VSIM — Value Source Identification Module
Architecture:
VEGA™ — Value Execution Governance Architecture
Parent Standard:
Value Attribution Standard (VAS)
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Core Module
Governed Space:
Value Source Identification Governance
Module ID: VEGA-VAS-VSIM-001
Origin ID: OOF.CM.VEGA.09.01.01
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 1 October 2026
Compatibility: OOF® Methodology OS™ · Modular OOF® Architecture Ecosystem
AI-Readable: Yes
Authority: OOF® — OriginOpen® Foundation
Protection:
MIP® — Methodological Intellectual Property
Canonical Language:
English (UCL™)
1. Canonical Definition
The Value Source Identification Module (VSIM) governs how the materially relevant sources from which validated Value emerged are identified, bounded, classified, evidenced, and represented before contribution attribution occurs.
VSIM establishes the traceable connection between a governed Value object and the operational sources capable of materially participating in its formation, preservation, realization, or validation.
It distinguishes the source of Value from the final output, visible participant, system owner, execution mechanism, resource provider, or entity receiving the resulting benefit.
VSIM does not determine how much Value should be attributed to a source.
It determines which sources legitimately enter the subsequent attribution process and why.
Its principal output is:
Governed Value Source State
Its foundational distinction is:
Visible Output Source ≠ Value Source.
2. Operational Role
The operational role of VSIM is:
to establish the evidence-grounded set of materially relevant Value sources that must be considered before contribution attribution can begin.
VAS asks:
“Who or what materially contributed to the validated Value, and how should that contribution be evidenced and attributed?”
VSIM asks:
“From which operational sources did the governed Value actually emerge?”
3. Position in the VAS Internal Flow
The VAS internal governance flow is:
Value Source Identification → Contribution Attribution → Human–AI Attribution → Multi-Agent Attribution → Attribution Evidence
VSIM governs:
Value Source Identification
Its principal upstream inputs include:
Validated Value
and relevant governed evidence from preceding VEGA™ Parent Standards.
Its principal output is:
Governed Value Source State
That state proceeds to:
Contribution Attribution Governance
4. Central Operational Question
Which human, artificial, technical, informational, infrastructural, methodological, and operational sources materially participated in forming, preserving, realizing, or validating the governed Value?
5. Value Source Object
Every VSIM assessment must begin with a sufficiently defined Value object.
The Value Source Object should identify:
the Value being examined,
the validated outcome associated with it,
the relevant operational process,
the applicable time period,
and the relevant execution context.
Without a defined Value object, source identification can expand indefinitely.
6. Validated Value Requirement
VSIM should operate against Value that has been sufficiently established through the applicable governance process.
Where the Value remains provisional, disputed, conditional, or evidence-limited, the source state should preserve that condition.
Unvalidated Output ≠ Validated Value Source Basis.
7. Value Source Boundary
The Value Source Boundary defines which operational space is examined for potential Value sources.
The boundary should be sufficiently broad to capture material sources but sufficiently constrained to prevent every remote background condition from becoming an attribution candidate.
The boundary may include:
humans,
AI systems,
agents,
models,
algorithms,
data,
tools,
software,
methodologies,
infrastructure,
operational processes,
and validation mechanisms.
8. Source Identification Horizon
Value may depend on sources acting at different times.
VSIM should therefore define a relevant Source Identification Horizon.
The horizon may include:
pre-execution contributions,
execution-time sources,
post-execution validation,
maintenance,
correction,
or preservation mechanisms.
Temporal Distance ≠ Source Irrelevance.
9. Source Candidate Identification
A Source Candidate is any identifiable source with a plausible material connection to the governed Value.
Candidate identification should initially favor sufficient coverage.
Candidate status does not establish material contribution.
It establishes eligibility for further source analysis.
10. Source Qualification
A Source Candidate should remain within the governed source set only where there is a defensible connection between that source and the Value formation process.
Qualification may consider:
functional relevance,
operational connection,
temporal relevance,
evidence availability,
and plausible material effect.
11. Material Value Source
A Material Value Source is a source whose capability, information, action, resource, structure, or intervention materially participates in producing, preserving, realizing, or validating the governed Value.
