Simulation Output Limitation Module (SOLM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Output Governance Standard (SOGS)
Operational Layer: Simulation Output Governance Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Simulation Output Limitation
Version: 1.0
Status: Canonical · Open Module
Effective Date: 19 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 Output Limitation
Canonical Definition
Simulation Output Limitation Module (SOLM) defines the governance frameworkthrough which material limitations attached to Simulation Outputs are
identified, classified, bounded, documented, propagated, and preserved so that
outputs cannot be interpreted, generalized, transferred, compared, or relied
upon beyond the conditions actually supported by the simulation that produced
them.
SOLM governs the boundary between:
what a Simulation Output shows
and:
what that output does not legitimately establish.
Its purpose is to prevent computational precision, high simulation volume,
visually convincing outputs, successful scenario execution, or apparently
stable results from expanding the legitimate meaning of a Simulation Output
beyond its governed simulation basis.
Operational Role
SOLM serves as the fourth internal governance space of the Simulation OutputGovernance Standard (SOGS).
The preceding modules establish:
SOIM — What exactly did the simulation produce?
SOCM — What type of Simulation Output is it?
SIM — What inference, if any, may legitimately be drawn from that output?
SOLM asks:
Where does the legitimate meaning of that output stop, what conditions
constrain its interpretation, and which limitations must remain attached
whenever the output is communicated, compared, reused, or relied upon?
The governed progression is:
Identified Simulation Output
↓Output Classification
↓Governed Inference
↓Limitation Identification
↓Limitation Materiality
↓Interpretation Boundary
↓Applicability Restriction
↓Transfer / Generalization Restriction
↓Limitation Propagation
↓Bounded Simulation Output
↓Output Documentation
Module Function
SOLM exists to prevent: scenario-specific results being presented as universalresults, model-specific behavior being presented as real-world certainty,
nominal-condition outputs being generalized to edge or stress conditions,
outputs produced under one environment being applied to another without
qualification, outputs based on one population being generalized to another,
simulated time horizons being extended beyond what was modeled, stochastic
results being represented as deterministic truth, point estimates being
presented without material uncertainty, averages hiding adverse distributions,
incomplete Scenario Coverage being forgotten, unknown Scenario Space
disappearing from downstream interpretation, simulation capability limits
being mistaken for system limits, inferred results being presented as direct
observations, correlation being presented as causation, output precision being
confused with output certainty, and output reuse occurring after the governing
simulation conditions have materially changed.
Simulation Output Limitation
A Simulation Output Limitation is any material condition restricting what aSimulation Output may legitimately: describe, imply, represent, compare,
generalize, predict, support, transfer, or be relied upon to establish.
A limitation may arise from any upstream SIMULOS® governance space.
Limitation Origin
A material output limitation should remain traceable to its origin wherepossible.
Possible origins include:
Simulation Object
Simulation Purpose & Scope
Reality Representation
Simulation Assumptions
Model & Environment
Simulation Inputs & Data
Scenario Design
Simulation Execution
Output Classification
or:Inference Process
Conceptually:Upstream Limitation → Simulation Output Limitation Upstream Limitation
Propagation
SIMULOS® establishes:
A material limitation does not disappear merely because the simulation
successfully produced an output.
If a limitation exists upstream and materially affects what the result can
mean, it should propagate into Output Governance.
Output Applicability Boundary
An Output Applicability Boundary defines where a Simulation Output maylegitimately apply.
Applicability may depend upon: Simulation Object version, system
configuration, population, environment, geography, time period, operating
state, scenario family, parameter range, input conditions, autonomy state, or
another relevant boundary.
Scope-Bound Output
A Simulation Output should remain bounded by the Simulation Purpose & Scopeunder which it was produced.
For example:
simulation of braking behavior at 0–80 km/h
does not automatically support conclusions about:
120 km/h behavior.
No Scope Expansion Principle
SIMULOS® establishes:A Simulation Output must not acquire a broader scope than the simulation
inquiry that produced it unless that broader applicability is independently
established.
