SIMULOS® Architecture Map

Simulation Governance Architecture — Simulation
Governance Layer

SIMULOS® serves as the official OOF® simulation governance reference map used
to identify, define, classify, navigate, govern, evaluate, and understand
governable simulation spaces across AI systems, autonomous systems, robotics,
digital twins, engineering systems, scientific models, critical
infrastructure, space systems, and future simulation environments.

The purpose of SIMULOS® is not to define the technical implementation of a
simulation system.

The purpose of SIMULOS® is to identify, define, separate, and map the
governable spaces through which a Simulation Object is identified, scoped,
represented, assumption-bounded, modeled, supplied with inputs, exposed to
scenarios, executed, interpreted through outputs, correlated with reality,
converted into bounded evidence, and governed throughout its continuing
lifecycle.

SIMULOS® serves as the official simulation governance reference map for:

  • AI Systems
  • Autonomous Systems
  • Autonomous Agents
  • Robotics
  • Digital Twins
  • Engineering Systems
  • Scientific Models
  • Physical Simulations
  • Hybrid Simulations
  • Mission Simulations
  • Space Systems
  • Safety-Critical Systems
  • Industrial Systems
  • Transportation Systems
  • Healthcare Simulations
  • Financial and Economic Simulations
  • Critical Infrastructure
  • Multi-System Environments
  • Future Simulation Ecosystems


Core Architectural Principle

Every simulation governance space answers one primary governance question.

No simulation governance space duplicates the purpose of another simulation
governance space.

Every simulation governance space has a defined beginning, a defined
operational purpose, and a defined governance boundary.

Every simulation governance space begins where the preceding simulation
governance requirement has been sufficiently established.

Together, these simulation governance spaces form the official simulation
governance reference map of SIMULOS®. Official Simulation Governance Flow

Simulation Object → Simulation Purpose & Scope → Simulation Reality
Representation → Simulation Assumptions → Simulation Model & Environment →
Simulation Inputs & Data → Simulation Scenario Design → Simulation Execution →
Simulation Output Governance → Simulation-to-Reality Correlation → Simulation
Evidence & Reliance Boundary → Simulation Lifecycle, Drift & Resimulation 1.
Simulation Object Standard (SOS)

Governed Space: Simulation Object Core Question

What exactly is being simulated, and how must the Simulation Object be
identified, bounded, versioned, and distinguished? Canonical Definition

Simulation Object Standard (SOS) defines the governance conditions under which
the object, system, process, environment, phenomenon, configuration,
relationship, or other subject being represented through simulation is
formally identified, bounded, versioned, configured, distinguished, and
documented as a governable Simulation Object. Governance Boundary

Begins when an intended simulation requires explicit identification of what is
actually being simulated.

Ends when the Simulation Object has been sufficiently identified, bounded,
versioned, configured, and documented to support definition of the simulation
purpose and scope. 2. Simulation Purpose & Scope Standard (SPSS)

Governed Space: Simulation Purpose & Scope Core Question

Why is the simulation being performed, what must it cover, and what remains
outside its simulation boundary? Canonical Definition

Simulation Purpose & Scope Standard (SPSS) defines the governance conditions
under which the purpose, intended inquiry, scope, exclusions, applicability
context, required depth, and required precision of a simulation are formally
established and documented. Governance Boundary

Begins when the Simulation Object has been sufficiently established and the
reason for simulating it must be formally defined.

Ends when the simulation purpose, scope, exclusions, applicability context,
depth, and precision have been sufficiently established to determine which
aspects of reality must be represented. 3. Reality Representation Standard
(RRS)

Governed Space: Simulation Reality Representation Core Question

Which aspects of reality must the simulation represent, at what abstraction
and fidelity, and which aspects may legitimately remain simplified or
excluded? Canonical Definition

Reality Representation Standard (RRS) defines the governance conditions under
which relevant aspects of reality are selected, abstracted, represented,
resolved, simplified, limited, or excluded within a simulation according to
the simulation's governed purpose and scope. Governance Boundary

Begins when the simulation purpose and scope are sufficiently established to
determine which aspects of reality materially require representation.