Materiality should be assessed relative to the specific Value object.
12. Incidental Source
An Incidental Source may appear in the operational environment without materially affecting the governed Value.
Examples may include:
unused tools,
irrelevant data,
nominal participants,
inactive services,
or infrastructure unrelated to the relevant execution.
Presence in the Environment ≠ Value Source.
13. Direct Value Source
A Direct Value Source participates directly in forming the content, decision, transformation, result, or other outcome from which Value emerges.
Directness should describe the source relationship.
It should not automatically determine attribution weight.
14. Indirect Value Source
An Indirect Value Source materially supports another source or mechanism through which Value is produced.
Indirect sources may include:
supporting data,
infrastructure,
methodology,
tooling,
or prior operational work.
Indirect Source ≠ Immaterial Source.
15. Enabling Value Source
An Enabling Value Source provides a capability or condition necessary for Value formation without necessarily determining the specific content of the final outcome.
Examples may include:
compute infrastructure,
data access,
software platforms,
tools,
or operational support.
16. Generative Value Source
A Generative Value Source materially produces new content, analysis, reasoning, decisions, transformations, or other operational outputs contributing to Value formation.
Human and artificial contributors may both function as generative sources.
17. Transformative Value Source
A Transformative Value Source materially changes an existing input into a form that contributes to the resulting Value.
Transformation may involve:
analysis,
compression,
translation,
reasoning,
classification,
optimization,
restructuring,
or synthesis.
18. Informational Value Source
An Informational Value Source provides information materially necessary for the governed Value.
Examples may include:
datasets,
retrieved documents,
sensor data,
market data,
domain knowledge,
or operational records.
Information availability alone does not establish material source status.
19. Decision Value Source
A Decision Value Source materially affects which action, option, configuration, or conclusion is selected.
This may include:
human judgment,
AI recommendation,
optimization logic,
policy logic,
or another decision mechanism.
20. Validation Value Source
A Validation Value Source materially establishes whether an outcome satisfies the conditions required for Value recognition.
It should remain distinguishable from the source that produced the underlying outcome.
Value Production Source ≠ Value Validation Source.
21. Corrective Value Source
A Corrective Value Source materially changes an otherwise deficient output or process so that Value can be produced, preserved, or validated.
Correction may therefore become a significant Value source even where its visible activity is small.
22. Preservation Value Source
A Preservation Value Source materially maintains existing Value against deterioration, failure, loss, or invalidation.
Preservation may involve:
maintenance,
monitoring,
continuity,
updating,
correction,
or operational support.
Value Preservation ≠ New Value Creation.
Both may nevertheless be legitimate Value sources.
23. Human Value Source
Human activity may become a Value source through:
problem definition,
judgment,
expertise,
instruction,
selection,
decision-making,
creative contribution,
review,
correction,
validation,
or intervention.
Human involvement alone does not establish source materiality.
24. AI Value Source
An AI system may become a Value source through:
generation,
reasoning,
analysis,
prediction,
classification,
planning,
optimization,
transformation,
or another material intelligence function.
AI Presence ≠ AI Value Source.
25. Agent Value Source
An autonomous or semi-autonomous agent may become a Value source where its actions materially affect the governed Value.
Agent source identification should reflect the actual function performed rather than the label “agent.”
26. Model Value Source
A model may constitute a distinct Value source where its capability materially affects the Value formation process.
Model invocation alone is insufficient.
Model Use ≠ Model Value Source.
27. Algorithmic Value Source
A deterministic or specialized algorithm may materially contribute to Value independently of AI model activity.
VSIM should remain mechanism-neutral.
A Value source need not be based on machine learning.
28. Tool Value Source
Tools may become Value sources through:
retrieval,
calculation,
search,
simulation,
execution,
external action,
or other materially relevant capabilities.
Tool contribution should remain distinguishable from the intelligence directing the tool.
29. Data Value Source
Data may become a material Value source where the resulting Value depends materially on its:
content,
availability,
freshness,
coverage,
accuracy,
or contextual relevance.
