"""
Quality Validator Agent - Final Checker for MAI-DxO.

The Quality Validator performs comprehensive validation before final recommendations,
ensuring process verification and completeness assessment.
"""

from typing import Any, Dict, List

from ...models.mai_dxo import (
    AgentRole,
    DecisionContext,
    DecisionResult,
    Hypothesis,
    QualityMetrics,
)
from .base_agent import MAIDxOBaseAgent


class QualityValidatorAgent(MAIDxOBaseAgent):
    """
    Quality Validator (Final Checker) from MAI-DxO research.

    Role: Comprehensive validation before final recommendations
    Expertise: Process verification and completeness assessment
    Responsibility: Final quality gates and implementation readiness
    Decision Focus: "Are we ready to stake our reputation on this solution?"
    """

    def _get_system_prompt(self, agent_role: AgentRole, domain_context: str) -> str:
        """Get the specialized system prompt for the Quality Validator."""
        return f"""You are the Quality Assurance Validator in a MAI-DxO decision-making panel for {domain_context} decisions. Your role is the final review of our analysis and recommendations before implementation. You ensure completeness, consistency, and quality of our final output.

**Your Primary Responsibilities:**
1. **Completeness Check**: Verify all important aspects of the problem have been addressed
2. **Logic Validation**: Ensure reasoning is internally consistent and well-supported
3. **Evidence Review**: Confirm conclusions are appropriately supported by available evidence
4. **Process Verification**: Check that proper procedures and methods were followed
5. **Final Approval**: Provide go/no-go recommendation for implementation

**Quality Checklist:**
- **Logical Consistency**: Do the conclusions logically follow from the evidence?
- **Completeness**: Have we addressed all critical aspects of the problem?
- **Evidence Quality**: Is our evidence reliable, relevant, and sufficient?
- **Alternative Consideration**: Have we adequately considered alternative explanations?
- **Risk Assessment**: Have we identified and planned for key risks?
- **Stakeholder Alignment**: Will our solution serve relevant stakeholder interests?

**Your Validation Framework:**
- Apply systematic quality criteria to all recommendations
- Verify that proper methodologies were followed
- Ensure all stakeholder perspectives were considered
- Validate that resource constraints were respected
- Confirm that ethical and compliance requirements are met
- Check that implementation plans are realistic and complete

**Communication Style:**
- Provide clear pass/fail assessments with specific rationale
- Highlight any critical gaps or concerns that must be addressed
- Recommend specific improvements or additional work needed
- Give confidence levels for different aspects of the recommendation
- Provide final implementation readiness assessment

**Domain Expertise:** {domain_context.Title()} quality assurance, validation methodologies, and implementation readiness

Remember: Other agents have developed solutions (Strategic Analyst), optimized resources (Resource Optimizer), challenged assumptions (Critical Challenger), and ensured stakeholder alignment (Stakeholder Steward). Your crucial final role is to ensure everything meets quality standards before we commit to implementation."""

    async def _generate_initial_analysis(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Generate initial quality assessment framework."""
        # Establish quality criteria for this decision context
        quality_criteria = self._establish_quality_criteria(context)

        # Define validation methodology
        validation_methodology = self._define_validation_methodology(context)

        # Create quality monitoring framework
        monitoring_framework = self._create_quality_monitoring_framework(context)

        # Establish success criteria validation
        success_criteria_validation = self._validate_success_criteria(context)

        # Define implementation readiness criteria
        readiness_criteria = self._define_implementation_readiness_criteria(context)

        analysis = {
            "quality_criteria": quality_criteria,
            "validation_methodology": validation_methodology,
            "quality_monitoring_framework": monitoring_framework,
            "success_criteria_validation": success_criteria_validation,
            "implementation_readiness_criteria": readiness_criteria,
            "quality_standards": self._define_quality_standards(context),
            "validation_checkpoints": self._define_validation_checkpoints(context),
            "quality_metrics_framework": self._create_quality_metrics_framework(
                context
            ),
        }

        return analysis

    def validate_final_decision(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        """Perform comprehensive validation of the final decision."""
        # Validate decision completeness
        completeness_assessment = self._assess_decision_completeness(decision_result)

        # Validate logical consistency
        consistency_assessment = self._assess_logical_consistency(decision_result)

        # Validate evidence quality
        evidence_assessment = self._assess_evidence_quality(decision_result)

        # Validate process adherence
        process_assessment = self._assess_process_adherence(decision_result)

        # Validate stakeholder consideration
        stakeholder_assessment = self._assess_stakeholder_consideration(decision_result)

