OOF™ Origin Open Foundation™

Independent Methodological Authority

Architecture Ecosystem: Structured Reality Standards™
Architecture Family: SIMULOS® — Simulation Governance Architecture
Parent Standard: Simulation Model & Environment Standard (SMES)
Operational Layer: Simulation Model & Environment Governance Layer
Category: Governance & Enforcement
Subcategory: Simulation Governance
Type: Parent Standard Module
Governed Space: Simulation Parameterization
Version: 1.0
Status: Canonical · Open Module
Effective Date: 17 August 2026
Compatibility: OOF® Methodology OS™ · GOA™ · ORA™ · VALIDOS® · INTEGROS® · ADIT® · AGA™ ·
AIG® · CLIA® · MGIA™ · ASGA™ · RIS™
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodological Intellectual Property
Canonical Language: English (UCL™)

Governed Space

Simulation Parameterization

Canonical Definition

Simulation Parameterization Module (SPHM) defines the governance framework for
identifying, defining, assigning, constraining, documenting, controlling,
changing, and reconstructing the parameters through which a governed
Simulation Model and Simulation Environment are configured for simulation
execution.

SPHM establishes the controlled parameter layer between the structural design
of the simulation and the specific configured conditions under which
simulation behavior is produced.

Operational Role

SPHM is the fourth internal governance space of the Simulation Model &
Environment Standard (SMES).

The preceding modules establish:

SMMM — What is the Simulation Model structurally composed of?

SECM — Within what configured Simulation Environment does that model operate?

SBIM — How do the model, environment, and connected domains interact across
their boundaries?

SPHM asks:

Which configurable values govern the behavior of the model and environment,
why were those values selected, what ranges and constraints apply, and which
parameter configuration produced a particular simulation execution?

The governed progression is:

Model Structure → Environment Configuration → Boundary Interaction → Parameter
Identification → Parameter Definition → Value Assignment → Constraint & Range
Control → Parameter Configuration → Execution Binding → Governed Simulation
Parameterization

Module Operational Space

SPHM governs: Simulation Parameter identification, parameter identity,
parameter definition, parameter classification, parameter ownership, parameter
source, parameter basis, parameter units, parameter types, parameter values,
parameter ranges, parameter distributions, parameter constraints, parameter
dependencies, parameter relationships, parameter inheritance, parameter
overrides, default parameters, fixed parameters, variable parameters, dynamic
parameters, stochastic parameters, derived parameters, calibrated parameters,
externally supplied parameters, model parameters, environment parameters,
boundary parameters, parameter sets, parameter configurations, parameter
baselines, parameter sensitivity, parameter change, parameter substitution,
parameter uncertainty references, parameter traceability, execution binding,
and parameter reconstruction.

Module Operational Scope

SPHM applies wherever configurable values materially influence simulation
behavior.

This may include: computational simulations, engineering simulations, robotics
simulations, autonomous-system simulations, AI simulations, digital twins,
scientific models, climate and environmental simulations, industrial
simulations, infrastructure simulations, financial simulations, economic
simulations, biological simulations, healthcare simulations, transportation
simulations, aerospace simulations, mission simulations, multi-agent
simulations, software-in-the-loop environments, hardware-in-the-loop
environments, and hybrid physical-digital simulations.

Governance depth should remain proportional to the material influence of
parameterization upon the simulation inquiry.

Module Function

A correctly structured model can produce materially different behavior
depending upon how it is parameterized.

For example:

Same Model + Different Parameters =
Different Simulation Behavior

A simulation may therefore fail methodologically even when its model structure
is correct if: material parameters are undocumented, default values are
silently inherited, parameter values lack an identifiable basis, permissible
ranges are undefined, incompatible parameters are combined, units are
inconsistent, values change during execution without visibility, parameter
dependencies are ignored, or the parameter set used for a result cannot later
be reconstructed.

SPHM prevents parameterization from becoming an invisible technical
configuration layer.

Simulation Parameter

A Simulation Parameter is a governed configurable property whose assigned
value, range, distribution, rule, or state may materially influence the
behavior of a Simulation Model, Simulation Environment, Simulation Boundary,
or simulation execution.

