ASNM — Assumption Sensitivity 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 Sensitivity
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 Sensitivity
Canonical Definition
Assumption Sensitivity Module (ASNM) defines the governance conditions under which the relationship between change in a Predictive Assumption and change in the governed prediction, predictive conditions, methodology applicability, probability, uncertainty, feasibility, or legitimate interpretation is identified, assessed, bounded, and represented.
ASNM determines how sensitive the prediction is to assumptions on which it depends.
It transforms:
Assumption Dependency
into:
Governed Assumption Sensitivity
by establishing how materially different assumption states may affect the predictive structure.
ASNM does not determine final Assumption Materiality and does not govern actual assumption-state change.
Those functions belong to:
AMAM — Assumption Materiality Module
and:
ACGM — Assumption Change Governance Module.
Foundational distinction:
Dependency ≠ Sensitivity
A prediction may depend upon an assumption while remaining relatively stable across reasonable changes in that assumption.
Another prediction may change dramatically when the same assumption moves only slightly.
Operational Role
ASNM serves as the third internal governance space of the Prediction Assumption Standard (PAS) .
It receives:
Identified Predictive Assumptions
Governed Assumption Dependency Map
and evaluates how changes in those assumptions may affect the prediction and its governed predictive conditions.
The module asks:
How much, in what direction, and under what conditions can the prediction change when a governed assumption changes, weakens, moves outside its expected range, or fails?
The governed progression is:
Governed Assumptions + Governed Dependency Structure → Sensitivity Identification → Sensitivity Direction → Sensitivity Range → Threshold Assessment → Propagation Assessment → Governed Assumption Sensitivity Profile
The resulting profile becomes input to:
AMAM — Assumption Materiality Module.
Internal Module Flow
Predictive Assumption Identification → Assumption Dependency Mapping → → Assumption Sensitivity → Assumption Materiality → Assumption Change Governance
Current Position: Assumption Sensitivity
Module Operational Space
ASNM governs:
assumption sensitivity identification; prediction sensitivity to assumptions; local sensitivity; global sensitivity; directional sensitivity; non-directional sensitivity; linear sensitivity; nonlinear sensitivity; threshold sensitivity; discontinuous sensitivity; conditional sensitivity; contextual sensitivity; temporal sensitivity; horizon sensitivity; boundary sensitivity; probability sensitivity;
uncertainty sensitivity; confidence sensitivity; feasibility sensitivity; methodology-applicability sensitivity; interpretation sensitivity; reliance sensitivity; compound sensitivity; dependency-mediated sensitivity; sensitivity propagation; sensitivity concentration; sensitivity interaction; sensitivity asymmetry; sensitivity range; tolerance range; sensitivity threshold; critical threshold; sensitivity uncertainty; sensitivity state; sensitivity change; historical sensitivity; and sensitivity traceability.
Module Function
Not every assumption affects a prediction in the same way.
Consider two assumptions:
Assumption A: Fuel price remains between 1.40 and 1.60.
Assumption B: The only distribution center remains operational.
A small change in fuel price may slightly affect the prediction.
Failure of the distribution center may fundamentally alter it.
Both are assumptions.
Both may be dependencies.
Their sensitivity characteristics are completely different.
ASNM makes that difference governable.
Assumption Sensitivity
Assumption Sensitivity represents the degree and form of change in the prediction or its governed predictive conditions resulting from change in a Predictive Assumption.
Sensitivity may concern changes in:
predicted outcome; probability; uncertainty; confidence; prediction range; feasibility; methodology applicability; interpretation; permitted reliance; prediction state.
Dependency ≠ Sensitivity
ADMM determines whether a dependency exists.
ASNM determines how consequential changes in that dependency may be to the prediction.
For example:
A demand forecast may depend upon temperature.
However, between 18°C and 22°C the forecast may change very little.
Above 30°C, demand may change substantially.
The dependency exists throughout.
The sensitivity changes across the condition range.
Dependency Presence ≠ Constant Sensitivity
Sensitivity Direction
Sensitivity may possess direction.