Ends when the required reality representation, abstraction, fidelity,
resolution, exclusions, and known representational limitations have been
sufficiently defined to support explicit governance of simulation assumptions.
4. Simulation Assumption Standard (SAS)

Governed Space: Simulation Assumptions Core Question

Which assumptions enable the simulation, and how must their basis,
dependencies, uncertainty, limitations, and consequences remain governed?
Canonical Definition

Simulation Assumption Standard (SAS) defines the governance conditions under
which assumptions enabling or materially influencing a simulation are
identified, justified, linked to their basis and dependencies, characterized
for uncertainty, bounded by limitations, and maintained as explicit elements
of the simulation basis. Governance Boundary

Begins when the selected representation of reality requires assumptions,
approximations, inferred conditions, or other propositions that cannot simply
be treated as established simulation facts.

Ends when material assumptions, their bases, dependencies, uncertainties, and
limitations have been sufficiently established to support construction and
configuration of the simulation model and environment. 5. Simulation Model &
Environment Standard (SMES)

Governed Space: Simulation Model & Environment Core Question

How must the simulation model and simulation environment be constructed,
configured, bounded, and controlled? Canonical Definition

Simulation Model & Environment Standard (SMES) defines the governance
conditions under which simulation models and simulation environments are
structurally constituted, configured, parameterized, bounded, versioned, and
controlled to represent the governed Simulation Object under the established
simulation basis. Governance Boundary

Begins when the Simulation Object, purpose, reality representation, and
material assumptions provide a sufficient basis for model and environment
construction.

Ends when the simulation model, environment, parameters, boundary
interactions, configuration, and version have been sufficiently established to
govern the inputs and data entering the simulation. 6. Simulation Input & Data
Standard (SIDS)

Governed Space: Simulation Inputs & Data Core Question

Which inputs and data may enter the simulation, and under what
representativeness, transformation, temporal, and uncertainty conditions?
Canonical Definition

Simulation Input & Data Standard (SIDS) defines the governance conditions
under which simulation inputs and data are identified, selected, assessed for
representativeness, transformed, temporally situated,
uncertainty-characterized, and admitted into a governed simulation. Governance
Boundary

Begins when the configured simulation model and environment require governed
inputs, parameters, datasets, signals, initial conditions, or other
simulation-driving information.

Ends when the simulation input basis has been sufficiently identified,
represented, transformed, temporally bounded, and uncertainty-characterized to
support governed scenario design. 7. Simulation Scenario Standard (SSS)

Governed Space: Simulation Scenario Design Core Question

Which scenarios must be simulated to represent expected conditions,
variations, edge cases, stresses, uncertainty, and materially relevant
alternatives? Canonical Definition

Simulation Scenario Standard (SSS) defines the governance conditions under
which simulation scenarios are identified, varied, structured, stress-tested,
extended toward relevant edge cases, and evaluated for coverage against the
governed simulation purpose and represented reality. Governance Boundary

Begins when the Simulation Object, represented reality, assumptions, model,
environment, and input basis are sufficiently established to determine which
conditions and variations require simulation.

Ends when the required scenario space, variations, edge cases, stress
conditions, and coverage have been sufficiently established to support
governed simulation execution. 8. Simulation Execution Standard (SES)

Governed Space: Simulation Execution Core Question

How must a simulation be instantiated, executed, observed, repeated,
controlled, and documented? Canonical Definition

Simulation Execution Standard (SES) defines the governance conditions under
which a simulation is instantiated, controlled, executed, observed, repeated,
and documented so that the execution process remains connected to its approved
Simulation Object, configuration, inputs, scenarios, and simulation basis.
Governance Boundary

Begins when the simulation object, model, environment, inputs, and required
scenario conditions are sufficiently established for execution.

Ends when governed simulation executions have been completed, observed,
repeated where required, and sufficiently documented to establish identifiable
simulation outputs. 9. Simulation Output Governance Standard (SOGS)

Governed Space: Simulation Output Governance Core Question

What exactly did the simulation produce, how must simulation outputs be
bounded and interpreted, and what may legitimately be inferred from them?
Canonical Definition

Simulation Output Governance Standard (SOGS) defines the governance conditions
under which simulation outputs are identified, classified, interpreted,
bounded, documented, and distinguished from derived inferences so that the
meaning and limitations of what the simulation produced remain explicit.
Governance Boundary

Begins when governed simulation execution produces observable, measurable,
derived, aggregated, or otherwise identifiable outputs.

