Value Attribution Standard (VAS)
Architecture:
VEGA™ — Value Execution Governance Architecture
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Parent Standard
Governed Space:
Value Attribution
Standard ID: VEGA-VAS-009
Origin ID: OOF.PS.VEGA.09.00.01
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 Attribution Standard (VAS) governs how materially relevant contributions to validated Value produced through intelligence-enabled activity are identified, evidenced, differentiated, connected to resulting Value, and represented as a governed attribution state.
VAS establishes a methodological basis for determining:
what materially contributed,
where the contribution occurred,
how it affected Value formation,
which human, artificial, technical, operational, or combined contributors were involved,
how multiple contributions interacted,
and what evidence supports the resulting attribution.
VAS distinguishes participation from contribution, contribution from causation, causation from Value creation, and Value Attribution from ownership, compensation, Accountability, responsibility, and Liability.
Its principal output is:
Governed Value Attribution State
Its foundational distinction is:
Participation ≠ Value Contribution.
2. Operational Role
The operational role of VAS is:
to establish an evidence-grounded representation of who or what materially contributed to validated Value and how those contributions relate to the Value produced.
VEGA™ governs the complete relationship between intelligence consumed and validated Value produced.
VAS makes the contribution structure within that relationship visible.
3. Core Question
Who or what materially contributed to the validated Value, and how should that contribution be evidenced and attributed?
4. Position in the VEGA™ Governance Lifecycle
The VEGA™ governance lifecycle is:
Intent → Investment → Configuration → Resource Allocation → Execution → Validation → Productivity → Value → Attribution → Optimization → Continuation Decision
VAS governs:
Attribution
Its principal upstream input is:
Governed Intelligence Sustainability State
together with relevant validated Value, execution, productivity, resource, economic, and Strategic Value evidence.
Its principal output is:
Governed Value Attribution State
That state proceeds to:
Decision Governance
5. Position in the VEGA™ Parent Flow
The VEGA™ Parent Standard flow is:
Value Governance → Execution Governance → Intelligence Productivity → Resource Governance → Computational Optimization → Intelligence Economy → Strategic Value Governance → Intelligence Sustainability → Value Attribution → Decision Governance
VAS is the ninth Parent Standard.
It converts validated Value and its supporting execution history into a governed representation of material contribution.
6. Governed Space
VAS governs:
Value Attribution
This includes methodological attribution of material contribution to validated Value across intelligence-enabled operational systems.
VAS does not govern legal ownership of Value.
It does not determine entitlement to revenue.
It does not determine wages.
It does not determine tax allocation.
It does not determine Liability.
It does not determine general organizational Accountability.
7. Attribution Object
Every attribution must identify the Value object being attributed.
The Attribution Object should be sufficiently specific to establish:
what Value was produced,
how it was validated,
the relevant operational boundary,
the relevant time period,
and the contributors potentially connected to it.
Value Attribution without a defined Value object is methodologically incomplete.
8. Validated Value Requirement
VAS attributes contribution to Value that has been sufficiently established through the applicable governance process.
Activity alone is not a sufficient attribution object.
Output alone is not necessarily Value.
Therefore:
No validated Value → No validated Value Attribution.
Where Value remains provisional, attribution should preserve that provisional status.
9. Attribution Boundary
Every VAS assessment should establish the boundary within which contribution is evaluated.
The boundary may include:
human activity,
AI execution,
agents,
models,
algorithms,
software,
data,
tools,
infrastructure,
operational processes,
and other materially relevant mechanisms.
The boundary should be broad enough to capture material contribution without expanding into every background condition of the organization.
10. Attribution Horizon
Contribution may occur before, during, or after the immediate execution producing Value.
The attribution horizon should therefore reflect the relevant Value formation period.
A contributor should not be excluded merely because its contribution occurred earlier than the final execution event.
Likewise, historical involvement does not automatically establish contribution to later Value.
11. Value Source Identification
VAS should identify the operational sources from which the validated Value emerged.
Potential sources may include:
human judgment,
AI-generated output,
automation,
data transformation,
model inference,
reasoning,
agent coordination,
software execution,
operational process,
infrastructure capability,
or combined activity.
Value Source Identification establishes where attribution analysis begins.
12. Contributor Identification
A Contributor is an identifiable human, artificial, technical, operational, or organizationally relevant source whose action, capability, resource, information, or intervention may have materially influenced the Value outcome.
Identification alone does not establish attribution.
