CATM — Contribution Attribution 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 Contribution Attribution Governance
Module ID: VEGA-VAS-CATM-002
Origin ID: OOF.CM.VEGA.09.02.01
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 Contribution Attribution Module (CATM) governs how materially relevant contributions from identified Value sources are established, differentiated, connected to validated Value, and represented as governed contribution attribution.
CATM begins after the materially relevant Value sources have been identified.
It determines whether and how those sources materially contributed to the formation, preservation, realization, correction, or validation of the governed Value.
CATM distinguishes participation from contribution, source presence from contribution, contribution from attribution weight, and material contribution from incidental activity.
It provides the methodological bridge between:
Governed Value Source State
and
Governed Contribution Attribution State
CATM does not determine legal ownership, compensation, tax treatment, Accountability, Responsibility, Harm, Liability, or Decision Authority.
Its principal output is:
Governed Contribution Attribution State
Its foundational distinction is:
Identified Value Source ≠ Attributed Value Contribution.
2. Operational Role
The operational role of CATM is:
to determine which identified Value sources materially contributed to the governed Value, how those contributions affected Value formation, and how the resulting contribution relationships should be represented without exceeding the available evidence.
VSIM asks:
“From which operational sources did the governed Value actually emerge?”
CATM asks:
“How did those identified sources materially contribute to the governed Value?”
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
CATM governs:
Contribution Attribution
Its principal upstream input is:
Governed Value Source State
Its principal output is:
Governed Contribution Attribution State
That state proceeds to:
Human–AI Attribution Governance
4. Central Operational Question
Which identified Value sources materially contributed to the governed Value, through what contribution mechanisms, and with what level of evidence, materiality, confidence, and attribution precision?
5. Contribution Attribution Object
Every CATM assessment must identify the specific Value object and source set being attributed.
The Contribution Attribution Object should preserve:
the governed Value,
the applicable Value state,
the Value Source State,
the relevant source boundary,
the relevant attribution horizon,
and the evidence available for contribution analysis.
6. Contribution Boundary
CATM should define the contribution boundary within which attribution occurs.
The boundary determines which effects are considered part of the relevant Value formation process.
A boundary that is too narrow may omit material contribution.
A boundary that is too broad may transform background conditions into artificial contribution.
7. Attribution Horizon
Contribution may occur across different stages of Value formation.
The attribution horizon may include:
problem definition,
preparation,
execution,
transformation,
review,
correction,
validation,
deployment,
realization,
or preservation.
The horizon should reflect the Value object rather than default to the final execution event.
8. Contribution Candidate
Every material Value source identified by VSIM becomes a potential Contribution Candidate.
Candidate status means:
the source is eligible for contribution assessment.
It does not mean:
the source has already been attributed Value.
9. Contribution Qualification
A Contribution Candidate qualifies for attribution when evidence establishes a sufficiently material relationship between the source and the governed Value.
Qualification should consider:
operational effect,
Value relevance,
materiality,
temporal connection,
dependency,
and available evidence.
10. Material Contribution
A Material Contribution is an identifiable input, action, capability, transformation, decision, resource, correction, validation, or intervention that materially affects the formation, preservation, realization, or validation of the governed Value.
Materiality is always relative to the Value object.
11. Contribution Materiality
Contribution Materiality represents the significance of a contribution to the governed Value.
Where useful, materiality may be represented as:
Critical
High
Material
Supporting
Limited
Incidental
Unresolved
These states are not universal numerical weights.
12. Materiality Basis
A materiality conclusion should identify why a contribution matters.
Possible bases include:
Value formation,
outcome quality,
decision change,
error prevention,
Value preservation,
operational completion,
validation,
or another evidenced effect.
Contribution Materiality must have a Value basis.
13. Participation vs Contribution
Participation means involvement.
Contribution means material effect on Value formation.
A person, model, agent, tool, or system may participate without materially contributing to the resulting Value.
Participation ≠ Value Contribution.
14. Activity vs Contribution
High activity does not establish high contribution.
A contributor may produce:
many outputs,
many tokens,
many actions,
many tool calls,
or substantial execution time
without materially affecting the resulting Value.
Activity Volume ≠ Contribution Materiality.
15. Output vs Contribution
Visible output is evidence of activity.
It is not automatically evidence of Value contribution.
A small intervention may create more Value than a large volume of output.
Output Volume ≠ Value Contribution.