Ends when simulation outputs and governed inferences have been sufficiently
identified, classified, bounded, limited, and documented to support comparison
with the reality they claim to represent. 10. Simulation-to-Reality
Correlation Standard (SRCS)

Governed Space: Simulation-to-Reality Correlation Core Question

To what extent do simulated behavior, conditions, and outputs correspond to
the reality they claim to represent? Canonical Definition

Simulation-to-Reality Correlation Standard (SRCS) defines the governance
conditions under which simulated behavior, states, conditions, relationships,
and outputs are systematically compared with available reality to determine
correspondence, deviation, alignment, uncertainty, and the limits of
simulation-to-reality correlation. Governance Boundary

Begins when governed simulation outputs exist and a relevant reality basis is
available or can legitimately be established for comparison.

Ends when the simulation-to-reality relationship has been sufficiently
characterized through comparison, correlation criteria, deviation analysis,
alignment assessment, and correlation uncertainty to support evaluation of
simulation-derived evidence.

Simulation similarity is not reality equivalence. 11. Simulation Evidence &
Reliance Boundary Standard (SERBS)

Governed Space: Simulation Evidence & Reliance Boundary Core Question

What evidential meaning may simulation results legitimately carry, and where
does simulation-specific reliance end before broader validation governance
becomes necessary? Canonical Definition

Simulation Evidence & Reliance Boundary Standard (SERBS) defines the
governance conditions under which simulation-derived information may
legitimately qualify as evidence, acquire simulation-specific evidential
weight, support explicitly bounded reliance, remain subject to defined use
limitations, and enter broader validation governance without losing the
conditions that establish its legitimate meaning. Governance Boundary

Begins when governed simulation outputs and, where relevant,
simulation-to-reality correlation provide a basis from which
simulation-derived evidence may be considered.

Ends when the simulation-derived evidence has been sufficiently qualified,
weighted, bounded for reliance, constrained by applicable use limitations, and
prepared for transfer into broader validation governance where required.

Simulation-derived evidence must not travel farther than its governed meaning
can legitimately support. 12. Simulation Lifecycle & Resimulation Standard
(SLRS)

Governed Space: Simulation Lifecycle, Drift & Resimulation Core Question

How must simulation relevance be monitored over time, and when must changed
reality trigger update, resimulation, replacement, or retirement? Canonical
Definition

Simulation Lifecycle & Resimulation Standard (SLRS) defines the governance
conditions under which the continuing relevance of a simulation is monitored,
material simulation drift is recognized, resimulation triggers are
established, resimulation scope is determined, and simulations are updated,
repeated, replaced, transitioned, or retired as the Simulation Object,
represented reality, assumptions, models, inputs, scenarios, operational
conditions, evidence basis, or intended reliance materially change. Governance
Boundary

Begins when a governed simulation and its outputs, correlations, or evidence
enter continuing use, reference, reliance, monitoring, or preservation beyond
the initial simulation process.

Continues throughout the remaining simulation lifecycle while the simulation,
its outputs, or simulation-derived evidence retain operational, evidential,
scientific, engineering, analytical, or decision relevance.

Where material change invalidates or weakens the existing simulation basis,
Simulation Lifecycle & Resimulation Standard may trigger governed re-entry
into the appropriate earlier SIMULOS® governance space rather than requiring
the entire lifecycle to restart automatically. Core Governed Simulation Spaces

  • Simulation Object
  • Simulation Purpose & Scope
  • Simulation Reality Representation
  • Simulation Assumptions
  • Simulation Model & Environment
  • Simulation Inputs & Data
  • Simulation Scenario Design
  • Simulation Execution
  • Simulation Output Governance
  • Simulation-to-Reality Correlation
  • Simulation Evidence & Reliance Boundary
  • Simulation Lifecycle, Drift & Resimulation


Recursive Simulation Lifecycle

SIMULOS® does not end when a simulation execution is completed.

Once simulation results, simulation-derived evidence, or simulation-supported
decisions remain in use, the architecture becomes recursive:

Governed Simulation → Simulation Output → Simulation-Derived Evidence →
Permitted Reliance → Lifecycle Monitoring → Material Change → Simulation Drift
Recognition → Resimulation Trigger → Resimulation Scope → Lifecycle Re-Entry →
Updated Simulation / Resimulation / Replacement / Retirement

Lifecycle re-entry occurs at the earliest materially affected governance
space.

A changed Simulation Object may require re-entry into Simulation Object
Standard.

A changed purpose may require re-entry into Simulation Purpose & Scope
Standard.