An assumption change may tend to:
increase predicted outcome; decrease predicted outcome; increase predicted probability; decrease predicted probability; widen uncertainty; narrow uncertainty; increase feasibility; decrease feasibility; change methodology applicability; change interpretation; restrict reliance.
Where directional behavior is known, it should remain identifiable.
Positive Sensitivity
Positive Sensitivity may exist where an increase or strengthening of an assumption condition tends to increase a predictive output.
For example:
Higher available production capacity → Higher predicted production output
where supported by the predictive structure.
Negative Sensitivity
Negative Sensitivity may exist where an increase in an assumption condition tends to reduce a predictive output.
For example:
Higher borrowing cost → Lower predicted investment activity
within the governed context.
Non-Directional Sensitivity
Some assumption changes affect prediction stability without a simple positive or negative direction.
A change may:
widen uncertainty; alter model applicability; move the prediction into a different state; require a different scenario; change permitted reliance.
ASNM does not force directional representation where the relationship is not meaningfully directional.
Sensitivity Magnitude
Sensitivity Magnitude represents how substantially the governed prediction or relevant predictive condition may change when an assumption changes.
Possible governed states may include:
Minimal
Low
Moderate
High
Extreme
Unable to Determine
The implementation may use quantitative measures where appropriate.
Low Sensitivity
A Low-Sensitivity Assumption may change materially without causing substantial change in the prediction within the relevant range.
Low sensitivity does not automatically mean the assumption is immaterial.
For example, an assumption may have little effect on predicted value but large implications for legitimate reliance.
Low Sensitivity ≠ Low Materiality by Default
High Sensitivity
A High-Sensitivity Assumption is one where relatively limited change may cause substantial change in the prediction or predictive conditions.
High sensitivity may justify closer monitoring or stronger disclosure.
It does not by itself determine materiality.
Extreme Sensitivity
Extreme Sensitivity exists where small assumption variation may fundamentally alter:
predicted outcome; probability structure; feasibility; methodology applicability; permitted interpretation.
Such assumptions may become candidates for Critical Assumption classification under AMAM.
Sensitivity Range
Sensitivity must be interpreted within a governed range.
An assumption may be stable within one interval and highly sensitive outside it.
For example:
Interest Rate Assumption: 2%–4%
may produce limited forecast change.
Above 6%, the prediction may materially shift.
Therefore:
Sensitivity at Range A ≠ Sensitivity at Range B
Tolerance Range
A Tolerance Range is a bounded interval within which assumption change produces no material or only limited predictive effect.
Example:
Supplier delivery variance ±2 days
may not materially affect a six-month production forecast.
Outside that range, sensitivity may increase rapidly.
Sensitivity Threshold
A Sensitivity Threshold is a point at which an assumption change begins to materially alter the prediction or a relevant predictive condition.
Thresholds may relate to:
value; probability; timing; operational state; system state; resource availability; boundary condition; external event.
Critical Threshold
A Critical Threshold is a sensitivity boundary beyond which the prediction may:
materially change;
become conditionally valid; become highly uncertain; lose methodology applicability; become infeasible; require reassessment.
Threshold Crossing ≠ Automatic Prediction Failure
It establishes a predictive governance condition.
Linear Sensitivity
Linear Sensitivity exists where assumption change produces approximately proportional predictive change within a defined range.
Example:
Every additional 1% cost increase reduces expected margin by approximately 0.4%.
ASNM may represent this relationship where methodologically justified.
Nonlinear Sensitivity
Nonlinear Sensitivity exists where predictive impact changes disproportionately across the assumption range.
Many real-world systems behave this way.
For example:
Server load may increase gradually without operational consequence until capacity approaches saturation, after which failure probability rises sharply.
Small Input Change ≠ Small Predictive Effect by Default
Discontinuous Sensitivity
Some assumption changes may trigger abrupt predictive transitions.
Example:
A regulatory authorization remains valid until revoked.
A small regulatory change may have little effect.
Formal revocation may immediately make an assumed commercial pathway impossible.
ASNM preserves discontinuities rather than forcing continuous representations.
Conditional Sensitivity
Sensitivity may exist only under another condition.