Reality Representation Limitation
Outputs are constrained by what the simulation represented.If Reality Representation excluded a material real-world feature, the
resulting Output cannot legitimately establish behavior involving that omitted
feature.
Therefore:
Unrepresented Reality → Unsupported Output
Claim Abstraction Limitation
A simulation may deliberately simplify reality.An output derived from that abstraction should not be presented as if the
omitted detail had been simulated.
Fidelity Limitation
Simulation fidelity may be sufficient for one purpose but insufficient foranother.
For example:
a low-fidelity simulation may support early design exploration while remaining
unsuitable for detailed safety reliance.
SOLM preserves that boundary.
Assumption-Bound Limitation
Simulation Outputs may depend upon assumptions established under SAS.Where an output materially depends upon an assumption, its interpretation
should remain conditional upon that assumption or upon the range within which
it remains applicable.
Conceptually:
Output Validity of Interpretation ≤ Applicability of Material Assumption No
Assumption Erasure Principle
A simulation assumption must not disappear from output interpretation when the
output materially depends upon that assumption.
Model-Bound Limitation
A Simulation Output reflects behavior of the governed simulation model.It does not automatically establish equivalent behavior of the real system.
This distinction is fundamental:
Model Behavior ≠ Operational Reality by
Default Environment-Bound Limitation
The simulation environment may constrain the legitimate meaning of the output.A result produced under:
Environment Version A
may not automatically transfer to:Environment Version B
where material conditions differ.Configuration Limitation
Outputs may apply only to a specific: hardware configuration, softwareversion, model version, sensor package, mission configuration, autonomy level,
or operational configuration.
Input-Bound Limitation
Simulation Outputs cannot legitimately exceed the representativeness, temporalapplicability, transformation basis, and uncertainty of the Simulation Inputs
on which they materially depend.
For example:
Input Population A
does not automatically support:Output Claim for Population B.
Representativeness Limitation
Where SIDS identifies incomplete representativeness, that limitation shouldremain attached to the output.
A simulation using input data representative only of:
urban daytime driving
cannot silently produce an output interpreted as representative of:
all driving environments.
Temporal Input Limitation
Historical or temporally restricted inputs may bound Output applicability.For example:
an economic simulation using pre-regime-change data may not automatically
support post-regime conclusions.
Input Uncertainty Limitation
Material uncertainty in the Simulation Input basis should constrain downstreaminterpretation.
A precise output derived from uncertain inputs may remain materially
uncertain.
No Precision Laundering Principle
SIMULOS® establishes:A simulation may transform uncertain inputs into computationally precise
outputs, but it may not transform uncertainty into certainty merely through
computation.
Scenario-Bound Limitation
Outputs may apply only to the Scenario or Scenario Family under which theywere generated.
A nominal-scenario Output does not automatically establish behavior under:
edge cases, stress conditions, degraded states, failure conditions,
transitions, or recovery scenarios.
Scenario Coverage Limitation
A Simulation Output may be locally strong while the overall Scenario Setremains incomplete.
SCCM may identify:
Scenario Coverage Gap
or:Critical Scenario Coverage Gap.
SOLM ensures that such gaps constrain any broader Output claim.
No Coverage Inflation Principle
SIMULOS® establishes:Strong results inside covered Scenario Space do not establish behavior inside
uncovered Scenario Space.
Unknown Scenario Space Limitation
Where unknown Scenario Space remains material, Output interpretation shouldavoid implying exhaustive behavioral knowledge.
Edge-Case Limitation
Outputs produced under nominal or ordinary variation should not be interpretedas demonstrating behavior at difficult edges unless those edges were
explicitly examined.
Stress-Condition Limitation
Successful performance under ordinary conditions does not establish robustnessunder stress.
Likewise, one stress condition does not establish robustness across all stress
combinations.
Execution-Bound Limitation
A Simulation Output inherits relevant limitations from the execution thatproduced it.
Examples include: execution deviation, interrupted run, incomplete run,
unexpected intervention, configuration mismatch, resource limitation,
execution drift, or incomplete observation.
Execution Completeness Limitation
An output from a partially completed execution should not be represented as ifit came from a complete execution where completion materially affects
interpretation.
