Value Attribution Standard (VAS)
Architecture:
VEGA™ — Value Execution Governance Architecture
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
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 to Value, where that contribution occurred, how human, artificial, technical, operational, and combined contributions interacted, and what evidence supports the resulting attribution.
Governed Space:
Value Attribution
Governance Flow:
Value Source Identification → Contribution Attribution → Human–AI Attribution → Multi-Agent Attribution → Attribution Evidence
Foundational Principle: Participation ≠ Value Contribution. Attribution must begin with validated Value and trace backward through evidence to the contributions that materially made that Value possible.
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About standard
About Value Attribution Standard (VAS)
Architecture:
VEGA™ — Value Execution Governance Architecture
Standard:
Value Attribution Standard (VAS)
Governed Space:
Value Attribution
What Is This Standard?
The Value Attribution Standard (VAS) is the VEGA™ Parent Standard for governing how materially relevant contributions to validated Value are identified, evidenced, differentiated, and attributed.
It provides a structured methodology for tracing Value backward to the human, AI, agent, model, tool, data, infrastructure, methodology, and operational contributions that materially helped produce, preserve, or validate it.
VAS does not assume that participation equals contribution or that every contributor contributed equally.
Its purpose is to make the formation of Value traceable enough to support evidence-based attribution without creating artificial precision.
Why Does This Standard Exist?
Modern intelligence systems rarely create Value through one identifiable contributor.
A business outcome may involve:
human expertise,
AI reasoning,
multiple agents,
models,
tools,
data,
software,
infrastructure,
validation,
and human correction.
Traditional attribution can become unreliable when it relies on job titles, system ownership, activity volume, compute consumption, or the visibility of the final output.
VAS instead begins with the governed Value and asks:
What materially contributed to producing this Value, and what evidence supports that conclusion?
This creates a methodological bridge between Value creation and the contributors that materially enabled it.
What This Standard Is Not
VAS is not a legal ownership framework.
It is not an intellectual-property allocation system.
It is not a compensation methodology.
It does not determine salaries, royalties, bonuses, commissions, or revenue shares.
It is not a tax-attribution or transfer-pricing methodology.
It does not determine Accountability, Responsibility, Harm, or Liability.
And it does not grant authority merely because a human, AI system, agent, model, or other mechanism contributed to Value.
VAS governs:
Methodological Value Attribution.
How It Works
VAS follows the governance flow:
Value Source Identification → Contribution Attribution → Human–AI Attribution → Multi-Agent Attribution → Attribution Evidence
The process begins with a sufficiently defined and validated Value object.
It then identifies the sources and contributors connected to that Value.
Their participation is tested for material contribution.
Where necessary, VAS distinguishes:
direct and indirect contribution,
enabling and operational contribution,
human and AI contribution,
individual and joint contribution,
positive and negative contribution,
production and validation contribution,
and observed evidence from counterfactual estimates.
Contribution may then be represented through Contribution Events, Chains, or Networks.
Evidence, provenance, uncertainty, confidence, overlap, and attribution conflicts remain attached to the resulting attribution state.
VAS therefore does not require every contributor to receive an artificial percentage.
Where the evidence supports precision, attribution may become more precise.
Where it does not, uncertainty remains visible.
Principal Governance Output
The principal output of VAS is:
Governed Value Attribution State
This state can identify:
what Value was attributed,
which contributors materially contributed,
what type of contribution occurred,
how contributions interacted,
where contribution was joint or overlapping,
what evidence supports the attribution,
how confident the attribution is,
and where uncertainty or conflict remains.
The output creates a traceable relationship:
Validated Value ↔ Value Source ↔ Contribution ↔ Contributor ↔ Contribution Event ↔ Evidence ↔ Attribution Conclusion
Canonical Phrases
Participation ≠ Value Contribution.
Contributor Presence ≠ Value Contribution.
AI Use ≠ AI Value Contribution.
Human Presence ≠ Human Value Contribution.
Productivity ≠ Value Attribution.
