Correlation Uncertainty Module (SCUM) defines the
governance conditions
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation-to-Reality Correlation Standard (SRCS)
Operational Layer: Simulation-to-Reality Correlation Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Correlation Uncertainty
Version: 1.0
Status: Canonical · Open Module
Effective Date: 19 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodological Intellectual Property
Canonical Language: English (UCL™)
Governed Space
Correlation Uncertainty
Canonical Definition
Correlation Uncertainty Module (SCUM) defines the governance conditions underwhich uncertainty affecting the relationship between simulation and
Operational Reality is identified, attributed where supportable, classified,
bounded, propagated, documented, communicated, and preserved throughout
Simulation-to-Reality Correlation.
SCUM governs uncertainty concerning:
how confidently a simulation can be said to correspond to reality within a
defined object, condition, scenario, environment, time, comparison basis, and
correlation scope.
Its purpose is not to eliminate uncertainty.
Its purpose is to prevent uncertainty from disappearing when simulation
results are compared with reality, aligned against observations, or
subsequently used as evidence.
Operational Role
SCUM is the fifth and final internal governance space of theSimulation-to-Reality Correlation Standard (SRCS).
The preceding modules establish:
SRCM — What simulation and reality observations may legitimately be compared?
SCCM — According to which criteria should correspondence be assessed?
SDMM — Where and how materially do simulation and reality differ?
SRAM — Which deviations require alignment, and how should that alignment
occur?
SCUM asks:
How certain are we about the resulting Simulation-to-Reality Correlation, what
remains unknown, and where does that uncertainty limit interpretation or
downstream reliance?
The complete internal progression is:
Simulation-Reality Comparison
↓Correlation Criteria
↓Simulation-Reality Deviation
↓Reality Alignment
↓Correlation Uncertainty
Module Function
SCUM prevents a correlation statement such as:“The simulation matches reality.”
from being treated as complete unless the uncertainty surrounding that
statement is also visible.
A governed correlation statement should instead be capable of expressing:
what corresponds,
under which conditions,
against which reality basis,
with what observed deviations,
with what remaining uncertainty,
and:
where that uncertainty becomes material.
Correlation Is Not Certainty
SIMULOS® establishes:Observed correlation does not eliminate uncertainty about the
simulation-reality relationship.
Strong correspondence may coexist with uncertainty concerning: unobserved
conditions, rare events, measurement limitations, model structure,
environmental variability, future conditions, human behavior, autonomous
behavior, interaction effects, or incomplete Operational Reality knowledge.
Correlation Uncertainty Object
Material Correlation Uncertainty should be capable of becoming a governedobject.
Conceptually:
Correlation Uncertainty ID
↓Affected Correlation Relationship
↓Uncertainty Source
↓Uncertainty Type
↓Affected Condition / Scenario
↓Magnitude / Bound where supportable
↓Dependencies
↓Materiality
↓
Reducibility
↓Downstream Consequence
↓Uncertainty Status Uncertainty Source
SCUM should identify the origin of uncertainty where evidence permits.Potential sources include: incomplete Operational Reality observations,
measurement error, sensor limitations, sparse data, uncertain Reality
Representation, uncertain assumptions, model simplification, unknown
environmental conditions, input variability, scenario incompleteness,
stochastic behavior, execution variability, incomplete comparison, uncertain
correlation criteria, deviation measurement limitations, alignment effects, or
unknown causes.
Unknown Source Must Remain Unknown
SIMULOS® establishes:Uncertainty must not be assigned a cause merely because governance requires a
classification.
Where its source cannot be sufficiently established:
Uncertainty Source Undetermined
is a legitimate governed state.Epistemic Uncertainty
Epistemic Uncertainty arises from incomplete knowledge.Examples include: insufficient observations, incomplete model knowledge,
uncertain parameter values, poorly understood interactions, limited
environmental knowledge, and incomplete causal understanding.
Epistemic uncertainty may potentially be reduced through additional:
evidence, measurement, modeling, experimentation, or observation.