Observation Limitation
SES may identify Observation Blind Spots.Where material internal events or states were not observable, Output
interpretation should not imply knowledge about those hidden regions.
Randomness Limitation
Where simulation behavior is stochastic, one run may not characterize thedistribution of possible outcomes.
SOLM may therefore preserve a limitation such as:
Single stochastic realization — not sufficient to characterize outcome
distribution.
Repetition Limitation
Repeated runs may remain insufficient where: repetitions are too few,configuration drift exists, seed coverage is narrow, relevant variation is
missing, or runs are not comparable.
Statistical Limitation
Simulation Outputs may have statistical limitations involving: sample size,variance, confidence, distribution tails, sampling strategy, rare-event
representation, convergence, or estimator uncertainty.
Mean Limitation
Average behavior may conceal: extremes, variance, multimodality, adversetails, or subgroup differences.
SIMULOS® establishes:
An average Simulation Output does not represent every simulated condition or
every possible outcome.
Distribution Limitation
A distribution estimated from simulation is bounded by: scenario generation,sampling method, assumptions, model behavior, input distributions, and number
of executions.
It is not automatically equivalent to the real-world distribution.
Rare-Event Limitation
Low observed frequency in simulation may result from: genuine rarity,inadequate sampling, scenario-selection bias, insufficient run count, or
missing Scenario Space.
A rare simulated event rate should therefore not automatically be interpreted
as a reliable real-world probability.
Resolution Limitation
Output resolution may constrain interpretation.Examples include: temporal resolution, spatial resolution, state resolution,
population granularity, behavioral granularity, or aggregated outputs.
No Resolution Inflation Principle
A Simulation Output cannot legitimately reveal distinctions that thesimulation did not represent or observe at sufficient resolution.
Temporal Output Limitation
A Simulation Output may apply only within a defined simulated time horizon.For example:
0–24 hours
does not automatically support:
30-day behavior.
Long-Horizon Limitation
Uncertainty, model drift, state accumulation, or changing environments mayincrease with simulated horizon.
Outputs should remain bounded accordingly.
Spatial Limitation
A simulation covering one: location, terrain, orbital region, climate,infrastructure topology, or geographic region
does not automatically support spatial generalization elsewhere.
Population Limitation
Outputs may be bounded to the population represented by: data, actor models,simulation scenarios, or relevant configuration.
Version Limitation
Simulation Outputs are attached to the versions that produced them.Conceptually:
Output O1 → Model v3.1 + Environment v5.0
+ Input Set 22 + Scenario Set 7
A later model or environment version should not automatically inherit theOutput.
No Version Inheritance Principle
SIMULOS® establishes:Simulation Outputs do not automatically remain applicable after a material
change in the Simulation Object, Model, Environment, Inputs, Parameters, or
Scenario Basis.
Inference Limitation
SIM — Simulation Inference Module establishes what may legitimately beinferred from a Simulation Output.
SOLM establishes the boundary beyond which that inference must not expand.
Direct Output vs Inferred Meaning
For example:Direct Output: simulated component temperature reached 112°C.
Possible inference:
Under the simulated conditions, the component model crossed the defined
thermal threshold.
Unsupported inference without further basis:
The real component will fail at exactly 112°C in all operational environments.
SOLM governs that distinction.
No Observation-to-Causation Principle
A simulated association does not automatically establish causal truth.If:
Condition A
and:
Output B
occur together in simulation, causality requires a sufficient simulation and
inference basis.
No Simulation-to-Reality Equivalence
Principle
SIMULOS® establishes:What occurred in simulation is evidence of simulated behavior under governed
simulated conditions; it is not automatically proof of identical behavior in
Operational Reality.
ORA™ and VALIDOS® may participate in downstream correlation and validation.
Generalization Limitation
A Generalization Limitation restricts extension of an output beyond therepresented conditions.
Generalization may concern: population, environment, geography, time,
configuration, system type, behavior, or operating condition.
Transfer Limitation
A Simulation Output generated for one Simulation Object should notautomatically be transferred to another object merely because the objects are
similar.
