"""
Stakeholder Steward Agent - Governance Specialist for MAI-DxO.

The Stakeholder Steward ensures ethical, sustainable, and compliant decisions
by analyzing long-term consequences and stakeholder impacts.
"""

from typing import Any, Dict, List

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


class StakeholderStewardAgent(MAIDxOBaseAgent):
    """
    Stakeholder Steward (Governance Specialist) from MAI-DxO research.

    Role: Ensures ethical, sustainable, and compliant decisions
    Expertise: Long-term consequences and stakeholder impact analysis
    Responsibility: Balances immediate goals with broader responsibilities
    Decision Focus: "Is this the right thing to do for all stakeholders?"
    """

    def _get_system_prompt(self, agent_role: AgentRole, domain_context: str) -> str:
        """Get the specialized system prompt for the Stakeholder Steward."""
        return f"""You are the Governance and Ethics Specialist in a MAI-DxO decision-making panel for {domain_context} decisions. Your role is to ensure our approach is responsible, sustainable, and aligned with broader stakeholder interests. You consider the long-term implications and ethical dimensions of our decisions.

**Your Primary Responsibilities:**
1. **Stakeholder Impact**: Consider effects on all relevant stakeholders, not just immediate beneficiaries
2. **Long-term Consequences**: Evaluate sustainability and long-term implications of proposed solutions
3. **Ethical Compliance**: Ensure approaches align with relevant ethical standards and professional codes
4. **Risk Management**: Identify potential negative consequences and mitigation strategies
5. **Value Alignment**: Verify decisions align with organizational values and social responsibility

**Key Considerations:**
- **Stakeholder Fairness**: Who benefits and who bears impacts from our decisions?
- **Transparency**: Are our methods and reasoning appropriately transparent to relevant parties?
- **Sustainability**: Are we optimizing for short-term gains at the expense of long-term value?
- **Professional Standards**: Do our approaches meet relevant industry or professional ethical standards?
- **Social Impact**: What are the broader societal implications of our recommendations?

**Your Analysis Framework:**
- Map all affected stakeholders and their interests
- Assess both intended and unintended consequences
- Evaluate decisions against ethical frameworks and standards
- Consider precedent-setting implications
- Balance competing stakeholder interests fairly

**Communication Style:**
- Clearly identify stakeholder impacts and trade-offs
- Highlight ethical considerations and compliance requirements
- Suggest modifications to improve stakeholder alignment
- Recommend stakeholder engagement and communication strategies
- Flag any red flags or unacceptable risks

**Domain Expertise:** {domain_context.title()} ethics, governance, stakeholder management, and regulatory compliance

Remember: Other agents will develop solutions (Strategic Analyst), optimize resources (Resource Optimizer), challenge assumptions (Critical Challenger), and validate quality (Quality Validator). Your crucial role is to ensure we do the right thing for all stakeholders and maintain long-term value and reputation."""

    async def _generate_initial_analysis(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Generate initial stakeholder and governance analysis."""
        # Map stakeholders and their interests
        stakeholder_mapping = self._map_stakeholders_and_interests(context)

        # Analyze ethical considerations
        ethical_assessment = self._assess_ethical_considerations(context)

        # Evaluate compliance requirements
        compliance_assessment = self._evaluate_compliance_requirements(context)

        # Assess long-term sustainability
        sustainability_analysis = self._analyze_sustainability_factors(context)

        # Identify potential conflicts of interest
        conflict_analysis = self._identify_potential_conflicts(
            context, stakeholder_mapping
        )

        # Evaluate transparency and communication needs
        transparency_assessment = self._assess_transparency_requirements(context)

        analysis = {
            "stakeholder_mapping": stakeholder_mapping,
            "ethical_assessment": ethical_assessment,
            "compliance_requirements": compliance_assessment,
            "sustainability_analysis": sustainability_analysis,
            "conflict_of_interest_analysis": conflict_analysis,
            "transparency_requirements": transparency_assessment,
            "governance_recommendations": self._generate_governance_recommendations(
                context
            ),
            "stakeholder_engagement_strategy": self._recommend_stakeholder_engagement(
                context, stakeholder_mapping
            ),
            "risk_mitigation_framework": self._develop_risk_mitigation_framework(
                context
            ),
        }

        return analysis

    def assess_hypothesis_stakeholder_impact(
        self, hypothesis: Hypothesis, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess stakeholder impact of a specific hypothesis."""
        # Map hypothesis impact to each stakeholder group
        stakeholder_impacts = self._map_hypothesis_stakeholder_impacts(
            hypothesis, context
        )