For example:
Fuel-price sensitivity may be high only where transport costs exceed a specified proportion of total product cost.
Conditional Sensitivity ≠ Universal Sensitivity
Contextual Sensitivity
The same assumption may exhibit different sensitivity across:
geographies; business units; populations; system configurations; market regimes; scenarios.
Sensitivity must therefore remain linked to predictive context.
Temporal Sensitivity
Sensitivity may change with time.
An assumption may have little importance early in the Prediction Horizon but become highly influential later.
Example:
A small financing shortfall may not affect operations this week but may materially constrain production six months later.
Horizon Sensitivity
The effect of an assumption may depend upon Prediction Horizon.
Low Short-Term Sensitivity ≠ Low Long-Term Sensitivity
For example:
Interest-rate changes may have limited effect on a seven-day sales forecast but substantial effect on a three-year investment forecast.
Boundary Sensitivity
An assumption may behave differently across predictive boundaries.
A labor-availability assumption may have minimal effect within one facility but high effect across an entire national production network.
Probability Sensitivity
An assumption change may materially affect the probability assigned to a prediction.
ASNM governs the sensitivity relationship.
PPUS later governs probability representation itself.
Probability Sensitivity ≠ Probability Governance
Uncertainty Sensitivity
Assumption weakening may increase uncertainty even where the central prediction changes little.
For example:
The expected output remains 1,000 units.
But uncertainty expands from:
±50
to:
±300.
This is a material sensitivity effect even if the expected value remains unchanged.
Confidence Sensitivity
An assumption change may affect confidence in the prediction without directly changing the predicted outcome.
Stable Predicted Value ≠ Stable Confidence
Feasibility Sensitivity
An assumption may affect whether a predicted outcome remains realistically achievable.
Example:
A construction completion prediction may remain numerically unchanged while labor availability deteriorates sufficiently to make the forecast infeasible.
PFES later governs final feasibility state.
ASNM governs the sensitivity relationship.
Methodology-Applicability Sensitivity
Some assumptions affect whether Prediction Methodology remains applicable.
For example:
A methodology may assume a sufficiently stationary environment.
If regime change occurs, methodology applicability may deteriorate sharply.
This may be more important than change in the prediction's numerical output.
Interpretation Sensitivity
Assumption change may alter what a prediction legitimately means.
A forecast may move from:
expected outcome
to:
conditional scenario
without changing its headline number.
ASNM preserves this interpretive sensitivity.
Reliance Sensitivity
Assumption changes may alter the degree to which a prediction can legitimately be relied upon.
PCRS governs final communication and reliance.
ASNM may identify assumptions whose change can alter reliance conditions.
Sensitivity Propagation
Sensitivity may propagate through the dependency structure established by ADMM.
Example:
Supplier Financial Stress
↓
Supplier Continuity Assumption Weakens
↓
Component Availability Assumption Weakens
↓
Production Continuity Assumption Weakens
↓
Output Prediction Sensitivity Increases
ADMM maps the dependency pathway.
ASNM evaluates how changes through that pathway affect the prediction.
Direct Sensitivity
Direct Sensitivity exists where changing an assumption materially affects the prediction without needing another assumption to change first.
Indirect Sensitivity
Indirect Sensitivity operates through one or more dependency relationships.
Indirect pathways may still produce substantial predictive effects.
Indirect Sensitivity ≠ Low Sensitivity
Compound Sensitivity
Several assumptions may jointly create a predictive effect greater or different from their individual effects.
For example:
High energy prices
Low inventory
Transport disruption
may jointly affect production more severely than any condition individually.
Sensitivity Interaction
Assumption sensitivities may interact.
The effect of Assumption A may depend on the state of Assumption B.
Example:
Interest-rate sensitivity may become much greater where corporate leverage is already high.
Sensitivity of A ≠ Independent of B by Default
Sensitivity Amplification
Multiple dependencies may amplify the effect of assumption change.
A moderate upstream change may generate a large downstream predictive effect.
Sensitivity Dampening
Predictive structures may also absorb assumption change.