Aleatory Uncertainty
Aleatory Uncertainty arises from inherent variability or stochastic behaviorin the represented reality.
It may not be eliminable simply by obtaining more information.
SCUM distinguishes:
uncertainty caused by what we do not know
from:
uncertainty caused by reality itself being variable.
Unknown Uncertainty
Some uncertainty cannot yet be meaningfully classified as epistemic oraleatory.
SCUM preserves:
Unknown / Unclassified Correlation
Uncertainty
rather than forcing artificial certainty about the nature of uncertaintyitself.
Reality-Reference Uncertainty
Operational Reality is not always perfectly observable.Reality observations may themselves contain: measurement uncertainty, sampling
limitations, missing states, incomplete telemetry, human reporting error,
sensor limitations, temporal gaps, or inaccessible conditions.
Therefore:
Reality Observation ≠ Perfect Reality Knowledge.
Simulation Uncertainty and Reality
Uncertainty Must Remain Distinguishable
A simulation-reality difference may arise from:Simulation Uncertainty
Reality Observation Uncertainty
or:Both.
SCUM should preserve this distinction where possible.
Comparison Uncertainty
Even where both simulation and reality observations are individually strong,uncertainty may exist in whether they are sufficiently comparable.
Possible causes include: different environmental conditions, different timing,
different configurations, different measurement resolution, different object
states, or incomplete contextual equivalence.
Criteria Uncertainty
Correlation Criteria may themselves contain uncertainty.For example, an organization may not know whether:
±2%
or:
±5%
represents a materially sufficient correspondence boundary for a particular
operational purpose.
SCUM preserves uncertainty in the criterion rather than hiding it inside the
correlation result.
Deviation Uncertainty
An observed deviation may have uncertainty around: magnitude, direction,frequency, localization, cause, propagation, or materiality.
Therefore:
Deviation Record
and:Deviation Uncertainty
should remain distinguishable.Alignment Uncertainty
A Reality Alignment action may improve observed correlation while creatinguncertainty about: generalization, unobserved scenarios, model interactions,
new parameter regions, or unintended downstream effects.
Alignment therefore does not automatically reduce total uncertainty.
Residual Correlation Uncertainty
After comparison, deviation assessment, and alignment, uncertainty may remain.This becomes:
Residual Correlation Uncertainty.
Residual uncertainty should be explicitly preserved rather than implicitly
converted into confidence.
Uncertainty Magnitude
Where evidence permits, uncertainty may be expressed quantitatively through:confidence intervals, probability distributions, error ranges, tolerance
intervals, sensitivity ranges, variance, credible intervals, or other
appropriate methods.
Where numerical quantification is not supportable, structured qualitative
classification may be used.
No False Precision Principle
SIMULOS® establishes:Correlation uncertainty must not be expressed more precisely than the
available evidence permits.
A numerical percentage can create misleading authority where the underlying
evidence cannot support that precision.
Qualitative Uncertainty Classification
Where quantitative measurement is inappropriate, implementation may useclassifications such as:
Low
Moderate
High
Critical
or:
Undetermined.
These classifications must remain bounded to the applicable correlation
context.
Uncertainty Bounds
Where possible, SCUM should establish the range within which the correlationrelationship is reasonably supported.
For example:
Strong Correlation — Nominal Conditions
Moderate Uncertainty — High Temperature
High Uncertainty — Extreme Combined
Conditions
Unknown — Unobserved Failure Configuration
This is more informative than a single global confidence score.Condition-Specific Uncertainty
Correlation uncertainty may vary significantly across conditions.Therefore:
Correlation Confidence is a surface, not necessarily a single number.
A simulation may be highly correlated with reality in one operational region
and poorly understood in another.
Scenario-Specific Uncertainty
Each materially relevant scenario may possess a different uncertainty profile.This is particularly important for: edge cases, stress conditions, rare
events, novel environments, and combinations never directly observed in
Operational Reality.
Temporal Uncertainty
A simulation may correspond well with current reality while uncertaintyincreases over time because: the environment changes, system behavior evolves,
components degrade, software changes, populations change, or Operational
Reality drifts.