Cross-System Transfer
For example:Robot Model A
may share architecture with:Robot Model B
but differences in: mass, software, sensors, actuators, control logic, orenvironment
may materially limit transfer.
Comparison Limitation
Two Simulation Outputs should not be compared as equivalent where materialdifferences exist in: Simulation Purpose, Scenario, Model, Environment,
Inputs, Parameters, execution conditions, output type, or inference basis.
No Apples-to-Oranges Principle
SIMULOS® establishes:Numerically similar Simulation Outputs are not necessarily methodologically
comparable.
Reuse Limitation
A Simulation Output may be reused in: another analysis, another scenario,another model, another validation process, another decision, or another
simulation.
Reuse should remain bounded by the Output's original applicability and
limitations.
No Context-Free Reuse Principle
A Simulation Output must not be detached from the conditions necessary tounderstand what it legitimately means.
Extrapolation Limitation
Outputs should not be extrapolated outside: modeled ranges, simulatedhorizons, Scenario Space, input distributions, or represented boundaries
without explicit governance.
Aggregation Limitation
Aggregated Simulation Outputs may hide important: scenario differences,subgroup differences, tail behavior, failures, or contradictory outcomes.
Output Selection Limitation
If only selected outputs or successful runs are presented, interpretation maybecome biased.
Material negative, anomalous, or contradictory outputs should remain visible
where they affect the legitimate conclusion.
No Cherry-Picked Output Principle
SIMULOS® establishes:A Simulation Output claim must not be strengthened by selectively excluding
materially relevant outputs that contradict or weaken that claim.
Output Contradiction
Simulation Outputs may conflict across: scenarios, repetitions, versions,parameter configurations, or model states.
A contradiction may itself create an Output Limitation.
Unresolved Contradiction Limitation
Where conflicting outputs cannot yet be reconciled, the legitimate inferencemay need to remain:
Conditional
Partial
Conflicted
or:
Undetermined.
SOLM preserves the limitation.
Decision-Use Limitation
A Simulation Output may support:exploration
without supporting:
deployment authorization.
It may support:
design comparison
without supporting:
safety certification.
It may support:
hypothesis generation
without supporting:
operational reliance.
The intended decision context matters.
Exploratory Output
An exploratory Simulation Output may legitimately support: investigation,hypothesis generation, design direction, anomaly discovery, or scenario
refinement.
It should not automatically be represented as validated evidence for
high-consequence reliance.
Operational Reliance Limitation
SOGS does not authorize operational reliance.VALIDOS®, ORA™, ASGA™, GOA™, or other relevant architectures may determine
what downstream reliance is legitimate.
SOLM preserves limitations that those downstream processes need to know.
Communication Limitation
Limitations should remain visible when Simulation Outputs are: reported,visualized, summarized, presented to management, published, transferred
between teams, used by AI systems, or integrated into decision-support tools.
No Limitation Loss Through Summarization
Principle
SIMULOS® establishes:A material Output Limitation must not disappear merely because the underlying
simulation record is summarized into a dashboard, chart, score, executive
summary, or AI-generated explanation.
Machine-Readable Limitation
Where Simulation Outputs are consumed automatically, limitations may requiremachine-readable representation.
Examples may include: applicability tags, scenario constraints, object-version
references, uncertainty flags, scope boundaries, comparison restrictions, or
reliance warnings.
Limitation Classification
A material Output Limitation may be classified as:Scope Limitation
Representation Limitation
Assumption Limitation
Model Limitation
Environment Limitation
Input Limitation
Scenario Limitation
Coverage Limitation
Execution Limitation
Observation Limitation
Statistical Limitation
Uncertainty Limitation
Temporal Limitation
Spatial Limitation
Population Limitation
Configuration Limitation
Version Limitation
Inference Limitation
Generalization Limitation
Transfer Limitation
Comparison Limitation
Reuse Limitation
or another simulation-specific class.Limitation Materiality
Not every known limitation requires equal prominence.A limitation is material where ignoring it could reasonably change:
interpretation, inference, comparison, decision, validation, operational
reliance, or assessment of the Simulation Object.