Redundancy, substitution, spare capacity, or alternative pathways may reduce downstream effect.
Dependency Change ≠ Equivalent Prediction Change
Sensitivity Concentration
A prediction may be disproportionately sensitive to a small number of assumptions.
This creates a sensitivity concentration profile.
The existence of many assumptions does not imply diversified predictive sensitivity.
Many Assumptions ≠ Diversified Sensitivity
Sensitivity Asymmetry
An assumption may affect a prediction differently depending on the direction of change.
Example:
Increasing production capacity by 10% may have little effect because demand is constrained.
Reducing production capacity by 10% may materially lower expected output.
Sensitivity Increase Direction ≠ Sensitivity Decrease Direction
Sensitivity Scenario
Where appropriate, sensitivity may be assessed using governed alternative states.
Example:
Assumption State A → Prediction State X
Assumption State B → Prediction State Y
Assumption State C → Prediction State Z
The purpose is not to replace simulation governance.
It is to govern sensitivity relationships.
SIMULOS® retains governance of formal simulation.
Single-Assumption Sensitivity
A prediction may be evaluated against one changing assumption while other relevant conditions are held sufficiently stable.
This can isolate local sensitivity.
However:
Single-Assumption Analysis ≠ Complete Predictive Sensitivity
Multi-Assumption Sensitivity
Where dependencies and interactions are material, multiple assumptions may need to be considered together.
This is particularly important where:
assumptions are correlated; dependencies are shared; thresholds interact; feedback exists.
Sensitivity Uncertainty
The sensitivity relationship itself may be uncertain.
Possible states may include:
Established
Estimated
Model-Dependent
Conditionally Established
Uncertain
Unable to Determine
Sensitivity uncertainty must remain visible.
Sensitivity Estimate ≠ Sensitivity Certainty
Sensitivity Confidence
Where applicable, systems may represent confidence in a sensitivity assessment.
This must remain distinct from:
prediction confidence; assumption confidence; probability.
Sensitivity Change
Sensitivity itself may change when:
Prediction Basis changes; methodology changes; system configuration changes; dependencies change; operational conditions change; Prediction Horizon changes; Predictive Boundary changes.
Therefore:
Historical Sensitivity ≠ Current Sensitivity
Historical Sensitivity State
A prediction reviewed retrospectively should preserve the sensitivity structure that applied when the prediction was formed or assessed.
Later discovery of stronger sensitivity must not silently rewrite the historical predictive state.
Later Sensitivity Knowledge ≠ Original Sensitivity Assessment
Sensitivity Reassessment
Sensitivity should be reassessed where material changes occur in:
assumption state; dependency structure; methodology; Prediction Basis; Prediction Horizon; Predictive Boundary; operational conditions.
Sensitivity Governance Sufficiency
Assumption Sensitivity is sufficiently governed when:
material sensitivity relationships are identifiable; direction is known where meaningful; sensitivity magnitude is represented where possible; relevant ranges are bounded; material thresholds are identifiable; nonlinear or discontinuous behavior is preserved where relevant; temporal and horizon sensitivity are considered; dependency-mediated propagation is visible; interactions among assumptions are identifiable where material; sensitivity uncertainty remains explicit;
current and historical sensitivity states remain distinguishable.
Governance Requirements
ASNM requires that:
1. material assumptions be assessed for predictive sensitivity where applicable; 2. dependency and sensitivity remain conceptually distinct; 3. sensitivity direction be represented where meaningful; 4. sensitivity magnitude be governed where material; 5. relevant sensitivity ranges remain identifiable; 6. tolerance ranges be represented where useful; 7. material thresholds be identifiable; 8. nonlinear sensitivity not be forced into linear representation; 9. discontinuous sensitivity remain visible; 10. conditional sensitivity not be represented as universal; 11. temporal sensitivity be considered where material; 12. horizon sensitivity remain visible; 13. probability sensitivity remain distinguishable from probability governance; 14. uncertainty sensitivity remain identifiable; 15. confidence sensitivity remain distinguishable from prediction confidence itself; 16. feasibility sensitivity remain distinguishable from final feasibility determination; 17. methodology-applicability sensitivity be identified where material; 18. dependency-mediated sensitivity propagation remain traceable; 19. assumption interactions be governed where material; 20. sensitivity amplification and dampening remain visible; 21. sensitivity concentration be identifiable; 22. asymmetric sensitivity be preserved where material; 23. sensitivity uncertainty remain explicit; 24. historical sensitivity states remain reconstructable; 25. later knowledge not overwrite historical sensitivity assessments.