Contributor Presence ≠ Value Contribution.
13. Contributor Classes
Where useful, contributors may be classified as:
Human Contributor
AI Contributor
Agent Contributor
Model Contributor
Algorithmic Contributor
Tool Contributor
Data Contributor
Infrastructure Contributor
Operational Process Contributor
Hybrid Contributor
Classification exists to improve attribution clarity.
It should not predetermine contribution weight.
14. Participation
Participation means involvement in the relevant operational process.
A participant may:
observe,
execute,
review,
approve,
provide data,
provide infrastructure,
coordinate,
validate,
or otherwise interact with the process.
Participation alone does not prove material contribution to Value.
15. Contribution
A Contribution is an identifiable input, action, capability, decision, transformation, resource, or intervention that materially affects the formation, quality, preservation, realization, or validation of the governed Value.
Contribution must be connected to the Value outcome through evidence.
16. Material Contribution
A Material Contribution is sufficiently significant that removing, degrading, or materially changing it could reasonably alter the Value outcome, Value quality, Value realization, or the ability to validate that Value.
Materiality should be assessed relative to the governed Value object.
17. Incidental Contribution
Some inputs may influence an execution without materially affecting Value.
VAS should distinguish incidental contribution from material contribution.
This prevents attribution from expanding indiscriminately to every system component or participant.
18. Enabling Contribution
An Enabling Contribution makes Value production possible without necessarily determining the specific content of the resulting Value.
Examples may include:
infrastructure availability,
tool access,
data availability,
or operational support.
Enabling contribution may be material while remaining distinct from direct Value formation.
19. Direct Contribution
A Direct Contribution materially affects the content, quality, decision, transformation, or outcome from which Value is produced.
Direct contribution should be evidenced through its relationship to the Value formation process.
20. Indirect Contribution
An Indirect Contribution materially supports another contributor or mechanism that produces Value.
Indirect contribution may be legitimate attribution where the relationship remains sufficiently traceable.
Indirect ≠ Immaterial.
21. Foundational Contribution
Some contributions establish capabilities or assets used repeatedly across later Value creation.
These may include:
model development,
methodology,
system architecture,
workflow design,
or reusable operational capability.
VAS may recognize Foundational Contribution where its connection to the governed Value remains methodologically defensible.
Foundational contribution should not automatically absorb attribution for every future use.
22. Operational Contribution
Operational Contribution occurs within the execution or operating process producing the governed Value.
It may include:
execution,
routing,
reasoning,
coordination,
decision support,
review,
correction,
or another material operational action.
23. Validation Contribution
Validation itself may materially contribute to Value where the Value depends on demonstrating that an outcome satisfies required conditions.
VAS should distinguish:
contribution to producing an outcome
from
contribution to validating that outcome.
Outcome Production ≠ Outcome Validation.
24. Preservation Contribution
A contribution may preserve Value that would otherwise deteriorate, disappear, or become unusable.
Examples may include:
maintenance,
monitoring,
correction,
continuity mechanisms,
or infrastructure support.
Value preservation may therefore constitute a legitimate contribution even where no new Value object is created.
25. Contribution Event
A Contribution Event is an identifiable occurrence through which a contributor materially affects the Value formation process.
The event may record:
contributor,
action,
input,
time,
context,
affected process,
result,
and supporting evidence.
Contribution Events enable attribution traceability.
26. Contribution Chain
Value may emerge through a sequence of contributions.
VAS may represent:
Contributor → Contribution Event → Intermediate Effect → Subsequent Contribution → Validated Outcome → Value
This forms a:
Contribution Chain
A Contribution Chain should preserve meaningful relationships without requiring exhaustive reconstruction of every insignificant event.
27. Contribution Network
Complex intelligence systems may produce Value through interacting contributors rather than a linear chain.
VAS may therefore represent a:
Contribution Network
where multiple humans, agents, models, tools, data sources, and operational processes contribute to the same Value object.
28. Contribution Dependency
One contribution may depend on another.
A model output may depend on data.
An agent decision may depend on a model.
A human decision may depend on an agent recommendation.
A validated outcome may depend on human correction.
VAS should preserve material contribution dependencies where they change attribution interpretation.
29. Contribution Sequence
The order of contributions may materially affect Value.
VAS may therefore preserve temporal sequence where:
earlier contributions constrain later actions,
later validation changes the status of earlier output,
or intervention timing materially affects the result.
30. Contribution Necessity
A contribution may be assessed for whether the governed Value could reasonably have been produced without it.