16. Direct Contribution
A Direct Contribution materially affects the content, decision, transformation, or operational result from which Value emerges.
Directness concerns the relationship to Value formation.
It does not automatically establish greater contribution than an indirect mechanism.
17. Indirect Contribution
An Indirect Contribution materially affects Value through another source, process, or contribution.
Examples may include:
supporting information,
methodology,
infrastructure,
coordination,
or prior operational work.
Indirect Contribution ≠ Immaterial Contribution.
18. Enabling Contribution
An Enabling Contribution establishes a capability or condition without which Value formation would be materially impaired or impossible.
Examples may include:
compute availability,
data access,
specialized software,
tools,
or infrastructure.
Enabling contribution should remain distinct from direct Value formation.
19. Generative Contribution
A Generative Contribution materially creates new:
content,
reasoning,
analysis,
decision options,
design,
or other operational output
that contributes to Value.
Generative contribution may originate from humans or artificial systems.
20. Transformative Contribution
A Transformative Contribution materially changes an existing input into a more valuable or operationally useful form.
Transformation may include:
reasoning,
synthesis,
optimization,
classification,
translation,
restructuring,
or compression.
21. Informational Contribution
An Informational Contribution provides evidence, data, knowledge, or context that materially affects the Value outcome.
Information volume alone does not determine contribution materiality.
22. Decision Contribution
A Decision Contribution materially affects which option, action, configuration, recommendation, or conclusion is selected.
The contributor need not hold formal Decision Authority to make a methodological contribution to the decision.
Decision Contribution ≠ Decision Authority.
23. Validation Contribution
A Validation Contribution materially establishes whether an outcome satisfies the conditions required for Value recognition.
It should remain distinct from contribution to producing the underlying outcome.
Outcome Contribution ≠ Validation Contribution.
24. Corrective Contribution
A Corrective Contribution materially improves or repairs an otherwise deficient contribution, output, or process.
Correction may:
remove an error,
change a recommendation,
restore validity,
prevent failure,
or preserve Value.
The amount of visible correction does not determine its materiality.
25. Preservation Contribution
A Preservation Contribution materially prevents existing Value from deteriorating, disappearing, becoming inaccessible, or becoming invalid.
Preservation may be achieved through:
maintenance,
monitoring,
updating,
continuity,
correction,
or operational support.
26. Foundational Contribution
A Foundational Contribution establishes a reusable capability that materially supports later Value formation.
Examples may include:
methodology,
architecture,
system design,
model development,
or reusable workflow logic.
Foundational contribution should not automatically receive full attribution for every later Value object.
27. Operational Contribution
An Operational Contribution occurs within the active process producing or realizing Value.
It may include:
execution,
routing,
coordination,
review,
intervention,
or other operational action.
28. Positive Contribution
A Positive Contribution materially increases, enables, preserves, or validates the governed Value.
Positive contribution should be supported by evidence of its effect.
29. Negative Contribution
A Negative Contribution materially reduces Value or increases the effort required to achieve it.
Examples may include:
incorrect output,
unnecessary complexity,
rework,
delay,
resource waste,
or misleading information.
Contribution ≠ Positive Contribution by default.
30. Neutral Contribution
A source may participate in the process while producing no material positive or negative effect on the governed Value.
Where supported by evidence, CATM may represent:
Neutral Contribution
rather than forcing positive attribution.
31. Mixed Contribution
A contributor may create both positive and negative effects.
For example, an AI model may provide useful analysis while introducing an error that requires human correction.
CATM should preserve both effects where material.
Net Success ≠ Absence of Negative Contribution.
32. Corrective Relationship
Where one contribution corrects another, CATM should preserve the relationship:
Deficient Contribution → Corrective Contribution → Changed Value Outcome
The successful final outcome should not erase the deficient contribution that preceded correction.
33. Contribution Event
A Contribution Event records an identifiable occurrence through which a source materially affects Value.
Where relevant, it may contain:
contributor,
source,
action,
input,
time,
context,
affected process,
effect,
Value relationship,
and evidence.
34. Contribution Sequence
The order in which contributions occur may materially affect Value.
CATM should preserve sequence where:
one contribution depends on another,
correction follows failure,
validation follows production,
or earlier actions constrain later outcomes.
35. Contribution Chain
A Contribution Chain represents a sequence such as:
Source → Contribution Event → Intermediate Effect → Subsequent Contribution → Validated Outcome → Value
Chains support traceability where Value formation is substantially sequential.