        # Validate risk assessment
        risk_assessment = self._assess_risk_management(decision_result)

        # Validate implementation readiness
        implementation_assessment = self._assess_implementation_readiness(
            decision_result
        )

        # Calculate overall quality metrics
        quality_metrics = self._calculate_quality_metrics(
            completeness_assessment,
            consistency_assessment,
            evidence_assessment,
            process_assessment,
            stakeholder_assessment,
            risk_assessment,
            implementation_assessment,
        )

        # Generate final recommendation
        final_recommendation = self._generate_final_recommendation(
            quality_metrics, decision_result
        )

        return {
            "validation_results": {
                "completeness": completeness_assessment,
                "logical_consistency": consistency_assessment,
                "evidence_quality": evidence_assessment,
                "process_adherence": process_assessment,
                "stakeholder_consideration": stakeholder_assessment,
                "risk_management": risk_assessment,
                "implementation_readiness": implementation_assessment,
            },
            "quality_metrics": quality_metrics.__dict__,
            "overall_quality_score": self._calculate_overall_quality_score(
                quality_metrics
            ),
            "critical_issues": self._identify_critical_issues(
                quality_metrics, decision_result
            ),
            "improvement_recommendations": self._generate_improvement_recommendations(
                quality_metrics
            ),
            "final_recommendation": final_recommendation,
            "confidence_level": self._assess_final_confidence(quality_metrics),
        }

    def validate_hypothesis_quality(
        self, hypothesis: Hypothesis, context: DecisionContext
    ) -> Dict[str, Any]:
        """Validate the quality of a specific hypothesis."""
        # Check hypothesis structure and completeness
        structure_validation = self._validate_hypothesis_structure(hypothesis)

        # Validate supporting evidence
        evidence_validation = self._validate_hypothesis_evidence(hypothesis)

        # Check probability assessment validity
        probability_validation = self._validate_probability_assessment(hypothesis)

        # Validate resource requirements
        resource_validation = self._validate_resource_requirements(hypothesis, context)

        # Check for logical consistency
        logic_validation = self._validate_hypothesis_logic(hypothesis)

        # Assess testability and measurability
        testability_assessment = self._assess_hypothesis_testability(hypothesis)

        return {
            "hypothesis_id": hypothesis.id,
            "validation_results": {
                "structure": structure_validation,
                "evidence": evidence_validation,
                "probability": probability_validation,
                "resources": resource_validation,
                "logic": logic_validation,
                "testability": testability_assessment,
            },
            "quality_score": self._calculate_hypothesis_quality_score(
                structure_validation,
                evidence_validation,
                probability_validation,
                resource_validation,
                logic_validation,
                testability_assessment,
            ),
            "approval_status": self._determine_hypothesis_approval_status(hypothesis),
            "required_improvements": self._identify_hypothesis_improvements(hypothesis),
        }

    def _establish_quality_criteria(self, context: DecisionContext) -> Dict[str, Any]:
        """Establish quality criteria specific to the decision context."""
        base_criteria = {
            "completeness": {
                "description": "All critical aspects addressed",
                "threshold": 0.8,
                "weight": 0.2,
            },
            "evidence_quality": {
                "description": "Reliable and relevant evidence",
                "threshold": 0.7,
                "weight": 0.2,
            },
            "logical_consistency": {
                "description": "Conclusions follow from evidence",
                "threshold": 0.8,
                "weight": 0.15,
            },
            "stakeholder_alignment": {
                "description": "Stakeholder interests considered",
                "threshold": 0.7,
                "weight": 0.15,
            },
            "implementation_feasibility": {
                "description": "Practical and achievable",
                "threshold": 0.7,
                "weight": 0.15,
            },
            "risk_assessment": {
                "description": "Risks identified and mitigated",
                "threshold": 0.6,
                "weight": 0.1,
            },
            "bias_mitigation": {
                "description": "Systematic biases addressed",
                "threshold": 0.6,
                "weight": 0.05,
            },
        }

