A prediction may contain many documented assumptions while still omitting a critical one.
Assumption governance therefore concerns material coverage rather than assumption count.
Number of Assumptions ≠ Assumption Governance Completeness
Assumption Governance Sufficiency
Prediction Assumption governance is sufficiently established when:
material assumptions are identifiable; assumptions are sufficiently defined; implicit material assumptions are surfaced where reasonably identifiable; material dependencies are mapped; sensitivity is understood where necessary; materiality is determined; critical assumptions are distinguishable; assumption uncertainty remains visible; material change triggers are governable; assumption changes can be propagated; historical assumption states remain reconstructable.
Prediction Basis Relationship
PBAS establishes:
what information supports the prediction.
PAS establishes:
what must hold for the prediction to remain meaningful.
Therefore:
Prediction Basis ≠ Prediction Assumptions
A Prediction Basis may provide information supporting an assumption without transforming that assumption into fact.
Prediction Methodology Relationship
PMES governs how the prediction is methodologically constructed.
A methodology may introduce assumptions.
PAS governs those assumptions as predictive conditions.
Therefore:
Methodological Assumption ≠ Prediction Methodology
Prediction Formation Relationship
PFOS later combines the governed predictive structure into a specific prediction.
PAS supplies the governed assumption conditions required for that formation.
The transition is:
Governed Prediction Methodology + Governed Prediction Basis + Governed Prediction Assumption Set → Prediction Formation
Probability & Uncertainty Relationship
PPUS later governs probability, confidence, ranges, uncertainty, and conditionality.
PAS establishes assumption conditions that may materially affect those representations.
Assumption Confidence ≠ Prediction Probability
Prediction Feasibility Relationship
PFES later evaluates whether the predicted outcome is realistically achievable under stated conditions.
PAS supplies assumptions relevant to that feasibility assessment.
Assumption Plausibility ≠ Prediction Feasibility
Monitoring & Revision Relationship
PMRS later governs when a prediction must be:
preserved; reviewed; revised; superseded; withdrawn.
PAS establishes assumption changes and breaches that may become triggers for those actions.
Assumption Change ≠ Automatic Prediction Revision
Operational Reality Relationship
ORA™ governs Operational Reality.
PAS may depend upon operational conditions and changes supplied by appropriate reality-governance sources.
PAS does not determine Operational Reality itself.
Assumed Operational Condition ≠ Governed Operational Reality
Evidence Governance Relationship
ADIT® governs evidence governance.
PAS may reference evidence supporting or challenging assumptions.
It does not determine general evidence validity or evidentiary sufficiency.
Assumption Support ≠ Evidence Validation
Governance Requirements
PAS requires that:
1. material predictive assumptions be identifiable; 2. assumptions be expressed with sufficient semantic clarity; 3. materially relevant implicit assumptions be surfaced where reasonably identifiable; 4. hidden material assumptions not remain silently embedded in the prediction; 5. assumptions remain distinguishable from facts; 6. assumptions remain distinguishable from Prediction Inputs; 7. assumption origin remain identifiable where material; 8. assumption scope and conditions remain governable; 9. material dependencies among assumptions remain identifiable; 10. material assumption chains remain governable; 11. sensitivity be assessed where necessary; 12. assumption materiality be determined; 13. critical assumptions remain distinguishable; 14. low probability of assumption failure not automatically imply low materiality; 15. assumption uncertainty remain visible; 16. unsupported material assumptions remain explicit; 17. material contradictions remain governable; 18. assumption change triggers be identifiable where necessary; 19. assumption breach remain distinguishable from prediction error; 20. material assumption changes be capable of propagation through relevant predictive relationships; 21. current and historical assumption states remain distinguishable; 22. later knowledge not rewrite historical assumption state; 23. the governed Assumption Set remain associated with the prediction it supports.
Governance Logic
PAS applies the following architecture-level governance logic:
Governed Prediction Object & Purpose
Governed Prediction Methodology
Governed Prediction Basis
↓
Predictive Assumptions Identified
↓
Implicit Material Assumptions Surfaced
↓
Assumption Scope and Conditions Defined
↓
Assumption Dependencies Mapped
↓
Assumption Sensitivity Assessed
↓
Assumption Materiality Determined
↓
Critical Assumptions Identified
↓
Assumption States Established
↓
Change Conditions and Triggers Established
↓
Historical Assumption State Preserved
↓
Governed Prediction Assumption Set
↓
PFOS — Prediction Formation Standard
Where assumption governance remains materially unresolved:
Prediction Assumption Governance Gap → Clarification / Assumption Disclosure / Restriction / Reassessment / Additional Analysis / Inability to Proceed
Governance Outputs
PAS may produce:
Governed Prediction Assumption Set; Predictive Assumption Register; assumption definitions; assumption scope records; assumption-state records; Assumption Dependency Map; Assumption Sensitivity Profile; Assumption Materiality Profile; Critical Assumption Register; assumption-support references; assumption-uncertainty records; assumption-conflict records; assumption-change triggers; assumption-breach records; assumption-change history; historical assumption-state records; unresolved assumption governance requirements.
