Simulation Evidence Qualification Module (SEQM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Evidence & Reliance Boundary Standard (SERBS)
Module: SEQM — Simulation Evidence Qualification Module
Operational Layer: Simulation Evidence Qualification Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Core Module
Governed Space: Simulation Evidence Qualification
Origin ID: SIMULOS-SERBS-SEQM-011.1
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 20 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodology Intellectual Property Protection
Canonical Language: English
Canonical Definition
Simulation Evidence Qualification Module (SEQM) is the first Core Module ofthe Simulation Evidence & Reliance Boundary Standard (SERBS) within SIMULOS® —
Simulation Governance Architecture.
SEQM governs the transformation of simulation-derived outputs, observations,
correlations, comparisons, and governed inferences into explicitly qualified
Simulation Evidence for a defined evidential purpose.
The Module establishes whether a simulation-derived information object is
sufficiently identified, traceable, context-bound, purpose-linked,
condition-aware, and methodologically interpretable to be treated as governed
evidence within the SIMULOS® lifecycle.
SEQM does not determine the ultimate strength, sufficiency, or validation
consequence of that evidence.
Its task is narrower and foundational:
to determine whether simulation-derived information legitimately qualifies as
evidence at all, and to define the evidential basis under which that
qualification is valid.
Core Question
What simulation-derived information legitimately qualifies as evidence, andfor what evidential purpose?
Architectural Position
SEQM is the first module within SERBS.It receives governed information from:
Simulation Output Governance Standard
(SOGS)
andSimulation-to-Reality Correlation Standard (SRCS).
It precedes:
SEWM — Simulation Evidence Weight Module.
Its architectural role is therefore to establish the evidence object before
any attempt is made to assign evidential weight, define reliance boundaries,
impose use limitations, or transfer the evidence into broader validation
governance.
Internal Flow Position
Governed Simulation Output Governed Inference Basis Simulation-to-RealityCorrelation Basis → Evidence Candidate Identification → Evidential Purpose
Definition → Simulation Basis Traceability → Qualification Condition
Assessment → Applicability Definition → Uncertainty Preservation → Evidence
Qualification State → Qualified Simulation Evidence → SEWM — Simulation
Evidence Weight Module
Operational Space
SEQM governs the point at which simulation-derived information crosses from:simulation result
into:
qualified simulation evidence.
This operational space includes: evidence candidate identification; evidential
purpose definition; proposition linkage; simulation basis traceability; output
provenance; inference provenance; correlation basis linkage; contextual
qualification; applicability definition; qualification conditions; uncertainty
preservation; temporal applicability; version applicability; scenario
applicability; configuration applicability; evidence qualification state;
evidence disqualification where the required basis is absent.
Operational Scope
SEQM applies to any simulation-derived information that an organizationintends to use as evidence.
Such information may include: numerical outputs; state transitions; observed
simulated behavior; event frequencies; failure patterns; scenario outcomes;
threshold crossings; sensitivity results; comparative results; stochastic
distributions; trajectory results; predicted behaviors; simulated
interactions; failure modes; anomaly patterns; scenario coverage observations;
simulation-to-reality comparisons; governed simulation inferences.
The Module applies equally to: a single simulation execution; repeated runs;
scenario portfolios; Monte Carlo campaigns; high-performance simulation
campaigns; digital twins; physical simulations; hybrid simulations; mission
simulations; engineering simulations; scientific simulations; safety
simulations; AI simulations; autonomous-system simulations; future simulation
technologies.
Evidence Candidate Principle
A simulation-derived result must first be identified as an Evidence Candidatebefore it can be qualified.
An Evidence Candidate is:
a defined simulation-derived information object proposed for use in
supporting, challenging, comparing, informing, or constraining a specific
proposition, question, assessment, or decision context.
An Evidence Candidate may originate from: one execution; multiple executions;
one scenario; a scenario set; a simulation campaign; a correlation result; a
pattern across outputs; a governed inference.
But existence alone does not establish evidential status. Simulation Output ≠
Simulation Evidence
SEQM enforces the distinction:
Simulation Output ≠ Simulation Evidence
A result becomes governed evidence only when the relationship between thatresult and an explicit evidential purpose is established.
For example:
“The simulator produced a maximum temperature of 912°C.”
is an output statement.