Governance Logic
ASNM applies the following governance logic:
Identified Predictive Assumptions
Governed Assumption Dependency Map
↓
Sensitivity Relationships Identified
↓
Sensitivity Direction Determined where meaningful
↓
Sensitivity Magnitude Assessed
↓
Applicable Range / Tolerance Range Established
↓
Thresholds and Critical Thresholds Identified
↓
Linear / Nonlinear / Discontinuous Behavior Distinguished
↓
Conditional / Contextual / Temporal Sensitivity Assessed
↓
Probability / Uncertainty / Confidence / Feasibility / Methodology Sensitivity Identified
↓
Dependency Propagation Effects Assessed
↓
Assumption Interaction / Amplification / Dampening Assessed
↓
Sensitivity Concentration Identified
↓
Sensitivity Uncertainty Preserved
↓
Historical Sensitivity State Preserved
↓
Governed Assumption Sensitivity Profile
↓
AMAM — Assumption Materiality Module
Where sensitivity cannot be sufficiently determined:
Assumption Sensitivity Governance Gap → Qualification / Additional Analysis / Scenario Examination / Explicit Uncertainty / Restricted Interpretation
Minimum Implementation Framework
A minimum implementation of ASNM must establish:
A. Assumption Reference
The Predictive Assumption being assessed.
B. Dependency Map Reference
The applicable dependency relationships established through ADMM.
C. Sensitivity Target
The predictive element potentially affected, such as:
Predicted Outcome / Probability / Uncertainty / Confidence / Feasibility / Methodology Applicability / Interpretation / Reliance / Other
D. Sensitivity Direction
Where meaningful:
Positive / Negative / Bidirectional / Non-Directional / Unable to Determine
E. Sensitivity Magnitude
At minimum:
Minimal / Low / Moderate / High / Extreme / Unable to Determine
F. Applicable Range
The assumption range over which the sensitivity assessment applies.
G. Tolerance Range
Where applicable, the range within which predictive impact remains limited.
H. Sensitivity Threshold
Any point at which predictive impact materially changes.
I. Sensitivity Form
Where applicable:
Linear / Nonlinear / Discontinuous / Conditional / Contextual / Temporal / Other
J. Propagation Path Reference
Material dependency pathways through which sensitivity may propagate.
K. Interaction Reference
Other assumptions materially affecting the sensitivity relationship.
L. Sensitivity Uncertainty State
At minimum:
Established / Estimated / Conditional / Uncertain / Unable to Determine
M. Historical Sensitivity State
The sensitivity state associated with the applicable prediction version and time.
Governance Outputs
ASNM may produce:
Governed Assumption Sensitivity Profile; Assumption Sensitivity Register; sensitivity-direction record; sensitivity-magnitude state; sensitivity-range record; tolerance-range record; Sensitivity Threshold Record; Critical Threshold Record; nonlinear-sensitivity record;
discontinuity record; conditional-sensitivity record; temporal-sensitivity profile; horizon-sensitivity profile; probability-sensitivity record; uncertainty-sensitivity record; confidence-sensitivity record; feasibility-sensitivity record; methodology-applicability sensitivity record; sensitivity-propagation map; assumption-interaction record; sensitivity-amplification record; sensitivity-dampening record; sensitivity-concentration profile; sensitivity-uncertainty record; historical sensitivity record; unresolved sensitivity requirements.
The primary governed output is:
Governed Assumption Sensitivity Profile
This becomes a principal input to:
AMAM — Assumption Materiality Module.