The primary governed output is:
Governed Prediction Assumption Set
Internal Module Architecture
PAS is operationalized through five Core Modules:
1. PAIM — Predictive Assumption Identification Module
Governs identification, explicit representation, scope, and initial state of assumptions materially supporting the prediction.
2. ADMM — Assumption Dependency Mapping Module
Governs relationships among assumptions and between assumptions, predictive conditions, inputs, methodology requirements, and other material dependencies.
3. ASNM — Assumption Sensitivity Module
Governs how materially the prediction or its predictive conditions may change when an assumption changes, weakens, or fails.
4. AMAM — Assumption Materiality Module
Governs determination of which assumptions are materially significant to prediction meaning, probability, uncertainty, feasibility, methodology applicability, or reliance.
5. ACGM — Assumption Change Governance Module
Governs assumption-state change, breach, reassessment triggers, historical preservation, and propagation of material assumption change through the Prediction Lifecycle.
The internal progression is:
Predictive Assumption Identification → Assumption Dependency Mapping → Assumption Sensitivity → Assumption Materiality → Assumption Change Governance
Critical Distinctions
Prediction Assumption ≠ Established Fact
Strongly Supported Assumption ≠ Established Fact
Implicit Assumption ≠ Governed Assumption
Prediction Input ≠ Prediction Assumption
Prediction Assumption ≠ Prediction
Historical Stability ≠ Future Stability
Assumption Validity at Horizon A ≠ Assumption Validity at Horizon B
Assumption Independence ≠ Default State
Material Assumption ≠ Most Probable Assumption
Low Probability of Failure ≠ Low Assumption Materiality
Supported Assumption ≠ Established Fact
Plausible Assumption ≠ Guaranteed Condition
Assumption Breach ≠ Prediction Error
Current Assumption ≠ Historical Assumption State
Later Knowledge ≠ Historical Assumption State
Individually Plausible Assumptions ≠ Coherent Assumption Set
Number of Assumptions ≠ Assumption Governance Completeness
Prediction Basis ≠ Prediction Assumptions
Methodological Assumption ≠ Prediction Methodology
Assumption Confidence ≠ Prediction Probability
Assumption Plausibility ≠ Prediction Feasibility
Assumption Change ≠ Automatic Prediction Revision
Assumed Operational Condition ≠ Governed Operational Reality
Assumption Support ≠ Evidence Validation
Cross-Architecture Boundaries
PBAS — Prediction Basis Standard governs the informational foundation supporting the prediction.
PMES — Prediction Methodology Standard governs the methodology by which the prediction is constructed.
PFOS — Prediction Formation Standard governs transformation of methodology, Prediction Basis, assumptions, and applicable conditions into a specific governed prediction.
PPUS — Prediction Probability & Uncertainty Standard governs probability, uncertainty, confidence, ranges, and conditionality.
PFES — Prediction Feasibility Standard governs whether the predicted outcome is realistically achievable under stated conditions.
PMRS — Prediction Monitoring & Revision Standard governs prediction-level monitoring, revision, supersession, and withdrawal.
ORA™ governs Operational Reality.
ADIT® governs evidence governance and audit evidence.
VALIDOS™ governs validation.
AGA™ governs accountability.
AIG® governs AI behavioral governance.
CLIA® governs cognition.
SIMULOS® governs simulation.
GOA™ provides overarching governance architecture.
PAS may reference outputs from these architectures without absorbing their governed spaces.
Standard Boundary
PAS governs the identification, explicit representation, scope, dependency, sensitivity, materiality, state, change, breach, historical preservation, and lifecycle relevance of assumptions materially supporting a prediction. It does not govern Prediction Basis itself, general evidence validity, Prediction Methodology design, Prediction Formation, probability representation, uncertainty determination, Prediction Feasibility, Operational Reality determination, prediction-level revision decisions, predictive performance, accountability allocation, or final prediction validity.
PAS governs:
what must hold for the prediction to remain meaningful.
It does not determine:
whether those conditions will actually hold.
Canonical Closing Statement
Every prediction depends not only on what is known, but also on what is assumed to remain true.
PAS makes those assumptions visible, bounded, and governable so that a prediction cannot silently depend upon conditions no one has identified.
A hidden assumption cannot remain a governed assumption.
Nahlad
Prediction Assumption Standard
Architecture:
PGA™ — Prediction Governance Architecture
Category:
Governance & Enforcement
Subcategory:
Prediction Governance
Canonical Definition: The Prediction Assumption Standard governs the assumptions that must hold, remain sufficiently plausible, or remain within defined conditions for a prediction and its stated probability, confidence, feasibility, or expected outcome to remain meaningful.
Governed Space:
Prediction Assumptions
Core Question: What assumptions must hold for the prediction and its stated probability, confidence, feasibility, or expected outcome to remain meaningful?
Prediction Assumption ≠ Established Fact
A hidden assumption cannot remain a governed assumption.
→ View Standard
Aboutbstandard
Governed Links
About Prediction Assumption Standard (PAS)
Prediction Assumption Identification Module (PAIM)
Assumption Dependency Mapping Module (ADMM)
Assumption Sensitivity Module (ASNM)
Assumption Materiality Assessment Module (AMAM)
Assumption Change Governance Module (ACGM)