“Under Scenario S-47, Configuration C-3, and the governed assumptions of
Simulation Model M-12, the simulation produced a maximum temperature of 912°C,
supporting evidence concerning thermal behavior under those represented
conditions.”
is an evidentially qualified statement.
The difference is governance context.
Evidential Purpose Principle
Every qualified Simulation Evidence object must have a defined EvidentialPurpose.
The Evidential Purpose specifies what the evidence is intended to inform.
It may relate to: a hypothesis; a design question; a performance question; a
safety proposition; a comparative assessment; a mission condition; a failure
hypothesis; a scenario question; a behavioral proposition; a threshold
condition; an engineering claim; a validation input; an operational question.
Evidence without a defined evidential purpose remains analytically informative
but methodologically underqualified.
Evidence-to-Proposition Relationship
SEQM requires a defined relationship between:Simulation Evidence and the proposition or question the evidence is intended
to inform.
The relationship may be: supporting; challenging; contradicting; comparative;
conditional; exclusionary; sensitivity-based; exploratory.
This distinction matters because the same simulation result may legitimately
serve different evidential functions in different contexts.
A failure result may weaken one proposition while strongly supporting another.
Simulation Basis Traceability
Qualified Simulation Evidence must remain traceable, where materiallyrelevant, to the governed simulation basis from which it originated.
The traceability chain may include:
Simulation Evidence → Simulation Output → Execution Record → Scenario → Input
Basis → Model & Environment → Assumptions → Reality Representation → Purpose &
Scope → Simulation Object
Where SRCS applies:
Simulation Evidence →
Simulation-to-Reality Correlation Basis
This chain ensures that evidential meaning cannot become detached from theconditions that created it.
Evidence Context Principle
Simulation evidence is inherently context-dependent.SEQM therefore requires relevant evidential context to remain visible.
Context may include: Simulation Object identity; object version;
configuration; purpose; scope; excluded conditions; reality representation;
abstraction level; fidelity level; assumptions; model version; environment
version; input set; temporal basis; scenario; parameter range; execution
conditions; output type; correlation basis; uncertainty.
Context is not metadata decoration.
It is part of the evidential meaning.
Qualification Conditions
An Evidence Candidate may be qualified only where the minimum materialconditions necessary to interpret it are established.
Qualification conditions may require: identifiable evidence object;
identifiable originating output; defined evidential purpose; traceable
simulation basis; known Simulation Object; known object version or
configuration; known scenario basis; known execution source; known material
assumptions; known output meaning; known correlation basis where reality
claims are involved; known uncertainty affecting interpretation; known
applicability conditions; known material exclusions.
The precise combination depends on the evidential purpose.
SEQM therefore does not impose unnecessary uniformity.
It requires sufficient contextual definition for legitimate evidential
interpretation.
Evidence Qualification State
SEQM establishes explicit qualification states.A Simulation Evidence Candidate may be
classified as: 1. Unqualified
The simulation-derived information exists but lacks sufficient evidentialcontext or purpose definition. 2. Conditionally Qualified
The information may function as evidence only under explicitly defined
conditions or limitations. 3. Qualified
The information has sufficient simulation-specific basis, traceability,
purpose alignment, and contextual definition to function as governed
Simulation Evidence. 4. Qualification Suspended
A previously qualified evidence object has encountered a material change,
unresolved discrepancy, missing provenance, or other condition requiring
reassessment. 5. Disqualified
The information cannot legitimately function as Simulation Evidence for the
stated evidential purpose.
Qualification state must remain distinguishable from evidential weight.
Qualified ≠ strong evidence.
Qualification determines whether the object may enter evidence governance.
SEWM determines how much simulation-specific evidential weight it may carry.
Conditional Qualification
Many simulation results can be evidentially valid only under specifiedconditions.
SEQM therefore permits conditional qualification.
Example:
Qualified only for Object Version V4, environmental range E2–E5, Scenario
Family S3, and model configuration M7.
Such evidence remains legitimate.
Its qualification is simply bounded.
Conditional qualification must not be silently transformed into universal
qualification downstream.
Negative Evidence Qualification
SEQM explicitly allows negative, failed, anomalous, unstable, or contradictoryresults to qualify as evidence.
