Simulation Assumption Uncertainty Module (SAUM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Assumption Standard (SAS)
Operational Layer: Simulation Assumption Governance Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Simulation Assumption Uncertainty
Version: 1.0
Status: Canonical · Open Module
Effective Date: 17 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · CLIA® · MGIA™ · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodological Intellectual Property
Canonical Language: English (UCL™)
Governed Space
Simulation Assumption Uncertainty
Canonical Definition
Simulation Assumption Uncertainty Module (SAUM) defines the governanceframework for identifying, characterizing, bounding, classifying, propagating,
reassessing, and preserving uncertainty associated with Simulation Assumptions
throughout the simulation lifecycle.
It establishes the methodological conditions under which uncertainty attached
to an assumption remains explicitly connected to the assumption, the
simulation context in which that assumption operates, the dependencies through
which its influence propagates, and the simulation outputs and conclusions
that may be materially affected by it.
SAUM does not attempt to eliminate uncertainty.
It governs what is uncertain about a Simulation Assumption, why that
uncertainty exists, how significant it may be within the simulation, where its
influence may propagate, and how that uncertainty must remain visible rather
than being converted into false certainty.
Operational Role
SAUM serves as the fourth internal governance space of the SimulationAssumption Standard (SAS).
The preceding modules establish:
SAIM — Which assumptions exist and which are materially relevant?
SABM — On what basis is each assumption being used?
SADM — What depends upon each assumption?
SAUM asks:
What uncertainty exists around each Simulation Assumption, how should that
uncertainty be represented, and where can it materially influence the
simulation?
The governed progression is:
Governed Simulation Assumption → Uncertainty Identification → Uncertainty
Source → Uncertainty Characterization → Uncertainty Bound → Dependency
Connection → Uncertainty Propagation → Materiality → Reassessment →
Uncertainty Record
The resulting uncertainty structure becomes an input to:
SALM — Simulation Assumption Limitation
Module
Module Function
Simulation Assumptions are often necessary precisely because completeknowledge is unavailable.
An assumption may represent: an estimated future condition, an incompletely
observed physical property, an uncertain human behavior, an environmental
condition, an engineering approximation, an unknown operational response, an
incomplete dataset condition, or another state that cannot be represented with
complete certainty.
The existence of an assumption therefore does not imply that its uncertainty
has been sufficiently governed.
SAUM transforms:
Assumption
into:
Assumption + Uncertainty + Uncertainty Basis + Bound + Dependency Context +
Propagation Consequence
The purpose is to prevent uncertainty from disappearing merely because an
uncertain condition has been expressed as a fixed simulation assumption.
Simulation Assumption Uncertainty
Simulation Assumption Uncertainty is the governed lack of complete certaintyconcerning the truth, value, range, stability, applicability, behavior, or
future state represented by a Simulation Assumption.
The relevant uncertainty must be understood relative to:
Simulation Object + Purpose + Scope + Reality Representation + Assumption +
Simulation Context
An assumption may therefore carry different uncertainty significance in
different simulations.
Uncertainty Identification
Every material Simulation Assumption should be examined for uncertainty.The inquiry asks:
What about this assumption is not known with sufficient certainty?
The answer may concern: the assumed value, expected range, probability,
stability, timing, frequency, applicability, underlying evidence,
environmental conditions, behavioral response, or another materially relevant
property.
Uncertainty Source
SAUM distinguishes the assumption from the source of its uncertainty.Uncertainty may arise because: measurements are incomplete, evidence is
limited, future conditions are unknown, observed populations are incomplete,
physical variability exists, behavior varies, environmental states change,
models simplify reality, historical information is sparse, expert judgment
differs, dependencies are incompletely understood, or relevant knowledge does
not yet exist.
Material uncertainty should remain attributable to its identifiable source
where possible.
Known Uncertainty
A Known Uncertainty exists where the existence and relevant nature ofuncertainty are recognized.
Example:
Wind speed is expected to vary between defined operational bounds.
The exact future wind speed is unknown, but the uncertainty itself is
understood sufficiently to be represented.
Partially Known Uncertainty
A Partially Known Uncertainty exists where some properties of the uncertaintyare understood while others remain unresolved.
For example:
a likely range may be known while the probability distribution remains
uncertain.
The unresolved portion must remain visible.