        # Assess ethical implications
        ethical_implications = self._assess_hypothesis_ethics(hypothesis, context)

        # Evaluate fairness and equity
        fairness_assessment = self._assess_fairness_and_equity(
            hypothesis, stakeholder_impacts
        )

        # Check for compliance issues
        compliance_check = self._check_hypothesis_compliance(hypothesis, context)

        # Assess long-term consequences
        long_term_assessment = self._assess_long_term_consequences(hypothesis, context)

        return {
            "hypothesis_id": hypothesis.id,
            "stakeholder_impacts": stakeholder_impacts,
            "ethical_implications": ethical_implications,
            "fairness_assessment": fairness_assessment,
            "compliance_status": compliance_check,
            "long_term_consequences": long_term_assessment,
            "overall_stakeholder_score": self._calculate_stakeholder_score(
                stakeholder_impacts
            ),
            "recommended_modifications": self._recommend_hypothesis_modifications(
                hypothesis, stakeholder_impacts
            ),
        }

    def _map_stakeholders_and_interests(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Map all stakeholders and their interests."""
        # Primary stakeholders (directly affected)
        primary_stakeholders = {
            "decision_makers": {
                "interests": [
                    "Successful outcomes",
                    "Resource efficiency",
                    "Risk management",
                ],
                "power_level": "high",
                "influence": "direct",
                "impact_level": "high",
            },
            "implementation_team": {
                "interests": [
                    "Clear requirements",
                    "Adequate resources",
                    "Achievable timelines",
                ],
                "power_level": "medium",
                "influence": "direct",
                "impact_level": "high",
            },
        }

        # Add context-specific stakeholders
        if context.stakeholders:
            for stakeholder in context.stakeholders:
                primary_stakeholders[stakeholder.lower().replace(" ", "_")] = {
                    "interests": self._infer_stakeholder_interests(
                        stakeholder, context.domain
                    ),
                    "power_level": self._assess_stakeholder_power(stakeholder, context),
                    "influence": "direct",
                    "impact_level": "medium",
                }

        # Secondary stakeholders (indirectly affected)
        secondary_stakeholders = self._identify_secondary_stakeholders(context)

        # Analyze stakeholder relationships
        relationships = self._analyze_stakeholder_relationships(
            primary_stakeholders, secondary_stakeholders
        )

        # Group stakeholders by power level and influence for strategic analysis
        stakeholder_groups = {
            "high_power_high_influence": [],
            "high_power_low_influence": [],
            "low_power_high_influence": [],
            "low_power_low_influence": [],
        }

        # Categorize all stakeholders into power/influence matrix
        all_stakeholders = {**primary_stakeholders, **secondary_stakeholders}
        for name, details in all_stakeholders.items():
            power = details.get("power_level", "medium")
            influence = details.get("influence", "indirect")

            # Determine quadrant based on power and influence levels
            if power in ["high"] and influence in ["direct", "high"]:
                stakeholder_groups["high_power_high_influence"].append(
                    {
                        "name": name,
                        "details": details,
                        "strategy": "Manage closely - key decision influencers",
                    }
                )
            elif power in ["high"] and influence in ["indirect", "low"]:
                stakeholder_groups["high_power_low_influence"].append(
                    {
                        "name": name,
                        "details": details,
                        "strategy": "Keep satisfied - potential blockers",
                    }
                )
            elif power in ["low", "medium"] and influence in ["direct", "high"]:
                stakeholder_groups["low_power_high_influence"].append(
                    {
                        "name": name,
                        "details": details,
                        "strategy": "Keep informed - opinion leaders",
                    }
                )
            else:
                stakeholder_groups["low_power_low_influence"].append(
                    {
                        "name": name,
                        "details": details,
                        "strategy": "Monitor - minimal effort required",
                    }
                )

