SIMULOS®

Simulation Governance Architecture — Simulation
Governance Layer

SIMULOS® serves as the official OOF® simulation governance architecture used
for simulation governance design, simulation space identification, simulation
methodology structuring, reality representation governance, assumption
governance, simulation execution governance, simulation output governance,
simulation-to-reality correlation, simulation evidence governance, reliance
boundary definition, lifecycle monitoring, and resimulation governance across
AI systems, autonomous systems, robotics, digital twins, engineering systems,
scientific models, critical infrastructure, space systems, organizations, and
future simulation environments.

Simulation Governance Architecture (SIMULOS®) does not define simulation
itself. SIMULOS® defines the governance architecture required to ensure that
simulations remain explicitly identified, purpose-bounded, reality-aware,
assumption-governed, structurally controlled, input-aware, scenario-governed,
execution-traceable, output-bounded, reality-correlated, evidentially
constrained, and lifecycle-governed throughout the complete simulation
lifecycle.

SIMULOS® consists of 12 Core Parent Standards and 60 Core Modules forming the
primary simulation governance backbone within the OOF® ecosystem.

Additional compatible standards, bridge standards, and specialized simulation
governance standards may extend the architecture when new simulation
governance spaces are identified. However, the standards listed below form the
official core simulation governance structure of SIMULOS®. Canonical
Definition

Simulation Governance Architecture (SIMULOS®) is the simulation governance
architecture that defines the structural and methodological conditions under
which a simulation is explicitly identified, purpose-bounded, connected to
represented reality, assumption-governed, modelled within controlled
environments, supplied with governed inputs, tested through relevant
scenarios, executed under defined conditions, interpreted through bounded
outputs, correlated with reality, converted into legitimately bounded
evidence, and continuously reconsidered as reality and simulation conditions
change throughout the complete simulation lifecycle. Core Question

How can simulations remain methodologically governed from the identification
of what is being simulated through representation, assumptions, modeling,
execution, outputs, reality correlation, evidential reliance, and eventual
resimulation, replacement, or retirement? Governance Flow

Simulation Object → Simulation Purpose & Scope → 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 & Resimulation Core Parent Standards
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? 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? 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? 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? 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? 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? 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? 8. Simulation Execution Standard (SES)

Governed Space: Simulation Execution

Core Question: How must a simulation be instantiated, executed, observed,
repeated, controlled, and documented? 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? 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? 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? 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?
Compatible OOF® Architectures

SIMULOS® interoperates with multiple OOF® architectures governing
complementary governance spaces.

GOA™ — Governance Architecture provides the constitutional governance layer
defining how governance itself is structured, bounded, delegated, and
maintained.

ORA™ — Operational Reality Architecture governs what is actually occurring in
Operational Reality, while SIMULOS® governs how selected aspects of that
reality are represented and simulated.

VALIDOS® — Validation Governance Architecture governs whether the total
validation basis sufficiently supports a Validation Determination and intended
reliance, while SIMULOS® determines what simulation and simulation-derived
evidence can legitimately represent and support.

INTEGROS® — Integrity Governance Architecture governs integrity across
objects, states, evidence, and processes, while SIMULOS® governs
simulation-specific structure, methodology, execution, representation,
correlation, evidence boundaries, and lifecycle.

ADIT® governs evidential observation, preservation, verification, and
auditability of what occurred, while SIMULOS® governs simulation execution
itself and the simulation-specific meaning of its resulting outputs and
evidence.

AGA™ — Accountability Governance Architecture governs authority,
responsibility, accountability, attribution, and decision relationships
surrounding simulation activities and reliance.

AIG® — AI Governance Architecture governs the behavioral governance lifecycle
of actual AI systems that may be represented, tested, or evaluated through
simulation.

ASGA™ — Autonomous Systems Governance Architecture governs actual autonomous
systems, agents, authority, constraints, and autonomous operational behavior
that may be represented or evaluated through simulation.

SIMULOS® serves as the common simulation governance architecture supporting
the broader OOF® ecosystem by ensuring that simulations remain
methodologically connected to the objects, realities, assumptions, conditions,
executions, outputs, evidence, and lifecycle states they claim to represent.
Architectural Position

SIMULOS® governs the complete simulation governance lifecycle.