A parameter may represent: physical property, behavioral property,
environmental condition, system capability, timing property, probability,
threshold, tolerance, resource quantity, interaction property, control value,
or another configurable simulation characteristic.

Parameter Identity

Each material parameter should be sufficiently identifiable.

Where relevant, Parameter Identity may establish: parameter name, parameter
identifier, parameter type, governed component, unit, source, basis,
permissible range, current value, and applicable configuration.

Parameter names alone may be insufficient where multiple components use
similarly named variables.

Parameter Definition

A parameter should have a sufficiently precise definition to establish what
the value actually represents.

For example:

Reaction Time = 0.8

is incomplete if the simulation record does not establish: whose reaction
time, measured in which unit, under which conditions, applied to which model
component, and whether the value is fixed, variable, or distributed.

Parameter Classification

Parameters may be classified according to their simulation role.

Possible classifications include:

Model Parameter

Environment Parameter

Boundary Parameter

Control Parameter

Initial-State Parameter

Behavioral Parameter

Physical Parameter

Timing Parameter

Resource Parameter

Stochastic Parameter

or another simulation-specific class.

Model Parameter

A Model Parameter configures a property of the Simulation Model without
changing its underlying structural identity unless the change becomes
structurally material.

Examples may include: mass, acceleration limit, control threshold, sensor
range, decision threshold, response rate, or behavioral coefficient.

Environment Parameter

An Environment Parameter configures a governed property of the Simulation
Environment.

Examples may include: temperature, traffic density, visibility, network
latency, market liquidity, resource availability, or environmental response
rate.

SECM establishes the environment and its configuration structure.

SPHM governs the controlled values through which configurable environmental
properties are instantiated.

Boundary Parameter

A Boundary Parameter configures a property of a governed Simulation Boundary
or interaction mechanism.

Examples may include: interface latency, transmission frequency, timeout,
conversion tolerance, synchronization interval, or signal range.

SBIM governs the boundary interaction.

SPHM governs its configurable parameter values.

Parameter Type

A parameter may be:

Fixed

Variable

Dynamic

Stochastic

Derived

Calibrated

Externally Supplied

Scenario-Controlled

or:

Execution-Controlled

where the distinction materially affects simulation behavior.

Fixed Parameter

A Fixed Parameter is intended to remain unchanged during the applicable
simulation execution or governed simulation configuration.

Its fixed status should not imply that its value requires no basis.

Variable Parameter

A Variable Parameter may take different governed values between simulation
runs or configurations.

The permissible variation should remain bounded where material.

Dynamic Parameter

A Dynamic Parameter may change during simulation execution according to
defined logic, state, events, or external conditions.

Its governing rule may be as important as its initial value.

Stochastic Parameter

A Stochastic Parameter is governed through a probability distribution or
stochastic mechanism rather than one fixed value.

Relevant governance may include: distribution type, parameters of the
distribution, permitted range, sampling rule, correlation, and seed
relationship where material.

Derived Parameter

A Derived Parameter is calculated from other governed values.

Conceptually:

Parameter A + Parameter B + Derivation
Rule → Derived Parameter C

The derivation should remain traceable where the resulting parameter
materially affects simulation behavior.

Calibrated Parameter

A Calibrated Parameter receives its value through a calibration process.

SPHM governs the parameterization role and resulting value.

It does not replace broader validation governance over whether the calibration
evidence is sufficient for an intended validation claim.

Externally Supplied Parameter

Some parameters originate outside the simulation.

Examples include: operational measurements, external datasets, sensor values,
market values, environmental feeds, engineering specifications, or third-party
model outputs.

Their external origin should remain identifiable.

Parameter Basis

Every material parameter should have an identifiable basis where practicable.

A Parameter Basis may derive from: measured reality, scientific literature,
engineering specification, historical data, expert judgment, Simulation
Assumption, calibration, scenario design, operational data, regulatory
requirement, or explicit experimental selection.

Parameter Basis ≠ Parameter Validity

SPHM governs:

Why was this parameter value selected and how is it represented inside the
simulation?

VALIDOS® may separately govern:

Is the evidentiary and methodological basis sufficient to validate reliance
upon the resulting simulation evidence?

This separation protects the architectural boundary between SIMULOS® and
VALIDOS®.