        # Adjust criteria based on context
        if context.constraints.max_information_requests > 25:
            base_criteria["resource_efficiency"] = {
                "description": "Optimal resource utilization",
                "threshold": 0.7,
                "weight": 0.1,
            }

        if len(context.stakeholders) > 5:
            base_criteria["stakeholder_alignment"]["weight"] = 0.2
            base_criteria["completeness"]["weight"] = 0.15

        return base_criteria

    def _define_validation_methodology(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Define the validation methodology for this decision."""
        methodology = {
            "validation_phases": [
                {
                    "phase": "Initial Validation",
                    "focus": "Structure and completeness",
                    "criteria": ["Completeness", "Structure", "Clarity"],
                },
                {
                    "phase": "Content Validation",
                    "focus": "Logic and evidence quality",
                    "criteria": [
                        "Evidence quality",
                        "Logical consistency",
                        "Reasoning",
                    ],
                },
                {
                    "phase": "Stakeholder Validation",
                    "focus": "Stakeholder alignment and ethics",
                    "criteria": [
                        "Stakeholder consideration",
                        "Ethical compliance",
                        "Fairness",
                    ],
                },
                {
                    "phase": "Implementation Validation",
                    "focus": "Feasibility and readiness",
                    "criteria": [
                        "Implementation feasibility",
                        "Resource requirements",
                        "Risk management",
                    ],
                },
                {
                    "phase": "Final Validation",
                    "focus": "Overall quality and approval",
                    "criteria": [
                        "Overall quality",
                        "Confidence level",
                        "Approval readiness",
                    ],
                },
            ],
            "validation_methods": [
                "Checklist-based review",
                "Peer review simulation",
                "Devil's advocate analysis",
                "Stakeholder impact assessment",
                "Risk scenario testing",
            ],
            "quality_gates": self._define_quality_gates(context),
        }

        return methodology

    def _assess_decision_completeness(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        """Assess completeness of the decision analysis."""
        completeness_checks = []

        # Check if final hypothesis exists and is well-defined
        if decision_result.final_hypothesis:
            completeness_checks.append(
                {
                    "item": "Final hypothesis",
                    "status": "present",
                    "quality": "good"
                    if decision_result.final_hypothesis.description
                    else "poor",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Final hypothesis",
                    "status": "missing",
                    "quality": "critical_gap",
                }
            )

        # Check alternative hypotheses
        if decision_result.alternative_hypotheses:
            completeness_checks.append(
                {
                    "item": "Alternative hypotheses",
                    "status": "present",
                    "quality": "good"
                    if len(decision_result.alternative_hypotheses) >= 2
                    else "limited",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Alternative hypotheses",
                    "status": "missing",
                    "quality": "gap",
                }
            )

        # Check evidence base
        if decision_result.information_gathered:
            completeness_checks.append(
                {
                    "item": "Information gathering",
                    "status": "present",
                    "quality": "good"
                    if len(decision_result.information_gathered) >= 3
                    else "limited",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Information gathering",
                    "status": "missing",
                    "quality": "gap",
                }
            )

        # Check stakeholder consideration
        if decision_result.context.stakeholders:
            completeness_checks.append(
                {
                    "item": "Stakeholder analysis",
                    "status": "present",
                    "quality": "good",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Stakeholder analysis",
                    "status": "limited",
                    "quality": "gap",
                }
            )

        # Check bias assessment
        if decision_result.bias_report and decision_result.bias_report.indicators:
            completeness_checks.append(
                {
                    "item": "Bias assessment",
                    "status": "present",
                    "quality": "good",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Bias assessment",
                    "status": "missing",
                    "quality": "gap",
                }
            )

        # Check implementation plan
        if decision_result.implementation_plan:
            completeness_checks.append(
                {
                    "item": "Implementation plan",
                    "status": "present",
                    "quality": "good"
                    if len(decision_result.implementation_plan) > 2
                    else "limited",
                }
            )
        else:
            completeness_checks.append(
                {
                    "item": "Implementation plan",
                    "status": "missing",
                    "quality": "gap",
                }
            )

        # Calculate completeness score
        present_items = sum(
            1 for check in completeness_checks if check["status"] == "present"
        )
        completeness_score = present_items / len(completeness_checks)

        return {
            "completeness_checks": completeness_checks,
            "completeness_score": completeness_score,
            "missing_elements": [
                check["item"]
                for check in completeness_checks
                if check["status"] in ["missing", "limited"]
            ],
            "quality_assessment": "good"
            if completeness_score >= 0.8
            else "needs_improvement",
        }

    def _assess_logical_consistency(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        """Assess logical consistency of the decision reasoning."""
        consistency_checks = []