The architecture progresses through:

Simulation Object → Simulation Purpose & Scope → 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 & Resimulation

Together these standards govern how a simulation target is identified, why
simulation is undertaken, how reality is represented, which assumptions make
the simulation possible, how models and environments are constituted, which
inputs enter the simulation, which scenarios are tested, how execution occurs,
what outputs are produced, how those outputs correspond to reality, what
evidential meaning may legitimately be derived from them, and when changed
reality requires resimulation, replacement, transition, or retirement.

Unlike a linear simulation methodology, SIMULOS® becomes recursive after
operational use begins.

The extended lifecycle becomes:

Simulation → Operational Use → Reality Change → Simulation Drift →
Resimulation Trigger → Resimulation Scope → Lifecycle Re-Entry → Resimulation
/ Replacement / Retirement

This enables a governed simulation to remain connected not merely to the
reality represented when it was created, but also to the reality that
continues to change after simulation results begin influencing operational
decisions. Core Governed Simulation Spaces

SIMULOS® establishes twelve distinct governable simulation spaces:

Simulation Object → what exactly is being simulated.

Simulation Purpose & Scope → why the simulation exists and what it must and
must not cover.

Simulation Reality Representation → which parts of reality are represented,
simplified, abstracted, or excluded.

Simulation Assumptions → which assumptions enable the simulation and how their
consequences remain visible.

Simulation Model & Environment → how the model and simulation environment are
structured, configured, and controlled.

Simulation Inputs & Data → what enters the simulation and under which
representativeness, transformation, temporal, and uncertainty conditions.

Simulation Scenario Design → what conditions, variations, edge cases,
stresses, and alternatives are actually tested.

Simulation Execution → how simulations are instantiated, controlled, observed,
repeated, and documented.

Simulation Output Governance → what the simulation produced and what may
legitimately be inferred from those outputs.

Simulation-to-Reality Correlation → how simulated behavior, conditions, and
outputs correspond to observed or governed reality.

Simulation Evidence & Reliance Boundary → what simulation-derived evidence may
legitimately mean and where simulation-specific reliance must stop.

Simulation Lifecycle, Drift & Resimulation → how continuing relevance is
monitored and when change requires resimulation, replacement, transition, or
retirement.

Each space answers a different methodological question.

Each can fail independently.

Their separation is therefore foundational to the SIMULOS® architecture.
Cross-Architecture Boundary Lock

SIMULOS® is intentionally bounded to prevent duplication with adjacent OOF®
architectures. SIMULOS® ↔ ORA™

ORA™: What is actually happening in Operational Reality?

SIMULOS®: How is selected reality represented and simulated?

Operational Reality ≠ Simulated Representation of Reality. SIMULOS® ↔ VALIDOS®

SIMULOS®: What does the simulation and simulation-derived evidence
legitimately represent and support?

VALIDOS®: Does the total validation basis sufficiently support the Validation
Determination and intended reliance?

Simulation Evidence ≠ Validation Determination. SIMULOS® ↔ INTEGROS®

SIMULOS®: governs simulation-specific structure, representation, assumptions,
execution, outputs, correlation, evidence boundaries, and lifecycle.

INTEGROS®: governs integrity across objects, states, evidence, and processes.

Simulation Governance ≠ General Integrity Governance. SIMULOS® ↔ ADIT®

SIMULOS®: governs simulation execution and the simulation-specific structures
through which simulation activity occurs.

ADIT®: governs evidential observation, preservation, verification,
examination, and auditability of what occurred.

Simulation Execution ≠ Audit of Simulation Execution. SIMULOS® ↔ AGA™

SIMULOS®: governs simulation methodology and simulation-specific conditions.

AGA™: governs who holds authority, responsibility, accountability, and
attribution for simulation-related decisions and failures.

Simulation Methodology ≠ Accountability Allocation. SIMULOS® ↔ AIG® / ASGA™

SIMULOS® may simulate AI systems, autonomous agents, robots, and autonomous
operational environments.

