Evidence Weighting

Evidence Weighting is the logic used to rank and reconcile evidence across sources. In allocator intelligence, the same claim may appear in multiple places with different reliability (e.g., registry filings vs marketing pages vs secondary databases). Weighting determines which data becomes canonical, which stays “possible,” and which is rejected.

From a product perspective, weighting prevents volatility. Without it, the platform flips between truths as sources update, creating instability and user distrust.

How teams define evidence weighting risk drivers

Teams evaluate weighting through:

• Source trust tiering: primary vs secondary vs self-reported
• Recency weighting: decay curves and “as-of” logic
• Corroboration rules: multiple independent sources increase confidence
• Specificity scoring: precise claims beat vague statements
• Conflict resolution: precedence and tie-breaker logic
• Human review triggers: thresholds that require verification
• Explainability: ability to show “why this is believed”

Allocator framing:
“When evidence conflicts, does the system act like an analyst—or like a coin flip?”

Where weighting matters most

• investment mandate classification (invests in hospitality, secondaries, etc.)
• contact roles and decision-maker mapping
• ownership links and relationship graphs
• change monitoring alerts (avoid false positives)

How weighting changes outcomes

Strong weighting discipline:

• produces stable, defensible outputs
• reduces whiplash updates and user confusion
• improves signal-to-noise in alerts
• supports reliable scoring and ranking systems

Weak weighting discipline:

• creates “data flip” behavior
• amplifies low-quality sources
• increases false positives in monitoring
• erodes trust in graphs and recommendations

How teams evaluate discipline

Confidence increases when:

• weighting tiers are explicit and consistent
• the system preserves conflicting evidence instead of overwriting blindly
• confidence scores are calibrated and interpretable
• review queues exist for high-impact or ambiguous claims

What slows decision-making and adoption

• black-box confidence scores with no rationale
• low-quality sources overriding better evidence
• lack of recency logic (old pages treated as current truth)
• no mechanism for dispute/correction workflows

Common misconceptions

• “More sources always helps” → low-quality sources add noise.
• “Weighting is subjective” → it’s rules + calibration + evidence.
• “Confidence scores solve it” → scores without explainability don’t.

Key questions during diligence

• How are sources tiered and weighted?
• What is your recency/decay logic?
• How do you treat conflicting evidence without losing context?
• What triggers human verification?
• Can users see why a value is considered true?