Simulation Data Representativeness Module (SDRM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Input & Data Standard (SIDS)
Operational Layer: Simulation Input & Data Governance Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Simulation Data Representativeness
Version: 1.0
Status: Canonical · Open Module
Effective Date: 18 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 Data Representativeness
Canonical Definition
Simulation Data Representativeness Module (SDRM) defines the governanceframework through which identified Simulation Inputs and datasets are assessed
to determine whether they sufficiently represent the populations,
environments, operating states, behaviors, distributions, conditions,
variations, and other materially relevant characteristics of the reality that
a defined simulation intends to represent.
SDRM establishes the representativeness layer of SIDS by determining whether
available information is sufficiently aligned with the Simulation Object,
Purpose, Scope, Reality Representation, Assumptions, Model & Environment, and
intended simulation context before that information is relied upon as a
representative basis for simulation activity.
Operational Role
SDRM serves as the second internal governance space of the Simulation Input &Data Standard (SIDS).
The preceding module, SIIM — Simulation Input Identification Module,
establishes what the material Simulation Inputs are, where they originate, how
they are classified, and what simulation roles they may influence.
SDRM asks:
Does the identified information sufficiently represent the reality,
population, environment, state, behavior, distribution, or condition that the
simulation intends it to represent?
The governed progression is:
Identified Simulation Input → Representation Target → Representativeness
Dimensions → Coverage Assessment → Gap Identification → Materiality Assessment
→ Representativeness Status → Representativeness Conditions & Restrictions
SDRM therefore prevents identified data from being treated as representative
merely because it is available, accurate within its original context,
technically compatible, or large in volume.
Module Function
SDRM exists to prevent: available data being assumed representative bydefault, large datasets being confused with representative datasets, one
population being silently generalized to another, normal-operation data being
generalized to failure conditions, one geographic environment being
generalized globally, one system version being treated as representative of
materially different versions, averages concealing relevant distributions,
majority populations hiding minority or rare cases, proxy data being treated
as direct representation, historical operating conditions being generalized to
structurally different environments, missing representation being interpreted
as absence of the represented phenomenon, and simulation outputs appearing
broadly applicable when the input basis supports only a narrower context.
Representativeness
Simulation Data Representativeness is the degree to which an identifiedSimulation Input or Input Set sufficiently reflects the materially relevant
characteristics of the reality segment it is intended to represent within a
defined simulation.
Representativeness is not an intrinsic universal property of data.
It is relational.
Conceptually:
Data + Representation Target + Simulation Context → Representativeness
Assessment Representation Target
A Representation Target defines what the input is expected to represent.
The target may be: population, environment, geographic region, operating
state, system configuration, behavior, physical condition, temporal condition,
distribution, event class, failure state, or another materially relevant
reality segment.
Without a defined Representation Target, the statement:
“The data is representative.”
is incomplete.
Representativeness Is Purpose-Relative
SIMULOS® establishes:No dataset is representative in the abstract; it is representative only
relative to a defined simulation inquiry and representation target.
The same dataset may therefore be:
Representative
for one simulation,and:
Non-Representative
for another. Representativeness ≠ AccuracyData may be highly accurate and still be unrepresentative.
For example:
a perfectly measured dataset from dry-road driving may accurately describe
dry-road conditions while being unsuitable as the sole representative basis
for snow-road simulation.
Therefore:
Accuracy asks whether the recorded information is correct.
Representativeness asks whether the information sufficiently reflects what the
simulation needs to represent. Representativeness ≠ Volume
A billion observations from one narrow operating condition may provide less
representative coverage than a much smaller dataset covering materially
different relevant conditions.
SIMULOS® establishes:
Data volume cannot compensate for missing representation of materially
relevant reality.
Population Representativeness
Where a simulation represents a population, SDRM determines whether materiallyrelevant population characteristics are sufficiently represented.
Population may include: humans, vehicles, machines, organizations,
infrastructure assets, biological entities, software systems, autonomous
agents, devices, or other classes of objects.
Relevant dimensions may include: subgroup, capability, age, configuration,
size, performance class, technical generation, behavioral profile, or another
simulation-relevant distinction.