Resource Consumption ≠ Value Contribution.
Higher Cost ≠ Greater Value Contribution.
Necessary Contribution ≠ Sufficient Contribution.
Indirect Contribution ≠ Immaterial Contribution.
Joint Contribution ≠ Equal Contribution by default.
Oversight Presence ≠ Material Oversight Contribution.
Model Invocation ≠ Model Value Contribution.
Counterfactual Estimate ≠ Observed Outcome.
Unknown Contribution ≠ Zero Contribution.
Attribution Precision must not exceed Evidence Precision.
Value Attribution ≠ Legal Ownership.
Value Contribution ≠ Compensation Entitlement.
Value Attribution ≠ Tax Attribution.
Value Contribution ≠ Accountability.
Value Attribution ≠ Liability Attribution.
Contribution does not create authority.
Value does not explain its own origin. Attribution makes that origin governable.
Potential Business Applications
VAS may support methodological services and assessments such as:
AI Value Attribution Assessment
Human–AI Contribution Assessment
Multi-Agent Value Attribution Assessment
AI Productivity Attribution Assessment
Automation Value Contribution Assessment
Human Oversight Contribution Assessment
AI Tool and Model Contribution Analysis
Value Formation Traceability Assessment
AI Workflow Contribution Mapping
Strategic Value Contribution Analysis
AI Value Attribution Audit
Contribution Evidence & Provenance Assessment
These applications can help organizations understand how Value actually emerges across increasingly complex human– AI operating environments without treating methodological attribution as legal ownership or compensation determination.
Use Case 1 — Human and AI Both Claim the Value
A professional uses an advanced AI system to produce an important business analysis.
The AI performs most of the research, synthesis, and initial reasoning.
The human defines the problem, selects the relevant evidence, identifies a critical error, changes the final recommendation, and validates the result before operational use.
The final analysis produces substantial validated Value.
Simply labeling the result “AI-generated” would ignore material human contribution.
Labeling it “human-created” would ignore substantial AI contribution.
VAS reconstructs the contribution structure.
It identifies:
the human problem-definition contribution,
the AI analytical contribution,
the human correction,
the validation contribution,
and the interaction through which the final Value emerged.
The result may therefore be represented as a governed Human–AI contribution rather than forcing the Value into a single- source attribution.
Human–AI Value can be jointly formed without being equally formed.
Use Case 2 — Ten Agents Participate, but Not All Create Value
An organization deploys a multi-agent system containing specialized agents for:
planning,
research,
retrieval,
analysis,
critique,
coordination,
validation,
and execution.
Ten agents participate in the workflow.
Execution logs show that several agents repeatedly produce information that does not materially affect the final outcome.
One research agent discovers the decisive evidence.
A reasoning agent transforms that evidence into the central conclusion.
A validation agent detects a material error.
A coordination mechanism routes the correction back into the workflow.
The final result produces validated Value.
VAS does not attribute Value equally across all ten agents merely because all participated.
It identifies which contributions materially affected the Value formation process and preserves the supporting evidence.
This can reveal both genuine contribution and unnecessary complexity.
More Agents ≠ More Value Contribution.
Canonical Closing Statement
Value may be visible long before its origin is understood.
In modern intelligence systems, the final outcome may emerge from humans, models, agents, tools, data, infrastructure, validation, correction, and coordination acting together.
Governed attribution cannot therefore begin with who owns the system, who consumed the most resources, who produced the most output, or who appeared last in the workflow.
It must begin with the Value.
Then it must follow the evidence backward.
Participation ≠ Value Contribution.
Productivity ≠ Value Attribution.
Resource Consumption ≠ Value Contribution.
Joint Contribution ≠ Equal Contribution by default.
Attribution Precision must not exceed Evidence Precision.
The purpose of VAS is not to assign credit by assumption.
It is to make material contribution visible enough to be identified, evidenced, differentiated, challenged, and governed.
VAS makes the path from Value to contribution traceable.
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)