Contribution Tolerance Band Model defines contribution not as a single exact value, but as an interval-based estimate with an explainable tolerance band.
It is designed for royalty allocation, trace evaluation, attribution systems, and dispute-resistant value distribution under imperfect evidence.
Contribution Tolerance Band Model v0.1 is an uncertainty-aware contribution assessment model that represents contribution as a range rather than a fixed point.
Instead of asking:
“Who contributed exactly 47%?”
this model asks:
“Within what explainable range can this contribution be fairly assessed?”
In AI-mediated creation, protocol design, writing, software development, research, and trace-based royalty systems, contribution is often difficult to measure precisely.
A contribution may appear as:
- an original question
- a structural idea
- a protocol design
- an implementation
- an edit
- a transformation
- an indirect influence
- a long-term trace
- a disputed or partially visible lineage
Because of this, exact contribution scoring can create false precision.
For example:
Contributor A: 47%
Contributor B: 38%
Contributor C: 15%
This looks precise, but the evidence may not justify that level of certainty.
Contribution Tolerance Band Model avoids false precision by using tolerance bands.
Contribution Range = estimated_value ± tolerance_band
Example:
Contributor A: 42% - 50%
Contributor B: 35% - 43%
Contributor C: 12% - 18%
The purpose is not to eliminate uncertainty.
The purpose is to make uncertainty visible, bounded, explainable, and governable.
Contribution should not be treated as a fixed point.
It should be treated as a flexible but auditable range.
C = μ ± ε
Where:
C= contribution rangeμ= estimated contribution valueε= tolerance bandμ - ε= lower boundμ + ε= upper bound
Example:
{
"contributor_id": "contributor_A",
"estimated_value": 0.46,
"tolerance_band": 0.04,
"range": {
"lower_bound": 0.42,
"upper_bound": 0.50
}
}A narrow band means stronger evidence and higher confidence.
A wide band means greater uncertainty and may trigger review, pooled allocation, deferred allocation, or dispute handling.
Do not force exactness where exactness cannot be justified.
This model does not claim to calculate the absolute truth of contribution.
Instead, it provides a structured way to estimate contribution under uncertainty.
The central principle is:
Make uncertainty visible, bounded, explainable, and governable.
This model distinguishes between controlled uncertainty and uncontrolled ambiguity.
Good uncertainty is explainable.
It may come from:
- incomplete but meaningful evidence
- partial structural similarity
- moderate trace confidence
- estimator disagreement within acceptable limits
- indirect but plausible influence
Example:
Contributor A: 42% - 50%
This is acceptable if the band width is justified.
Bad ambiguity is too broad to support safe allocation.
Example:
Contributor A: 10% - 90%
This should not be used for automatic allocation.
It should trigger review, dispute handling, deferred allocation, or shared pool handling.
A Contribution Tolerance Band assessment may include:
- contribution assessment metadata
- target work information
- contributor assessments
- estimated values
- tolerance bands
- contribution ranges
- evidence bundles
- estimator outputs
- uncertainty factors
- contribution layers
- allocation recommendations
- review triggers
- governance metadata
- relationships to other trace or royalty systems
For human readability, contribution may also be expressed as layers.
Instead of relying only on numbers, the model can classify contributors as:
Primary Contribution Layer
Near Contribution Layer
Supporting Contribution Layer
Shared Influence Layer
Unresolved / Disputed Layer
This reduces unnecessary conflict caused by excessive numerical precision.
Example:
{
"primary_contribution_layer": ["contributor_A"],
"near_contribution_layer": ["contributor_B"],
"supporting_contribution_layer": ["contributor_C"],
"shared_influence_layer": ["contributor_D"],
"unresolved_layer": []
}Contribution Tolerance Band Model does not directly enforce payment.
It provides allocation guidance.
Possible allocation outcomes include:
automatic_allocation
pooled_allocation
equalized_allocation
human_review
dispute_registry
deferred_allocation
common_culture_pool
no_allocation
Used when:
- evidence is strong
- tolerance bands are narrow
- estimator agreement is high
- dispute risk is low
Used when:
- contributor ranges overlap
- contributions are difficult to separate
- influence is shared
- strict ranking would create unnecessary conflict
Used when:
- tolerance bands are too wide
- evidence is conflicting
- estimator agreement is low
- high-value distribution is involved
Used when:
- influence may be real but not individually attributable
- origin is unclear
- contribution has become broadly cultural
- evidence is too weak for individual allocation
This model may be interpreted as a Yin-Yang balance system.
