#!/usr/bin/env python3
"""
Operator Brief — Complex Decision Surfaces with Uncertainty Thresholds

Handles multi-factor decisions with explicit uncertainty quantification.
Delegates complex decisions when confidence < threshold.
"""

import json
from datetime import datetime


class OperatorBrief:
    def __init__(self, confidence_threshold=0.7):
        self.threshold = confidence_threshold
        self.decisions = []
        self.uncertainty_log = []

    def evaluate(self, decision, factors, uncertainty=0.0):
        """
        Evaluate decision with explicit uncertainty.
        
        Args:
            decision: Decision string
            factors: Dict of contributing factors with weights
            uncertainty: Explicit uncertainty score (0.0-1.0)
        """
        confidence = 1.0 - uncertainty
        
        if confidence >= self.threshold:
            # Direct decision
            result = {
                'decision': decision,
                'confidence': confidence,
                'factors': factors,
                'uncertainty': uncertainty,
                'timestamp': datetime.utcnow().isoformat(),
                'action': 'EXECUTE'
            }
        else:
            # Defer to higher-level analysis
            result = {
                'decision': decision,
                'confidence': confidence,
                'factors': factors,
                'uncertainty': uncertainty,
                'timestamp': datetime.utcnow().isoformat(),
                'action': 'DEFER',
                'reason': f'Confidence {confidence:.2f} < threshold {self.threshold:.2f}'
            }
        
        self.decisions.append(result)
        self.uncertainty_log.append(uncertainty)
        return result

    def aggregate_uncertainty(self):
        """Return average uncertainty across all decisions."""
        if not self.uncertainty_log:
            return 0.0
        return sum(self.uncertainty_log) / len(self.uncertainty_log)

    def to_json(self):
        """Export current state for logging."""
        return json.dumps({
            'decisions_count': len(self.decisions),
            'avg_uncertainty': self.aggregate_uncertainty(),
            'recent_decisions': self.decisions[-10:]
        }, indent=2)


# Singleton instance
operator_brief = OperatorBrief(confidence_threshold=0.7)


if __name__ == "__main__":
    # Test usage
    result = operator_brief.evaluate(
        "Deploy MTP monitoring to production",
        {
            'monitoring_active': True,
            'data_collection': True,
            'resource_impact': 'moderate'
        },
        uncertainty=0.2
    )
    print(f"Decision: {result['action']}")
    print(f"Confidence: {result['confidence']:.2f}")