VSIM identifies the methodological source relationship.
It does not determine data ownership or licensing rights.
30. Infrastructure Value Source
Infrastructure may become an enabling Value source where Value production materially depends on its:
compute,
storage,
networking,
hardware,
availability,
or execution capability.
Infrastructure should not automatically be treated as the source of the intelligence operating through it.
31. Methodology Value Source
A methodology may become a Value source where it materially determines:
process,
analysis,
measurement,
validation,
decision logic,
governance logic,
or execution structure.
Methodology source identification does not determine intellectual-property ownership.
32. Software Value Source
Software may become a Value source where its logic or capability materially affects the governed outcome.
This may include:
workflow software,
orchestration,
specialized applications,
automation,
or execution logic.
Software merely hosting an activity is not automatically a material Value source.
33. Operational Process Value Source
The structure of an operational process may itself materially contribute to Value.
Examples may include:
sequencing,
review stages,
escalation,
quality control,
routing,
or coordination.
VSIM should therefore not reduce all Value to individual human or technological contributors.
34. Composite Value Source
Some Value emerges from a configuration whose components cannot meaningfully be treated as independent sources at the chosen level of analysis.
VSIM may represent a:
Composite Value Source
where this better reflects operational reality.
Composite representation should not be used merely to avoid available attribution evidence.
35. Hybrid Human–AI Value Source
Where human and AI activity interact so tightly that the relevant Value source is the combined process, VSIM may identify a:
Hybrid Human–AI Value Source
This does not predetermine how subsequent attribution should be distributed.
It preserves the operational source structure for later analysis.
36. Source Chain
A Value source may depend on another source.
VSIM may represent:
Underlying Source → Intermediate Source → Operational Source → Value
For example:
Data → Retrieval Tool → AI Analysis → Human Decision → Validated Value
The Source Chain helps prevent the visible final actor from absorbing the entire source structure.
37. Source Network
Complex Value formation may involve multiple interacting sources.
VSIM may represent a:
Value Source Network
where sources interact non-linearly.
A network may include humans, models, agents, data, tools, infrastructure, and validation mechanisms.
38. Primary Value Source
Where evidence supports meaningful differentiation, one or more sources may be identified as Primary Value Sources.
A Primary Value Source has a particularly direct or material relationship to the Value object.
Primary status should not imply sole contribution.
39. Supporting Value Source
A Supporting Value Source materially assists Value formation without functioning as the principal operational source.
Supporting status should remain evidence-based.
40. Source Necessity
VSIM may assess whether a source was necessary for the Value produced under the observed operating configuration.
Possible states may include:
Necessary
Material but Non-Necessary
Supporting
Substitutable
Incidental
Unresolved
Necessity is a source property within the observed Value formation process.
It is not attribution weight.
41. Source Substitutability
A Value source may be material while being replaceable by another source.
VSIM may identify whether another:
human,
model,
agent,
algorithm,
tool,
dataset,
methodology,
or infrastructure mechanism
could plausibly perform the required source function.
Substitutable Source ≠ Immaterial Source.
42. Source Uniqueness
A source may provide a capability not credibly available elsewhere within the governed context.
Source Uniqueness may strengthen the relevance of the source but should not automatically determine attribution magnitude.
43. Source Dependency
Some Value sources cannot function independently.
A model may depend on data.
An agent may depend on a model and tools.
A human decision may depend on AI analysis.
VSIM should preserve material source dependencies where they affect subsequent attribution.
44. Source Overlap
Two sources may provide substantially overlapping functions.
VSIM should identify overlap where failing to do so could later create double attribution.
Multiple Sources ≠ Multiple Independent Contributions.
45. Source Interaction
Value may arise because two or more sources interact.
The combined effect may exceed or differ from what either source would produce independently.
VSIM should preserve material interaction without prematurely assigning attribution weight.
46. Source Sequence
Where the sequence of sources materially affects Value formation, VSIM should preserve that sequence.
For example:
Human Intent → AI Analysis → Tool Execution → Human Correction → Validation
Changing the order may change the resulting Value.
47. Source Activation
A potential source may exist but never become operationally relevant to the Value object.