Assumption-to-Parameter Traceability

Simulation Assumptions may become parameter values.

Conceptually:

Simulation Assumption → Parameter
Definition → Parameter Value

For example:

Assumption: Average Human Reaction Time =
1 second

may become:

Reaction_Time = 1.0 s

Where material, the relationship should remain traceable.

Reality-Representation-to-Parameter
Traceability

Reality Representation requirements may also determine parameterization.

Conceptually:

Represented Reality Property → Simulation Variable → Parameter Definition →
Parameter Value / Distribution

This establishes continuity between what the simulation claims to represent
and how that representation is configured.

Parameter Value

A Parameter Value is the assigned value or state applied to a parameter within
a defined Simulation Configuration.

A value should remain interpretable together with: parameter identity, unit,
applicable range, source or basis, configuration, and execution context.

Parameter Units

Where parameters represent measurable quantities, units should be explicit.

Examples:

Velocity = 30 m/s

Latency = 120 ms

Temperature = 25 °C

Mass = 1,500 kg

Unit ambiguity can materially alter simulation behavior.

Unit Compatibility

Parameters participating in shared calculations or interactions should use
compatible units or governed conversion.

Silent unit conversion should not create an invisible simulation
transformation.

Parameter Range

A Parameter Range establishes the values within which a parameter may
legitimately operate for a defined simulation configuration.

Conceptually:

Minimum ≤ Parameter ≤ Maximum

Ranges may be: physical, methodological, empirical, scenario-defined,
operational, safety-related, or technically imposed.

Parameter Range Violation

A parameter value outside its governed range should not silently enter
simulation execution.

Possible treatments may include:

Reject

Flag

Constrain

Require Authorization

Create Exception

or another governed response.

Parameter Constraint

A Parameter Constraint defines a condition restricting permissible parameter
values or combinations.

Constraints may include: minimum, maximum, allowed states, prohibited
combinations, conditional ranges, dependency conditions, or cross-parameter
relationships.

Cross-Parameter Constraint

Some values may be legitimate individually but illegitimate together.

For example:

Payload Mass = Valid

and:

Fuel Level = Valid

may nevertheless create an impossible combined configuration.

SPHM therefore governs parameter relationships as well as individual values.

Parameter Dependency

A Parameter Dependency exists where the value, range, interpretation, or
effect of one parameter depends materially upon another parameter or governed
simulation condition.

Conceptually:

Parameter A → constrains / determines / modifies → Parameter B Parameter
Dependency Map

Where dependencies are material, SPHM should support:

Parameter → Dependency → Dependent
Parameter → Effect

This becomes particularly important in complex models containing hundreds or
thousands of configurable variables.

Parameter Distribution

Some simulation properties are more appropriately represented by distributions
than single values.

Examples include: human reaction times, equipment failure rates, weather
variation, demand behavior, network latency, biological variability, or market
behavior.

The distribution itself becomes a governed parameterization object.

Distribution Governance

Where material, governance may establish: distribution type, central tendency,
dispersion, bounds, truncation, correlation, sampling method, and applicable
population or condition.

Parameter Correlation

Parameters may be statistically or structurally related.

Treating correlated parameters as independent may create unrealistic
simulation states.

Material correlation assumptions should therefore remain visible.

Default Parameter

A Default Parameter is a parameter value automatically applied when no
alternative value is explicitly selected.

Defaults are not methodologically neutral.

A default can materially shape simulation behavior.

No Invisible Default Principle

A material parameter must not escape governance merely because its value was
supplied automatically by software, inherited from a template, or left
unchanged from a previous configuration.

Parameter Inheritance

Parameters may be inherited from: parent configurations, templates, model
defaults, environment defaults, prior simulation versions, or scenario
configurations.

Material inheritance should remain traceable.

Parameter Override

A Parameter Override occurs when an inherited, default, or previously
established value is replaced for a specific configuration or execution.

An override should identify: affected parameter, previous value, new value,
reason, applicable scope, and execution relationship

where material.

Parameter Set

A Parameter Set is a governed collection of parameters intended to operate
together.

Examples:

Baseline Parameter Set

Adverse Weather Parameter Set

High-Load Parameter Set

Failure Parameter Set

Experimental Parameter Set Parameter
Configuration

A Parameter Configuration is the complete materially relevant combination of
parameter values, distributions, constraints, dependencies, and overrides
applicable to a defined simulation execution or group of executions.