SCUM therefore allows correlation uncertainty to be time-dependent.
Spatial Uncertainty
Correlation may be strong in observed geographic or physical regions whileremaining uncertain elsewhere.
This matters for: autonomous vehicles, robotics, climate simulation,
aerospace, infrastructure, and geographically distributed systems.
Behavioral Uncertainty
Behavioral uncertainty concerns uncertainty about whether simulated actors orsystems behave sufficiently like their real counterparts.
This can be particularly material for: humans, AI agents, autonomous systems,
multi-agent systems, adaptive systems, and adversarial environments.
Interaction Uncertainty
Even where individual components are well represented, their interactions mayremain uncertain.
SCUM therefore governs uncertainty arising from:
component × component
human × system
agent × agent
system × environment
and other interaction relationships.
Uncertainty Dependency
Uncertainties may depend upon one another.For example:
Environmental Uncertainty
↓Sensor Uncertainty
↓Perception Uncertainty
↓Behavioral Uncertainty
↓Trajectory Correlation Uncertainty
SCUM should preserve material dependencies where identifiable.Uncertainty Interaction
Multiple individually modest uncertainties may interact to produce amaterially larger uncertainty.
For example:
Sensor Noise Weather Variability Timing
Uncertainty
may create:High Autonomous Behavior Correlation Uncertainty.
Uncertainty Propagation
Correlation uncertainty may propagate downstream through the simulationlifecycle and into later governance architectures.
Conceptually:
Correlation Uncertainty
↓Simulation Output Interpretation
↓Validation Evidence
↓Validation Determination
↓Operational Reliance
SCUM therefore requires material uncertainty to remain transferable ratherthan disappearing at the SIMULOS® boundary.
No Uncertainty Laundering Principle
SIMULOS® establishes:Uncertainty identified within Simulation-to-Reality Correlation must not
disappear merely because the simulation output enters another governance
process.
If VALIDOS® uses the simulation as validation evidence, the relevant
uncertainty must travel with it.
Uncertainty Accumulation
Where multiple uncertain relationships contribute to one correlationconclusion, SCUM should determine whether uncertainty accumulates materially.
A simulation should not claim high overall certainty merely because each
individual uncertainty appears small when assessed separately.
Uncertainty Reduction
Where uncertainty is reducible, SCUM may identify actions such as: additionalOperational Reality observations, additional measurements, broader scenario
execution, targeted experiments, higher-fidelity representation, model
refinement, parameter reassessment, additional comparison, or repeated
execution.
Reducible vs Irreducible Uncertainty
SCUM distinguishes:Reducible Uncertainty
from:Currently Irreducible Uncertainty.
This matters because governance responses differ.
Reducible uncertainty may create an evidence or simulation improvement
requirement.
Irreducible uncertainty may instead require:
bounded interpretation, explicit conditions, or restricted reliance.
Uncertainty Reduction Has a Cost
Reducing uncertainty may require: computation, testing, physical experiments,additional missions, new sensors, more data, time, or operational exposure.
SIMULOS® does not require uncertainty to be reduced indefinitely.
The appropriate reduction depth depends upon the simulation purpose and
downstream consequence.
Material Uncertainty
Uncertainty becomes material when it can materially affect: interpretation,correlation classification, simulation output meaning, validation, safety
assessment, operational decision-making, autonomous-system behavior, mission
planning, or reliance.
Uncertainty Threshold
Where appropriate, implementations may establish thresholds beyond which:additional evidence,
additional simulation,
alignment,
validation restriction,
or:
reliance limitation
is required.
SIMULOS® does not prescribe one universal uncertainty threshold.
Correlation Confidence
An implementation may derive a bounded Correlation Confidence representationfrom the governed uncertainty basis.
However:
Correlation Confidence must never conceal the conditions, scope, or unresolved
uncertainty upon which it depends. Correlation Confidence ≠ Probability of
Reality
A confidence representation must not imply that the simulation itself has
become reality or that all future real-world behavior has been
probabilistically captured.
