Weighing the reputation of participants

1) Why weigh reputation

• consider context (role, risk, geo, class QoS);
• regulate the impact of v治理 and access to limits;
• provide resistance to collusion and sibyl attacks;
• associate reputation with economic liability (S-pledges, penalties) and contractual rights (RNFT).

2) Reputation objects and roles

Subjects: people, organizations, nodes/validators, service accounts.
Роли: Creator, Node/Validator, Provider (compute/DA), Operator, Affiliate, Curator, Oracle, Regulator/Auditor.
Context domains: product, region, risk class, QoS (Q4/Q3/...).

Reputation is always contextual: R (subject, role, domain, timeframe).

3) Signal sources (what we measure)

Quality of actions: moderation accuracy, validity of oracle responses, SLA nodes, uptime, latency, error rate.
Reliability and responsibility: execution of RNFT contracts, absence of violations, timely reports.
Economics: returns/chargebacks, share of disputed transactions, Cost-to-Serve entity influence.
Social affirmations: review/scores with author weights (meta-reputation).
Negative events: S-pledge slashing, fraud patterns, compliance blocks.

All signals have signatures, ULID/trace, TTL, source trust class.

4) Normalization and scales

• Robust z: (\hat x =\frac {x -\text {median}} {\text {MAD}}) (trim the tails to P1/P99).
• Min-max robust: on ([P5, P95]) in the time window.
• EWMA smoothing for robustness.

Signals are reduced to unified scale ([-1; + 1]).

5) Basic weighting formula

[
R_{c} = \sigma!\left(\sum_i w_i(c), s_i - \lambda_c\right),
]

where (w_i (c)) are the weights for context (c) (role/domain/risk), (\lambda _ c) is the trust threshold, (\sigma) is the compression (tanh/Logit) for robustness.

[
R = \sum_{c \in \text{contexts}} \alpha_c, R_c,\quad \sum \alpha_c = 1.
]

6) Weights and context (policy-driven)

Risk weighing: for high-risk actions (payments, cross-chain confirmation) ↑ weight of accuracy/incidents, ↓ weight of "social likes."

Geo/Compliance: Strict jurisdictions ↑ thresholds (\lambda _ c).
QoS: for Q4 (critical commands) it is stronger to fine p95/p99 tails.
Prescription: fresh signals are more significant, old - through decay.

All weights and thresholds are upravlyayutsya治理 (see § 15) and published in the Governance Registry.

7) Time, decay, amnesty

Decay: (R_t = R_{t-1}\cdot e ^ {-\delta\Delta t} +\Delta R) - natural "forgetting."

Amnesties: legal procedures for reducing the negative footprint with long-term impeccable behavior.
Freezing contexts: for rare roles (for example, an auditor), we fix the "windows" of the assessment.

8) Relationship with rights and limits

• Rights/quotas: API limits, resource access, RNFT bullet sizes.
• 治理 -weight: voice modifier (g '= g\cdot f (R)), where (f) is a monotonic function (corridor [0. 5..1. 5]).
• Economy: discounts/premiums, take-rate decline/growth, collateral for S-pledges.

9) Anti-fraud and anti-collusion

Sybil: entry thresholds (minimum S-pledge), behavioral signatures, device fingerprint with private hashes.
Rings of mutual ratings: graph analysis (PageRank/TrustRank), cutting "loops," reducing the weight of mutual reviews.
Light metrics pharming: hidden control tasks, blind-run quality checks.
Selective attack on registers: signatures, merkly roots, audited logs.
Transfer of reputation between accounts: prohibited; badges-proofs of skills/audits without "points" are allowed.

10) Reputations and Liens (R + S)

R - trust in quality; S is economic responsibility.
For high-risk roles, the R&S rule applies: a minimum of R and a minimum of S are required; fines - through S slashing with entry in negative R magazine.
At steadily high R - reduction of requirements for S (in koridore治理).

11) Cross chain and tolerability

R locality: Reputation remains in the original trust domain.
Transfer - through provable units: badges "SLA-. 90d≥99 9%," "0 disputes/quarter," etc.
Condition snapshots: publication of aggregates with evidence; recipients apply their own weights/thresholds.

12) Privacy and compliance

DID + VC: minimization of personal data; attributes via verifiable credits.
ZK proofs: confirmation of R thresholds without disclosing details (e.g. "R≥0. 7»).
Right of challenge: formalized appeal; transparent post-mortem.

13) Reputation Program Metrics and KPIs

Discriminatory ability: ROC-AUC to separate bona fides from violators.
Robustness: emission sensitivity, TailAmplification impact.
Economics: R correlation with LTV/retention/margin.
Safety: reduced incident/fraud rate with stable response.
Fairness: no systematic skew across segments with equal inputs.

14) Dashboards and observability

R-Panel: R distribution by roles/geo, share of "low," "medium," "high."

Signal quality: accuracy/recall of control tasks by role.
Incidents/penalties: Slashing frequency, dynamics after weight changes.
na治理 effect: correlation of R and votes/outcomes, Gini index of influence.
Economics: Quota/price dependence on R, effect on Cost-to-Serve.

15) 治理 of weights and thresholds

Proposals: change (w_i (c) ,\lambda _ c ,\delta) through voting.
R-modifier: voice weight depends on R in the quality/safety domain, in the corridor [0. 8..1. 2].
Sunset clauses: temporary changes with auto-rollback without re-approval.

Publicity: publishing versions of scales, comparative report "before/after."

16) Embedding in contracts/services

Reputation Registry: storing aggregates, scale versions, event log.
Policy Engine (ABAC): access/quota rules with conditions on R.
Rewards Router: bonuses for quality participants (↓take -rate, ↑limity).
Dispute/Escrow: Integrating Appeals, Compensations, and Fines into the RNFT.
Compliance Gate: geo/age thresholds R, reporting to auditor.

17) Implementation playbook

1. Role and risk mapping. Identify contexts (c) and critical signals.
2. Collection and normalization. Signatures, ULID, anti-duplicates, robust scales.
3. Starting weights/thresholds. Governance-version 1. 0, pilot windows.
4. Quality control. Synthetic tasks/honey checks, ROC-AUC.
5. Integration. ABAC, RNFT rules, Rewards/Slashing, Compliance Gate.
6. Dashboards and alerts. R distributions, incidents, economics.
7. Pilot and retro-calibration. 1-2 quarters; tuning scales/thresholds.
8. Scaling and interconnection. Publication of badge aggregates, local weights for recipients.

18) Delivery checklist

• Defined contexts (role/domain/risk/geo/QoS)
• Robust normalization and EWMA implemented
• Configured weights/thresholds s治理 -process and sunset
• Implemented anti-sybil/anti-collusion and control tasks
• Associated R with rights/quotas/voice and S-pledges
• Privacy (DID/VC, ZK) and appeals included
• Dashboards, ROC-AUC and reporting available
• Pilot passed, scales recalibrated

19) Glossary

R (Reputation): non-transferable reputation of quality/trust.
S (Stake): pledge of economic responsibility and source of fines.
RNFT: contract of rights/limits/obligations and KPIs in the relationship.
ABAC: access by attributes (including R thresholds).
Decay: attenuation of the influence of old events.
Sunset: temporary parameter changes with auto-rollback.

20) The bottom line

Reputation weighting is a trust management system, not just "points." Contextual weights, robust normalization, a bunch of R↔S↔RNFT i治理 make a reputation a tool for safe growth: the best get more rights and lower costs, violators - predictable restrictions and economic responsibility.