A changed understanding of reality may require re-entry into Reality
Representation Standard.

A failed assumption may require re-entry into Simulation Assumption Standard.

A changed model or environment may require re-entry into Simulation Model &
Environment Standard.

Changed data may require re-entry into Simulation Input & Data Standard.

A newly relevant operating condition may require re-entry into Simulation
Scenario Standard.

The architecture therefore supports targeted governed resimulation rather than
uncontrolled repetition. Architectural Position

SIMULOS® governs the complete simulation governance lifecycle.

The architecture progresses through:

Simulation Object → Simulation Purpose & Scope → Simulation Reality
Representation → Simulation Assumptions → Simulation Model & Environment →
Simulation Inputs & Data → Simulation Scenario Design → Simulation Execution →
Simulation Output Governance → Simulation-to-Reality Correlation → Simulation
Evidence & Reliance Boundary → Simulation Lifecycle, Drift & Resimulation

Together, these simulation governance spaces govern how the object of
simulation is formally established, why simulation is performed, how reality
is represented, which assumptions enable that representation, how models and
environments are constituted, which inputs enter the simulation, which
scenarios are examined, how simulations are executed, what their outputs
legitimately mean, how those outputs correspond to reality, what evidential
significance and reliance they may support, and when changing conditions
require governed resimulation, replacement, transition, or retirement.

SIMULOS® therefore provides a complete governance path from the first
identification of what is being simulated to the continuing determination of
whether that simulation remains relevant enough to be relied upon. What
SIMULOS® Defines

SIMULOS® defines:

- where simulation governance begins, - how a Simulation Object is formally
identified and bounded, - why a simulation is being performed, - what the
simulation must and must not cover, - which aspects of reality must be
represented, - what abstraction, fidelity, and resolution are appropriate, -
which assumptions enable or materially affect the simulation, - how the
simulation model and environment are governed, - which inputs and data may
enter the simulation, - which scenarios, variations, edge cases, and stress
conditions require simulation, - how simulation execution is governed, - how
simulation outputs are identified and interpreted, - where output
interpretation ends and inference begins, - how simulated behavior is compared
with reality, - how simulation-to-reality deviation and uncertainty are
represented, - what simulation-derived information may legitimately become
evidence, - how much simulation-specific evidential weight that evidence may
carry, - where legitimate simulation-specific reliance ends, - which uses and
interpretations must remain limited, - how simulation-derived evidence
interfaces with VALIDOS® validation governance, - how simulation relevance is
monitored over time, - what constitutes material simulation drift, - when
resimulation must be triggered, - where lifecycle re-entry must occur, - when
a simulation should be updated, replaced, transitioned, or retired.

SIMULOS® provides the official simulation governance language used for
simulation-space identification, simulation lifecycle navigation, governance
classification, structural diagnosis, evidence-boundary determination,
resimulation governance, and simulation architecture design across complex
operational environments. Critical Architectural Distinctions

Simulation Object ≠ Validation Object

Represented Reality ≠ Operational Reality

Simulation Assumption ≠ Established Fact

Representation Fidelity ≠ Reality Equivalence

Model Sophistication ≠ Model Applicability

Input Volume ≠ Input Representativeness

Scenario Count ≠ Scenario Coverage

Simulation Execution ≠ Audit of Simulation Execution

Simulation Output ≠ Simulation Evidence

Simulation Inference ≠ Direct Simulation Observation

Simulation-to-Reality Correlation ≠ Reality Equivalence

Evidence Volume ≠ Evidential Strength

Repeated Simulation ≠ Independent Evidence

High Evidential Weight ≠ Broad Reliance

Simulation Evidence ≠ Validation Determination

Past Simulation Relevance ≠ Continuing Simulation Relevance

Resimulation ≠ Failure of the Original Simulation Cross-Architecture
Governance Boundaries SIMULOS® ↔ ORA™ — Operational Reality Architecture

Operational Reality Architecture governs what exists and occurs in actual
Operational Reality.

SIMULOS® — Simulation Governance Architecture governs how selected aspects of
that reality are represented, modeled, simulated, compared, and governed
inside the simulation lifecycle.

Operational Reality ≠ Simulated Reality. SIMULOS® ↔ VALIDOS® — Validation
Governance Architecture

SIMULOS® — Simulation Governance Architecture determines what
simulation-derived evidence can legitimately represent, mean, and support
within its simulation-specific reliance boundary.