Subgroup Representation
A population-level dataset may conceal weak coverage of materially relevantsubgroups.
SDRM therefore asks:
Which subgroups materially affect simulation behavior, and are they
sufficiently represented?
Overall population coverage does not automatically establish subgroup
coverage.
Environmental Representativeness
Environmental data should represent the environments within which simulatedbehavior is expected to be examined.
Relevant environmental dimensions may include: weather, terrain, temperature,
lighting, infrastructure, electromagnetic conditions, network conditions,
traffic density, physical obstacles, or other environmental factors.
Geographic Representativeness
Data obtained in one geographic region may not sufficiently represent anotherwhere material differences exist.
Possible differences include: infrastructure, climate, terrain, regulation,
traffic behavior, architecture, population characteristics, or environmental
conditions.
Geographic transfer should therefore remain explicit where material.
Operational-State Representativeness
Data from one operational state should not automatically represent another.Relevant states may include:
Normal
Degraded
Failure
Emergency
Recovery
Maintenance
Startup
Shutdown
or another defined operating state.
A simulation intended to explore failures may require data that represents
more than normal operation.
System-Version Representativeness
Simulation data may originate from a different version or configuration of therepresented system.
SDRM asks whether differences between:
Data-Origin System
and:Simulation-Represented System
are material to simulation behavior.Behavioral Representativeness
Where behavior affects simulation outcomes, input data should sufficientlyrepresent materially relevant behavioral variation.
This may include: human behavior, autonomous behavior, organizational
behavior, market behavior, system response, interaction behavior, or
adversarial behavior.
Distributional Representativeness
Representativeness includes more than averages.Relevant characteristics may include: mean, variance, distribution shape,
tails, correlations, clusters, multimodality, extremes, and conditional
distributions.
Average ≠ Representative Distribution
SIMULOS® establishes:A representative average does not establish a representative distribution.
Two populations may have the same average while producing materially different
simulation behavior.
Range Representativeness
The observed range of an input should be compared with the range relevant tothe simulation.
For example:
Training / observed temperature range:
0°C–30°C
Simulation scope: −30°C–50°C
The input basis does not fully represent the simulation range.Variation Representativeness
Simulation behavior may depend upon variation rather than absolute valuesalone.
SDRM therefore evaluates whether material variation patterns are sufficiently
represented.
This may include: rapid changes, cyclic changes, random variation, seasonal
variation, load variation, behavioral variation, or environmental variation.
Rare-Condition Representation
Rare events may have low frequency but high simulation importance.Examples include: catastrophic failures, extreme weather, unusual human
behavior, collision conditions, cascading infrastructure failures, unusual
system interactions, or low-frequency safety events.
SIMULOS® establishes:
Low frequency does not imply low representational importance.
Consequence-Weighted Representativeness
The required representation of a condition may increase where failure torepresent it could materially affect a high-consequence simulation conclusion.
This does not mean every rare condition must always be included.
It means representativeness decisions should reflect the purpose and
consequence of the simulation.
Boundary-Condition Representativeness
Simulation behavior may change near operational boundaries.Data concentrated in the center of an operating range may therefore fail to
represent behavior near: maximum load, minimum load, speed limits, thermal
limits, stability limits, communication limits, control limits, or other
material boundaries.
Representation Coverage
Representation Coverage describes how much of the materially relevantRepresentation Target is sufficiently represented by the available input
basis.
Coverage may be assessed across multiple dimensions rather than reduced to one
percentage.
Multidimensional Representativeness
A dataset may be representative along one dimension and weak along another.For example:
Population Coverage — Strong
Geographic Coverage — Strong
Failure-State Coverage — Weak
Extreme-Weather Coverage — Missing
SDRM preserves these distinctions.No Single-Score Requirement
SDRM does not require representativeness to be compressed into one numericalscore.
A single score may hide material gaps.
Representativeness may instead be represented through: dimensional
assessments, coverage maps, statuses, conditions, restrictions, and gap
records.
Representation Gap
A Simulation Data Representativeness Gap exists where available informationdoes not sufficiently represent a materially relevant part of the
Representation Target.
