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https://github.com/samuraiwriter7/contribution-tolerance-band-model

An uncertainty-aware contribution assessment model for interval-based royalty allocation, trace evaluation, and dispute-resistant value distribution.
https://github.com/samuraiwriter7/contribution-tolerance-band-model

attribution contribution dispute-resolution interval-estimation kazene provenance royalty-os tolerance-band trace-protocol uncertainty

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An uncertainty-aware contribution assessment model for interval-based royalty allocation, trace evaluation, and dispute-resistant value distribution.

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README 

          
# Contribution Tolerance Band Model



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.



---



## One-Line Definition



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?”



---



## Why This Exists



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:



```text

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.



```text

Contribution Range = estimated_value ± tolerance_band

```



Example:



```text

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.



---



## Core Concept



Contribution should not be treated as a fixed point.



It should be treated as a flexible but auditable range.



```text

C = μ ± ε

```



Where:



- `C` = contribution range

- `μ` = estimated contribution value

- `ε` = tolerance band

- `μ - ε` = lower bound

- `μ + ε` = upper bound



Example:



```json

{

  "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.



---



## Core Principle



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.



---



## Good Uncertainty vs Bad Ambiguity



This model distinguishes between controlled uncertainty and uncontrolled ambiguity.



### Good Uncertainty



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:



```text

Contributor A: 42% - 50%

```



This is acceptable if the band width is justified.



### Bad Ambiguity



Bad ambiguity is too broad to support safe allocation.



Example:



```text

Contributor A: 10% - 90%

```



This should not be used for automatic allocation.



It should trigger review, dispute handling, deferred allocation, or shared pool handling.



---



## Model Structure



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



---



## Contribution Layers



For human readability, contribution may also be expressed as layers.



Instead of relying only on numbers, the model can classify contributors as:



```text

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:



```json

{

  "primary_contribution_layer": ["contributor_A"],

  "near_contribution_layer": ["contributor_B"],

  "supporting_contribution_layer": ["contributor_C"],

  "shared_influence_layer": ["contributor_D"],

  "unresolved_layer": []

}

```



---



## Allocation Behavior



Contribution Tolerance Band Model does not directly enforce payment.



It provides allocation guidance.



Possible allocation outcomes include:



```text

automatic_allocation

pooled_allocation

equalized_allocation

human_review

dispute_registry

deferred_allocation

common_culture_pool

no_allocation

```



### Automatic Allocation



Used when:



- evidence is strong

- tolerance bands are narrow

- estimator agreement is high

- dispute risk is low



### Pooled Allocation



Used when:



- contributor ranges overlap

- contributions are difficult to separate

- influence is shared

- strict ranking would create unnecessary conflict



### Human Review



Used when:



- tolerance bands are too wide

- evidence is conflicting

- estimator agreement is low

- high-value distribution is involved



### Common Culture Pool



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



---



## Yin-Yang Balance Interpretation



This model may be interpreted as a Yin-Yang balance system.



### Yang Evidence



Yang represents visible, explicit, externalized evidence.



Examples:



- citation

- signature

- timestamp

- code commit

- publication history

- explicit reference

- repository history

- C2PA-style provenance

- signed attestation



### Yin Influence



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.



```text

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.



---



## Repository Structure