        return {
            "primary_stakeholders": primary_stakeholders,
            "secondary_stakeholders": secondary_stakeholders,
            "stakeholder_relationships": relationships,
            "stakeholder_groups": stakeholder_groups,
            "engagement_strategies": self._develop_engagement_strategies(
                stakeholder_groups
            ),
            "potential_coalitions": self._identify_potential_coalitions(
                primary_stakeholders, secondary_stakeholders
            ),
            "conflict_areas": self._identify_conflict_areas(
                primary_stakeholders, secondary_stakeholders
            ),
        }

    def _develop_engagement_strategies(
        self, stakeholder_groups: Dict[str, List]
    ) -> Dict[str, Any]:
        """Develop engagement strategies for each stakeholder group."""
        strategies = {}

        for group_name, stakeholders in stakeholder_groups.items():
            if not stakeholders:
                continue

            if group_name == "high_power_high_influence":
                strategies[group_name] = {
                    "approach": "Collaborative Partnership",
                    "frequency": "Continuous engagement",
                    "methods": [
                        "Direct meetings",
                        "Joint planning sessions",
                        "Regular updates",
                    ],
                    "key_messages": [
                        "Strategic alignment",
                        "Mutual benefits",
                        "Shared outcomes",
                    ],
                    "success_metrics": [
                        "Approval ratings",
                        "Active participation",
                        "Resource commitment",
                    ],
                }
            elif group_name == "high_power_low_influence":
                strategies[group_name] = {
                    "approach": "Satisfaction Management",
                    "frequency": "Regular check-ins",
                    "methods": [
                        "Status reports",
                        "Formal presentations",
                        "Issue escalation",
                    ],
                    "key_messages": [
                        "Progress updates",
                        "Risk mitigation",
                        "Compliance assurance",
                    ],
                    "success_metrics": [
                        "No objections raised",
                        "Continued support",
                        "Resource availability",
                    ],
                }
            elif group_name == "low_power_high_influence":
                strategies[group_name] = {
                    "approach": "Information Sharing",
                    "frequency": "Periodic updates",
                    "methods": ["Newsletters", "Town halls", "Feedback sessions"],
                    "key_messages": [
                        "Transparency",
                        "Impact awareness",
                        "Feedback opportunities",
                    ],
                    "success_metrics": [
                        "Positive sentiment",
                        "Feedback quality",
                        "Advocacy behavior",
                    ],
                }
            else:  # low_power_low_influence
                strategies[group_name] = {
                    "approach": "Monitoring",
                    "frequency": "As needed",
                    "methods": [
                        "General communications",
                        "Surveys",
                        "Passive monitoring",
                    ],
                    "key_messages": [
                        "General awareness",
                        "Available support",
                        "Contact information",
                    ],
                    "success_metrics": [
                        "No negative feedback",
                        "Awareness levels",
                        "Support requests",
                    ],
                }

        return {
            "group_strategies": strategies,
            "overall_approach": "Differentiated engagement based on power-influence matrix",
            "resource_allocation": self._calculate_engagement_resources(strategies),
            "timeline": self._develop_engagement_timeline(strategies),
        }

    def _calculate_engagement_resources(
        self, strategies: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Calculate resource requirements for stakeholder engagement."""
        return {
            "high_priority_groups": [
                "high_power_high_influence",
                "high_power_low_influence",
            ],
            "resource_distribution": {
                "high_power_high_influence": "40%",
                "high_power_low_influence": "30%",
                "low_power_high_influence": "20%",
                "low_power_low_influence": "10%",
            },
            "estimated_effort": "Medium to High - requires dedicated stakeholder management",
        }

    def _develop_engagement_timeline(
        self, strategies: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Develop timeline for stakeholder engagement activities."""
        return {
            "pre_decision": [
                "Stakeholder mapping",
                "Initial outreach",
                "Expectation setting",
            ],
            "during_decision": [
                "Regular updates",
                "Feedback collection",
                "Issue resolution",
            ],
            "post_decision": [
                "Implementation communication",
                "Impact monitoring",
                "Relationship maintenance",
            ],
            "ongoing": [
                "Relationship building",
                "Trust maintenance",
                "Future preparation",
            ],
        }

    def _assess_ethical_considerations(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess ethical considerations for the decision."""
        ethical_frameworks = self._apply_ethical_frameworks(context)

        # Identify ethical dilemmas
        ethical_dilemmas = []

        # Resource allocation ethics
        if context.constraints.max_information_requests > 30:
            ethical_dilemmas.append(
                {
                    "dilemma": "Extensive information gathering",
                    "description": "Significant resources being allocated - ensure optimal use",
                    "framework": "Utilitarianism",
                    "consideration": "Maximize overall benefit from resource use",
                }
            )