36. Contribution Network
Where Value emerges through interacting contributors, CATM may represent a:
Contribution Network
A network may capture:
parallel contributions,
feedback,
correction,
coordination,
shared dependencies,
and iterative Value formation.
37. Contribution Dependency
A contribution may depend on another contribution.
For example:
AI reasoning may depend on retrieved information.
Human judgment may depend on AI analysis.
Validation may depend on evidence produced by another mechanism.
CATM should preserve material dependency relationships.
38. Contribution Interaction
Two contributions may interact in a way that materially changes Value.
The combined effect may be:
additive,
complementary,
amplifying,
corrective,
constraining,
or conflicting.
Interaction should be represented where independent attribution would distort operational reality.
39. Contribution Necessity
CATM may assess whether a contribution was necessary for the observed Value outcome.
Possible states may include:
Necessary
Material but Non-Necessary
Supporting
Substitutable
Incidental
Unresolved
Necessity does not automatically establish attribution magnitude.
40. Contribution Sufficiency
A contribution is sufficient only where it could independently satisfy the relevant Value-producing condition.
Many contributions are necessary but not sufficient.
Others are neither necessary nor sufficient individually but become material through interaction.
Necessary Contribution ≠ Sufficient Contribution.
41. Contribution Substitutability
CATM may assess whether another source could plausibly have provided the same material contribution.
Substitutability may affect interpretation of contribution but does not erase the contribution that actually occurred.
Substitutable Contribution ≠ Non-Contribution.
42. Contribution Uniqueness
A contribution may provide a capability, insight, intervention, or effect that no credible alternative within the governed process could provide.
Uniqueness may increase its structural significance.
It does not automatically establish a numerical attribution percentage.
43. Incremental Contribution
Where evidence permits, CATM may assess the incremental effect of a contribution.
The central question is:
What material difference to the Value outcome is associated with this contribution?
The result may be:
quantitative,
categorical,
comparative,
bounded,
or unresolved.
44. Baseline Comparison
Incremental Contribution may require comparison against an appropriate baseline.
A baseline may represent:
execution without the contribution,
a previous process,
an alternative mechanism,
a control condition,
or another defensible reference.
The baseline should be explicitly identified.
45. Counterfactual Contribution
Where appropriate, CATM may assess:
What would plausibly have occurred without this contribution?
Counterfactual analysis can support contribution reasoning.
But:
Counterfactual Estimate ≠ Observed Outcome.
The assumptions underlying the counterfactual must remain visible.
46. Contribution Overlap
Two or more contributions may affect the same portion of Value.
CATM should identify overlap where separate attribution would otherwise count the same Value effect multiple times.
Contribution Overlap ≠ Independent Contribution.
47. Double-Counting Control
CATM should prevent the same Value effect from being attributed repeatedly without methodological justification.
Double-counting may arise where:
a model and its agent wrapper are both credited for the same effect,
a team and its members are separately counted,
a tool and the process using it are both assigned the full effect,
or enabling and direct contributions are treated as independent copies of the same Value.
48. Joint Contribution
Joint Contribution exists where Value emerges from interaction among contributors and cannot reasonably be decomposed without creating misleading precision.
CATM may preserve the contribution jointly.
Joint Contribution ≠ Equal Contribution by default.
49. Contribution Decomposition
Where evidence supports it, a joint contribution may be decomposed into distinct contribution components.
Decomposition should occur only where the resulting representation improves accuracy.
Decomposition without evidence creates false precision.
50. Contribution Weight
CATM may represent relative contribution weight where a defensible methodology and sufficient evidence exist.
Weight may be:
quantitative,
categorical,
ordinal,
bounded,
or comparative.
VAS does not require universal percentage allocation.
51. Attribution Precision
The precision of contribution attribution should never exceed the precision supported by the evidence and methodology.
Attribution Precision must not exceed Evidence Precision.
Where only broad contribution classes can be defended, CATM should preserve broad classes.
52. Contribution Normalization
Where materially different contribution types must be compared, CATM may use a governed normalization method.
Normalization should preserve:
the original contribution types,
the normalization methodology,
assumptions,
limitations,
and resulting uncertainty.
Normalization does not make fundamentally different contributions naturally equivalent.
53. Contribution Confidence
Each material attribution may include a confidence state.
Possible states include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Unresolved
Confidence should reflect the quality of evidence supporting the attribution.