```text

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

```



---



## Start Here



- [`docs/contribution-tolerance-band-model.md`](docs/contribution-tolerance-band-model.md)  

  Main explanatory document for the Contribution Tolerance Band Model.



- [`schemas/contribution-tolerance-band-v0.1.schema.json`](schemas/contribution-tolerance-band-v0.1.schema.json)  

  JSON Schema for validating Contribution Tolerance Band records.



- [`examples/contribution-tolerance-band.sample.json`](examples/contribution-tolerance-band.sample.json)  

  Example record showing interval-based contribution assessment.



- [`.github/workflows/validate-specs.yml`](.github/workflows/validate-specs.yml)  

  GitHub Actions workflow for validating schema compliance and numerical consistency.



---



## Related Documents



### Core Model



- [`docs/contribution-tolerance-band-model.md`](docs/contribution-tolerance-band-model.md)  

  Main explanatory document for the Contribution Tolerance Band Model.



- [`docs/band-width-function.md`](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`](docs/model-weighting-notes.md)  

  Explains how multiple estimators should be weighted, aggregated, adjusted, and audited.



- [`docs/temporal-adjustment-notes.md`](docs/temporal-adjustment-notes.md)  

  Explains how contribution ranges, confidence, evidence strength, and allocation readiness may change over time.



### Governance and Allocation



- [`docs/allocation-readiness.md`](docs/allocation-readiness.md)  

  Defines when a contribution assessment is stable enough to influence allocation.



- [`docs/review-status-notes.md`](docs/review-status-notes.md)  

  Explains assessment lifecycle states, review reasons, and review transitions.



- [`docs/governance-bridge-notes.md`](docs/governance-bridge-notes.md)  

  Connects assessment, review, readiness, dispute handling, and Royalty OS allocation policy.



- [`docs/anti-gaming-rules.md`](docs/anti-gaming-rules.md)  

  Defines safeguards against artificial ambiguity, trace spam, overclaiming, false provenance, bad-faith disputes, and other manipulation risks.



### Relationship Documents



- [`docs/relationship-to-royalty-os.md`](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`](docs/relationship-to-ksd.md)  

  Explains how KSD and Structure Fingerprint provide structural evidence for contribution assessment.



- [`docs/relationship-to-trace-protocol.md`](docs/relationship-to-trace-protocol.md)  

  Explains how Trace Protocol records become trace evidence for contribution ranges.



- [`docs/relationship-to-dispute-registry.md`](docs/relationship-to-dispute-registry.md)  

  Explains how disputed or contested contribution assessments should be governed.



- [`docs/lineage-engine-integration.md`](docs/lineage-engine-integration.md)  

  Explains how lineage records connect to contribution ranges, allocation readiness, dispute handling, and Royalty OS policy.



---



## Schema Usage



This repository uses JSON Schema Draft 2020-12.



The main schema is located at:



```text

schemas/contribution-tolerance-band-v0.1.schema.json

```



The example file is located at:



```text

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



---



## Validation



This repository includes a GitHub Actions workflow:



```text

.github/workflows/validate-specs.yml

```



The workflow performs both JSON Schema validation and additional custom consistency checks.



### JSON Schema Validation



The workflow validates:



- schema compliance

- required fields

- allowed enum values

- object structure

- value ranges

- URI and date-time formats



### Custom Numerical Validation



The workflow also checks numerical consistency that JSON Schema alone cannot fully enforce.



It validates:



```text

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.



---



## Example



Minimal contributor range example:



```json

{

  "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:



```text

42% - 50%

```



---



## Review Triggers



A contribution assessment may require review when:



```text

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



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:



```text

strong evidence → narrower band

weak evidence   → wider band

```



See also: [`docs/band-width-function.md`](docs/band-width-function.md)



---



## Model Weighting



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`](docs/model-weighting-notes.md)



---



## Temporal Adjustment



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`](docs/temporal-adjustment-notes.md)



---



## Anti-Gaming Rules



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`](docs/anti-gaming-rules.md)



---



## Relationship to Royalty OS



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`](docs/relationship-to-royalty-os.md)



---



## Relationship to KSD / Structure Fingerprint



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`](docs/relationship-to-ksd.md)



---



## Relationship to Trace Protocol



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`](docs/relationship-to-trace-protocol.md)



---



## Relationship to Lineage Engine



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`](docs/lineage-engine-integration.md)



---



## Relationship to Signed Impact Attestation



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



---



## Relationship to Dispute Registry



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`](docs/relationship-to-dispute-registry.md)



---



## Allocation Readiness



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`](docs/allocation-readiness.md)



---



## Review Status



Review Status defines how contribution assessments express lifecycle state and review requirements.



An assessment may be:



```text

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`](docs/review-status-notes.md)



---



## Governance Bridge



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`](docs/governance-bridge-notes.md)



---



## Non-Goals



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.



---



## Design Philosophy



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.



---



## Current Status



```text

Version: v0.1

Status: Draft

Schema: JSON Schema Draft 2020-12

Validation: GitHub Actions

```



This repository currently provides:



```text

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

```



---



## License



See [`LICENSE`](LICENSE).



---



## Summary



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.