Possible states may include:
Necessary
Material but Non-Necessary
Supporting
Substitutable
Incidental
Unresolved
Necessity should not be inferred merely from presence in the workflow.
31. Contribution Sufficiency
A contribution may be necessary without being sufficient to produce Value.
Likewise, a contribution may materially increase Value without independently creating it.
VAS therefore preserves:
Necessary Contribution ≠ Sufficient Contribution.
32. Contribution Substitutability
A contribution may be replaceable by another mechanism while remaining material to the actual Value produced.
Substitutability does not erase historical contribution.
It indicates whether another contributor could plausibly have fulfilled the same function.
33. Contribution Increment
Where evidence permits, VAS may evaluate the incremental effect associated with a contribution.
The relevant question is:
What changed in the Value outcome because this contribution occurred?
Incremental contribution may be represented quantitatively, categorically, comparatively, or through another defensible method.
No universal formula is required.
34. Counterfactual Contribution Analysis
Where methodologically appropriate, attribution may examine:
What would likely have occurred if the contribution had been absent or materially different?
Counterfactual analysis can strengthen attribution.
It must not be presented as observed fact.
Counterfactual Estimate ≠ Observed Outcome.
35. Contribution Confidence
Attribution should preserve the confidence supported by available evidence.
Possible states may include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Unresolved
Confidence should reflect evidence quality rather than institutional preference.
36. Human Contribution Attribution
VAS governs methodological attribution of human contribution where human activity materially affects validated Value.
Human contributions may include:
problem definition,
judgment,
decision-making,
instruction,
selection,
review,
correction,
creative input,
domain expertise,
validation,
or operational intervention.
Human presence alone does not establish Value contribution.
37. AI Contribution Attribution
AI contribution may include:
generation,
reasoning,
classification,
prediction,
optimization,
transformation,
analysis,
planning,
or other material intelligence activity.
VAS should attribute the contribution actually demonstrated.
It should not attribute Value merely because an AI system participated.
AI Use ≠ AI Value Contribution.
38. Human–AI Contribution
Many Value outcomes emerge through combined human and artificial intelligence.
VAS should preserve the interaction rather than forcing the outcome into an exclusively human or exclusively AI attribution where the evidence does not support that simplification.
Possible patterns may include:
Human-Led / AI-Supported
AI-Led / Human-Validated
Joint Human–AI Formation
Human Correction of AI Output
AI Transformation of Human Input
Iterative Human–AI Formation
These are descriptive attribution structures, not rankings.
39. Human Oversight Attribution
Human oversight should be attributed only where it materially contributes to the governed Value or its validation.
Nominal approval without substantive intervention should not automatically receive the same attribution as material review, correction, or judgment.
Oversight Presence ≠ Material Oversight Contribution.
40. Human Correction Attribution
Where human intervention corrects an AI output and that correction materially affects Value, the correction should remain visible in the attribution structure.
This enables distinction between:
AI-generated Value,
AI-assisted Value,
and Value preserved or created through human correction.
41. Multi-Agent Attribution
Where multiple agents contribute to the same Value object, VAS should identify material agent contributions without assuming equal contribution.
Relevant roles may include:
planning,
research,
execution,
validation,
coordination,
critique,
routing,
or specialized transformation.
42. Agent Coordination Contribution
Coordination itself may create Value where it materially improves the combination, sequencing, routing, or integration of agent activity.
VAS should distinguish:
agent task contribution
from
coordination contribution.
43. Model-Level Attribution
Where multiple models participate in an execution configuration, VAS may attribute contribution at model level where evidence supports meaningful differentiation.
Model invocation alone does not establish material Value contribution.
44. Tool Contribution
Tools may materially contribute to Value through:
retrieval,
calculation,
execution,
search,
data access,
simulation,
validation,
or external action.
VAS should distinguish the intelligence directing the tool from the capability provided by the tool where that distinction materially affects attribution.
45. Data Contribution
Data may materially contribute to Value by enabling:
context,
evidence,
training,
retrieval,
decision support,
or validation.
VAS may govern methodological contribution of data to Value.
It does not determine legal ownership, licensing rights, copyright, or compensation entitlement associated with that data.
46. Infrastructure Contribution
Infrastructure may enable Value production through:
compute,
storage,
networking,
hardware,
availability,
or execution capacity.
Infrastructure availability does not automatically mean infrastructure created the Value.
VAS should distinguish enabling infrastructure contribution from intelligence or operational contribution.