Conceptually:

Simulation Parameter Configuration = Parameter Set + Values + Ranges +
Constraints + Dependencies + Overrides Parameter Configuration Identifier

Material parameter configurations should be uniquely distinguishable where
different configurations may produce different simulation behavior.

This enables:

Simulation Result → Simulation Execution → Parameter Configuration Parameter
Baseline

A Parameter Baseline establishes the approved reference parameter
configuration against which changes may be identified.

The baseline may support: repeatability, comparability, sensitivity analysis,
scenario variation, configuration control, and change detection.

Baseline ≠ Universal Correctness

A baseline establishes a controlled reference.

It does not establish that the baseline perfectly represents reality.

Parameter Variation

Parameter Variation may be intentional.

Examples include: sensitivity testing, stress testing, scenario exploration,
uncertainty exploration, optimization, robustness testing, or boundary
testing.

SPHM governs the variation itself.

Scenario Design governance determines how parameter variation is organized
into governed scenarios.

Parameter Variation Range

Where parameter variation is intentional, the tested range should remain
identifiable.

For example:

Wind Speed: 0–150 km/h

rather than simply:

Wind varied.

Parameter Sweep

A Parameter Sweep systematically varies one or more parameters across defined
values or ranges.

SPHM may govern: parameter identity, variation range, step, distribution,
combinations, exclusions, and resulting configuration identifiers.

Parameter Sensitivity

Parameter Sensitivity describes the degree to which changes in a parameter
materially alter simulation behavior or outputs.

SPHM governs the identification and configuration relevance of sensitivity.

It does not replace the downstream interpretation or validation of simulation
outputs.

High-Sensitivity Parameter

A parameter may be classified as high-sensitivity where small changes can
materially alter simulation behavior.

Such parameters may justify stronger: basis documentation, range control,
change control, traceability, and execution binding.

Parameter Sensitivity ≠ Uncertainty
Governance

Sensitivity asks:

How strongly does simulation behavior respond when this parameter changes?

Uncertainty asks:

How uncertain are we about the parameter or its basis?

Simulation Assumption governance and other relevant spaces may govern
uncertainty.

SPHM preserves the parameterization relationship without duplicating those
spaces.

Parameter Substitution

A parameter may be substituted when the originally intended value or source is
unavailable.

For example:

Measured Wind Data → Synthetic Wind
Distribution

The substitution should remain visible where material.

Parameter Change

A Parameter Change may involve: value change, range change, distribution
change, unit change, constraint change, dependency change, source change,
basis change, inheritance change, override, or parameter removal.

Parameter Change Record

Material changes should preserve:

Previous Parameter State → Change → New Parameter State → Applicable
Configuration Parameter Drift

Parameter Drift occurs where parameter values or parameterization behavior
progressively depart from the governed configuration without an equivalent
governed decision.

Drift may arise through: automated adaptation, external feeds, configuration
changes, software updates, calibration updates, environment changes, or
inherited defaults.

Dynamic Parameter Change

A parameter changing during execution according to its governed rule is not
necessarily drift.

The distinction is:

Governed Dynamic Change

versus:

Uncontrolled Parameter Change Parameter
Lock

A parameter may be locked where its value must not change during a defined
execution or comparison set.

Locking may be important for: repeatability, controlled experimentation,
comparative simulation, benchmark execution, or forensic reconstruction.

Parameter Freeze

A Parameter Freeze may preserve a complete parameter configuration for a
defined simulation execution campaign.

This creates a stable configuration reference.

Parameterization and Scenario Design

SPHM governs:

Which parameters exist and how their values may be configured.

Scenario Design governs:

Which combinations of conditions and parameter configurations are
intentionally selected as scenarios for simulation.

Therefore:

Parameterization ≠ Scenario Design

This boundary prevents duplication between Parent Standards.

Parameterization and Input Data

A parameter should also remain distinguishable from Input Data.

A parameter configures the simulation.

Input Data supplies data consumed during simulation execution.

In some systems the distinction may be technically blurred.

The governance classification should follow operational function rather than
software implementation.