Correlation Uncertainty Status
Possible implementation states may include:Sufficiently Bounded
Conditionally Bounded
Material Uncertainty Present
High Uncertainty
Critical Uncertainty
or:Uncertainty Undetermined.
Correlation Uncertainty Record
A governed record may include:Correlation Uncertainty ID
Simulation Object / Version
Reality Reference
Comparison Basis
Affected Scenario / Condition
Uncertainty Source
Uncertainty Type
Magnitude / Range
Dependencies
Propagation Path
MaterialityReducibility
Reduction Requirement
Residual Uncertainty
Downstream Limitation
StatusEvidence Basis
Timestamp / Version Context Uncertainty
Across Versions
SCUM should support comparison of uncertainty across simulation versions.For example:
Simulation v1 → High uncertainty in
thermal extremes
Simulation v2 → Moderate uncertainty
Simulation v3 → Low uncertainty within tested range / Unknown beyond testedrange
This creates a more meaningful improvement record than simply declaring newer
versions “more accurate.” Uncertainty Trend
Repeated simulation-reality correlation may reveal:
Decreasing Uncertainty
Stable Uncertainty
Increasing Uncertainty
Migrating Uncertainty
or:Newly Emergent Uncertainty.
These trends may indicate where simulation governance should re-enter upstream
spaces.
Correlation Coverage and Uncertainty
High correlation inside a narrow observed region does not establish lowuncertainty outside that region.
Therefore:
Observed Correlation Coverage must remain distinguishable from Unobserved
Correlation Uncertainty.
Unknown Regions
Where no sufficient Operational Reality basis exists, SCUM should permitexplicit classification such as:
Correlation Unknown — Insufficient Reality Observation.
This is especially important where simulation exists precisely because direct
real-world testing is difficult or impossible.
Novel Conditions
A simulation may be used to explore conditions that have never occurred orcannot safely be reproduced.
SCUM does not prohibit this.
Instead, it requires the distinction between:
Empirically Correlated Region
and:Simulation-Only / Uncorrelated Region.
Extrapolation Uncertainty
Where simulation extends beyond observed reality, SCUM should identify:Extrapolation Uncertainty.
The farther the simulation moves beyond the governed correlation basis, the
more carefully its correspondence claims should be bounded.
No Correlation Transfer by Similarity
SIMULOS® establishes:Correlation established for one condition, environment, configuration, or
system version must not automatically transfer to another merely because the
two appear similar.
Transfer requires a governed basis.
Cross-Version Reality Correlation
A new system or simulation version may inherit some correlation evidence froma previous version only where the relevant relationship remains materially
comparable.
Otherwise new correlation assessment is required.
Correlation Uncertainty and Decision
Consequence
The same uncertainty level may be acceptable for:early design exploration
but unacceptable for:
safety-critical deployment authorization.
SCUM therefore preserves downstream consequence context without itself making
the final reliance decision.
Uncertainty Communication
Material uncertainty should be communicated in a form usable by: engineers,decision-makers, validators, auditors, regulators, autonomous systems, and
downstream AI systems.
The communication should preserve both:
what is known
and:
what remains uncertain.
Machine-Readable Uncertainty
For AI-readable implementations, uncertainty should be capable of beingrepresented structurally.
Conceptually:
Correlation Scope
Correlation Status
Uncertainty Type
Uncertainty Bound
Applicable Conditions
Excluded Conditions
Known Deviations
Unknown Regions
Downstream Restrictions
This allows downstream systems to consume uncertainty rather than merelyconsuming a correlation score.
Relationship to SRCM
SRCM determines whether simulation and reality observations are sufficientlycomparable.
SCUM preserves uncertainty remaining in that comparison basis.
Relationship to SCCM
SCCM establishes the criteria for judging correspondence.SCUM governs uncertainty affecting the interpretation and application of those
criteria.
Relationship to SDMM
SDMM governs observed Simulation-Reality Deviations.SCUM governs uncertainty concerning those deviations and the broader
correlation relationship.
Therefore:
Deviation ≠ Uncertainty.
A deviation is an observed difference.
Uncertainty concerns how confidently that difference and the resulting
correlation relationship are understood.
