Reputation

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What is reputation

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Dimensions & signals

• Accuracy & agreement — outcomes of validations on your CFs; pass rate, dispute rate, and reversal history.
• Behavioral reliability — commitment and responsiveness (on‑time delivery, review turnaround), sustained throughput, and task completion quality.
• Attested attributes — third‑party attestations (e.g., licenses, affiliations, certifications) provided by human participants or trusted registries, and protocol‑verified checks (e.g., passing tutorials/quizzes/tests).
• Recency — fresh activity counts more; long inactivity decays gently.
• Staking‑as‑confidence — backing claims with stake; audits can boost or slash reputation.

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List of Sample Events which can impact reputation

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What reputation does (functions)

• Unlocks better work & pay. Higher reputation unlocks higher‑paying tasks and roles that expect quality and commitment. Some tasks also require additional qualifications (e.g., professional license, education level, domain expertise); reputation complements these requirements, it doesn’t replace them.
• Weights contributions during assembly. Atomic contributions from higher‑reputation identities can receive more weight in Data Assembly (e.g., lower review quorum, higher inclusion priority). This improves quality while reducing redundant reviews.
• Influences ownership fractions (governed). Within governance‑defined bounds, higher‑reputation contributors may earn a larger share of ownership fractions for equivalent contribution types, reflecting trust and expected quality. Exact policies live in Tokenized Ownership Proofs and are explained by the Royalty Engine during payout.
• Boosts via economic commitment. You can boost reputation through staking‑as‑confidence. Passing audits increases trust; failed audits decrease it. Skin‑in‑the‑game ties the signal to real consequences.

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How reputation updates (lifecycle)

• New CFs start neutral; validations tilt the score up or down.
• Behavioral signals accumulate continuously.
• Staking acts as a multiplier (positive or negative) after audits.
• Tiers update on a schedule/cooldown to avoid flapping and gaming.

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Safety, fairness & recovery

• Sybil & collusion resistance: aggregate across time; require validator diversity; rate‑limit risky patterns.
• Cooldowns & decay: apply changes on a schedule; introduce gentle decay during inactivity to keep the signal fresh.
• Appeals & disputes: appeal outcomes are recorded; fair corrections help recovery.
• No black boxes: every change maps to recorded events; the model and weights are versioned.

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Privacy & disclosure

• Public: your tier, coarse score band, and high‑level reasons (e.g., “high agreement”, “verified license”).
• Private/permissioned: raw artifacts/PII; prefer verifiable credentials and encrypted evidence over plaintext.
• Selective disclosure: anonymous credentials / ZK proofs are experimental and may change.

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Invariants

• Event‑driven & append‑only: reputation changes come from recorded events; history isn’t rewritten.
• Deterministic from inputs: same events + same model version ⇒ same reputation outcome.
• Minimal disclosure: reveal only what policy requires for a decision.
• Auditability: each change links to underlying CFs, validations, or attestations.

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Interfaces

• Identity: who the score belongs to; linked wallets/DIDs; verified attributes.
• Contribution Fingerprints: validations and audits on your CFs drive updates.
• Data Assembly: reputation supplies weighting hints and can reduce review needs.
• Tokenized Ownership Proofs & Royalty Engine: governed policies may map reputation to ownership fractions and explain splits at payout time.
• Access & Metering: tiers and attributes map to policy (who can do what; how much review is needed).