Yang represents visible, explicit, externalized evidence.
Examples:
- citation
- signature
- timestamp
- code commit
- publication history
- explicit reference
- repository history
- C2PA-style provenance
- signed attestation
Yin represents hidden, structural, indirect, or long-term influence.
Examples:
- conceptual resonance
- structural similarity
- origin-question influence
- implicit inspiration
- design lineage
- cultural diffusion
- long-term transformation
A healthy contribution model should not rely only on Yang evidence.
It should also recognize Yin influence.
However, it should not rely only on Yin influence either.
Unverified influence claims must not automatically generate strong allocation rights.
Therefore, this model seeks a middle balance.
Too much Yang → only explicit records matter
Too much Yin → vague influence claims dominate
Middle Balance → evidence and structure are both considered
This is the willow model of allocation:
flexible enough to bend with uncertainty,
rooted enough to remain auditable.
contribution-tolerance-band-model/
├─ .github/
│ └─ workflows/
│ └─ validate-specs.yml
├─ docs/
│ ├─ contribution-tolerance-band-model.md
│ ├─ relationship-to-royalty-os.md
│ ├─ relationship-to-ksd.md
│ ├─ relationship-to-trace-protocol.md
│ ├─ relationship-to-dispute-registry.md
│ ├─ allocation-readiness.md
│ ├─ review-status-notes.md
│ ├─ governance-bridge-notes.md
│ ├─ band-width-function.md
│ ├─ model-weighting-notes.md
│ ├─ temporal-adjustment-notes.md
│ ├─ anti-gaming-rules.md
│ └─ lineage-engine-integration.md
├─ examples/
│ └─ contribution-tolerance-band.sample.json
├─ schemas/
│ └─ contribution-tolerance-band-v0.1.schema.json
├─ LICENSE
└─ README.md
-
docs/contribution-tolerance-band-model.md
Main explanatory document for the Contribution Tolerance Band Model. -
schemas/contribution-tolerance-band-v0.1.schema.json
JSON Schema for validating Contribution Tolerance Band records. -
examples/contribution-tolerance-band.sample.json
Example record showing interval-based contribution assessment. -
.github/workflows/validate-specs.yml
GitHub Actions workflow for validating schema compliance and numerical consistency.
-
docs/contribution-tolerance-band-model.md
Main explanatory document for the Contribution Tolerance Band Model. -
docs/band-width-function.md
Defines how tolerance band width should widen or narrow based on evidence, trace confidence, estimator variance, and dispute risk. -
docs/model-weighting-notes.md
Explains how multiple estimators should be weighted, aggregated, adjusted, and audited. -
docs/temporal-adjustment-notes.md
Explains how contribution ranges, confidence, evidence strength, and allocation readiness may change over time.
-
docs/allocation-readiness.md
Defines when a contribution assessment is stable enough to influence allocation. -
docs/review-status-notes.md
Explains assessment lifecycle states, review reasons, and review transitions. -
docs/governance-bridge-notes.md
Connects assessment, review, readiness, dispute handling, and Royalty OS allocation policy. -
docs/anti-gaming-rules.md
Defines safeguards against artificial ambiguity, trace spam, overclaiming, false provenance, bad-faith disputes, and other manipulation risks.
-
docs/relationship-to-royalty-os.md
Explains how this model serves as the uncertainty-aware contribution layer for Royalty OS. -
docs/relationship-to-ksd.md
Explains how KSD and Structure Fingerprint provide structural evidence for contribution assessment. -
docs/relationship-to-trace-protocol.md
Explains how Trace Protocol records become trace evidence for contribution ranges. -
docs/relationship-to-dispute-registry.md
Explains how disputed or contested contribution assessments should be governed. -
docs/lineage-engine-integration.md
Explains how lineage records connect to contribution ranges, allocation readiness, dispute handling, and Royalty OS policy.
This repository uses JSON Schema Draft 2020-12.