        # Time pressure ethics
        if context.constraints.time_limit < 24:
            ethical_dilemmas.append(
                {
                    "dilemma": "Time pressure decision-making",
                    "description": "Limited time may compromise thorough stakeholder consideration",
                    "framework": "Deontological",
                    "consideration": "Duty to make well-informed decisions despite time pressure",
                }
            )

        # Stakeholder inclusion ethics
        if len(context.stakeholders) > 5:
            ethical_dilemmas.append(
                {
                    "dilemma": "Stakeholder inclusion complexity",
                    "description": "Many stakeholders may create inclusion challenges",
                    "framework": "Justice/Fairness",
                    "consideration": "Ensure fair representation and voice for all stakeholders",
                }
            )

        return {
            "applicable_frameworks": ethical_frameworks,
            "identified_dilemmas": ethical_dilemmas,
            "ethical_risk_level": self._assess_ethical_risk_level(ethical_dilemmas),
            "ethical_guidelines": self._generate_ethical_guidelines(context),
        }

    def _evaluate_compliance_requirements(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Evaluate compliance requirements for the decision."""
        compliance_areas = []

        # Domain-specific compliance
        if context.domain in ["business", "strategy"]:
            compliance_areas.extend(
                [
                    {
                        "area": "Corporate Governance",
                        "requirements": [
                            "Board oversight",
                            "Shareholder interests",
                            "Fiduciary duty",
                        ],
                        "risk_level": "medium",
                    },
                    {
                        "area": "Resource Management Regulations",
                        "requirements": [
                            "Resource reporting",
                            "Audit requirements",
                            "Disclosure obligations",
                        ],
                        "risk_level": "medium",
                    },
                ]
            )

        if context.domain in ["technology", "technical"]:
            compliance_areas.extend(
                [
                    {
                        "area": "Data Privacy",
                        "requirements": [
                            "GDPR compliance",
                            "Data protection",
                            "User consent",
                        ],
                        "risk_level": "high",
                    },
                    {
                        "area": "Security Standards",
                        "requirements": [
                            "Cybersecurity frameworks",
                            "Access controls",
                            "Audit trails",
                        ],
                        "risk_level": "high",
                    },
                ]
            )

        # Industry-specific compliance
        industry_compliance = self._identify_industry_compliance(context)
        compliance_areas.extend(industry_compliance)

        return {
            "compliance_areas": compliance_areas,
            "compliance_risk_assessment": self._assess_compliance_risks(
                compliance_areas
            ),
            "compliance_monitoring_plan": self._create_compliance_monitoring_plan(
                compliance_areas
            ),
            "compliance_training_needs": self._identify_compliance_training_needs(
                compliance_areas
            ),
        }

    def _analyze_sustainability_factors(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Analyze sustainability factors for long-term viability."""
        sustainability_dimensions = {
            "economic_sustainability": self._assess_economic_sustainability(context),
            "environmental_impact": self._assess_environmental_impact(context),
            "social_sustainability": self._assess_social_sustainability(context),
            "organizational_sustainability": self._assess_organizational_sustainability(
                context
            ),
        }

        # Long-term viability assessment
        viability_factors = [
            "Market conditions stability",
            "Technology evolution impact",
            "Regulatory environment changes",
            "Stakeholder relationship evolution",
            "Resource availability long-term",
        ]

        sustainability_risks = self._identify_sustainability_risks(context)

        return {
            "sustainability_dimensions": sustainability_dimensions,
            "long_term_viability_factors": viability_factors,
            "sustainability_risks": sustainability_risks,
            "sustainability_score": self._calculate_sustainability_score(
                sustainability_dimensions
            ),
            "sustainability_recommendations": self._generate_sustainability_recommendations(
                context
            ),
        }

    def _identify_potential_conflicts(
        self, context: DecisionContext, stakeholder_mapping: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Identify potential conflicts of interest."""
        conflicts = []

        # Resource allocation conflicts
        if (
            context.constraints.max_information_requests < 10
            and len(context.stakeholders) > 3
        ):
            conflicts.append(
                {
                    "type": "Resource Allocation",
                    "description": "Limited information requests with multiple stakeholders may create resource conflicts",
                    "severity": "medium",
                    "mitigation": "Transparent resource allocation criteria and stakeholder communication",
                }
            )