47. Methodology Contribution
A methodology may materially influence how Value is produced by defining:
process,
decision logic,
validation logic,
measurement,
governance,
or execution structure.
Where methodology materially shapes the Value outcome, its contribution may be represented.
This does not itself determine intellectual-property ownership or economic entitlement.
48. Negative Contribution
Not every contribution improves Value.
Some actions may:
reduce quality,
increase cost,
cause rework,
introduce errors,
delay completion,
or reduce validated Value.
Where relevant, VAS may represent:
Positive Contribution
Neutral Contribution
Negative Contribution
Mixed Contribution
This prevents attribution systems from recognizing only beneficial participation.
49. Corrective Contribution
A contributor may create Value by correcting a prior negative or deficient contribution.
Corrective contribution should remain distinguishable from the contribution that created the original deficiency.
50. Contribution Overlap
Two or more contributors may affect the same portion of Value.
VAS should avoid automatically double-counting overlapping contributions.
Where contribution cannot be cleanly separated, the attribution state should preserve the shared or unresolved relationship.
51. Joint Contribution
Joint Contribution exists where Value emerges through interaction that cannot reasonably be decomposed into independent contributor shares without creating false precision.
In such cases, VAS may represent the contribution jointly.
Joint Contribution ≠ Equal Contribution by default.
52. Attribution Granularity
Attribution may occur at different levels:
execution,
task,
workflow,
project,
system,
capability,
or Value object.
The chosen granularity should correspond to the decision or governance purpose.
Greater granularity is not automatically more accurate.
53. Attribution Weight
Where evidence supports it, VAS may represent relative contribution weight.
Weight may be:
quantitative,
categorical,
ordinal,
bounded,
or another defensible representation.
VAS does not require universal percentages.
Attribution Precision must not exceed Evidence Precision.
54. Attribution Normalization
Where different contribution types must be compared or consolidated, a governed normalization method may be used.
Normalization should preserve:
method,
assumptions,
scope,
evidence,
and limitations.
Normalization must not imply that fundamentally different contributions are naturally equivalent.
55. Attribution Uncertainty
Attribution frequently involves uncertainty because Value formation may be:
distributed,
iterative,
partially observable,
interdependent,
or counterfactual.
Uncertainty should remain visible rather than being converted into artificial certainty.
56. Attribution Conflict
Different evidence sources or methodologies may produce competing attribution interpretations.
VAS should preserve material conflict where it cannot be resolved.
Possible states may include:
Consistent
Partially Conflicting
Materially Conflicting
Evidence-Incomplete
Unresolved
An unresolved attribution should not be converted into an arbitrary definitive allocation.
57. Attribution Evidence
Attribution evidence may include:
execution records,
workflow records,
human actions,
agent traces,
model outputs,
tool calls,
data provenance,
validation evidence,
correction records,
version history,
decision records,
resource records,
or other relevant evidence.
Evidence requirements should correspond to attribution materiality.
58. Attribution Provenance
Material attribution should preserve sufficient provenance to determine:
what evidence was used,
where it originated,
which version applied,
when the relevant event occurred,
what methodology was used,
and how the attribution conclusion was produced.
Provenance supports reproducibility and challenge.
59. Attribution Reproducibility
Where practicable, another competent assessor should be able to reconstruct the material reasoning behind an attribution from the preserved evidence and methodology.
Reproducibility does not require identical judgment where uncertainty exists.
It requires sufficient transparency to understand how the attribution was formed.
60. Attribution Traceability
VAS should preserve a traceable relationship:
Validated Value
↔ Value Source
↔ Contribution
↔ Contributor
↔ Contribution Event
↔ Evidence
↔ Attribution Conclusion
Traceability is central to governed attribution.
61. Attribution Integrity
Attribution must not be manipulated merely to support a preferred organizational, commercial, political, legal, or compensation outcome.
Integrity failures may include:
excluding material contributors,
inflating nominal participation,
double-counting contribution,
selective evidence,
unsupported precision,
retrospective reclassification,
or assigning Value to a contributor solely because it holds authority.
Authority ≠ Value Contribution.
62. Attribution Revision
Attribution may change when:
new evidence emerges,
Value is revalidated,
contribution evidence changes,
errors are discovered,
or the attribution methodology is legitimately improved.
Revisions should preserve:
previous attribution,
new attribution,
reason for change,
supporting evidence,
methodology version,
and effective time.