The main schema is located at:
schemas/contribution-tolerance-band-v0.1.schema.json
The example file is located at:
examples/contribution-tolerance-band.sample.json
The schema validates the structure of a Contribution Tolerance Band assessment, including:
- required metadata
- contributor records
- contribution ranges
- evidence bundles
- estimator outputs
- allocation recommendations
- governance metadata
- relationship records
This repository includes a GitHub Actions workflow:
.github/workflows/validate-specs.yml
The workflow performs both JSON Schema validation and additional custom consistency checks.
The workflow validates:
- schema compliance
- required fields
- allowed enum values
- object structure
- value ranges
- URI and date-time formats
The workflow also checks numerical consistency that JSON Schema alone cannot fully enforce.
It validates:
lower_bound <= upper_bound
lower_bound = estimated_value - tolerance_band
upper_bound = estimated_value + tolerance_band
allocation_share total = 1.0
shared_pool_policy.pool_share consistency
review_required / review_reasons consistency
This prevents invalid contribution ranges from passing validation.
Minimal contributor range example:
{
"contributor_id": "contributor_A",
"estimated_value": 0.46,
"tolerance_band": 0.04,
"range": {
"lower_bound": 0.42,
"upper_bound": 0.50
},
"confidence": 0.82,
"evidence_strength": 0.78,
"trace_confidence": 0.74,
"estimator_agreement": 0.81,
"dispute_risk": 0.12,
"contribution_layer": "primary"
}This means the contributor is not treated as having exactly 46%.
Instead, the contributor is assessed within an explainable range:
42% - 50%
A contribution assessment may require review when:
tolerance_band is too wide
evidence_strength is too low
trace_confidence is too low
estimator_agreement is too low
dispute_risk is too high
ranges overlap in a conflict-sensitive way
counter-evidence exists
high-value distribution is involved
manual review is requested
When review is required, allocation may be:
- deferred
- pooled
- sent to human review
- sent to dispute handling
- placed into a shared influence pool
- placed into a common culture pool
Band Width Function defines how the tolerance_band should be calculated or justified.
The band should widen or narrow based on:
- evidence strength
- structure ambiguity
- trace uncertainty
- estimator variance
- dispute risk
- temporal uncertainty
- manipulation risk
Core idea:
strong evidence → narrower band
weak evidence → wider band
See also: docs/band-width-function.md
Model Weighting explains how multiple estimators should be combined.
Estimators may include:
- structure estimator
- provenance estimator
- trace estimator
- similarity estimator
- human reviewer
- community review
- dispute-aware estimator
- domain expert
The goal is to avoid dependence on a single estimator.
See also: docs/model-weighting-notes.md
Temporal Adjustment explains how contribution assessments may change over time.
Time may:
- strengthen evidence
- weaken trace confidence
- clarify lineage
- increase cultural diffusion
- reveal counter-evidence
- resolve disputes
- require supersession
Core idea:
Time should not erase contribution, but time may change how contribution is interpreted.
See also: docs/temporal-adjustment-notes.md
Anti-Gaming Rules protect the model from manipulation.
They address risks such as:
- artificial ambiguity
- range inflation
- trace spam
- false provenance
- structure overclaim
- similarity overclaim
- bad-faith disputes
- retroactive claim insertion
- evidence flooding
- model weighting manipulation
- temporal gaming
Core rule:
Uncertainty should not become entitlement.
See also: docs/anti-gaming-rules.md
Contribution Tolerance Band Model can serve as an uncertainty-aware contribution layer for Royalty OS and trace-based allocation systems.
Royalty OS may use this model to determine whether a contribution case should result in:
- automatic allocation
- pooled allocation
- equalized allocation
- human review
- dispute registry handling
- deferred allocation
- common culture pool allocation
This model does not replace Royalty OS.
It strengthens Royalty OS by preventing false precision in contribution scoring.
See also: docs/relationship-to-royalty-os.md
KSD and Structure Fingerprint provide structural evidence.
They help determine:
- whether two works share structural lineage
- whether a question, pattern, or architecture has been inherited
- whether similarity is superficial or structural
- whether indirect influence may exist
Contribution Tolerance Band Model uses this evidence to adjust the tolerance band.
Strong structural evidence narrows uncertainty.
Weak or ambiguous structural evidence widens uncertainty.
See also: docs/relationship-to-ksd.md
Trace Protocol provides trace paths.
