identity-canon/research/commercial-identity/reputation-assurance-gradient.md

Reputation and Counterparty Assurance Gradient

Source Type

Cross-domain synthesis. Online reputation systems, credit reporting, contract bonding theory, payment dispute automation, and alternative dispute resolution (ADR) / litigation practice.

Domain

How counterparties move from weak social proof to enforceable commercial reliance — and how identity-canon should model that journey without collapsing tiers.

Why This Source Matters

"Reputation" is overloaded: a five-star Yelp review, a D&B PAYDEX score, a performance bond, and a court judgment all influence whether a counterparty is trusted — but they differ radically in evidence quality, gaming risk, attribution strength, and enforceability. Software often stores them in one "rating" field. Canon must preserve the gradient so downstream systems do not treat gamable opinion as legal fact or ignore contractual stakes already modeled elsewhere.

The Assurance Gradient (Journey)

Counterparty assurance typically escalates through four tiers. Higher tiers do not replace lower ones; they constrain how much weight lower tiers may carry for a given decision.

Tier 1  OPINION        Star ratings, reviews, karma, badges
        (weak/gamable) Low cost to fake; Sybil-prone; scope-local

Tier 2  OBSERVED       PAYDEX, on-time %, chargeback rate, audit logs,
        (evidence)     verified transaction history, KYC outcome

Tier 3  COMMITTED      Contract, bond, escrow, guarantee, insurance,
        (financial)    SLA penalties, payment mandate, subscription lock-in

Tier 4  ADJUDICATED    Arbitration award, court judgment, regulator action,
        (legal)        enforced settlement, lien, bankruptcy filing

Tier 1 — Opinion signals (weak, gamable)

Examples: Amazon/Yelp star ratings, eBay feedback scores, Stack Overflow reputation, Uber driver rating, Trustpilot reviews, Airbnb host score.

Properties:

• Low cost of manipulation — fake reviews, review bombing, sock puppets, Sybil accounts (Jøsang reputation attack taxonomy).
• Scope-local — reputation on eBay does not transfer to Etsy without explicit portability (reputation bank problem).
• Voluntary participation bias — satisfied and angry customers over-represent; silent majority absent.
• Identity attribution weak — reviewer may be unverified persona; linkage to Natural Person or Organization often absent.
• Economic effect real but bounded — eBay seller ratings correlate with price premium, but platforms add escrow and buyer protection because ratings alone insufficient.

Canon mapping: Reputation Signal — an Evidence Source with assurance_tier: opinion. Attach to Profile, Commercial Record, or Actor with explicit Scope (platform namespace). Default synonymity and trust strength: weak. Do not promote to Commercial Commitment.

Gaming defenses (downstream): verified-purchase flags, rate limits, graph analysis, moderation — model as separate Evidence Source metadata, not as tier upgrade by itself.

Tier 2 — Observed metrics (evidence-based)

Examples: D&B PAYDEX, business credit scores, platform completion rate, on-time delivery statistics, SLA attainment dashboards, chargeback ratio, sanctions-screen clear result, KYC pass, LEI renewal status.

Properties:

• Grounded in observable events — payment dates, shipment scans, registry lookups, transaction logs.
• Stronger attribution — usually tied to Registry Identifier, Commercial Record, or verified Account history.
• Third-party or platform issuer — D&B, credit bureaus, marketplace operator, KYC vendor acts as Evidence Source issuer.
• Still revisable — metrics update; disputes may correct; not legally conclusive.
• Monitoring lifecycle — ongoing CDD and PAYDEX refresh mirror Lifecycle State on evidence, not one-time truth.

Canon mapping: Performance Evidence — Evidence Source with assurance_tier: observed. Link to Commercial Record / Organization via Registry Identifier or Commercial Relationship. Supports Trust Relationship with medium-to-strong confidence when issuer is authoritative.

Tier 3 — Committed stakes (contractual / financial)

Examples: Performance bonds, surety bonds, letters of credit, escrow deposits, service-level agreements with liquidated damages, signed MSAs, active subscription with payment mandate, insurance certificates, qualified electronic seals on contracts (eIDAS).

Properties:

• Costly to breach — Klein-Leffler bonding: quality assurance through market forces when reputation alone insufficient; hostages and penalties.
• Explicit parties — Legal Person / Organization actors bound via Commercial Commitment and Representation chains.
• Automated enforcement partial — smart-contract escrow, Stripe retention, auto-renewal billing, SLA breach triggers — automation executes committed rules without yet reaching court.
• Identity stakes rise — counterparties need stable Registry Identifier, Commercial Record, and often Beneficial Ownership Relationship because liability is real.

Canon mapping: Commercial Commitment (contract, subscription, payment mandate, bond) with Evidence Source attesting execution. Assurance tier: committed. Trust Relationship here should cite the commitment ID, not opinion aggregates.

Distinction: A five-star rating is not a bond. A bond is not a review. Model separately; combine only in downstream risk engines with explicit weighting.