Critical Output Limitation
A Critical Output Limitation exists where ignoring the limitation couldproduce materially misleading or high-consequence interpretation.
Limitation Severity
A simulation implementation may classify limitations as:Informational
MaterialMajor
Critical
or another justified structure.
SIMULOS® does not mandate one universal severity scale.
Limitation Status
Possible statuses may include:Active
Conditionally Resolved
ResolvedSuperseded
Unresolved
or:
Applicability Undetermined.
Resolved Limitation
A limitation may be resolved only when an appropriate governed processestablishes that the restriction no longer materially applies.
For example:
an earlier population limitation may be resolved after additional simulations
extend the represented population sufficiently.
No Silent Resolution Principle
SIMULOS® establishes:A Simulation Output Limitation must not be treated as resolved merely because
later results are favorable.
Limitation Propagation
A material limitation should follow the Output into downstream use.Conceptually:
Simulation Output
↓Output Limitation
↓Inference
↓
Documentation
↓Validation / Analysis / Decision /
Reliance
The limitation should remain visible wherever its absence could materiallydistort meaning.
Limitation Propagation Failure
A Limitation Propagation Failure occurs where the original simulation recordcontains a material limitation but downstream reporting or use no longer
communicates it.
Examples include: dashboard removes uncertainty, executive summary removes
scope restriction, reused data loses Scenario reference, AI-generated answer
omits model limitation, or published graph removes excluded conditions.
Output Limitation Register
SOLM may maintain a Simulation Output Limitation Register.A limitation record may identify: Limitation ID, affected Output, limitation
class, origin, description, materiality, applicability, affected inference,
affected comparison, downstream propagation requirement, status, version, and
reassessment trigger.
Limitation-to-Output Binding
Every material limitation should be bound to the relevant: individual Output,Output Set, Scenario Output, Campaign Output, derived result, or inference.
Limitation-to-Inference Binding
Where a specific limitation constrains a particular inference:Output → Inference → Limitation
should remain traceable.Limitation-to-Decision Binding
Where an Output is used for decision support, material limitations shouldremain available to that decision process.
Limitation Reassessment Trigger
Reassessment may be required when: additional scenarios are executed, coverageexpands, new data becomes available, model fidelity improves, environment
changes, assumptions change, input uncertainty decreases, new validation
evidence appears, the Output is reused for another purpose, or a new reliance
context is proposed.
Output Limitation Change
A limitation may: narrow, expand, become more severe, become less severe,resolve, or become newly discovered
as the simulation lifecycle evolves.
Historical Limitation Preservation
Later resolution of a limitation must not rewrite the limitation state ofearlier Output use.
Point-in-time reconstruction should remain possible.
No Retrospective Strengthening Principle
SIMULOS® establishes:A later improvement in simulation capability must not retroactively strengthen
what an earlier Simulation Output legitimately established at the time it was
produced.
Point-in-Time Output Interpretation
SOLM should support questions such as:What limitations were known when this Output was produced?
Which assumptions constrained it?
Which Scenario Space was uncovered?
Which model version produced it?
Which inference was allowed?
Which generalizations were prohibited?
Was the limitation later resolved?
Was the Output reused outside its original context?
Simulation Output Boundary Statement
A governed Output may carry a concise boundary statement such as:This Simulation Output applies only to the identified Simulation Object
version, Simulation Scope, Scenario Set, Model & Environment configuration,
Input & Data Basis, execution conditions, and Output limitations recorded for
the producing simulation activity.
Space and Mission Systems
Space and mission simulation creates particularly important Output Limitationquestions because operational reality may be difficult or impossible to
reproduce before deployment.
A mission simulation may produce:
95.8% successful autonomous recovery
across a large Monte Carlo campaign.
That number alone does not establish:
95.8% real-mission recovery probability.
SOLM asks:
Which mission conditions were represented?
Which communication states were absent?
Which hardware failures were modeled?
Which dependency combinations remained uncovered?
Which environmental uncertainties were simplified?
Was the recovery model validated against comparable operational behavior?