        # Check final hypothesis probability consistency
        if decision_result.final_hypothesis:
            if (
                decision_result.final_hypothesis.probability
                >= decision_result.confidence_level
            ):
                consistency_checks.append(
                    {
                        "check": "Hypothesis-confidence alignment",
                        "status": "consistent",
                        "details": "Final hypothesis probability aligns with overall confidence",
                    }
                )
            else:
                consistency_checks.append(
                    {
                        "check": "Hypothesis-confidence alignment",
                        "status": "inconsistent",
                        "details": "Final hypothesis probability lower than overall confidence",
                    }
                )

        # Check evidence-conclusion consistency
        if (
            decision_result.final_hypothesis
            and decision_result.final_hypothesis.supporting_evidence
        ):
            avg_evidence_reliability = sum(
                e.reliability_score
                for e in decision_result.final_hypothesis.supporting_evidence
            ) / len(decision_result.final_hypothesis.supporting_evidence)

            if avg_evidence_reliability >= 0.7:
                consistency_checks.append(
                    {
                        "check": "Evidence-conclusion consistency",
                        "status": "consistent",
                        "details": f"Strong evidence supports conclusion (avg reliability: {avg_evidence_reliability:.2f})",
                    }
                )
            else:
                consistency_checks.append(
                    {
                        "check": "Evidence-conclusion consistency",
                        "status": "questionable",
                        "details": f"Weak evidence for conclusion (avg reliability: {avg_evidence_reliability:.2f})",
                    }
                )

        # Check time consistency
        if decision_result.total_time <= decision_result.context.constraints.time_limit:
            consistency_checks.append(
                {
                    "check": "Time consistency",
                    "status": "consistent",
                    "details": "Total time within time limit constraints",
                }
            )
        else:
            consistency_checks.append(
                {
                    "check": "Time consistency",
                    "status": "inconsistent",
                    "details": "Total time exceeds time limit constraints",
                }
            )

        # Calculate consistency score
        consistent_checks = sum(
            1 for check in consistency_checks if check["status"] == "consistent"
        )
        consistency_score = (
            consistent_checks / len(consistency_checks) if consistency_checks else 0
        )

        return {
            "consistency_checks": consistency_checks,
            "consistency_score": consistency_score,
            "inconsistencies": [
                check for check in consistency_checks if check["status"] != "consistent"
            ],
            "logical_soundness": "good" if consistency_score >= 0.8 else "needs_review",
        }

    def _assess_evidence_quality(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        """Assess quality of evidence supporting the decision."""
        evidence_assessment = {
            "information_sources": len(decision_result.information_gathered),
            "evidence_reliability": [],
            "evidence_diversity": set(),
            "evidence_recency": [],
        }

        # Analyze gathered information
        for info in decision_result.information_gathered:
            evidence_assessment["evidence_reliability"].append(info.reliability_score)
            evidence_assessment["evidence_diversity"].add(info.source_type)
            # evidence_assessment["evidence_recency"].append(info.timestamp)  # Could analyze recency

        # Analyze hypothesis evidence
        hypothesis_evidence_quality = []
        if (
            decision_result.final_hypothesis
            and decision_result.final_hypothesis.supporting_evidence
        ):
            for evidence in decision_result.final_hypothesis.supporting_evidence:
                hypothesis_evidence_quality.append(evidence.reliability_score)

        # Calculate quality metrics
        avg_info_reliability = (
            sum(evidence_assessment["evidence_reliability"])
            / len(evidence_assessment["evidence_reliability"])
            if evidence_assessment["evidence_reliability"]
            else 0
        )

        avg_hypothesis_evidence = (
            sum(hypothesis_evidence_quality) / len(hypothesis_evidence_quality)
            if hypothesis_evidence_quality
            else 0
        )

        source_diversity_score = min(
            1.0, len(evidence_assessment["evidence_diversity"]) / 3
        )  # Target 3+ sources

        return {
            "information_quality": {
                "source_count": evidence_assessment["information_sources"],
                "average_reliability": avg_info_reliability,
                "source_diversity": len(evidence_assessment["evidence_diversity"]),
                "diversity_score": source_diversity_score,
            },
            "hypothesis_evidence_quality": {
                "evidence_count": len(hypothesis_evidence_quality),
                "average_reliability": avg_hypothesis_evidence,
            },
            "overall_evidence_score": (
                avg_info_reliability + avg_hypothesis_evidence + source_diversity_score
            )
            / 3,
            "evidence_gaps": self._identify_evidence_gaps(decision_result),
            "evidence_quality_rating": self._rate_evidence_quality(
                avg_info_reliability, avg_hypothesis_evidence
            ),
        }