Tier 4 — Adjudicated outcomes (automated dispute → legal resolution)

Escalation path:

1. Platform automation — chargeback dispute rules, marketplace arbitration (eBay Money Back Guarantee), payment processor outcome.
2. Contractual ADR — mandatory arbitration clause (AAA, ICC, JAMS); neutral award binding per contract and statute.
3. Courts — breach of contract, fraud, collections, judgment lien, bankruptcy.

Properties:

• Third-party or state authority — arbitrator, court, regulator issues outcome.
• High attribution — parties identified in proceeding; ties to Legal Entity.
• Enforceable beyond platform — judgments attach to legal persons; credit reporting may follow.
• Lifecycle durable — satisfied, appealed, vacated, enforced — explicit Lifecycle State.

Canon mapping: Adjudication Outcome — Evidence Source with assurance_tier: adjudicated. May trigger Commercial Commitment state change (breached, fulfilled), Trust Relationship revocation, or Lifecycle State on Commercial Record. Do not model as "bad review."

Cross-Tier Dynamics

Transition	What changes	Canon event
Opinion → Observed	Platform verifies purchase; metric computed from logs	New Performance Evidence; optional Synonymity link reviewer Account to transaction
Observed → Committed	Parties sign contract / post bond	Commercial Commitment created; Trust Relationship cites commitment
Committed → Adjudicated	Breach → ADR/court	Adjudication Outcome Evidence; commitment lifecycle update
Adjudicated → Observed	Judgment paid; credit file updated	Performance Evidence refresh (credit bureau)

De-escalation: Adjudicated fraud finding may invalidate opinion signals (moderation) but should not silently delete Evidence — supersede with lifecycle.

Identity coupling: Higher tiers require stronger actor attribution. Opinion may attach to Persona; adjudication attaches to Legal Entity + Registry Identifier.

Relationship to Existing Canon

Concept	Role in assurance gradient
Evidence Source	Carrier for all tiers; use assurance_tier metadata
Trust Relationship	Counterparty reliance; must cite tier basis
Commercial Commitment	Tier 3 anchor
Commercial Relationship	Scope for which assurance applies
Registry Identifier	Attribution for tiers 2–4
Beneficial Ownership Relationship	Liability chain for tier 3–4 entity customers
Assurance Level (NIST)	Orthogonal — identity/auth proofing, not commercial performance
Synonymity Assertion	Link platform persona to legal entity when tiers mix

Reputation Systems Literature (Practical)

Jøsang survey and Resnick criteria for effective reputation systems:

1. Long-lived entities with predictable future interaction.
2. Capture and distribute feedback from prior interactions.
3. Use feedback to guide trust.

Implication for canon: Tier 1 only works when Scope is stable and interaction history is modeled as Evidence with temporal bounds. Reputation capital (economic value of good history) is aggregate Performance Evidence over time — not a separate ontological root.

Attacks: self-promotion, Sybil, slandering, whitewashing — map to integrity_risk metadata on opinion-tier Evidence; downstream concern, but canon should flag tier-1 default weakness.

Candidate Canonical Mappings

Source artifact	Canonical mapping
Star rating / review	Reputation Signal (Evidence Source, tier: opinion)
Verified purchase review	Reputation Signal + Performance Evidence link
PAYDEX / credit score	Performance Evidence (tier: observed)
SLA dashboard	Performance Evidence on Commercial Relationship
Signed MSA / bond	Commercial Commitment + Evidence Source (tier: committed)
Escrow release	Commercial Commitment lifecycle event
Arbitration award	Adjudication Outcome (tier: adjudicated)
Court judgment	Adjudication Outcome + may affect Legal Entity lifecycle
"Trust score" UI	Downstream projection — not canonical root

Resolved Canon Question

Do not add Reputation as a first-class entity.

Instead:

1. Counterparty Assurance Gradient — modeling pattern (four tiers).
2. Evidence Source specializations by tier: Reputation Signal (opinion), Performance Evidence (observed), Adjudication Outcome (adjudicated); tier 3 uses existing Commercial Commitment.
3. Trust Relationship carries assurance_basis referencing tier + evidence IDs.

Convenience term only: "Reputation" in prose — resolve to specific tier and Evidence Source before modeling.

Open Questions

(none — settled in commercial-identity-nuance-settlement.md)

Settled

• assurance_tier primary; optional numeric_score + score_scale downstream.
• Segregated escrow → Commercial Commitment commitment_type: escrow.
• Reputation portability → Synonymity linked_to, weak default.
• Oracle release → observed; ADR/court → adjudicated.

References

• Josang, "A survey of trust and reputation systems for online service provision" — https://doi.org/10.1016/j.dss.2005.05.019
• Hoffman et al., "A survey of attack and defense techniques for reputation systems" — ACM Computing Surveys
• Klein and Leffler (1981), quality assurance through bonding / price premiums
• RFC 7070, An Architecture for Reputation Reporting — https://www.rfc-editor.org/rfc/rfc7070
• Wikipedia, Reputation system — https://en.wikipedia.org/wiki/Reputation_system
• Internal: commercial-trust-binding-theory.md, duns-commercial-credit-identity.md, legal-person-agency-contract.md, kyc-aml-commercial-identity-binding.md