63. Attribution Versioning
Where Value or contribution structures evolve over time, attribution should preserve version continuity.
A later attribution should not silently overwrite an earlier governed state.
This enables reconstruction of how the Value Attribution changed and why.
64. Attribution State
VAS may represent attribution through states such as:
Directly Attributed
Jointly Attributed
Indirectly Attributed
Enabling Contribution
Corrective Contribution
Negative Contribution
Conditionally Attributed
Evidence-Limited
Conflicted
Unattributed
Unresolved
These states describe methodological attribution.
They do not establish legal or economic entitlement.
65. Governed Value Attribution State
The principal output of VAS is:
Governed Value Attribution State
It should identify, where applicable:
Value object,
Value validation state,
attribution boundary,
attribution horizon,
Value sources,
contributors,
contributor classes,
Contribution Events,
contribution types,
materiality,
contribution dependencies,
Contribution Chains or Networks,
human contribution,
AI contribution,
Human–AI contribution,
Multi-Agent contribution,
model contribution,
tool contribution,
data contribution,
infrastructure contribution,
methodology contribution,
negative or corrective contribution,
overlap,
joint contribution,
contribution necessity,
contribution sufficiency,
substitutability,
incremental contribution,
attribution weight where defensible,
uncertainty,
confidence,
conflicts,
evidence,
provenance,
methodology,
and version.
66. Attribution and Value Change
If the underlying validated Value changes materially, the attribution state may require reassessment.
A contribution attributed to an initial Value state should not automatically retain the same significance after:
Value deterioration,
Value expansion,
Value invalidation,
or substantial change in the Value object.
Changed Value may require changed Attribution.
67. Attribution and Productivity
Productivity evidence may help explain contribution but does not itself establish Value Attribution.
A contributor may produce substantial output with little Value.
Another may make a small intervention that materially changes the final Value.
Therefore:
Productivity ≠ Value Attribution.
68. Attribution and Resource Consumption
Resource consumption may support contribution analysis but does not establish contribution magnitude.
A contributor consuming more compute, time, energy, or infrastructure is not automatically responsible for more Value.
Resource Consumption ≠ Value Contribution.
69. Attribution and Economic Cost
Cost may be associated with a contributor or contribution.
But:
Higher Cost ≠ Greater Value Contribution.
VAS attributes contribution to Value.
IES governs Intelligence Economy.
The two may interact without becoming equivalent.
70. Attribution and Strategic Value
Some contributions may primarily affect Strategic Value rather than immediate operational Value.
Where the relevant Value object is governed through SVGS, VAS may attribute contributions to that governed Strategic Value.
It must preserve the distinction between immediate and strategic Value.
71. Attribution and Sustainability
Sustainability evidence may show that a contributor or mechanism helps preserve Value over time.
VAS may attribute material preservation contribution where supported by evidence.
ISS remains responsible for determining the sustainability state itself.
72. Attribution and Optimization
Attribution can reveal which mechanisms actually contribute to Value.
This may support later optimization by identifying:
high-contribution mechanisms,
low-contribution activity,
redundant participation,
negative contribution,
or unnecessary complexity.
VAS provides attribution evidence.
It does not independently authorize optimization changes.
73. Attribution and Decision Governance
The Governed Value Attribution State may inform:
continuation,
scaling,
migration,
reconfiguration,
or retirement decisions.
DGS governs those lifecycle decisions.
Attribution Evidence ≠ Decision Authority.
74. Attribution and Ownership
VAS does not determine who legally owns:
Value,
output,
data,
models,
software,
methodology,
intellectual property,
or resulting assets.
Value Attribution ≠ Legal Ownership.
Legal ownership must be determined through the competent legal or contractual framework.
75. Attribution and Compensation
Methodological contribution may inform a separate compensation process.
VAS does not determine:
salary,
bonus,
royalty,
revenue share,
fee,
commission,
or other compensation.
Value Contribution ≠ Compensation Entitlement.
76. Attribution and Taxation
VAS does not determine:
taxable income,
transfer pricing,
permanent establishment,
tax residency,
tax ownership,
tax allocation,
or other jurisdiction-specific tax treatment.
Methodological attribution may become an input to a competent tax analysis where appropriate.
Value Attribution ≠ Tax Attribution.
77. Attribution and Accountability
Contribution to Value does not establish general Accountability.
A contributor may materially create Value without holding governance authority.
A person may hold Accountability without materially contributing to a specific Value object.
Value Contribution ≠ Accountability.