54. Contribution Uncertainty
Uncertainty may arise from:
incomplete evidence,
complex interaction,
unobservable processes,
overlapping contributions,
counterfactual assumptions,
or unclear causal relationships.
Uncertainty should remain visible.
Uncertainty ≠ Zero Contribution.
55. Contribution Conflict
Evidence may support competing interpretations of the same contribution.
Possible states may include:
Consistent
Partially Conflicting
Materially Conflicting
Evidence-Incomplete
Unresolved
CATM should not manufacture certainty merely because a definitive attribution would be convenient.
56. Contribution Evidence
Contribution attribution should be supported by evidence appropriate to the Value object and materiality of the contribution.
Evidence may include:
execution traces,
workflow records,
human actions,
model outputs,
agent activity,
tool calls,
data provenance,
validation records,
correction records,
decision records,
system logs,
and controlled comparisons.
57. Evidence-to-Contribution Link
Evidence should be connected to the specific contribution claim it supports.
A large evidence repository is not sufficient if the relationship between evidence and attribution cannot be reconstructed.
CATM should preserve:
Contribution Claim ↔ Supporting Evidence
58. Contribution Provenance
Material contribution attribution should preserve sufficient provenance to establish:
which source acted,
which version applied,
what action occurred,
when it occurred,
which Value object was affected,
what evidence supports the relationship,
and which attribution methodology was used.
59. Contribution Traceability
CATM should preserve a traceable relationship:
Value Source → Contribution Event → Contribution Effect → Value Relationship → Evidence → Attribution State
This relationship forms the core of governed contribution attribution.
60. Contribution Integrity
Contribution attribution must not be manipulated to produce a preferred distribution of credit.
Integrity failures may include:
inflating nominal participation,
omitting material contribution,
hiding negative contribution,
double-counting,
selective evidence,
unsupported weighting,
boundary manipulation,
or assigning contribution because of authority, status, ownership, or cost.
Authority ≠ Value Contribution.
61. Attribution Bias Control
Where relevant, CATM should identify systematic attribution bias.
Potential bias may arise from:
human preference,
automation bias,
organizational hierarchy,
system ownership,
technology visibility,
recency,
output volume,
cost,
or institutional incentives.
Bias identification should support methodological correction rather than assume intent.
62. Contribution Revision
Contribution attribution may be revised when:
new evidence appears,
the Value object changes,
Value is revalidated,
a contribution was incorrectly classified,
a source relationship changes,
or the attribution methodology is legitimately improved.
Revision should preserve the prior governed state.
63. Contribution Versioning
CATM should preserve:
previous attribution,
current attribution,
methodology version,
reason for change,
supporting evidence,
and effective time
where material attribution changes.
Silent overwriting weakens attribution traceability.
64. Governed Contribution Attribution State
The principal output of CATM is:
Governed Contribution Attribution State
Possible contribution-level states may include:
Directly Attributed
Indirectly Attributed
Enabling Contribution
Generative Contribution
Transformative Contribution
Informational Contribution
Decision Contribution
Validation Contribution
Corrective Contribution
Preservation Contribution
Foundational Contribution
Operational Contribution
Positive Contribution
Negative Contribution
Neutral Contribution
Mixed Contribution
Jointly Attributed
Conditionally Attributed
Evidence-Limited
Conflicted
Unattributed
Unresolved
The state should preserve why the contribution received that classification.
65. Minimum Implementation Framework
A conforming implementation of CATM must provide exactly the following six capabilities:
1. Contribution Object, Boundary & Qualification Governance
Capability to establish the Contribution Attribution Object, attribution boundary, attribution horizon, Contribution Candidates, and qualification criteria.
2. Contribution Type & Materiality Governance
Capability to identify and distinguish direct, indirect, enabling, generative, transformative, informational, decision, validation, corrective, preservation, foundational, operational, positive, negative, neutral, and mixed contributions and assess their materiality.
3. Contribution Structure & Relationship Governance
Capability to represent Contribution Events, sequences, Chains, Networks, dependencies, interactions, necessity, sufficiency, substitutability, uniqueness, overlap, and joint contribution.
4. Incremental, Comparative & Weight Governance
Capability to assess Incremental Contribution, baselines, counterfactuals, decomposition, Contribution Weight, normalization, and attribution precision where methodologically defensible.