SIMULOS®: governs their simulated representation and simulation lifecycle.

AIG® / ASGA™: govern the behavior, autonomy, authority, constraints, and
operation of the actual systems.

Simulating an autonomous system ≠ governing the autonomy of the actual system.
Architectural Origin

SIMULOS® evolved from the foundational:

SIMULOS® — Simulation Integrity Standard

which established the original methodological concern that simulation must
remain structurally connected to the reality, assumptions, conditions,
execution basis, and outputs it claims to represent.

As the governed space expanded, simulation integrity was identified as only
one part of a substantially larger governance problem.

The architecture therefore evolved from a single integrity-oriented standard
into a complete simulation governance architecture.

Its foundational principles are distributed primarily across:

Reality Representation → Simulation Assumptions → Simulation Model &
Environment → Simulation Execution → Simulation Output Governance →
Simulation-to-Reality Correlation → Simulation Evidence & Reliance Boundary

The original standard remains part of the architectural history of SIMULOS® as
its Foundational Reference Standard, while the twelve Parent Standards
constitute the current canonical architecture. Architectural Structure

  • Architecture: SIMULOS® — Simulation Governance Architecture
  • Category: Governance & Enforcement
  • Subcategory: Simulation Governance
  • Core Parent Standards: 12
  • Core Modules: 60
  • Architecture Model: Lifecycle-Based · Modular · Recursive
  • Primary Governance Domain: Simulation Governance


The architecture deliberately separates governable spaces that can
independently fail.

A simulation may:

- represent the wrong object; - pursue an unclear purpose; - represent reality
inadequately; - depend upon unsupported assumptions; - use an inappropriate
model; - consume unrepresentative inputs; - omit decisive scenarios; - execute
incorrectly; - produce misinterpreted outputs; - correlate poorly with
reality; - generate evidence used beyond its legitimate meaning; - remain
operational after reality has materially changed.

SIMULOS® treats these not as one generic simulation quality problem, but as
distinct governance spaces requiring distinct methodological controls.
Recursive Lifecycle Principle

SIMULOS® does not end when a simulation run ends.

A simulation may remain operationally influential for months, years, or
decades after execution.

During that period:

Reality may change. The Simulation Object may change. Assumptions may become
obsolete. Inputs may shift. New scenarios may emerge. Models may become
outdated. Correlation may deteriorate. Evidence may lose applicability.

SIMULOS® therefore closes recursively through SLRS:

Operational Use → Reality Change → Simulation Drift → Resimulation Trigger →
Resimulation Scope → Lifecycle Re-Entry → Resimulation / Replacement /
Retirement

This makes lifecycle change part of simulation governance rather than an
external maintenance concern. Foundational Architecture Principles

A simulation cannot legitimately represent an object more precisely than that
object has been identified and bounded.

Simulation purpose determines what the simulation must represent, not what the
technology happens to be capable of simulating.

Represented reality is not Operational Reality.

Simulation similarity is not reality equivalence.

An assumption that remains hidden cannot remain governed.

Model sophistication does not compensate for an incorrectly represented
problem.

More data does not correct an incorrectly bounded simulation.

Scenario volume is not scenario coverage.

Execution precision does not guarantee interpretive legitimacy.

Simulation output is not automatically simulation evidence.

Evidence volume is not evidential strength.

Simulation-derived evidence must not travel farther than its governed meaning
can legitimately support.

A simulation that was once relevant does not remain relevant merely because it
continues to execute.

Changed reality may require changed simulation.

Resimulation is part of simulation governance, not an admission that the
original simulation failed. Canonical Principle

SIMULOS® does not govern Operational Reality itself, general validation
sufficiency, general integrity, auditability, accountability, or the behavior
and authority of actual AI and autonomous systems. SIMULOS® governs the
methodological conditions under which reality is represented through
simulation, simulation activity is structured and executed, simulation outputs
acquire bounded meaning, simulation-derived evidence acquires legitimate
reliance boundaries, and continuing changes in reality trigger governed
resimulation, replacement, transition, or retirement throughout the complete
simulation lifecycle.