Critical Distinctions
Dependency ≠ Sensitivity
Dependency Presence ≠ Constant Sensitivity
Low Sensitivity ≠ Low Materiality by Default
High Sensitivity ≠ Automatic Criticality
Sensitivity at Range A ≠ Sensitivity at Range B
Threshold Crossing ≠ Automatic Prediction Failure
Small Input Change ≠ Small Predictive Effect by Default
Conditional Sensitivity ≠ Universal Sensitivity
Low Short-Term Sensitivity ≠ Low Long-Term Sensitivity
Probability Sensitivity ≠ Probability Governance
Stable Predicted Value ≠ Stable Confidence
Indirect Sensitivity ≠ Low Sensitivity
Sensitivity of A ≠ Independent of B by Default
Dependency Change ≠ Equivalent Prediction Change
Many Assumptions ≠ Diversified Sensitivity
Sensitivity Increase Direction ≠ Sensitivity Decrease Direction
Single-Assumption Analysis ≠ Complete Predictive Sensitivity
Sensitivity Estimate ≠ Sensitivity Certainty
Historical Sensitivity ≠ Current Sensitivity
Later Sensitivity Knowledge ≠ Original Sensitivity Assessment
Enterprise Application
ASNM enables organizations to understand which predictive assumptions can change significantly without materially affecting a prediction and which can destabilize it after only limited change.
This is critical in:
financial forecasting + AI prediction + supply chains + industrial operations + infrastructure + strategic planning + insurance + healthcare + markets + autonomous systems.
Without sensitivity governance, two assumptions may appear equally important simply because both are documented.
In reality:
one may tolerate substantial variation;
the other may sit immediately next to a predictive tipping point.
ASNM makes that distinction governable before AMAM determines final Assumption Materiality.
Use Case 1 — A Financial Forecast Looks Stable Until Interest Rates Cross One Threshold
A company forecasts that a major investment project will remain profitable.
The prediction assumes that borrowing rates remain within a reasonable range.
Analysts initially treat interest-rate changes as a simple linear risk.
Sensitivity assessment shows something different.
Between:
3% and 4.5%
the expected return changes only moderately.
Above:
5.2%
debt-service requirements cause available cash flow to deteriorate rapidly.
Above:
6%
the projected investment case becomes fundamentally different.
The critical governance question is:
Can an assumption be meaningfully governed when its effect is represented as gradual even though the prediction becomes radically more sensitive after a specific threshold?
ASNM identifies:
the tolerance range; nonlinear sensitivity; critical thresholds; direction and magnitude of change.
Dependency Presence ≠ Constant Sensitivity
Use Case 2 — The AI Forecast Keeps the Same Number While Its Uncertainty Explodes
An AI forecasting system predicts:
1,000 units of demand next month.
The prediction assumes that customer behavior remains comparable to recent history.
A major market disruption begins weakening that continuity assumption.
The central prediction remains approximately:
1,000 units.
At first glance, nothing appears to have changed.
However, the credible prediction range expands from:
950–1,050
to:
700–1,300.
The critical governance question is:
Can a prediction legitimately be described as stable merely because its central value has not changed when weakening assumptions have dramatically increased uncertainty around that value?
ASNM identifies the prediction's uncertainty sensitivity even though the headline prediction remains unchanged.
Stable Predicted Value ≠ Stable Predictive Condition
Canonical Boundary
ASNM governs the identification, direction, magnitude, range, threshold, form, propagation, interaction, uncertainty, and historical preservation of the sensitivity relationships between Predictive Assumptions and prediction outcomes, probability, uncertainty, confidence, feasibility, methodology applicability, interpretation, and reliance conditions. It does not govern assumption identification, dependency mapping itself, final Assumption Materiality, Assumption Change Governance, probability representation, final feasibility determination, Prediction Formation, Operational Reality, or prediction-level revision.
ASNM establishes:
how the prediction may respond when its assumptions change.
It does not determine:
whether that sensitivity is sufficiently important to make the assumption material or what action must follow when the assumption actually changes.
Canonical Closing Statement
An assumption does not become important merely because a prediction depends upon it. Its governance also requires understanding what happens when that assumption moves.
ASNM makes predictive sensitivity visible before assumption importance is determined.