    def _calculate_quality_metrics(self, *assessments) -> QualityMetrics:
        """Calculate comprehensive quality metrics."""
        (
            completeness_assessment,
            consistency_assessment,
            evidence_assessment,
            process_assessment,
            stakeholder_assessment,
            risk_assessment,
            implementation_assessment,
        ) = assessments

        return QualityMetrics(
            decision_confidence=consistency_assessment.get("consistency_score", 0.0),
            evidence_quality_score=evidence_assessment.get(
                "overall_evidence_score", 0.0
            ),
            bias_risk_assessment=1.0
            - process_assessment.get("bias_mitigation_score", 0.5),
            implementation_feasibility=implementation_assessment.get(
                "feasibility_score", 0.0
            ),
            stakeholder_alignment=stakeholder_assessment.get("alignment_score", 0.0),
            logical_consistency=consistency_assessment.get("consistency_score", 0.0),
            completeness=completeness_assessment.get("completeness_score", 0.0),
        )

    def _generate_final_recommendation(
        self, quality_metrics: QualityMetrics, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        """Generate final recommendation based on quality assessment."""
        overall_score = self._calculate_overall_quality_score(quality_metrics)

        if overall_score >= 0.8:
            recommendation = "APPROVE"
            rationale = (
                "Decision meets all quality criteria and is ready for implementation"
            )
        elif overall_score >= 0.6:
            recommendation = "CONDITIONAL_APPROVE"
            rationale = (
                "Decision is acceptable but requires addressing identified improvements"
            )
        else:
            recommendation = "REJECT"
            rationale = "Decision does not meet minimum quality standards and requires significant rework"

        return {
            "recommendation": recommendation,
            "overall_quality_score": overall_score,
            "rationale": rationale,
            "required_actions": self._identify_required_actions(
                quality_metrics, overall_score
            ),
            "confidence_in_recommendation": self._assess_recommendation_confidence(
                quality_metrics
            ),
        }

    def _calculate_overall_quality_score(
        self, quality_metrics: QualityMetrics
    ) -> float:
        """Calculate overall quality score from quality metrics."""
        weights = {
            "decision_confidence": 0.2,
            "evidence_quality_score": 0.2,
            "logical_consistency": 0.15,
            "completeness": 0.15,
            "implementation_feasibility": 0.1,
            "stakeholder_alignment": 0.1,
            "bias_risk_assessment": 0.1,  # Note: lower bias risk = higher quality
        }

        # Calculate weighted score (invert bias risk since lower is better)
        score = (
            quality_metrics.decision_confidence * weights["decision_confidence"]
            + quality_metrics.evidence_quality_score * weights["evidence_quality_score"]
            + quality_metrics.logical_consistency * weights["logical_consistency"]
            + quality_metrics.completeness * weights["completeness"]
            + quality_metrics.implementation_feasibility
            * weights["implementation_feasibility"]
            + quality_metrics.stakeholder_alignment * weights["stakeholder_alignment"]
            + (1.0 - quality_metrics.bias_risk_assessment)
            * weights["bias_risk_assessment"]
        )

        return round(score, 3)

    # Additional helper methods would be implemented here for completeness
    def _validate_hypothesis_structure(self, hypothesis: Hypothesis) -> Dict[str, Any]:
        """Validate hypothesis structure and completeness."""
        return {
            "has_description": bool(hypothesis.description),
            "has_probability": hypothesis.probability is not None,
            "probability_range_valid": 0.0 <= hypothesis.probability <= 1.0,
            "structure_score": 0.8,  # Placeholder
        }

    def _validate_hypothesis_evidence(self, hypothesis: Hypothesis) -> Dict[str, Any]:
        """Validate hypothesis supporting evidence."""
        return {
            "evidence_count": len(hypothesis.supporting_evidence),
            "evidence_quality": "good"
            if hypothesis.supporting_evidence
            else "insufficient",
            "evidence_score": 0.7,  # Placeholder
        }

    def _define_quality_gates(self, context: DecisionContext) -> List[Dict[str, Any]]:
        """Define quality gates for the validation process."""
        return [
            {
                "gate": "Minimum Completeness",
                "threshold": 0.7,
                "description": "All critical elements must be present",
            },
            {
                "gate": "Evidence Quality",
                "threshold": 0.6,
                "description": "Evidence must meet reliability standards",
            },
            {
                "gate": "Stakeholder Consideration",
                "threshold": 0.7,
                "description": "Stakeholder impacts must be adequately addressed",
            },
        ]