The Output may remain extremely useful.
But its legitimate meaning must remain bounded by the simulated conditions.
Autonomous Systems
An autonomous system may achieve:99.9% simulated task success.
SOLM prevents that number from silently becoming:
99.9% operational safety
or:
99.9% real-world reliability
unless the required governance basis independently supports those claims.
The Output might legitimately establish:
99.9% success within the defined governed Scenario Set and simulation
conditions.
That is a stronger methodological statement because its boundary is explicit.
AI Agent Systems
An AI agent may complete:98% of simulated workflows
within a test environment.
SOLM may reveal that: tool permissions were fixed, external systems were
stable, memory was always available, human instructions were unambiguous, and
authority transitions were not simulated.
The legitimate Output claim therefore remains bounded to that condition space.
Industrial Simulation
A manufacturer may simulate equipment under:normal + elevated load
and observe no failures.
SOLM prevents that result from being generalized automatically to: extreme
temperature, degraded maintenance, sensor failure, long-duration operation, or
combined load and environmental stress
if those conditions were not represented.
Existing Simulation Program Diagnostic
SOLM can be applied to existing simulation programs by asking:What claims are being made from our Simulation Outputs?
Which output limitations are explicit?
Which limitations exist upstream but disappear from reporting?
Do dashboards show precision without uncertainty?
Are nominal outputs being generalized to uncovered Scenario Space?
Are outputs from old Model Versions still being reused?
Are Monte Carlo frequencies being presented as real-world probabilities
without qualification?
Are outputs being used for decisions beyond the purpose for which the
simulation was designed?
Can each output claim be traced to its limiting conditions?
This can reveal that the key weakness is not the quality of the simulation
itself but the way its outputs are interpreted and communicated.
Module Boundary
SOLM begins when a Simulation Output has been sufficiently: identified underSOIM, classified under SOCM, and associated with a governed inference basis
under SIM.
SOLM ends when the materially relevant limitations affecting: interpretation,
inference, applicability, generalization, comparison, transfer, reuse, and
downstream reliance
have been sufficiently identified, classified, bounded, documented, and
attached to the affected Simulation Output.
SOLM does not govern: general integrity, data integrity, output tamper
resistance, validation determination, operational deployment authorization,
general risk management, or final reliance authorization.
Integrity remains under INTEGROS®.
Architectural Boundary
SOLM specifically avoids the concept:Output Integrity
because that governance space belongs to INTEGROS®.The architectural distinction is:
SOLM → What limitations constrain the meaning and use of this Simulation
Output?
INTEGROS® → Has the relevant Output or record preserved the integrity
properties required of it?
This separation prevents SIMULOS® from duplicating integrity governance.
Minimum Implementation Framework
1. Identify Material Output LimitationsDetermine which upstream or output-specific conditions materially constrain
interpretation. 2. Classify Limitation Origin
Identify whether the limitation arises from: scope, representation,
assumption, model, environment, inputs, scenarios, execution, observation,
statistics, uncertainty, or inference.
3. Assess Limitation Materiality
Determine whether ignoring the limitation could materially alter
interpretation, comparison, validation, or reliance. 4. Establish Output
Applicability Boundary
Define where the Output may and may not legitimately apply. 5. Establish
Inference Boundary
Determine what the Output may support and which broader conclusions remain
unsupported. 6. Establish Generalization and Transfer Restrictions
Identify restrictions upon use across: populations, environments, versions,
systems, scenarios, time periods, and configurations.
7. Bind Limitations to Outputs
Ensure each material limitation remains attached to the affected Output or
Output Set. 8. Establish Propagation Requirements
Determine where limitations must remain visible in downstream: documentation,
reporting, validation, analysis, decision support, and reuse.
9. Define Reassessment Triggers
Identify which new simulations, evidence, model changes, Scenario expansions,
or reuse contexts require limitation reassessment. 10. Preserve Historical
Limitation State
Maintain the limitation record applicable at the time the Output was produced
and used.