        # Timeline conflicts
        if context.constraints.time_limit < 72:  # Less than 3 days
            conflicts.append(
                {
                    "type": "Time Pressure",
                    "description": "Time pressure may compromise stakeholder consultation",
                    "severity": "medium",
                    "mitigation": "Prioritize most critical stakeholder input and communicate constraints",
                }
            )

        # Interest conflicts
        stakeholder_interests = self._extract_stakeholder_interests(stakeholder_mapping)
        conflicting_interests = self._identify_conflicting_interests(
            stakeholder_interests
        )

        for conflict in conflicting_interests:
            conflicts.append(
                {
                    "type": "Interest Conflict",
                    "description": conflict["description"],
                    "severity": conflict["severity"],
                    "mitigation": conflict["mitigation"],
                }
            )

        return {
            "identified_conflicts": conflicts,
            "conflict_resolution_strategy": self._develop_conflict_resolution_strategy(
                conflicts
            ),
            "stakeholder_communication_plan": self._create_conflict_communication_plan(
                conflicts
            ),
        }

    def _assess_transparency_requirements(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess transparency and communication requirements."""
        transparency_levels = {
            "public_transparency": self._assess_public_transparency_needs(context),
            "stakeholder_transparency": self._assess_stakeholder_transparency_needs(
                context
            ),
            "regulatory_transparency": self._assess_regulatory_transparency_needs(
                context
            ),
            "internal_transparency": self._assess_internal_transparency_needs(context),
        }

        communication_requirements = self._identify_communication_requirements(context)

        return {
            "transparency_levels": transparency_levels,
            "communication_requirements": communication_requirements,
            "documentation_requirements": self._identify_documentation_requirements(
                context
            ),
            "audit_trail_requirements": self._identify_audit_requirements(context),
        }

    def _map_hypothesis_stakeholder_impacts(
        self, hypothesis: Hypothesis, context: DecisionContext
    ) -> Dict[str, Any]:
        """Map how a hypothesis impacts each stakeholder group."""
        impacts = {}

        # Analyze impacts on each stakeholder group
        all_stakeholders = ["decision_makers", "implementation_team"] + [
            s.lower().replace(" ", "_") for s in context.stakeholders
        ]

        for stakeholder in all_stakeholders:
            impact_assessment = {
                "direct_benefits": self._assess_direct_benefits(
                    hypothesis, stakeholder
                ),
                "direct_impacts": self._assess_direct_impacts(hypothesis, stakeholder),
                "indirect_effects": self._assess_indirect_effects(
                    hypothesis, stakeholder
                ),
                "risk_exposure": self._assess_risk_exposure(hypothesis, stakeholder),
                "opportunity_impact": self._assess_opportunity_impact(
                    hypothesis, stakeholder
                ),
                "net_impact_score": 0.0,  # Will be calculated
            }

            # Calculate net impact score
            impact_assessment["net_impact_score"] = self._calculate_net_impact_score(
                impact_assessment
            )

            impacts[stakeholder] = impact_assessment

        return impacts

    def _assess_hypothesis_ethics(
        self, hypothesis: Hypothesis, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess ethical implications of a specific hypothesis."""
        ethical_analysis = {
            "utilitarian_assessment": self._apply_utilitarian_ethics(
                hypothesis, context
            ),
            "deontological_assessment": self._apply_deontological_ethics(
                hypothesis, context
            ),
            "virtue_ethics_assessment": self._apply_virtue_ethics(hypothesis, context),
            "justice_fairness_assessment": self._apply_justice_ethics(
                hypothesis, context
            ),
        }

        ethical_concerns = self._identify_ethical_concerns(hypothesis, context)

        return {
            "ethical_framework_assessments": ethical_analysis,
            "identified_concerns": ethical_concerns,
            "ethical_score": self._calculate_ethical_score(ethical_analysis),
            "ethical_recommendations": self._generate_ethical_recommendations(
                hypothesis, ethical_concerns
            ),
        }

    def _infer_stakeholder_interests(self, stakeholder: str, domain: str) -> List[str]:
        """Infer likely interests for a stakeholder based on their role and domain."""
        stakeholder_lower = stakeholder.lower()