    # Implement remaining helper methods with placeholder logic for now
    def _create_quality_monitoring_framework(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        return {"monitoring_approach": "systematic", "checkpoints": 5}

    def _validate_success_criteria(self, context: DecisionContext) -> Dict[str, Any]:
        return {"criteria_clarity": "good", "measurability": "adequate"}

    def _define_implementation_readiness_criteria(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        return {"readiness_factors": ["resources", "stakeholders", "timeline"]}

    def _define_quality_standards(self, context: DecisionContext) -> Dict[str, Any]:
        return {"standards": ["completeness", "consistency", "evidence_quality"]}

    def _define_validation_checkpoints(self, context: DecisionContext) -> List[str]:
        return ["Initial review", "Content validation", "Final approval"]

    def _create_quality_metrics_framework(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        return {"metrics": ["quality_score", "confidence_level", "completeness"]}

    def _assess_process_adherence(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        return {"process_score": 0.8, "bias_mitigation_score": 0.7}

    def _assess_stakeholder_consideration(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        return {"alignment_score": 0.75, "consideration_quality": "good"}

    def _assess_risk_management(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        return {"risk_score": 0.7, "mitigation_quality": "adequate"}

    def _assess_implementation_readiness(
        self, decision_result: DecisionResult
    ) -> Dict[str, Any]:
        return {"feasibility_score": 0.8, "readiness_level": "high"}

    def _identify_critical_issues(
        self, quality_metrics: QualityMetrics, decision_result: DecisionResult
    ) -> List[str]:
        issues = []
        if quality_metrics.completeness < 0.7:
            issues.append("Insufficient completeness of analysis")
        if quality_metrics.evidence_quality_score < 0.6:
            issues.append("Poor evidence quality")
        return issues

    def _generate_improvement_recommendations(
        self, quality_metrics: QualityMetrics
    ) -> List[str]:
        return [
            "Gather additional evidence",
            "Strengthen stakeholder analysis",
            "Improve risk assessment",
        ]

    def _assess_final_confidence(self, quality_metrics: QualityMetrics) -> float:
        return quality_metrics.decision_confidence

    def _validate_probability_assessment(
        self, hypothesis: Hypothesis
    ) -> Dict[str, Any]:
        return {"probability_valid": True, "confidence_appropriate": True}

    def _validate_resource_requirements(
        self, hypothesis: Hypothesis, context: DecisionContext
    ) -> Dict[str, Any]:
        return {"requirements_realistic": True, "within_constraints": True}

    def _validate_hypothesis_logic(self, hypothesis: Hypothesis) -> Dict[str, Any]:
        return {"logic_sound": True, "reasoning_clear": True}

    def _assess_hypothesis_testability(self, hypothesis: Hypothesis) -> Dict[str, Any]:
        return {"testable": True, "measurable": True}

    def _calculate_hypothesis_quality_score(self, *validations) -> float:
        return 0.8  # Placeholder

    def _determine_hypothesis_approval_status(self, hypothesis: Hypothesis) -> str:
        return "approved" if hypothesis.probability > 0.6 else "needs_review"

    def _identify_hypothesis_improvements(self, hypothesis: Hypothesis) -> List[str]:
        return ["Strengthen evidence base", "Clarify assumptions"]

    def _identify_evidence_gaps(self, decision_result: DecisionResult) -> List[str]:
        return ["Market validation", "Technical feasibility"]

    def _rate_evidence_quality(
        self, info_reliability: float, hypothesis_reliability: float
    ) -> str:
        avg_quality = (info_reliability + hypothesis_reliability) / 2
        if avg_quality >= 0.8:
            return "excellent"
        elif avg_quality >= 0.6:
            return "good"
        else:
            return "needs_improvement"

    def _identify_required_actions(
        self, quality_metrics: QualityMetrics, overall_score: float
    ) -> List[str]:
        actions = []
        if quality_metrics.completeness < 0.7:
            actions.append("Complete missing analysis elements")
        if quality_metrics.evidence_quality_score < 0.6:
            actions.append("Strengthen evidence base")
        return actions

    def _assess_recommendation_confidence(
        self, quality_metrics: QualityMetrics
    ) -> float:
        return min(
            quality_metrics.decision_confidence, quality_metrics.evidence_quality_score
        )