78. Attribution and Responsibility
Operational Responsibility and Value Contribution may overlap but are not equivalent.
Responsibility may exist because of assigned role.
Contribution must be evidenced through the Value formation process.
Responsibility ≠ Value Contribution.
79. Attribution and Liability
VAS does not determine legal or methodological Liability.
A contributor may create substantial Value without being liable for resulting Harm.
A party may incur Liability without having contributed positively to Value.
Value Attribution ≠ Liability Attribution.
LIGA™ governs Liability.
80. Attribution and Harm
Positive Value Contribution does not erase Harm.
Likewise, Harm does not automatically negate every Value contribution.
HAGA™ governs Harm.
VAS governs contribution to Value.
Value Contribution ≠ Harm Acceptability.
81. Attribution and Authority
A system, model, agent, human, or organization may materially contribute to Value without gaining additional execution or governance authority.
Capability does not create authority.
Contribution does not create authority.
Authority remains governed by the competent governance architecture or institution.
82. Attribution Under Automation
Increasing automation may change the contribution structure.
Tasks previously performed by humans may become:
AI-executed,
algorithmically executed,
tool-executed,
or infrastructure-mediated.
VAS should update attribution according to actual contribution rather than preserving historical assumptions about who created Value.
83. Attribution Under Model Change
Changing the underlying model may alter:
output quality,
reasoning contribution,
human correction requirements,
tool usage,
cost,
or workflow structure.
Where Value formation changes materially, attribution should be reassessed.
84. Attribution Under Multi-Agent Change
Adding, removing, or reconfiguring agents may change contribution relationships.
VAS should not assume that additional agents create additional Value.
More Agents ≠ More Value Contribution.
85. Attribution Under Human Intervention
Human intervention may increase, reduce, preserve, or fail to affect Value.
VAS should represent the actual evidenced effect.
Human involvement should neither be systematically privileged nor systematically discounted.
86. Attribution Under Failure and Correction
Where one contributor produces a deficient result and another corrects it, VAS should preserve both:
the negative or deficient contribution,
and
the corrective contribution.
The final successful outcome should not erase the path through which it was produced.
87. Attribution Under Reuse
A reusable model, methodology, dataset, software component, or operational asset may contribute across many Value objects.
VAS should avoid automatically assigning its full contribution repeatedly where doing so would distort attribution.
The applicable methodology should define how reusable contribution is represented.
88. Attribution Under Shared Infrastructure
Shared infrastructure may support many Value-producing activities simultaneously.
VAS should attribute infrastructure contribution only to the extent methodologically relevant to the governed Value object.
Raw infrastructure utilization alone is not sufficient attribution.
89. Attribution Under Unobservable Contribution
Some contribution may be materially plausible but insufficiently observable.
VAS should permit:
Evidence-Limited
or
Unresolved
states rather than inventing precision.
Unknown Contribution ≠ Zero Contribution.
90. Attribution Under Conflicting Claims
Multiple contributors may claim primary contribution to the same Value.
VAS should resolve such claims through evidence and methodology where possible.
Where evidence cannot support a definitive conclusion, the conflict should remain visible.
Governance should not convert institutional power into attribution certainty.
91. Attribution Quality
A high-quality attribution should be:
Value-grounded
Evidence-supported
Traceable
Boundary-defined
Methodologically explicit
Proportionate
Versioned
Challengeable
Uncertainty-aware
Quality does not require artificial numerical precision.
92. Attribution Failure Modes
Material failure modes include:
Participation Inflation
Contributor Omission
Double Attribution
Contribution Double-Counting
Authority-Based Attribution
Cost-Based Attribution
Output-Based Attribution without Value
False Precision
Hidden Joint Contribution
Unresolved Contribution Presented as Certain
Historical Attribution Persistence after Value Change
Attribution Boundary Manipulation
93. Attribution Challenge
Material attribution conclusions should be capable of challenge where evidence or methodology is disputed.
A challenge may question:
the Value object,
boundary,
contributor identification,
materiality,
evidence,
causal connection,
weight,
methodology,
or confidence.
Challenge does not automatically invalidate attribution.
It triggers appropriate review.
94. Attribution Reassessment
Reassessment may be required following:
new evidence,
Value revalidation,
system reconfiguration,
model change,
agent change,
human-role change,
workflow change,
methodology change,
or identification of a material attribution error.
95. Attribution Continuity
Attribution should remain connected across versions where Value creation continues over time.