VSIM should distinguish:
Available Source
from
Activated Source
and
Material Value Source.
48. Source Contribution Path
For every material source where practicable, VSIM should identify a traceable path:
Source → Operational Effect → Outcome Effect → Value Relationship
This creates the basis for subsequent Contribution Attribution Governance.
49. Source Evidence
Source identification should be supported by evidence proportionate to the materiality of the Value object.
Evidence may include:
execution records,
workflow traces,
human actions,
agent traces,
model records,
tool calls,
data provenance,
software logs,
infrastructure records,
validation records,
methodology records,
or other relevant operational evidence.
50. Source Evidence Quality
Source evidence should be evaluated for relevant qualities such as:
identity,
integrity,
completeness,
specificity,
temporal relevance,
provenance,
and reproducibility.
The existence of evidence does not guarantee that it establishes the claimed source relationship.
51. Source Provenance
VSIM should preserve sufficient provenance to determine:
where source evidence originated,
which source version applied,
when the source was active,
what operational process connected it to Value,
and how the source conclusion was formed.
52. Source Confidence
The confidence associated with Value Source Identification should reflect available evidence.
Possible states may include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Unresolved
Source confidence should remain separate from subsequent attribution weight.
53. Source Uncertainty
Uncertainty may arise because:
execution is partially observable,
multiple sources overlap,
historical evidence is incomplete,
source interactions are complex,
or causal relationships cannot be fully reconstructed.
VSIM should preserve material uncertainty.
Uncertain Source ≠ No Source.
54. Source Conflict
Different evidence may support conflicting interpretations of the Value source structure.
Possible states may include:
Consistent
Partially Conflicting
Materially Conflicting
Evidence-Incomplete
Unresolved
Conflict should remain visible until sufficiently resolved.
55. Source Change
The source structure of Value may change over time.
Changes may arise from:
model replacement,
automation,
new agents,
human-role changes,
data changes,
tool changes,
infrastructure changes,
or workflow redesign.
Source identification should therefore be version-sensitive.
56. Source Drift
Value Source Drift occurs where the mechanisms producing or preserving Value gradually change without an explicit redesign.
Examples include:
greater AI involvement,
reduced human correction,
increased reliance on one dataset,
new automated routing,
or changing infrastructure.
Drift may require source reassessment.
57. Source Revision
A Governed Value Source State may be revised when:
new evidence emerges,
the Value object changes,
source relationships change,
an error is discovered,
or the methodology is legitimately updated.
Revision should preserve the previous state and reason for change.
58. Source Integrity
Source identification must not be manipulated to support a preferred attribution outcome.
Integrity failures may include:
excluding inconvenient sources,
including nominal participants as material sources,
hiding automated contribution,
hiding human correction,
collapsing source chains,
ignoring shared sources,
or selecting boundaries that predetermine attribution.
Source Identification must precede Attribution Preference.
59. Governed Value Source State
The principal output of VSIM is:
Governed Value Source State
Possible source-level states may include:
Material Source
Primary Source
Supporting Source
Direct Source
Indirect Source
Enabling Source
Generative Source
Transformative Source
Informational Source
Decision Source
Validation Source
Corrective Source
Preservation Source
Composite Source
Hybrid Human–AI Source
Incidental Source
Evidence-Limited Source
Conflicted Source
Unresolved Source
The state should preserve the evidence and source relationship rather than rely on labels alone.
60. Minimum Implementation Framework
A conforming implementation of VSIM must provide exactly the following six capabilities:
1. Value Object & Source Boundary Governance
Capability to identify the governed Value object, relevant Value state, source boundary, source horizon, and candidate source universe.
2. Source Identification & Qualification Governance
Capability to identify Source Candidates and determine which humans, AI systems, agents, models, algorithms, tools, data, infrastructure, methodologies, software, and operational processes qualify as material Value sources.
3. Source Structure & Relationship Governance
Capability to represent direct, indirect, enabling, generative, transformative, informational, decision, validation, corrective, preservation, composite, and hybrid sources together with Source Chains, Networks, dependencies, overlap, interaction, and sequence.