Parameterization and Assumptions

Simulation Assumptions may justify or constrain parameter values.

However:

Assumption Governance ≠ Parameter
Governance

SAS governs the assumption and its role.

SPHM governs how that assumption becomes an operational simulation
configuration.

Parameterization and Environment
Configuration

SECM establishes:

what environmental configuration exists.

SPHM establishes:

which configurable values instantiate or control material properties of that
environment.

Parameterization and Boundary Interaction

SBIM establishes:

what may cross a Simulation Boundary and under which interaction conditions.

SPHM governs:

the configurable values controlling those interaction properties.

Parameter Configuration Completeness

Parameter Configuration Completeness asks:

Have all material parameters required to reconstruct the governed simulation
execution been identified and preserved?

Completeness does not require documenting every technically available setting.

It requires visibility over settings capable of materially affecting the
simulation inquiry.

Parameter Configuration Consistency

Parameter Configuration Consistency asks whether: units are compatible, values
fall within applicable ranges, dependencies are satisfied, constraints are
respected, inherited values remain applicable, and combined values form a
coherent simulation configuration.

Parameter Configuration Coherence

A parameter configuration may contain individually valid values while
producing an incoherent overall simulation state.

SPHM therefore evaluates configuration-level coherence rather than only
individual parameter validity.

Parameter Comparability

Two simulation executions should not be treated as parameter-equivalent unless
their materially relevant parameter configurations are sufficiently aligned.

Differences should remain visible where they affect interpretation.

Controlled Comparison

For controlled comparative simulation:

Compared Executions should differ only in the variables intentionally changed
or otherwise explicitly disclosed.

This enables parameter-level causal interpretation without assuming broader
methodological validation.

Parameter Change Propagation

A material parameter change may propagate through later SIMULOS® spaces.

Conceptually:

Parameter Change

↓

Scenario Configuration

↓

Simulation Execution

↓

Simulation Output

↓

Reality Correlation

↓

Simulation Evidence

SPHM preserves the parameter change.

The downstream spaces govern their respective consequences.

Parameter-to-Execution Binding

Every material simulation execution should be bindable to the Parameter
Configuration under which it occurred.

Conceptually:

Simulation Execution ID → Parameter
Configuration ID

Without this relationship, repeatability and reconstruction may become
impossible.

Parameter-to-Output Traceability

Where material, simulation outputs should remain traceable to the parameter
configuration that contributed to their production.

Conceptually:

Parameter Configuration → Execution →
Simulation Output

SPHM establishes the parameter side of that traceability chain.

Parameter Reconstruction

SPHM should enable reconstruction sufficient to answer:

Which parameters were active?

Which values were assigned?

Which units applied?

Which ranges and constraints applied?

Which values were inherited or overridden?

Which parameters were dynamic or stochastic?

Which dependencies existed?

Which parameter configuration produced the execution?

Did any material parameter change during execution?

Parameterization Traceability

A complete parameterization traceability path may be represented as:

Simulation Purpose & Scope

↓

Reality Representation

↓

Simulation Assumptions

↓

Simulation Model & Environment

↓

Simulation Parameter

↓

Parameter Basis

↓

Parameter Value / Distribution

↓

Parameter Configuration

↓

Simulation Execution

Minimum Implementation Framework

1. Identify Material Simulation Parameters

Determine which configurable properties may materially influence simulation
behavior. 2. Define and Classify Parameters

Establish meaning, type, unit, role, and governed component. 3. Establish
Parameter Basis

Identify the basis supporting material parameter values or distributions. 4.
Establish Values, Ranges, and Distributions

Define applicable values and permissible variation. 5. Govern Constraints and
Dependencies

Identify relationships that limit or modify parameter configuration. 6.
Control Defaults, Inheritance, and Overrides

Prevent invisible parameterization through technical defaults or inherited
configurations. 7. Establish Parameter Sets and Baselines

Create controlled parameter configurations for simulation activity. 8. Govern
Material Parameter Change

Preserve changes, substitutions, drift, and dynamic behavior. 9. Bind
Parameter Configuration to Execution

Ensure each material execution can identify its applicable parameter
configuration. 10. Preserve Parameter Reconstruction

Maintain sufficient traceability to reconstruct the parameterization that
produced simulation behavior.