This allows governance to distinguish:
persistent contribution,
new contribution,
retired contribution,
changed contribution,
and newly discovered contribution.
96. Minimum Governance Requirements
A conforming VAS implementation must be capable of:
1. defining the Value Attribution Object; 2. establishing the attribution boundary and horizon; 3. verifying the relevant Value state; 4. identifying material Value sources; 5. identifying potential contributors; 6. distinguishing participation from contribution; 7. determining contribution materiality;
8. representing direct, indirect, enabling, foundational, operational, validation, preservation, negative, and corrective contributions where applicable; 9. representing human, AI, Human–AI, Multi-Agent, model, tool, data, infrastructure, and methodology contribution where relevant; 10. establishing Contribution Events, Chains, or Networks where required; 11. assessing necessity, sufficiency, substitutability, and incremental contribution where methodologically appropriate; 12. controlling overlap and double-counting; 13. preserving uncertainty and confidence; 14. maintaining attribution evidence and provenance; 15. preserving methodology and version history; 16. supporting attribution challenge and reassessment; 17. producing a Governed Value Attribution State; 18. preserving the boundary between attribution and ownership, compensation, taxation, Accountability, Responsibility, Harm, Liability, and Decision Authority.
97. Governance Logic
The canonical VAS governance logic is:
Governed Intelligence Sustainability State
↓
Identify Validated Value Object
↓
Confirm Value State
↓
Define Attribution Boundary / Horizon
↓
Identify Value Sources
↓
Identify Potential Contributors
↓
Identify Contribution Events
↓
Determine Material Contribution
↓
Classify Contribution Type
↓
Map Contribution Chains / Networks
↓
Assess Contribution Dependencies
↓
Assess Human / AI / Human–AI Contribution
↓
Assess Multi-Agent / Model / Tool Contribution
↓
Assess Data / Infrastructure / Methodology Contribution
↓
Identify Negative / Corrective Contribution
↓
Assess Necessity / Sufficiency / Substitutability
↓
Assess Incremental Contribution where appropriate
↓
Control Overlap / Joint Contribution / Double-Counting
↓
Assess Attribution Granularity / Weight where defensible
↓
Assess Evidence / Provenance
↓
Assess Uncertainty / Confidence / Conflict
↓
Test Attribution Integrity
↓
Establish Governed Value Attribution State
↓
Handoff to Decision Governance
98. Governance Outputs
VAS may produce:
Value Attribution Object
Attribution Boundary
Attribution Horizon
Value Source Record
Contributor Record
Contributor Classification
Contribution Event Record
Contribution Materiality State
Direct Contribution State
Indirect Contribution State
Enabling Contribution State
Foundational Contribution State
Operational Contribution State
Validation Contribution State
Preservation Contribution State
Human Contribution State
AI Contribution State
Human–AI Contribution State
Multi-Agent Contribution State
Model Contribution State
Tool Contribution State
Data Contribution State
Infrastructure Contribution State
Methodology Contribution State
Negative Contribution State
Corrective Contribution State
Contribution Chain
Contribution Network
Contribution Dependency Record
Contribution Necessity State
Contribution Sufficiency State
Contribution Substitutability State
Contribution Increment State
Contribution Overlap State
Joint Contribution State
Attribution Granularity
Attribution Weight
Attribution Confidence
Attribution Uncertainty State
Attribution Conflict State
Attribution Evidence Record
Attribution Provenance Record
Attribution Methodology Record
Attribution Version
The principal output is:
Governed Value Attribution State
99. Critical Distinctions
VAS preserves the following distinctions:
Participation ≠ Value Contribution
Contributor Presence ≠ Value Contribution
Activity ≠ Value
Output ≠ Value
Productivity ≠ Value Attribution
Resource Consumption ≠ Value Contribution
Higher Cost ≠ Greater Value Contribution
Direct Contribution ≠ Sole Contribution
Indirect Contribution ≠ Immaterial Contribution
Necessary Contribution ≠ Sufficient Contribution
Joint Contribution ≠ Equal Contribution by default
AI Use ≠ AI Value Contribution
Human Presence ≠ Human Value Contribution
Oversight Presence ≠ Material Oversight Contribution
Model Invocation ≠ Model Value Contribution
Infrastructure Availability ≠ Infrastructure Value Creation
Counterfactual Estimate ≠ Observed Outcome
Attribution Precision must not exceed Evidence Precision
Unknown Contribution ≠ Zero Contribution
Value Attribution ≠ Legal Ownership
Value Contribution ≠ Compensation Entitlement
Value Attribution ≠ Tax Attribution
Value Contribution ≠ Accountability
Responsibility ≠ Value Contribution
Value Attribution ≠ Liability Attribution
Value Contribution ≠ Harm Acceptability
Contribution ≠ Authority
Attribution Evidence ≠ Decision Authority
100. Cross-Architecture Boundary
VAS governs methodological Value Attribution.