VALIDOS® — Validation Governance Architecture determines whether the complete
validation basis is sufficient to support a Validation Determination and
intended reliance.

Simulation Evidence ≠ Validation Determination. SIMULOS® ↔ INTEGROS® —
Integrity Governance Architecture

SIMULOS® — Simulation Governance Architecture governs simulation-specific
structure, conditions, execution, outputs, correlation, evidence boundaries,
and lifecycle.

INTEGROS® — Integrity Governance Architecture governs integrity across
governed objects, structures, conditions, states, evidence, and processes.

Simulation Governance ≠ Integrity Governance. SIMULOS® ↔ ADIT® — Audit in Real
Time Architecture

SIMULOS® — Simulation Governance Architecture governs simulation execution and
the simulation lifecycle itself.

ADIT® — Audit in Real Time Architecture governs evidential observation,
preservation, verification, traceability, and auditability of what occurred
where applicable.

Execution Governance ≠ Audit Governance. SIMULOS® ↔ AGA™ — Accountability
Governance Architecture

SIMULOS® — Simulation Governance Architecture governs the methodological
conditions under which simulation activities and simulation-derived evidence
acquire legitimate meaning.

AGA™ — Accountability Governance Architecture governs who holds authority,
responsibility, accountability, attribution, and decision ownership
surrounding those activities.

Methodological Governance ≠ Accountability Governance. SIMULOS® ↔ AIG® — AI
Governance Architecture

SIMULOS® may simulate AI systems and their behavior.

AIG® — AI Governance Architecture continues to govern the behavioral
governance lifecycle of actual AI systems.

Simulated AI Behavior ≠ Governance of Actual AI Behavior. SIMULOS® ↔ ASGA™ —
Autonomous Systems Governance Architecture

SIMULOS® may simulate autonomous agents, robots, vehicles, machines, and
multi-agent systems.

ASGA™ — Autonomous Systems Governance Architecture continues to govern the
authority, autonomy, constraints, execution, coordination, escalation, and
operational behavior of actual autonomous systems.

Simulated Autonomy ≠ Governed Operational Autonomy. Compatible OOF®
Architectures

  • GOA™ — Governance Architecture
  • ORA™ — Operational Reality Architecture
  • VALIDOS® — Validation Governance Architecture
  • INTEGROS® — Integrity Governance Architecture
  • OBIDENITY® — Origin-Bound Identity Governance Architecture
  • AGA™ — Accountability Governance Architecture
  • AIG® — AI Governance Architecture
  • ASGA™ — Autonomous Systems Governance Architecture
  • CLIA® — Cognitive Governance Intelligence Architecture
  • MGIA™ — Memory Governance Intelligence Architecture
  • ADIT® — Audit in Real Time Architecture


Canonical Principle

SIMULOS® does not define Operational Reality, general validation sufficiency,
general integrity, accountability, auditability, or the operational authority
and behavior of actual AI and autonomous systems. SIMULOS® governs the
conditions under which a Simulation Object is identified, simulation purpose
is bounded, reality is represented, assumptions are made explicit, models and
environments are constituted, inputs are admitted, scenarios are designed,
simulations are executed, outputs are interpreted, simulation-to-reality
relationships are established, simulation-derived evidence acquires bounded
meaning and reliance, and material change triggers governed lifecycle
re-entry, resimulation, replacement, transition, or retirement. Canonical
Closing Statement

SIMULOS® serves as the official simulation governance reference map for AI
systems, autonomous systems, robotics, digital twins, engineering systems,
scientific models, critical infrastructure, space systems, and future
simulation environments. By defining the primary governable spaces of the
complete simulation lifecycle, SIMULOS® provides the architecture used to
identify what is being simulated, establish why it is being simulated,
determine how reality is represented, govern assumptions, models,
environments, inputs, scenarios and execution, establish what simulation
outputs legitimately mean, determine how simulated results correspond to
reality, bound the evidential meaning and reliance of simulation-derived
evidence, and continuously determine whether material change requires
lifecycle re-entry, resimulation, replacement, transition, or retirement.
SIMULOS® therefore establishes simulation not as an isolated computational
event, but as a continuously governable relationship between a Simulation
Object, a representation of reality, a governed simulation process,
simulation-derived evidence, and the changing reality within which that
evidence may ultimately be relied upon.