Unresolved Uncertainty
An Unresolved Uncertainty exists where material uncertainty is known to existbut cannot yet be sufficiently characterized or bounded.
SIMULOS® establishes:
Unknown magnitude does not justify zero uncertainty.
Where uncertainty cannot be sufficiently quantified, it should remain
explicitly represented as unresolved.
Quantitative Uncertainty
Where justified, uncertainty may be represented quantitatively through:ranges, intervals, distributions, probabilities, tolerances, confidence
bounds, sensitivity values, or another applicable quantitative representation.
The representation should not imply greater precision than the underlying
uncertainty basis supports.
Qualitative Uncertainty
Not all uncertainty can legitimately be reduced to numbers.Qualitative uncertainty may be represented through governed classifications
such as:
Low
Moderate
High
Critical
or another defined classification scheme.
The classification basis should remain explicit.
No False Quantification Principle
SIMULOS® establishes:Uncertainty must not be assigned numerical precision merely to make the
simulation appear more rigorous.
Where the basis supports only qualitative characterization, quantitative
representation should not manufacture unsupported certainty.
Bounded Uncertainty
A Bounded Uncertainty exists where an assumption can reasonably be constrainedwithin defined limits.
Example:
Temperature Assumption: 18–25°C
The bound should identify: lower limit, upper limit, basis, applicablecontext, and conditions under which the bound remains meaningful.
Unbounded Uncertainty
Some uncertainty cannot be sufficiently bounded.Where a material assumption contains unbounded uncertainty, SAUM requires that
condition to remain visible.
Unbounded uncertainty may materially constrain: simulation interpretation,
scenario confidence, output confidence, or the legitimate strength of
conclusions.
SAUM identifies the condition without making the external validation
determination.
Uncertainty Range
An Assumption Uncertainty Range defines the governed interval within which therelevant assumed condition is reasonably expected to vary.
A range should not automatically be interpreted as:
all values equally likely
unless that interpretation is independently justified.
Uncertainty Distribution
Where sufficient basis exists, uncertainty may be represented through aprobability distribution.
The distribution itself may depend upon assumptions.
SAUM therefore requires the basis of the distribution to remain
distinguishable from the uncertainty being represented.
Uncertainty Confidence
A simulation may possess uncertainty not only about an assumed value, butabout the uncertainty estimate itself.
Example:
Assumed Range: 10–15
may itself have:Low Confidence
if based upon limited observations.SAUM permits this second-order uncertainty to remain visible rather than
treating the range as absolute.
Epistemic Uncertainty
Epistemic Uncertainty arises from incomplete knowledge.It may potentially be reduced through: better evidence, additional
measurement, improved observation, further research, or stronger
understanding.
Examples include uncertainty concerning: material properties, system behavior,
causal relationships, or poorly observed conditions.
Variability-Related Uncertainty
Some uncertainty arises because reality itself varies.Examples include: weather, human behavior, traffic, biological response,
market behavior, manufacturing variability, or environmental fluctuation.
This uncertainty may persist even where extensive information exists.
SAUM preserves the distinction where it materially affects simulation
governance.
Uncertainty Materiality
Not every uncertainty requires the same governance depth.Materiality may depend upon: uncertainty magnitude, affected assumption,
dependency breadth, dependency depth, affected model components, sensitivity,
output consequence, scenario consequence, and intended simulation use.
The governing question is:
Could this uncertainty materially change simulation behavior, outputs, or
conclusions?
Uncertainty Sensitivity
An assumption may contain substantial uncertainty while the simulation remainsrelatively insensitive to it.
Another assumption may contain only small uncertainty while simulation outputs
are highly sensitive to small changes.
SAUM therefore separates:
Uncertainty Magnitude
from:Simulation Sensitivity
Conceptually:Small Uncertainty + High Sensitivity
may be more material than:Large Uncertainty + Low Sensitivity
Uncertainty Dependency
SADM establishes what depends upon an assumption.SAUM uses that dependency structure to determine where assumption uncertainty
may matter.
Conceptually:
Assumption
↓
Assumption Uncertainty
↓Dependency Path
↓Affected Simulation Element
↓Affected Output Uncertainty Propagation
Simulation Assumption Uncertainty Propagation occurs where uncertaintyattached to an assumption influences downstream simulation elements.