Within VEGA™:
VGS governs what constitutes legitimate and validated Value.
XGS governs intelligence execution.
IPS governs Intelligence Productivity.
RGS governs execution resources.
COS governs Computational Optimization.
IES governs Intelligence Execution Economics.
SVGS governs Strategic Value.
ISS governs Intelligence Sustainability.
VAS governs attribution of material contribution to governed Value.
DGS governs lifecycle Decision Governance.
Outside VEGA™:
HAGA™ governs Harm.
LIGA™ governs Liability.
Accountability architectures govern Accountability.
Identity architectures may establish identity and provenance relevant to contributors.
VALIDOS™ may govern validation methodology.
ADIT® may govern evidence and auditability.
VAS may consume governed outputs from these architectures without replacing their governed domains.
101. Canonical Boundary
VAS begins when a sufficiently defined and governed Value object exists for methodological contribution attribution.
VAS governs:
Value Attribution Objects,
attribution boundaries,
attribution horizons,
Value Source Identification,
Contributor Identification,
participation,
material contribution,
incidental contribution,
enabling contribution,
direct contribution,
indirect contribution,
foundational contribution,
operational contribution,
validation contribution,
preservation contribution,
Contribution Events,
Contribution Chains,
Contribution Networks,
contribution dependencies,
contribution sequence,
necessity,
sufficiency,
substitutability,
incremental contribution,
counterfactual contribution analysis,
Human Contribution,
AI Contribution,
Human–AI Contribution,
human oversight contribution,
human correction contribution,
Multi-Agent Contribution,
agent coordination contribution,
model contribution,
tool contribution,
data contribution,
infrastructure contribution,
methodology contribution,
negative contribution,
corrective contribution,
overlap,
joint contribution,
attribution granularity,
attribution weight,
normalization,
uncertainty,
confidence,
conflict,
evidence,
provenance,
reproducibility,
traceability,
integrity,
revision,
versioning,
Value-change reassessment,
and attribution continuity.
VAS does not independently determine:
legal ownership,
intellectual-property rights,
compensation,
salary,
royalties,
revenue sharing,
tax allocation,
transfer pricing,
general Accountability,
Responsibility,
Liability,
Harm,
execution authority,
investment authority,
or final continuation, scaling, migration, and retirement decisions.
VAS ends when materially relevant contribution to the governed Value has been represented as a:
Governed Value Attribution State
for subsequent Decision Governance.
102. Canonical Closing Statement
Value does not explain its own origin.
A validated outcome may be visible while the contribution structure behind it remains hidden.
A human may participate without materially changing the Value.
An AI system may generate most of the output while a small human correction determines whether that output becomes valuable.
Several agents may participate while one coordination mechanism creates the decisive contribution.
Infrastructure may enable the entire process without being the source of the intelligence that forms the Value.
And a contributor may consume the most resources while contributing less Value than a smaller intervention elsewhere in the system.
Governed attribution therefore cannot begin with titles, authority, cost, activity, or technological visibility.
It must begin with the Value itself and work backward through evidence.
Participation ≠ Value Contribution.
Productivity ≠ Value Attribution.
Resource Consumption ≠ Value Contribution.
Joint Contribution ≠ Equal Contribution by default.
Attribution Precision must not exceed Evidence Precision.
Value Attribution ≠ Legal Ownership.
Value Contribution ≠ Compensation Entitlement.
Value Attribution ≠ Liability Attribution.
Contribution does not create authority.
The purpose of attribution is not to force every contribution into a percentage.
It is to make the formation of Value sufficiently visible that material contribution can be identified, evidenced, challenged, and governed without pretending to know more than the evidence can support.
VAS governs the traceable path from validated Value back to the contributions that materially made that Value possible.
Mahlad
Governed Links
About Value Attribution Standard (VAS)
Value Source Identification Module (VSIM)
Contribution Attribution Module (CATM)
Human–AI Attribution Module (HAAM)
Multi-Agent Attribution Module (MAAM)
Attribution Evidence Module (AEM)