Trust Computation
February 28, 2026 · View on GitHub
Formula
$ \text{effective\_trust} = \text{t} \times \text{authority\_weight} \times \text{temporal\_decay} \times (1 + \text{cumulative\_penalty}) $
Components
Base confidence (t): The raw trust score assigned to the claim [0.0 - 1.0].
Authority weight: Derived from the claim's tier:
| Tier | Weight | Description | Example |
|---|---|---|---|
| 1 | 1.00 | Primary source | SEC filings, official records |
| 2 | 0.85 | Authoritative secondary | Analyst reports, peer-reviewed |
| 3 | 0.70 | General secondary | News, industry reports |
| 4 | 0.50 | Internal estimate | CRM data, team assessments |
| 5 | 0.30 | AI inference | Model outputs, extrapolations |
Temporal decay: 0.5^(age_days / half_life_days)
If no decay field is set, temporal decay is 1.0 (no decay).
Cumulative penalty: Sum of all pen values from the provenance chain. Penalties are negative, so (1 + penalty) reduces the score.
Decision Thresholds
| Score | Decision | Meaning |
|---|---|---|
| >= 0.70 | ACCEPT | High confidence, suitable for decisions |
| >= 0.40 | LOW | Moderate confidence, use with caution |
| < 0.40 | REJECT | Low confidence, not suitable for decisions |
Worked Example: Woodgrove Bank
Sarah's Claims
Claim 1: "Revenue was $4.2B" — t=0.98, tier=1 (SEC filing)
effective = 0.98 × 1.00 × 1.0 × 1.0 = 0.98 → ACCEPT
Claim 2: "Cloud growth 15-18%" — t=0.85, tier=2 (Gartner)
effective = 0.85 × 0.85 × 1.0 × 1.0 = 0.7225 → ACCEPT
Claim 3: "Pipeline strong" — t=0.72, tier=4 (CRM estimate)
effective = 0.72 × 0.50 × 1.0 × 1.0 = 0.36 → REJECT
Copilot's AI Claims
Claim 4: "AI adoption 34%" — t=0.78, tier=2 (McKinsey survey)
effective = 0.78 × 0.85 × 1.0 × 1.0 = 0.663 → LOW
Claim 5: "H2 will accelerate to 25%" — t=0.63, tier=5 (AI inference)
effective = 0.63 × 0.30 × 1.0 × 1.0 = 0.189 → REJECT
After Agent Consumption (penalty = -0.03)
Claims surviving threshold 0.5 filter: Claims 1, 2 (above 0.5 effective trust)
After -0.03 penalty:
Claim 1: 0.95 × 1.00 × 1.0 × 0.97 = 0.9215 → ACCEPT
Claim 2: 0.82 × 0.85 × 1.0 × 0.97 = 0.6760 → LOW
Temporal Decay Example
Claim 1 with decay=90 (quarterly):
Day 0: 0.98 × 1.00 × 1.000 = 0.980 → ACCEPT
Day 45: 0.98 × 1.00 × 0.707 = 0.693 → LOW
Day 90: 0.98 × 1.00 × 0.500 = 0.490 → LOW
Day 180: 0.98 × 1.00 × 0.250 = 0.245 → REJECT
Day 365: 0.98 × 1.00 × 0.060 = 0.059 → REJECT