Relationship to SRAM
SRAM governs Reality Alignment.SCUM determines what uncertainty remains before, during, and after alignment.
Therefore:
Alignment may reduce deviation without eliminating uncertainty.
Relationship to ORA™
ORA™ governs Operational Reality.SCUM does not assign certainty to Operational Reality itself.
It governs how uncertainty in the available reality basis affects the
simulation-to-reality relationship.
Relationship to VALIDOS®
VALIDOS® may use simulation and Simulation-to-Reality Correlation as part of aValidation Evidence basis.
SCUM ensures that material correlation uncertainty remains attached to that
evidence.
Therefore:
VALIDOS® must not receive simulation correlation as stronger evidence than
SIMULOS® has actually established.
Relationship to INTEGROS®
INTEGROS® may govern integrity properties of: observations, measurements,datasets, records, and uncertainty artifacts.
SCUM governs the simulation-specific uncertainty relationship.
Relationship to SOGS
SOGS governs what simulation outputs are and what may legitimately be inferredfrom them.
SCUM provides a correlation-uncertainty basis that may materially constrain
those inferences where outputs are interpreted as representations of reality.
Relationship to Operational Reliance
SCUM does not itself authorize operational reliance.It provides the uncertainty basis required by downstream governance to
determine whether reliance is legitimate.
Therefore:
Correlation Uncertainty → Reliance Input
not:Correlation Uncertainty → Reliance Authorization.
Autonomous Systems Application
An autonomous system may show excellent simulation-reality correlation acrossmillions of nominal simulation events.
However, limited real-world observations may exist for: rare pedestrian
behavior, unusual weather combinations, degraded sensors, adversarial
interactions, infrastructure failures, multi-agent conflicts, or emergency
recovery.
SCUM prevents nominal correlation from being generalized automatically into
low uncertainty across the entire autonomous operating domain.
A machine-readable Correlation Uncertainty Map can identify where the system's
simulation evidence is strong and where real-world correspondence remains
uncertain.
Space and Mission Simulation Application
Space agencies frequently rely on simulation precisely where direct realityevidence is sparse.
A spacecraft or planetary-landing simulation may possess strong correlation
evidence for: subsystem tests, terrestrial hardware testing, known orbital
conditions, previous mission telemetry, and observed environmental parameters.
But uncertainty may remain high for: never-observed surface interactions,
extreme radiation combinations, long-duration degradation, unknown atmospheric
behavior, rare subsystem combinations, autonomous recovery, or first-of-kind
mission conditions.
SCUM allows those regions to remain explicitly:
Simulation-Supported but
Reality-Correlation Uncertain
rather than silently inheriting the confidence of well-tested regions.This can reveal exactly where additional testing, observation,
instrumentation, scenario coverage, or conservative mission constraints
provide the greatest value.
Minimum Implementation Framework
1. Establish the Correlation BasisIdentify the simulation, Operational Reality reference, comparison basis,
criteria, deviations, and alignment state. 2. Identify Material Uncertainty
Determine where the correlation relationship remains uncertain. 3. Identify
the Uncertainty Source
Attribute uncertainty where evidence permits.
Otherwise record:
Source Undetermined. 4. Classify the
Uncertainty
Determine whether uncertainty is: epistemic, aleatory, mixed, or unknown.5. Localize the Uncertainty
Identify the affected: condition, scenario, component, environment, behavior,
interaction, time, or output.
6. Bound or Quantify Where Supportable
Use numerical or qualitative bounds appropriate to the evidence. 7. Map
Dependencies and Interaction
Determine whether uncertainties depend upon or amplify one another. 8. Assess
Propagation
Identify where uncertainty can affect downstream interpretation or governance.
9. Determine Materiality
Assess whether the uncertainty materially affects the intended simulation
purpose or downstream use. 10. Determine Reducibility
Identify whether additional evidence or simulation activity can materially
reduce the uncertainty. 11. Preserve Residual Uncertainty
Record what remains unresolved after available reduction or alignment actions.