Examples include: missing population, missing environment, missing state,
missing behavior, missing distribution tail, missing failure condition,
missing configuration, or insufficient range.
Representation Gap Classification
A gap may be classified as:Non-Material
The gap is not expected to materially affect the defined simulation inquiry.Material but Bounded
The gap matters but its effect can be sufficiently restricted or isolated.Conditional
Simulation use remains possible only under explicit conditions.
Material and Unresolved
The gap materially weakens the simulation basis and has not been sufficientlyaddressed.
Representativeness Undetermined
Available information is insufficient to determine materiality or coverage.Underrepresentation
Underrepresentation occurs where a relevant condition exists in the target
reality but appears insufficiently in the simulation input basis.
Underrepresentation can produce artificially weak exposure to that condition.
Overrepresentation
Overrepresentation occurs where a condition appears disproportionately in the
input basis relative to the Representation Target.
This may distort simulation behavior if the simulation interprets the data as
naturally distributed.
Representation Imbalance
A dataset may contain all required classes while still representing them inproportions inconsistent with the intended simulation context.
SDRM therefore distinguishes:
Presence
from:
Representative Proportion.
Intentional Oversampling
Overrepresentation is not always a defect.Rare or high-consequence conditions may intentionally be oversampled for
testing.
Where intentional, the difference between:
Natural Representation
and:Test Amplification
should remain explicit.Proxy Representation
A Proxy Input represents a target indirectly because direct data isunavailable or impractical.
For example:
data from one machine type may be used as a proxy for another similar machine.
Proxy use may be legitimate.
SDRM requires the representational relationship to remain explicit. Proxy ≠
Target
SIMULOS® establishes:
Similarity between a proxy and a target does not establish representational
equivalence.
Material differences must remain visible.
Cross-Context Transfer
Data may be transferred from one context to another.Conceptually:
Source Context → Transfer Basis → Target
Simulation Context
SDRM governs whether the transfer preserves sufficient representativeness.Cross-Context Representativeness
Conditions
Transfer may depend upon similarities in: population, environment, systemconfiguration, behavior, operating range, physical properties, or causal
structure.
Where differences matter, restrictions should follow the transferred data.
Synthetic Representation
Synthetic data may intentionally fill representativeness gaps.For example:
real-world datasets may contain too few critical failure events.
Synthetic data may increase coverage.
SDRM asks:
Does the synthetic information sufficiently represent the intended condition?
Its synthetic origin remains governed under SIIM. Synthetic Coverage ≠
Real-World Frequency
Synthetic datasets may deliberately alter frequencies.
Therefore:
Synthetic Scenario Coverage
should not automatically be interpreted as:Observed Real-World Distribution.
Representativeness and Simulation
Assumptions
Simulation Assumptions may influence representativeness requirements.For example:
if the simulation assumes operation only within a narrow environmental range,
data outside that range may not be required.
If the scope expands, the representativeness requirement may change.
Representativeness and Reality
Representation
RRS — Reality Representation Standard determines which aspects of reality thesimulation must represent.
SDRM determines whether the input basis sufficiently represents those selected
aspects.
The relationship is:
Selected Reality → Representation Requirement → Input Representation Target →
Representativeness Assessment Representativeness and Scenario Design
SDRM does not determine which scenarios must be executed.
However, representativeness gaps may become important inputs to SSS —
Simulation Scenario Standard.
For example:
a weakly represented rare failure may justify deliberate scenario coverage.
Representativeness Materiality
Not every representational difference matters.Materiality depends upon whether the difference could reasonably affect:
simulation behavior, simulation outputs, scenario conclusions, downstream
validation, or operational reliance.
Materiality Assessment
A representativeness gap should be evaluated relative to:Simulation Object
Simulation Purpose
Simulation Scope
Reality Representation
Simulation Assumptions
Model & Environment
and where relevant:intended downstream reliance.
Representativeness Restriction
An input may remain usable despite limited representativeness if its use isexplicitly restricted.
For example:
Representative only for dry-road daytime operation.
The restriction should propagate to downstream simulation interpretation.