Example:
Uncertain Road Friction
↓Braking Model
↓Stopping Distance
↓Collision Probability
The uncertainty does not disappear merely because it passes through a model.No Uncertainty Laundering Principle
SIMULOS® establishes:Uncertainty attached to a material assumption does not become certainty merely
because the assumption passes through mathematical, computational, or
simulation processing.
Transformation may change the uncertainty.
It does not automatically eliminate it.
Uncertainty Amplification
A simulation dependency may amplify assumption uncertainty.Example:
±1% uncertainty in Input Assumption
may produce:
±15% variation in Output
where the model is highly sensitive.
Such amplification should remain identifiable.
Uncertainty Dampening
Simulation structure may reduce the downstream effect of assumptionuncertainty.
Where demonstrated, SAUM may record that uncertainty is dampened through the
dependency path.
Dampening should be demonstrated rather than presumed.
Uncertainty Accumulation
Multiple assumptions may contribute uncertainty to the same simulation elementor output.
Conceptually:
Assumption A Uncertainty Assumption B
Uncertainty Assumption C Uncertainty
↓Output Uncertainty
SAUM requires material accumulation to remain visible.Correlated Uncertainty
Uncertainties may not be independent.Two assumptions may derive from: the same evidence, the same environmental
condition, the same dataset, the same expert judgment, or the same underlying
physical process.
Treating correlated uncertainty as independent may materially misrepresent
simulation confidence.
SAUM therefore requires material correlation to be identified where known.
Compound Uncertainty
A Compound Uncertainty exists where several uncertain assumptions interact ina way that cannot be adequately understood by evaluating each one
independently.
This may occur in: nonlinear systems, coupled models, multi-agent simulations,
complex environmental models, or highly interdependent systems.
Uncertainty Concentration
A simulation may contain many assumptions while most material uncertainty isconcentrated in only a small number.
SAUM may identify:
High-Uncertainty Assumption Concentration
where a limited set of assumptions dominates uncertainty in key outputs.This can become an important simulation-governance signal.
Uncertainty Interaction
Assumption uncertainties may interact.For example:
uncertainty in:
Reaction Time
and:Road Friction
may jointly influence collision outcomes more strongly than either uncertaintyconsidered independently.
Material interactions should remain governable.
Uncertainty Threshold
A simulation may establish thresholds beyond which assumption uncertaintybecomes materially incompatible with the intended simulation purpose.
For example:
Uncertainty ≤ Threshold X
may be acceptable for exploratory simulation.The same uncertainty may be unacceptable for high-consequence simulation.
SAUM therefore treats thresholds as context-dependent governance conditions.
Uncertainty and Simulation Depth
Required uncertainty governance may increase with: simulation consequence,simulation complexity, decision significance, required precision, reliance
expectations, and Simulation Depth.
A low-consequence exploratory simulation may legitimately tolerate broader
uncertainty.
A simulation supporting high-consequence engineering decisions may require
substantially stronger characterization.
Uncertainty and Precision
Simulation output precision should not be confused with uncertainty.A simulation may output:
12.347821
while the governing assumption supports only:
approximately 10–15
Numerical output precision does not eliminate upstream uncertainty.
No Precision Inflation Principle
SIMULOS® establishes:Simulation output precision must not be interpreted as greater certainty than
the assumptions and governed simulation basis can support. Uncertainty vs.
Assumption Basis
SABM governs:
Why is the assumption being used?
SAUM governs:
What remains uncertain about that assumption?
A strong assumption basis may still contain material uncertainty.
The two governance questions are distinct.
Uncertainty vs. Dependency
SADM governs:What depends upon the assumption?
SAUM governs:
How can uncertainty associated with that assumption influence those
dependencies?
This preserves clean module boundaries.
Uncertainty vs. Limitation
SAUM governs uncertainty.SALM subsequently governs:
Where the assumption ceases to remain applicable, what constraints it creates,
and which limitations must remain visible.
Uncertainty may create a limitation.
It is not itself identical to limitation governance. Uncertainty vs.
Validation
SAUM does not determine whether a Simulation Assumption is universally valid
or whether a simulation may support an external validation claim.
Those questions belong to the appropriate validation governance under
VALIDOS®.
SAUM supplies the governed uncertainty representation needed for such
downstream evaluation where applicable. Uncertainty vs. Integrity
SAUM does not replace INTEGROS®.
INTEGROS® may govern whether relevant uncertainty records, evidence, states,
or processes preserve required integrity.
