/**
 * Confidence Calibration Analytics - empirical evidence for "is confidence trustworthy?"
 *
 * Wraps EscalationLedger (mirroring how UsageAnalytics wraps SpendingTracker) to answer
 * with data, not assumption: within each confidence bucket, what fraction of escalations
 * were aborted/modified vs. proceeded? A well-calibrated system should show aborts/modifies
 * concentrated in low buckets — if a high-confidence bucket shows a meaningful abort rate,
 * that's measured evidence of overconfidence.
 */
import { type EscalationLedger } from './escalation-ledger.js';
export interface ConfidenceBucketSummary {
    abortCount: number;
    label: string;
    modifyCount: number;
    proceedCount: number;
    totalCount: number;
}
export interface ConfidenceCalibrationOptions {
    sinceDate?: string;
    stage?: string;
}
export interface ConfidenceCalibrationPeriod {
    from: null | string;
    to: null | string;
}
export interface ConfidenceCalibrationSummary {
    byConfidenceBucket: ConfidenceBucketSummary[];
    byTriggeredCriterion: TriggeredCriterionSummary[];
    period: ConfidenceCalibrationPeriod;
    totalEscalations: number;
}
export interface TriggeredCriterionSummary {
    count: number;
    criterion: string;
}
export declare class ConfidenceCalibrationAnalytics {
    private readonly ledger;
    constructor(ledger: EscalationLedger);
    analyze(options?: ConfidenceCalibrationOptions): ConfidenceCalibrationSummary;
    generateJsonReport(options?: ConfidenceCalibrationOptions): string;
    generateMarkdownReport(options?: ConfidenceCalibrationOptions): string;
    private bucketByConfidence;
    private computePeriod;
    private tallyTriggeredCriteria;
}
export declare function getConfidenceCalibrationAnalytics(): ConfidenceCalibrationAnalytics;
export declare function resetConfidenceCalibrationAnalytics(): void;
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