Representativeness Condition
A Simulation Input may receive a condition such as:Suitable only when ambient temperature remains between 0°C and 30°C.
Conditional representativeness should not become unconditional merely because
simulation execution succeeds.
Representativeness Status
A material Simulation Input or Input Set may receive a status such as:Representative
Conditionally Representative
Partially Representative
Non-Representative
or:Representativeness Undetermined
The status is always relative to a defined Representation Target andsimulation context.
No Universal Representative Status
SIMULOS® establishes:Representative for one simulation does not mean representative for all
simulations.
Representativeness status must therefore remain context-bound.
Input-Level vs Input-Set
Representativeness
Individual inputs may be representative while the complete Input Set is not.Conversely, several individually incomplete datasets may together provide
sufficient representation.
SDRM therefore supports assessment at:
Individual Input Level
and:Simulation Input Set Level.
Complementary Representation
Multiple datasets may complement one another.For example:
Dataset A — Normal Operation Dataset B — Failure Operation Dataset C — Extreme
Weather
may collectively provide stronger representation than any dataset alone.
Representativeness Conflict
Different datasets may imply materially different representations of the sametarget.
For example:
Dataset A: failure frequency 0.1%
Dataset B: failure frequency 2.5%
The conflict should remain visible rather than being silently averaged away.Representation Basis
A Simulation Representation Basis records why an input or Input Set isconsidered sufficiently representative for a defined simulation context.
It may reference: target definition, coverage, gaps, materiality,
restrictions, conditions, proxies, synthetic supplementation, and unresolved
uncertainty.
Representativeness Traceability
SDRM establishes:Simulation Input
↓Representation Target
↓Relevant Representation Dimensions
↓Coverage
↓
Gap
↓
Materiality
↓
Representativeness Status
↓Conditions / Restrictions
↓Simulation Use
This enables downstream users to determine what the data actually supports.Representativeness Change
Representativeness may change even when the dataset itself does not.For example:
the Simulation Scope may expand.
A new environment may be introduced.
A new population may be added.
The represented system may change configuration.
The same data may therefore become less representative because the target
changed.
Representativeness Reassessment Trigger
Reassessment may be required when there is material change in: SimulationObject, Purpose, Scope, Reality Representation, Assumptions, Model,
Environment, target population, operational conditions, system configuration,
or available input basis.
No Representativeness Inheritance
Principle
SIMULOS® establishes:Representativeness must not be inherited automatically from a previous
simulation when the Representation Target or simulation context has materially
changed.
No Availability-to-Representativeness
Principle
Available data is not representative data by default.No Size-to-Representativeness Principle
More data does not resolve a missing reality dimension if the additional datacomes from the same unrepresentative condition.
No Majority-to-Whole Principle
Strong representation of the majority does not establish representation ofevery materially relevant subgroup or condition.
No Normal-to-Extreme Principle
Data representative of normal operation does not automatically representdegraded, failure, emergency, boundary, or extreme conditions.
No Proxy Equivalence Principle
Proxy data must not silently acquire the identity or representativeness statusof the target it approximates.
No Successful-Execution Principle
A simulation running successfully with an input does not establish that theinput was representative of the reality being simulated.
Representativeness Limitation Propagation
Material representativeness limitations should propagate downstream.Conceptually:
Representativeness Gap → Scenario / Execution Limitation → Output
Interpretation Limitation → Downstream Reliance Limitation
The relevant downstream Parent Standards govern those later stages.
SDRM ensures the original limitation remains visible.
Minimum Implementation Framework
1. Identify the Representation TargetDefine what each material Simulation Input or Input Set is expected to
represent. 2. Identify Material Representation Dimensions
Determine which dimensions matter, such as: population, environment,
geography, operating state, behavior, configuration, distribution, range,
variation, or rare conditions.
3. Assess Representation Coverage
Determine which materially relevant portions of the target are sufficiently
represented. 4. Identify Representation Gaps
Record missing, weak, imbalanced, proxy-based, or uncertain representation. 5.