SAUM governs specifically the uncertainty associated with Simulation
Assumptions inside the simulation governance lifecycle.
Uncertainty Change
Assumption uncertainty may change when: new evidence appears, measurementsimprove, the simulation environment changes, the Simulation Object changes,
the assumption is revised, dependency relationships change, new scenarios are
introduced, or new uncertainty is discovered.
The uncertainty state should therefore remain capable of reassessment.
Uncertainty Reduction
New evidence or improved knowledge may reduce uncertainty.SAUM permits:
Previous Uncertainty → New Basis → Reduced
Uncertainty
The historical uncertainty should remain traceable to the simulation versionsfor which it applied.
Uncertainty Expansion
New evidence may reveal that prior uncertainty was underestimated.SAUM permits:
Previous Bound → New Evidence → Expanded
Bound
Expansion is not a governance failure.Failure would occur if material new uncertainty were concealed while the
previous narrower representation continued to be used.
Uncertainty Reclassification
An uncertainty may move between states such as:Unresolved → Partially Characterized →
Bounded
or:Bounded → Unresolved
when new information changes the available basis.Uncertainty Change Trigger
Reassessment may be triggered by: assumption modification, basis modification,dependency modification, model change, environment change, new evidence,
contradictory evidence, new scenarios, unexpected simulation behavior, or
changed simulation purpose and scope.
Uncertainty Version Binding
Every material uncertainty representation should remain bound to the relevant:Simulation Object, assumption version, simulation configuration, model
version, environment version, and applicable lifecycle state
where these distinctions materially affect the uncertainty.
Historical Uncertainty Preservation
Later reduction of uncertainty should not erase the fact that earliersimulation outputs were produced under greater uncertainty.
Historical uncertainty records support: simulation reconstruction, comparison,
audit, learning, and lifecycle accountability.
Uncertainty Gap
A Simulation Assumption Uncertainty Gap exists where material uncertainty: hasnot been identified, cannot be characterized, lacks sufficient basis, has
unknown bounds, has unknown propagation, has unresolved correlation, or cannot
be connected to affected simulation elements.
The gap itself should become a governed condition.
No Unknown-to-Zero Principle
SIMULOS® establishes:Uncertainty that cannot currently be determined must not be represented as
zero uncertainty.
The correct state may be:
Uncertainty Undetermined
rather than:No Uncertainty Uncertainty Disclosure
Material assumption uncertainty should remain visible to downstream simulationgovernance.
Disclosure may include: uncertainty type, magnitude or classification, basis,
confidence, unresolved elements, affected dependencies, affected outputs, and
applicable conditions.
Uncertainty Traceability
SAUM preserves the chain:Simulation Assumption
↓Uncertainty Source
↓Uncertainty Characterization
↓Uncertainty Bound / Status
↓Dependency Path
↓Affected Simulation Element
↓Affected Output / Conclusion
where applicable.Simulation Assumption Uncertainty Record
For each material assumption uncertainty, SAUM may establish a recordcontaining: Uncertainty ID, Simulation Assumption ID, uncertainty description,
uncertainty source, uncertainty type, quantitative or qualitative
representation, lower and upper bounds where applicable, distribution where
applicable, confidence, materiality, sensitivity, dependency references,
propagation path, correlation references, interaction references, threshold
status, unresolved uncertainty, uncertainty change history, simulation
version, and traceability references.
Assumption Uncertainty Map
Material uncertainties may be represented through a Simulation AssumptionUncertainty Map connecting:
Assumptions → Uncertainties → Dependencies
→ Affected Outputs
The map allows simulation operators and reviewers to identify whereuncertainty is concentrated and where it may materially influence simulation
outcomes.
Uncertainty Status
An assumption uncertainty may be classified as:Identified
Characterized
BoundedPartially Bounded
UnboundedConditional
Material
Critical
Under Reassessment
Resolvedor:
Undetermined
according to the applicable governance context.Minimum Implementation Framework
1. Identify Material Assumption UncertaintyExamine each material Simulation Assumption for uncertainty relevant to the
simulation. 2. Identify the Uncertainty Source
Determine why the uncertainty exists and what information supports its
characterization. 3. Characterize the Uncertainty
Determine whether the uncertainty is quantitative, qualitative, bounded,
partially bounded, unbounded, known, or unresolved. 4. Establish Appropriate
Bounds
Where justified, define ranges, distributions, tolerances, confidence levels,
or other appropriate representations. 5. Connect Uncertainty to Dependencies
Use the governed dependency structure to identify which simulation elements
may be affected. 6. Assess Materiality and Sensitivity
Determine whether the uncertainty can materially influence model behavior,
scenarios, outputs, or conclusions. 7. Assess Interaction and Accumulation
Identify material correlation, compound uncertainty, concentration,
amplification, or interaction between uncertain assumptions. 8. Trace
Uncertainty Propagation
Preserve how material uncertainty can propagate through the simulation. 9.