5. Contribution Evidence & Integrity Governance
Capability to preserve evidence-to-contribution links, provenance, traceability, confidence, uncertainty, conflict, bias controls, revision, versioning, and attribution integrity.
6. VAS Human–AI Attribution Handoff Governance
Capability to produce a Governed Contribution Attribution State sufficiently explicit for subsequent Human–AI Attribution Governance.
66. Governance Logic
The canonical CATM governance logic is:
Governed Value Source State
↓
Identify Contribution Attribution Object
↓
Define Contribution Boundary / Horizon
↓
Identify Contribution Candidates
↓
Qualify Material Contributions
↓
Determine Contribution Materiality
↓
Classify Contribution Type
↓
Identify Positive / Negative / Neutral / Mixed Effects
↓
Identify Contribution Events
↓
Map Contribution Sequence / Chains / Networks
↓
Assess Contribution Dependencies / Interaction
↓
Assess Necessity / Sufficiency
↓
Assess Substitutability / Uniqueness
↓
Assess Incremental Contribution
↓
Establish Appropriate Baseline / Counterfactual where justified
↓
Identify Overlap / Joint Contribution
↓
Control Double-Counting
↓
Determine Contribution Weight where defensible
↓
Control Attribution Precision
↓
Assess Evidence / Provenance / Traceability
↓
Assess Confidence / Uncertainty / Conflict
↓
Test Attribution Bias / Integrity
↓
Establish Governed Contribution Attribution State
↓
Handoff to Human–AI Attribution Governance
67. Governance Outputs
CATM may produce:
Contribution Attribution Object
Contribution Boundary
Attribution Horizon
Contribution Candidate Record
Contribution Qualification State
Contribution Materiality State
Direct Contribution State
Indirect Contribution State
Enabling Contribution State
Generative Contribution State
Transformative Contribution State
Informational Contribution State
Decision Contribution State
Validation Contribution State
Corrective Contribution State
Preservation Contribution State
Foundational Contribution State
Operational Contribution State
Positive Contribution State
Negative Contribution State
Neutral Contribution State
Mixed Contribution State
Contribution Event Record
Contribution Sequence Record
Contribution Chain
Contribution Network
Contribution Dependency Record
Contribution Interaction State
Contribution Necessity State
Contribution Sufficiency State
Contribution Substitutability State
Contribution Uniqueness State
Incremental Contribution State
Contribution Baseline
Counterfactual Contribution State
Contribution Overlap State
Joint Contribution State
Contribution Decomposition Record
Contribution Weight
Contribution Normalization Record
Contribution Confidence State
Contribution Uncertainty State
Contribution Conflict State
Contribution Evidence Record
Evidence-to-Contribution Link
Contribution Provenance Record
Contribution Traceability Record
Attribution Bias State
Contribution Version
The principal output is:
Governed Contribution Attribution State
68. Critical Distinctions
CATM preserves the following distinctions:
Identified Value Source ≠ Attributed Value Contribution
Participation ≠ Value Contribution
Activity Volume ≠ Contribution Materiality
Output Volume ≠ Value Contribution
Direct Contribution ≠ Sole Contribution
Indirect Contribution ≠ Immaterial Contribution
Contribution ≠ Positive Contribution by default
Outcome Contribution ≠ Validation Contribution
Necessary Contribution ≠ Sufficient Contribution
Substitutable Contribution ≠ Non-Contribution
Contribution Necessity ≠ Attribution Weight
Contribution Overlap ≠ Independent Contribution
Joint Contribution ≠ Equal Contribution by default
Decomposition without evidence creates false precision
Counterfactual Estimate ≠ Observed Outcome
Attribution Precision must not exceed Evidence Precision
Uncertainty ≠ Zero Contribution
Higher Cost ≠ Greater Contribution
Higher Resource Consumption ≠ Greater Contribution
Authority ≠ Value Contribution
Decision Contribution ≠ Decision Authority
Contribution Attribution ≠ Legal Ownership
Contribution Attribution ≠ Compensation Entitlement
Contribution Attribution ≠ Liability Attribution
69. Cross-Architecture Boundary
CATM governs methodological attribution of contribution to governed Value.
VSIM identifies materially relevant Value sources.
CATM determines how those sources materially contributed.
Subsequent VAS mechanisms govern specialized Human–AI Attribution, Multi-Agent Attribution, and Attribution Evidence.
VGS governs Value.
XGS governs execution.