Assess Gap Materiality
Determine whether each gap could materially affect the defined simulation
inquiry. 6. Assess Distribution and Range
Determine whether averages, ranges, tails, variation, and dependencies are
sufficiently represented where material. 7. Assess Rare and Boundary
Conditions
Determine whether low-frequency or boundary conditions require representation
because of their simulation relevance or consequence. 8. Govern Proxy and
Cross-Context Use
Document the basis and limitations where information is transferred from
another population, environment, configuration, or context. 9. Assess
Synthetic Supplementation
Where synthetic information fills representation gaps, preserve its role and
the limits of what it represents. 10. Establish Conditions and Restrictions
Bound simulation use where representativeness is incomplete but sufficiently
manageable. 11. Assign Representativeness Status
Determine whether the input basis is: Representative, Conditionally
Representative, Partially Representative, Non-Representative, or
Representativeness Undetermined.
12. Preserve Representativeness Traceability
Maintain the relationship between target, coverage, gaps, materiality, status,
restrictions, and simulation use.
Governance Outputs
SDRM may produce: Representation Target Record, Representation Dimension Map,Population Representativeness Assessment, Subgroup Representation Record,
Environmental Representativeness Assessment, Geographic Representativeness
Assessment, Operational-State Representativeness Assessment, System-Version
Representativeness Assessment, Behavioral Representativeness Assessment,
Distributional Representativeness Assessment, Range Representativeness Record,
Variation Representativeness Record, Rare-Condition Representation Record,
Boundary-Condition Representation Record, Representation Coverage Map,
Representation Imbalance Record, Underrepresentation Record,
Overrepresentation Record, Representation Gap Record, Representation Gap
Materiality Assessment, Proxy Representation Record, Cross-Context Transfer
Record, Synthetic Representation Record, Complementary Representation Record,
Representativeness Conflict Record, Representativeness Condition Record,
Representativeness Restriction Record, Representativeness Status, Simulation
Representation Basis, Representativeness Reassessment Trigger,
Representativeness Change Record, and Simulation Data Representativeness
Traceability Map.
Use Case 1 — Autonomous Vehicle Simulation
ScenarioAn autonomous vehicle simulation is intended to assess operation across:
daytime and nighttime, dry and wet roads, urban and rural environments, normal
traffic, emergency braking, and pedestrian interactions.
The available driving dataset contains millions of kilometers of data.
However, almost all observations come from:
daytime + dry roads + urban operation. Application
SDRM does not treat dataset size as proof of representativeness.
It identifies material gaps in: nighttime conditions, wet-road behavior, rural
environments, emergency braking, and uncommon pedestrian interactions.
Result
The dataset receives:
Partially Representative
status for the declared simulation scope.Its use may remain legitimate for defined urban dry-road scenarios while
additional real or synthetic information is required for broader scenario
coverage. Use Case 2 — Spacecraft Landing Simulation
Scenario
A spacecraft landing simulation relies upon atmospheric and terrain
information derived primarily from previously observed regions.
The planned landing zone contains terrain and atmospheric conditions that are
only partially represented by those observations. Application
SDRM establishes:
Observed Region ≠ Landing Target Region
and evaluates: terrain coverage, atmospheric ranges, geographic transfer,boundary conditions, and proxy assumptions.
Result
The available input basis may be classified:
Conditionally Representative
with explicit limitations requiring additional synthetic or model-derivedcoverage for materially underrepresented landing conditions.
Architectural Position
Simulation Data Representativeness is the second internal governance space ofthe Simulation Input & Data Standard (SIDS).
The complete SIDS progression is:
Simulation Input Identification
↓Simulation Data Representativeness
↓Simulation Data Transformation
↓Simulation Temporal Data
↓Simulation Input Uncertainty
SIIM determines:What information is entering the simulation?
SDRM determines:
What reality can that information legitimately represent?
The next module, SDTM — Simulation Data Transformation Module, governs what
happens when that identified and representationally understood information
must be transformed before simulation use.
Foundational Principle
Simulation data becomes representative only relative to a definedrepresentation target, and no simulation may legitimately generalize its input
basis beyond the populations, environments, states, behaviors, distributions,
conditions, and boundaries that the available information can sufficiently
represent.