12. Establish Correlation Uncertainty Status
Assign a bounded status appropriate to the applicable correlation scope. 13.
Transfer Material Uncertainty Downstream
Ensure that simulation outputs, validation processes, audits, or reliance
decisions receive the relevant uncertainty information. 14. Preserve
Traceability
Maintain the relationship:
Reality Basis → Comparison → Criteria → Deviation → Alignment → Uncertainty →
Downstream Use.
Governance Outputs
SCUM may produce: Correlation Uncertainty ID, Correlation Uncertainty Record,Uncertainty Source Record, Uncertainty Classification, Epistemic Uncertainty
Record, Aleatory Uncertainty Record, Unknown Uncertainty Record,
Reality-Reference Uncertainty Record, Comparison Uncertainty Record, Criteria
Uncertainty Record, Deviation Uncertainty Record, Alignment Uncertainty
Record, Residual Correlation Uncertainty Record, Quantitative Uncertainty
Bound, Qualitative Uncertainty Classification, Condition-Specific Uncertainty
Map, Scenario-Specific Uncertainty Map, Temporal Uncertainty Record, Spatial
Uncertainty Record, Behavioral Uncertainty Record, Interaction Uncertainty
Record, Uncertainty Dependency Map, Uncertainty Propagation Map, Uncertainty
Accumulation Assessment, Uncertainty Reduction Requirement, Reducibility
Assessment, Extrapolation Uncertainty Record, Unknown Correlation Region
Record, Correlation Uncertainty Status, Correlation Confidence Record,
Downstream Uncertainty Transfer Record, and Governed Simulation-to-Reality
Correlation Uncertainty Basis.
Use Case 1 — Autonomous Driving Beyond
Tested Conditions
An autonomous driving simulation demonstrates strong correlation withOperational Reality across extensive urban driving tests.
However, only limited real-world evidence exists for:
heavy snow + partially blocked sensors + unusual pedestrian movement + low
visibility.
SCUM prevents the strong nominal correlation from automatically extending into
this region.
The system records:
Nominal Urban Conditions → Correlation
Uncertainty Low
Heavy Snow → Correlation Uncertainty
Moderate
Heavy Snow + Sensor Degradation + Rare Pedestrian Interaction → CorrelationUncertainty High
This uncertainty can then travel with the simulation evidence into validation
and operational decision-making. Use Case 2 — First-of-Kind Space Mission
A space agency uses simulation to assess an autonomous landing sequence for a
destination where direct Operational Reality observations are necessarily
limited.
The simulation correlates strongly with: terrestrial hardware testing,
propulsion tests, sensor testing, and comparable prior mission data.
However, the exact combination of:
surface properties + environmental conditions + autonomous response + landing
dynamics
has never been directly observed.
SCUM prevents correlation from known regions from being silently transferred
into the unknown mission region.
The resulting correlation basis may state:
Subsystem Correlation — Strong
Known Environmental Range — Sufficiently
Bounded
Exact Landing Environment — Material
Epistemic Uncertainty
Combined First-of-Kind Landing Interaction — Correlation Unknown /Simulation-Supported
This gives engineers and decision-makers a governed map of where additional
evidence can reduce uncertainty and where uncertainty must instead remain an
explicit mission constraint.
Architectural Position
SCUM is the fifth and final module of the Simulation-to-Reality CorrelationStandard (SRCS).
The complete SRCS progression is:
SRCM — Simulation-Reality Comparison
↓SCCM — Simulation Correlation Criteria
↓SDMM — Simulation-Reality Deviation
↓SRAM — Reality Alignment
↓SCUM — Correlation Uncertainty
Together, these five modules answer:What can be compared?
↓
According to what criteria?
↓
Where does simulation differ from reality?
↓
What should be aligned?
↓
How certain are we about the resulting relationship?
This closes the governed Simulation-to-Reality Correlation space without
converting correlation into validation or simulation into reality.
Foundational Principle
Simulation similarity is not reality equivalence, and correlation confidencemust never exceed the reality evidence, comparison basis, observed conditions,
and unresolved uncertainty that actually support it.