Evidence qualification must not depend upon whether the result supports the
preferred hypothesis.
Examples include: failure to achieve mission objectives; model divergence;
repeated instability; scenario-specific failure; unexpected collision; thermal
exceedance; non-convergence; behavioral anomaly; disagreement with operational
data; unexpected sensitivity.
If the result is relevant, traceable, and contextually interpretable, it may
qualify as evidence.
Inference Qualification
Some simulation evidence is directly observed in simulation outputs.Other evidence is derived through inference.
SEQM requires this distinction to remain visible.
Direct Simulation Observation ≠ Derived
Simulation Inference
Where evidence depends upon an inference, the qualification basis mustidentify: originating outputs; inference method; inferential assumptions;
transformation or aggregation; inference scope; uncertainty introduced by
inference.
The existence of a governed inference does not automatically increase or
decrease evidence weight.
It simply changes the evidential pathway that must remain traceable.
Aggregated Evidence Qualification
Simulation campaigns frequently aggregate outputs across large numbers ofruns.
SEQM requires aggregated evidence to identify: aggregation basis; included
executions; excluded executions; scenario composition; common assumptions;
common model dependencies; statistical method; aggregation transformation;
treatment of failed runs; treatment of anomalous runs; dependence structure
where known.
An aggregate metric must not obscure the evidence objects from which it was
created.
Evidence Independence Awareness
SEQM does not determine final evidential weight, but it must preserveinformation relevant to later assessment of evidence dependence.
Examples include: repeated use of the same model; shared assumptions;
identical input generation; common scenario design; shared execution
infrastructure; common data transformations; common correlation basis.
This enables SEWM to distinguish between:
many evidence objects
and
many independently informative evidence objects.
Applicability Definition
Every qualified evidence object should identify the context within which itsevidential meaning applies.
Applicability may be defined by: object; configuration; scenario; environment;
parameter range; temporal interval; operational condition; mission phase;
behavior class; model state; input family; uncertainty range.
Applicability defines where the evidence may meaningfully enter subsequent
governance.
It does not yet define the maximum reliance boundary; that is owned by SRBM.
Temporal Qualification
Evidence qualification may depend upon time.Evidence can become underqualified where: the Simulation Object changes; a
model is replaced; assumptions become obsolete; new operational data appears;
reality correlation changes; input distributions materially shift; scenario
relevance changes.
SEQM must therefore support:
Qualification Review and Qualification Reassessment.
This creates the interface to SLRS lifecycle governance.
Evidence Provenance
SEQM requires simulation evidence to preserve sufficient provenance toidentify how the evidence came into existence.
Provenance may include: originating simulation; originating execution ID;
simulation campaign ID; scenario ID; model version; environment version;
object version; input package; output reference; transformation history;
inference history; aggregation history; qualification record.
SEQM governs the simulation-specific evidential provenance relationship.
General provenance integrity and audit assurance remain outside its boundary
where governed by INTEGROS® or ADIT®.
Qualification Failure Conditions
An Evidence Candidate should not qualify where material information necessaryto understand its evidential meaning is absent.
Examples include: unknown originating execution; unknown Simulation Object
version; missing scenario basis; unclear evidential purpose; untraceable
transformed output; unidentified material assumption; unknown model
configuration; missing relationship to the proposition being supported; output
used outside its represented context; correlation claim without a governed
correlation basis; aggregation that conceals materially relevant failures;
evidence whose meaning cannot be reconstructed.
Qualification failure does not necessarily mean the underlying simulation is
invalid.
It means the information cannot currently function as governed Simulation
Evidence for the intended purpose.
SEQM Boundary with SOGS
SOGS governs: what the simulation produced; output identity; outputclassification; inference; output limitation; output documentation.
SEQM governs: whether that governed output or inference may legitimately
function as evidence; for what evidential purpose; under what qualification
conditions; with what simulation basis.
Therefore:
Governed Output ≠ Qualified Evidence.
SEQM Boundary with SRCS
SRCS governs the correlation relationship between simulation and reality.SEQM may use that governed correlation relationship as part of an evidence
qualification basis.
It does not recalculate or redefine correlation.
Therefore:
Correlation Result → Evidence
Qualification Input
notEvidence Qualification → Correlation Determination.