IPS governs Productivity.
RGS governs resource allocation.
COS governs Computational Optimization.
IES governs Intelligence Execution Economics.
SVGS governs Strategic Value.
ISS governs Intelligence Sustainability.
DGS governs lifecycle decisions.
CATM may consume evidence from these domains without replacing them.
HAGA™ governs Harm.
LIGA™ governs Liability.
Accountability architectures govern Accountability.
Identity architectures may establish contributor identity.
VALIDOS™ may govern validation methodology.
ADIT® may govern evidence and auditability.
70. Canonical Boundary
CATM begins when VSIM has established a sufficiently explicit:
Governed Value Source State
CATM governs:
Contribution Attribution Objects,
contribution boundaries,
attribution horizons,
Contribution Candidates,
qualification,
materiality,
direct contribution,
indirect contribution,
enabling contribution,
generative contribution,
transformative contribution,
informational contribution,
decision contribution,
validation contribution,
corrective contribution,
preservation contribution,
foundational contribution,
operational contribution,
positive contribution,
negative contribution,
neutral contribution,
mixed contribution,
Contribution Events,
sequences,
Chains,
Networks,
dependencies,
interactions,
necessity,
sufficiency,
substitutability,
uniqueness,
Incremental Contribution,
baselines,
counterfactuals,
overlap,
double-counting,
Joint Contribution,
decomposition,
Contribution Weight,
normalization,
attribution precision,
confidence,
uncertainty,
conflict,
evidence,
evidence-to-contribution links,
provenance,
traceability,
bias,
integrity,
revision,
and versioning.
CATM does not independently determine:
Value Source Identification,
specialized Human–AI attribution,
specialized Multi-Agent attribution,
legal ownership,
intellectual-property rights,
compensation,
tax treatment,
Accountability,
Responsibility,
Harm,
Liability,
or Decision Authority.
CATM ends when material contribution relationships have been represented as a:
Governed Contribution Attribution State
for subsequent Human–AI Attribution Governance.
71. Use Case 1 — Most of the Work Was Automated, but One Human Intervention Changed the Outcome
An AI-enabled workflow produces a commercial analysis.
The automated system performs:
data retrieval,
analysis,
drafting,
comparison,
and recommendation generation.
A human specialist performs only one substantive intervention.
The specialist identifies that the model has interpreted a regulatory constraint incorrectly and changes the recommendation.
The correction determines whether the final output can be operationally used.
CATM does not measure contribution by:
time spent,
word count,
number of actions,
or output volume.
It evaluates the material effect on Value.
The automated system may retain substantial generative and analytical contribution while the human receives a material Corrective Contribution.
Output Volume ≠ Value Contribution.
72. Use Case 2 — Several Systems Receive Credit for the Same Value
A multi-component intelligence workflow uses:
a retrieval system,
a reasoning model,
an agent framework,
a validation tool,
and an orchestration layer.
Internal reporting independently assigns each component substantial credit for the successful outcome.
When the attribution is combined, the same Value effect is effectively counted several times.
CATM maps the Contribution Chain and identifies where contributions overlap.
The reasoning model may generate the analysis.
The agent framework may route the execution.
The retrieval system may supply decisive evidence.
The validation tool may establish that the result satisfies requirements.
The orchestration layer may enable coordination.
CATM preserves these distinct contribution types without assigning the entire Value independently to every component.
Contribution Overlap ≠ Independent Contribution.
73. Canonical Closing Statement
Identifying a source is only the beginning of attribution.
A source can exist without materially changing Value.
A contributor can perform most of the visible work while another intervention determines whether the outcome becomes valuable.
Several mechanisms can participate in the same result while affecting the same portion of Value.
And a successful final outcome can contain positive, negative, and corrective contributions at the same time.
Contribution attribution therefore cannot be governed by activity, visibility, hierarchy, cost, or intuition alone.
It must follow the effect of contribution on Value.
Identified Value Source ≠ Attributed Value Contribution.
Participation ≠ Value Contribution.
Activity Volume ≠ Contribution Materiality.
Necessary Contribution ≠ Sufficient Contribution.
Joint Contribution ≠ Equal Contribution by default.
Attribution Precision must not exceed Evidence Precision.
CATM does not ask who appeared in the process.
It asks what materially changed the Value and what evidence supports that conclusion.
CATM converts identified Value sources into governed contribution relationships without assigning more certainty than the evidence can justify.