These may include:
- access trace
- influence trace
- audit trace
- reference trace
- transformation trace
Contribution Tolerance Band Model uses trace confidence to determine whether the contribution range should be narrow or wide.
Clear trace paths generally narrow the tolerance band.
Missing, indirect, or disputed trace paths generally widen the tolerance band.
See also: docs/relationship-to-trace-protocol.md
Lineage Engine identifies possible origin, derivation, adaptation, influence, or structural inheritance.
Contribution Tolerance Band Model converts those lineage signals into uncertainty-aware contribution ranges.
Lineage may include:
- origin candidate
- derived from
- adapted from
- influenced by
- translated from
- remixed from
- implemented from
- extended from
- structurally similar to
- conceptually related to
Lineage is evidence of relationship.
It is not automatic proof of contribution share.
See also: docs/lineage-engine-integration.md
Signed Impact Attestation provides accountable claims.
If an evaluator, institution, or reviewer claims that a contributor had a certain impact, that claim can be included as evidence.
However, a signed claim does not automatically determine final contribution.
It may affect:
- evidence strength
- dispute risk
- estimator confidence
- review status
If a contribution estimate is challenged, the case may be sent to Dispute Registry.
Dispute Registry may record:
- objection
- correction
- supersession
- revocation
- reaffirmation
- review outcome
Contribution Tolerance Band Model should not hide disputes.
It should make uncertain or contested contribution visible.
See also: docs/relationship-to-dispute-registry.md
Allocation Readiness defines when a Contribution Tolerance Band assessment is sufficiently stable to influence allocation.
A contribution assessment should not automatically become an allocation decision.
It must pass a readiness gate based on:
- tolerance band width
- evidence strength
- trace confidence
- estimator agreement
- dispute risk
- review status
- value at risk
See also: docs/allocation-readiness.md
Review Status defines how contribution assessments express lifecycle state and review requirements.
An assessment may be:
draft
active
review_required
disputed
superseded
revoked
archived
Review status prevents unstable assessments from being mistaken for final decisions.
See also: docs/review-status-notes.md
Governance Bridge connects assessment, review, allocation readiness, dispute handling, and Royalty OS allocation policy.
Its core rule is:
A contribution range should never become an allocation decision without governance.
See also: docs/governance-bridge-notes.md
This model does not:
- determine legal ownership
- replace copyright law
- prove absolute originality
- guarantee perfect fairness
- calculate the metaphysical truth of influence
- enforce payment by itself
- eliminate all disputes
- replace human judgment in high-risk cases
This model provides an uncertainty-aware structure for fairer contribution assessment.
Contribution is not always a fixed number.
In creative, technical, and AI-mediated environments, contribution often behaves like a flexible structure.
It bends with evidence.
It shifts with new traces.
It narrows with stronger proof.
It widens under uncertainty.
It may require review when conflict appears.
Therefore, a mature royalty or attribution system should not pretend to have perfect certainty.
It should preserve flexibility without losing accountability.
The system must be like a willow:
flexible enough to bend with uncertainty,
rooted enough to remain auditable.
Version: v0.1
Status: Draft
Schema: JSON Schema Draft 2020-12
Validation: GitHub Actions
This repository currently provides:
schemas/
contribution-tolerance-band-v0.1.schema.json
examples/
contribution-tolerance-band.sample.json
docs/
contribution-tolerance-band-model.md
relationship-to-royalty-os.md
relationship-to-ksd.md
relationship-to-trace-protocol.md
relationship-to-dispute-registry.md
allocation-readiness.md
review-status-notes.md
governance-bridge-notes.md
band-width-function.md
model-weighting-notes.md
temporal-adjustment-notes.md
anti-gaming-rules.md
lineage-engine-integration.md
.github/workflows/
validate-specs.yml
See LICENSE.
Contribution Tolerance Band Model v0.1 introduces:
- interval-based contribution estimation
- explainable tolerance bands
- evidence-aware uncertainty
- estimator consensus
- model weighting
- temporal adjustment
- anti-gaming safeguards
- lineage integration
- range overlap handling
- review triggers
- allocation readiness
- shared pool handling
- dispute-aware allocation guidance
- governance-aware review flow
- machine-readable validation
Its central principle is:
Do not force exactness where exactness cannot be justified.
Make uncertainty visible, bounded, explainable, and governable.