4. Source Materiality, Necessity & Substitutability Governance
Capability to assess source materiality, primary/supporting status, necessity, substitutability, uniqueness, activation, and contribution path without prematurely assigning attribution weight.
5. Source Evidence, Change & Integrity Governance
Capability to preserve source evidence, quality, provenance, confidence, uncertainty, conflict, change, drift, revision, versioning, and source-identification integrity.
6. VAS Contribution Attribution Handoff Governance
Capability to produce a Governed Value Source State sufficiently explicit for subsequent Contribution Attribution Governance.
61. Governance Logic
The canonical VSIM governance logic is:
Validated Value
↓
Identify Value Source Object
↓
Confirm Applicable Value State
↓
Establish Source Boundary / Horizon
↓
Identify Source Candidates
↓
Qualify Material Sources
↓
Classify Source Functions
↓
Identify Human / AI / Agent / Model Sources
↓
Identify Algorithm / Tool / Data Sources
↓
Identify Infrastructure / Methodology / Software / Process Sources
↓
Identify Direct / Indirect / Enabling Sources
↓
Identify Generative / Transformative / Informational Sources
↓
Identify Decision / Validation / Corrective / Preservation Sources
↓
Map Source Chains / Networks
↓
Assess Source Dependencies / Overlap / Interaction / Sequence
↓
Assess Necessity / Substitutability / Uniqueness
↓
Establish Source Contribution Paths
↓
Assess Evidence / Provenance
↓
Assess Confidence / Uncertainty / Conflict
↓
Test Source Integrity
↓
Establish Governed Value Source State
↓
Handoff to Contribution Attribution Governance
62. Governance Outputs
VSIM may produce:
Value Source Object
Value Source Boundary
Source Identification Horizon
Source Candidate Record
Source Qualification State
Material Value Source Record
Direct Value Source State
Indirect Value Source State
Enabling Value Source State
Generative Value Source State
Transformative Value Source State
Informational Value Source State
Decision Value Source State
Validation Value Source State
Corrective Value Source State
Preservation Value Source State
Human Value Source State
AI Value Source State
Agent Value Source State
Model Value Source State
Algorithmic Value Source State
Tool Value Source State
Data Value Source State
Infrastructure Value Source State
Methodology Value Source State
Software Value Source State
Operational Process Value Source State
Composite Value Source State
Hybrid Human–AI Value Source State
Source Chain
Source Network
Primary Value Source State
Supporting Value Source State
Source Necessity State
Source Substitutability State
Source Uniqueness State
Source Dependency Record
Source Overlap State
Source Interaction Record
Source Sequence Record
Source Activation State
Source Contribution Path
Source Evidence Record
Source Evidence Quality State
Source Provenance Record
Source Confidence State
Source Uncertainty State
Source Conflict State
Source Drift State
Source Version
The principal output is:
Governed Value Source State
63. Critical Distinctions
VSIM preserves the following distinctions:
Visible Output Source ≠ Value Source
Presence in the Environment ≠ Value Source
Contributor Presence ≠ Material Value Source
Human Involvement ≠ Human Value Source
AI Presence ≠ AI Value Source
Model Use ≠ Model Value Source
Tool Use ≠ Tool Value Source
Data Availability ≠ Data Value Source
Infrastructure Availability ≠ Infrastructure Value Creation
Direct Source ≠ Sole Source
Indirect Source ≠ Immaterial Source
Value Production Source ≠ Value Validation Source
Value Preservation ≠ New Value Creation
Available Source ≠ Activated Source
Activated Source ≠ Material Value Source
Substitutable Source ≠ Immaterial Source
Multiple Sources ≠ Multiple Independent Contributions
Source Necessity ≠ Attribution Weight
Source Confidence ≠ Attribution Weight
Uncertain Source ≠ No Source
Source Identification ≠ Contribution Attribution
Source Identification ≠ Legal Ownership
Source Identification ≠ Compensation Entitlement
Source Identification ≠ Liability Attribution
64. Cross-Architecture Boundary
VSIM governs identification of materially relevant sources of governed Value.
VGS determines the applicable Value state.
