03 — Surrogate Calibration Instrument
Goal
Make the refuter EARN its veto: track refuter-prediction vs measured-outcome agreement and down-weight an uncalibrated refuter. The 2-of-2 prediction failures say the surrogate is unvalidated.
Implementation Sketch
- calibration_record appended per measured bet: {refuter_majority_verdict, measured_direction, agreed: bool}.
- veto weight = f(rolling agreement); a refuter set with low agreement cannot unilaterally withdraw a bet — it can only de-prioritize.
- Append-only; calibration history is read via the §13 read API.
Spec References
- scripts/ledger_evolution_runtime/verification.py (refuter records)
- learning-ledger.md §11.2 (verification_record), §3 teeth
- Evidence: 2-of-2 measured bets contradicted the a-priori refuter prediction
Work Items
- Implement calibration tracking: for every empirically-measured bet, record refuter-prediction vs measured-outcome agreement into the calibration_record (the 2-of-2-predictions-wrong evidence motivates this).
- Feed calibration back into verification: an under-calibrated refuter loses veto weight (calibration gates the gate). Plan-agnostic; the calibration history lives in the plan’s own evolutions[], never in generic tooling.
- Cover with scripts/ledger_evolution_runtime/tests/test_calibration.py (agreement recorded + veto-weight feedback); update learning-ledger.md §11.
Fresh intel (regenerated)
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(full dossier: surrogate-calibration—s-e40e5a1c.intel.json)