Record Uncertainty Gaps
Keep unresolved or insufficiently characterized material uncertainty visible.
10. Maintain Uncertainty Lifecycle Traceability
Bind uncertainty representations to the relevant assumption and simulation
versions and reassess them when material change occurs.
Governance Outputs
SAUM may produce: Simulation Assumption Uncertainty Identifier, SimulationAssumption Uncertainty Record, Uncertainty Source Record, Uncertainty
Characterization Record, Quantitative Uncertainty Record, Qualitative
Uncertainty Record, Uncertainty Bound, Uncertainty Range Record, Uncertainty
Distribution Record, Uncertainty Confidence Record, Unresolved Uncertainty
Record, Uncertainty Materiality Assessment, Uncertainty Sensitivity Record,
Uncertainty Dependency Record, Uncertainty Propagation Path, Uncertainty
Amplification Record, Uncertainty Dampening Record, Uncertainty Accumulation
Record, Correlated Uncertainty Record, Compound Uncertainty Record,
Uncertainty Interaction Record, Uncertainty Concentration Record, Uncertainty
Threshold Record, Uncertainty Change Record, Uncertainty Reassessment Record,
Uncertainty Gap Record, Simulation Assumption Uncertainty Map, Simulation
Assumption Uncertainty Traceability Chain, and Governed Simulation Assumption
Uncertainty Set.
Use Case 1 — Autonomous Vehicle Simulation
ScenarioA vehicle simulation assumes:
Average human pedestrian reaction behavior remains within a defined response
range.
The assumption is required for
collision-avoidance scenarios. Application
SAUM identifies that the response range contains substantial humanvariability.
The simulation is highly sensitive to the upper tail of delayed pedestrian
reactions.
The uncertainty is therefore connected through:
Pedestrian Response Assumption
↓Behavioral Model
↓Collision-Avoidance Scenario
↓Stopping / Evasion Outcome Result
The assumption is classified as:Material Uncertainty — High Output
Sensitivity
The uncertainty remains visible in the simulation record rather than beinghidden behind one fixed pedestrian-response value. Use Case 2 — Space Mission
Simulation
Scenario
A mission simulation assumes a defined degradation rate for a spacecraft power
subsystem over a multi-year mission. Application
The degradation rate contains uncertainty resulting from: limited
long-duration observations, radiation exposure variability, temperature
cycles, and manufacturing variability.
SAUM maps uncertainty propagation through:
Degradation Assumption
↓Power Generation
↓Battery Availability
↓Thermal / Communication Capacity
↓Mission Availability Result
The simulation records a bounded uncertainty range together with a residualunresolved component.
Mission outputs cannot therefore represent the degradation assumption as a
perfectly known future state.
Architectural Position
Simulation Assumption Uncertainty is the fourth internal governance space ofthe Simulation Assumption Standard (SAS).
The complete SAS progression is:
Simulation Assumption Identification → Simulation Assumption Basis →
Simulation Assumption Dependency → Simulation Assumption Uncertainty →
Simulation Assumption Limitation
SAIM establishes:
Which assumptions exist?
SABM establishes:
Why are they being used?
SADM establishes:
What depends upon them?
SAUM establishes:
What remains uncertain about them and where can that uncertainty propagate?
SALM establishes:
Where do those assumptions cease to remain applicable and what limitations
follow?
Together, the five modules ensure that Simulation Assumptions cannot function
as invisible premises, unsupported simplifications, isolated model conditions,
falsely certain inputs, or unbounded representations of reality.
Foundational Principle
Uncertainty does not disappear when an uncertain condition becomes aSimulation Assumption; it must remain identifiable, bounded where possible,
connected to its dependencies, and traceable through every materially affected
simulation outcome.