XGS may provide execution evidence.
IPS may provide productivity evidence.
RGS may provide resource evidence.
COS may provide computational configuration evidence.
IES may provide economic evidence.
SVGS may provide Strategic Value evidence.
ISS may provide sustainability evidence.
VSIM uses these governed outputs to identify relevant Value sources.
It does not reproduce their governance functions.
Within VAS, VSIM identifies the Value source structure.
Subsequent VAS mechanisms govern:
Contribution Attribution,
Human–AI Attribution,
Multi-Agent Attribution,
and Attribution Evidence.
HAGA™ governs Harm.
LIGA™ governs Liability.
Identity architectures may establish source identity and provenance.
VALIDOS™ may govern validation methodology.
ADIT® may govern evidence and auditability.
65. Canonical Boundary
VSIM begins when a sufficiently defined Value object exists for Value Source Identification.
VSIM governs:
Value Source Objects,
source boundaries,
source horizons,
Source Candidates,
source qualification,
material Value sources,
incidental sources,
direct sources,
indirect sources,
enabling sources,
generative sources,
transformative sources,
informational sources,
decision sources,
validation sources,
corrective sources,
preservation sources,
human sources,
AI sources,
agent sources,
model sources,
algorithmic sources,
tool sources,
data sources,
infrastructure sources,
methodology sources,
software sources,
operational process sources,
Composite Value Sources,
Hybrid Human–AI Value Sources,
Source Chains,
Source Networks,
primary sources,
supporting sources,
source necessity,
substitutability,
uniqueness,
dependency,
overlap,
interaction,
sequence,
activation,
contribution paths,
evidence,
evidence quality,
provenance,
confidence,
uncertainty,
conflict,
change,
drift,
revision,
versioning,
and integrity.
VSIM does not determine:
final contribution attribution,
attribution percentages,
legal ownership,
intellectual-property rights,
compensation,
tax allocation,
Accountability,
Responsibility,
Harm,
Liability,
or Decision Authority.
VSIM ends when the materially relevant sources associated with the governed Value have been represented as a:
Governed Value Source State
for subsequent Contribution Attribution Governance.
66. Use Case 1 — The Final AI Model Appears to Have Created the Value
An organization uses an AI model to produce a high-value operational recommendation.
The final recommendation is visibly generated by the model.
Source analysis shows, however, that the result depends materially on:
a human-defined problem,
a proprietary dataset,
a retrieval system,
a specialized analytical methodology,
the AI model,
and a human validation process.
Treating the visible model as the sole Value source would collapse the actual Value formation structure.
VSIM identifies the materially relevant sources and maps their relationship before attribution begins.
Visible Output Source ≠ Value Source.
67. Use Case 2 — Small Human Correction Changes the Entire Value Outcome
An AI system produces a detailed technical analysis.
Most of the final document originates from AI execution.
A specialist reviews the result and changes one assumption.
That small correction changes the recommendation and prevents an operationally invalid decision.
The specialist produced very little of the visible output.
Yet the correction materially changed the Value outcome.
VSIM identifies the human correction as a material Corrective Value Source rather than measuring source importance by output volume.
The resulting Governed Value Source State can then proceed to Contribution Attribution Governance.
Output Volume ≠ Source Materiality.
68. Canonical Closing Statement
Value is often easiest to see at the end of a process.
Its sources are not.
The final model may be visible while the decisive data remains hidden.
The final human may approve the outcome while earlier machine reasoning produced most of its content.
Infrastructure may enable the entire process without forming the decision.
And a correction occupying one line may matter more to the Value than thousands of lines produced before it.
Source identification must therefore precede attribution.
Visible Output Source ≠ Value Source.
Presence in the Environment ≠ Value Source.
AI Presence ≠ AI Value Source.
Direct Source ≠ Sole Source.
Output Volume ≠ Source Materiality.
Source Identification ≠ Contribution Attribution.
The purpose of VSIM is not to decide who deserves the Value.
It is to establish, before attribution begins, where that Value materially came from.
VSIM makes the sources behind Value visible before VAS determines how their contributions should be attributed.