Governance Outputs

SPHM may produce: Simulation Parameter Register, Simulation Parameter
Identifier, Parameter Definition Record, Parameter Classification Record,
Parameter Basis Record, Parameter Source Record, Parameter Unit Record,
Parameter Value Record, Parameter Range Record, Parameter Distribution Record,
Parameter Constraint Record, Cross-Parameter Constraint Record, Parameter
Dependency Map, Parameter Correlation Record, Default Parameter Record,
Parameter Inheritance Record, Parameter Override Record, Parameter Set,
Parameter Configuration, Parameter Configuration Identifier, Parameter
Baseline, Parameter Variation Record, Parameter Sweep Record, Parameter
Sensitivity Record, High-Sensitivity Parameter Record, Parameter Substitution
Record, Parameter Change Record, Parameter Drift Record, Parameter Lock
Record, Parameter Freeze Record, Assumption-to-Parameter Traceability Map,
Reality-Representation-to-Parameter Traceability Map, Parameter Configuration
Completeness Assessment, Parameter Configuration Consistency Assessment,
Parameter Configuration Coherence Assessment, Parameter-to-Execution Binding,
Parameter Reconstruction Record, and Simulation Parameterization Traceability
Map.

Use Case 1 — Autonomous Vehicle Simulation

Scenario

An autonomous vehicle simulation uses configurable values for:

Vehicle Mass + Sensor Range + Reaction Delay + Road Friction + Visibility +
Traffic Density Application

SPHM identifies each material parameter, establishes units and ranges,
preserves the source or assumption supporting each value, and binds the
resulting Parameter Configuration to the simulation execution.

A change from:

Road Friction = 0.8

to:

Road Friction = 0.3

creates a materially different parameter configuration. Result

The resulting vehicle behavior can be traced to the exact parameter
configuration under which it was produced. Use Case 2 — Spacecraft Mission
Simulation

Scenario

A spacecraft simulation includes:

Vehicle Mass + Fuel Load + Engine Performance + Communication Delay + Thermal
Conditions + Orbital Parameters Application

SPHM governs the values, units, permissible ranges, dependencies, and
parameter sets.

If spacecraft mass changes, dependent fuel and propulsion parameters can be
identified rather than remaining silently inconsistent. Result

Mission simulation executions remain reconstructable and parameter-comparable.
Use Case 3 — Financial Stress Simulation

Scenario

A financial simulation varies:

Interest Rate + Default Probability + Liquidity + Asset Correlation + Market
Volatility Application

SPHM governs parameter ranges, distributions, correlations, parameter sweeps,
and the specific configuration applied to each execution. Result

A stress result can be distinguished from a baseline result through its
governed Parameter Configuration rather than only through its final output.

Architectural Position

Simulation Parameterization is the fourth internal governance space of the
Simulation Model & Environment Standard (SMES).

The complete SMES progression is:

Simulation Model Structure → Simulation Environment Configuration → Simulation
Boundary Interaction → Simulation Parameterization → Simulation Environment
Versioning

SPHM receives: the governed Simulation Model Structure from SMMM, the governed
Simulation Environment Configuration from SECM, and the governed Simulation
Boundary Interaction structure from SBIM.

It then establishes the controlled parameter configuration required to
determine which specific configuration of the model and environment is
actually prepared for simulation activity.

The resulting governed configuration becomes an input to Simulation
Environment Versioning and later SIMULOS® lifecycle spaces.

Foundational Principle

A simulation cannot be meaningfully reconstructed, compared, or governed when
the material parameters shaping its behavior are unknown, uncontrolled, or
detached from the execution they produced.

Canonical Closing Statement

Simulation Parameterization Module establishes the parameter governance layer
of the Simulation Model & Environment Standard by transforming configurable
model, environment, and boundary properties into explicit, bounded, traceable,
and reconstructable Simulation Parameter Configurations. Through parameter
identification, definition, classification, basis, units, values, ranges,
distributions, constraints, dependencies, defaults, inheritance, overrides,
sensitivity, controlled variation, change governance, configuration baselines,
and execution binding, SPHM ensures that materially different simulation
behavior cannot be produced by invisible parameter choices while being
represented as if it arose from the same governed simulation configuration.