Governance Outputs
SOLM may produce: Simulation Output Limitation Identifier, Simulation OutputLimitation Record, Output Applicability Boundary, Scope Limitation Record,
Reality Representation Limitation Record, Abstraction Limitation Record,
Fidelity Limitation Record, Assumption-Bound Limitation Record, Model-Bound
Limitation Record, Environment-Bound Limitation Record, Configuration
Limitation Record, Input-Bound Limitation Record, Representativeness
Limitation Record, Temporal Input Limitation Record, Input Uncertainty
Limitation Record, Scenario-Bound Limitation Record, Scenario Coverage
Limitation Record, Unknown Scenario Space Limitation Record, Edge-Case
Limitation Record, Stress-Condition Limitation Record, Execution-Bound
Limitation Record, Execution Completeness Limitation Record, Observation
Limitation Record, Randomness Limitation Record, Repetition Limitation Record,
Statistical Limitation Record, Distribution Limitation Record, Rare-Event
Limitation Record, Resolution Limitation Record, Temporal Output Limitation
Record, Spatial Limitation Record, Population Limitation Record, Version
Limitation Record, Inference Limitation Record, Generalization Limitation
Record, Transfer Limitation Record, Comparison Limitation Record, Reuse
Limitation Record, Extrapolation Limitation Record, Aggregation Limitation
Record, Output Contradiction Limitation Record, Decision-Use Limitation
Record, Critical Output Limitation, Output Limitation Register, Limitation
Propagation Requirement, Limitation Propagation Failure Record, Limitation
Reassessment Trigger, Limitation Change Record, Point-in-Time Limitation
Record, Output Boundary Statement, and Simulation Output Limitation
Traceability Map.
Use Case 1 — Spacecraft Autonomous Landing
Simulation
ScenarioA spacecraft landing simulation produces a result showing:
99.4% successful autonomous landing
across a large governed Simulation Campaign.
The number appears highly convincing.
Application
SOLM identifies material limitations: only the modeled terrain classes wererepresented, communication-loss conditions were limited, certain
sensor-failure combinations remained outside Scenario Coverage, atmospheric
uncertainty was bounded to a defined range, the Output applies to one vehicle
configuration, and the result is a simulated campaign frequency, not a direct
empirical mission-success probability.
Result
The legitimate Output statement becomes:
99.4% simulated landing success across the governed Scenario Set, Model &
Environment configuration, Input basis, and uncertainty conditions represented
by the identified Simulation Campaign.
The limitation does not weaken the simulation.
It prevents a strong simulation result from being converted into a stronger
real-world claim than the simulation can legitimately support. Use Case 2 —
Autonomous Vehicle Safety Simulation
Scenario
An autonomous-driving platform reports:
99.99% collision-free simulation performance. Application
SOLM identifies that most Simulation Executions occur within: mapped urban
environments, defined weather ranges, supported sensor configurations, known
road-user classes, and a bounded Scenario Set.
Some compound edge conditions remain
uncovered. Result
The organization cannot legitimately communicate the Output as:99.99% real-world safety.
The governed Output remains:
99.99% collision-free performance within the identified simulated conditions
and Scenario Coverage basis.
The remaining limitations become explicit inputs to further Scenario
expansion, validation, and operational reliance assessment.
Architectural Position
Simulation Output Limitation Module (SOLM) is the fourth internal governancespace of the Simulation Output Governance Standard (SOGS).
The complete SOGS progression is:
Simulation Output Identification
↓Simulation Output Classification
↓Simulation Inference
↓Simulation Output Limitation
↓Simulation Output Documentation
SOIM establishes:What exactly was produced?
SOCM establishes:
What kind of Simulation Output is it?
SIM establishes:
What may legitimately be inferred from it?
SOLM establishes:
Where does that legitimate interpretation stop?
SODM then preserves:
the Output, its classification, inference, limitations, provenance, and
documentation basis.
Foundational Principle
A Simulation Output is only as broadly meaningful as the governed conditionsunder which it was produced, and no output may legitimately escape the
assumptions, representation, data, scenarios, execution conditions,
uncertainty, and coverage boundaries that materially constrain what it can
demonstrate.