SEQM Boundary with SEWM
SEQM determines whether simulation-derived information qualifies as evidence.SEWM determines the simulation-specific evidential weight of that qualified
evidence.
Therefore:
Qualification ≠ Weight.
A result may be fully qualified yet carry weak evidential weight.
A result may also be conditionally qualified yet carry strong evidential
weight within a narrow context.
SEQM Boundary with VALIDOS®
SEQM does not determine whether evidence is sufficient for a broaderValidation Determination.
It governs only the qualification of simulation-derived information as
Simulation Evidence.
The boundary is:
SEQM: Does this simulation-derived information legitimately qualify as
evidence for this defined evidential purpose?
VALIDOS®: Does the total validation evidence basis sufficiently support the
Validation Determination and intended reliance?
SEQM Boundary with INTEGROS®
SEQM does not own general evidence integrity.It may require evidence provenance, traceability, and contextual completeness
as conditions of qualification.
INTEGROS® governs broader integrity properties of the evidence and its
supporting structures.
SEQM Boundary with ADIT®
SEQM may require a reconstructable evidential basis.ADIT® may audit whether records, evidence, observations, and execution traces
are sufficiently preserved and verifiable.
SEQM does not own auditability governance.
Minimum Implementation Framework
A minimum SEQM implementation should establish: 1. Evidence CandidateIdentification
Identify the exact simulation-derived information proposed as evidence. 2.
Evidential Purpose Definition
Define the proposition, question, comparison, or decision context the evidence
is intended to inform. 3. Evidence Source Identification
Identify the originating outputs, observations, correlations, comparisons, or
inferences. 4. Simulation Basis Traceability
Link the Evidence Candidate to the relevant governed simulation lifecycle
elements. 5. Context Definition
Identify object, version, configuration, scenario, model, environment,
assumptions, inputs, and other materially relevant conditions. 6.
Applicability Definition
Define where the evidential meaning
applies. 7. Uncertainty Preservation
Preserve material uncertainty affecting interpretation. 8. QualificationCondition Assessment
Determine whether the minimum required conditions for legitimate evidential
interpretation are satisfied. 9. Qualification State Assignment
Assign the Evidence Candidate a governed qualification state. 10.
Qualification Record Creation
Document the basis supporting the qualification decision.
Governance Outputs
SEQM may produce: Simulation Evidence Candidate Record Simulation EvidentialPurpose Statement Simulation Evidence Source Record Simulation Evidence
Traceability Chain Simulation Evidence Context Record Simulation Evidence
Applicability Statement Simulation Evidence Qualification Condition Record
Simulation Evidence Uncertainty Link Simulation Evidence Qualification State
Simulation Evidence Disqualification Record Simulation Evidence Qualification
Record Qualified Simulation Evidence Object
These outputs provide the input basis for SEWM — Simulation Evidence Weight
Module.
Enterprise Application
SEQM enables organizations to prevent a common simulation governance failure:treating every technically generated result as evidence.
An organization may use SEQM to determine: whether a particular simulation
result legitimately supports a stated proposition; whether the result remains
connected to the correct Simulation Object version; whether scenario and
execution context are preserved; whether a derived inference is traceable to
its source outputs; whether failed and anomalous outcomes remain evidentially
visible; whether aggregated campaign results conceal material dependencies;
whether evidence is still applicable after material change; whether an output
should remain unqualified, become conditionally qualified, become fully
qualified, or be disqualified; whether simulation-derived information is ready
to proceed into evidential weight assessment.
This becomes especially important in very large simulation programs where
millions or billions of outputs may exist but only a subset may legitimately
function as evidence for a particular claim.
SEQM therefore creates an explicit methodological gate between simulation
production and simulation evidence governance.
Foundational Principles
Simulation output is not automatically simulation evidence.Evidence qualification requires an explicit evidential purpose.
Evidence must remain connected to the simulation basis that gives it meaning.
Qualification is contextual, not universal.
Qualified evidence may still carry weak evidential weight.
Conditional evidence must remain conditional.
Negative simulation results may qualify as evidence.
Aggregated evidence must not erase its originating simulation basis.
Evidence provenance is part of evidential qualification.
A simulation result that cannot be meaningfully traced, contextualized, and
interpreted should not be treated as governed Simulation Evidence.