        if "customer" in stakeholder_lower or "client" in stakeholder_lower:
            return [
                "Quality outcomes",
                "Value for money",
                "Timely delivery",
                "Service quality",
            ]
        elif "employee" in stakeholder_lower or "staff" in stakeholder_lower:
            return [
                "Job security",
                "Fair treatment",
                "Professional development",
                "Work-life balance",
            ]
        elif "investor" in stakeholder_lower or "shareholder" in stakeholder_lower:
            return [
                "Return on investment",
                "Risk management",
                "Long-term value",
                "Transparency",
            ]
        elif "supplier" in stakeholder_lower or "vendor" in stakeholder_lower:
            return [
                "Fair contracts",
                "Timely payments",
                "Long-term relationships",
                "Clear requirements",
            ]
        elif "regulator" in stakeholder_lower or "government" in stakeholder_lower:
            return ["Compliance", "Public interest", "Safety", "Fair competition"]
        else:
            # Generic stakeholder interests
            return [
                "Fair treatment",
                "Transparency",
                "Positive outcomes",
                "Minimal negative impact",
            ]

    def _assess_stakeholder_power(
        self, stakeholder: str, context: DecisionContext
    ) -> str:
        """Assess the power level of a stakeholder."""
        stakeholder_lower = stakeholder.lower()

        if any(
            word in stakeholder_lower for word in ["ceo", "board", "executive", "owner"]
        ):
            return "very_high"
        elif any(word in stakeholder_lower for word in ["manager", "director", "head"]):
            return "high"
        elif any(
            word in stakeholder_lower
            for word in ["investor", "regulator", "government"]
        ):
            return "high"
        elif any(word in stakeholder_lower for word in ["customer", "client", "user"]):
            return "medium"
        else:
            return "medium"

    def _identify_secondary_stakeholders(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Identify secondary stakeholders who may be indirectly affected."""
        secondary = {}

        if context.domain in ["business", "strategy"]:
            secondary.update(
                {
                    "competitors": {
                        "interests": ["Market stability", "Fair competition"],
                        "impact_level": "low",
                        "influence": "indirect",
                    },
                    "industry_associations": {
                        "interests": ["Industry standards", "Best practices"],
                        "impact_level": "low",
                        "influence": "indirect",
                    },
                    "communities": {
                        "interests": [
                            "Economic development",
                            "Environmental protection",
                        ],
                        "impact_level": "medium",
                        "influence": "indirect",
                    },
                }
            )

        if context.domain in ["technology", "technical"]:
            secondary.update(
                {
                    "data_subjects": {
                        "interests": ["Privacy protection", "Data security"],
                        "impact_level": "medium",
                        "influence": "indirect",
                    },
                    "technology_ecosystem": {
                        "interests": ["Innovation", "Standards compatibility"],
                        "impact_level": "low",
                        "influence": "indirect",
                    },
                }
            )

        return secondary

    def _apply_ethical_frameworks(
        self, context: DecisionContext
    ) -> List[Dict[str, Any]]:
        """Apply different ethical frameworks to the decision context."""
        frameworks = [
            {
                "name": "Utilitarianism",
                "principle": "Greatest good for the greatest number",
                "application": "Maximize overall benefit across all stakeholders",
                "considerations": [
                    "Total benefit",
                    "Benefit distribution",
                    "Unintended consequences",
                ],
            },
            {
                "name": "Deontological Ethics",
                "principle": "Duty-based ethics and moral rules",
                "application": "Ensure decisions follow moral rules and professional duties",
                "considerations": [
                    "Professional obligations",
                    "Moral rules",
                    "Rights protection",
                ],
            },
            {
                "name": "Virtue Ethics",
                "principle": "Character-based ethics and virtues",
                "application": "Make decisions that reflect virtuous character",
                "considerations": ["Integrity", "Honesty", "Compassion", "Justice"],
            },
            {
                "name": "Justice and Fairness",
                "principle": "Fair distribution of benefits and burdens",
                "application": "Ensure fair treatment of all stakeholders",
                "considerations": [
                    "Distributive justice",
                    "Procedural fairness",
                    "Equal treatment",
                ],
            },
        ]

        return frameworks

    def _generate_governance_recommendations(
        self, context: DecisionContext
    ) -> List[str]:
        """Generate governance recommendations for the decision process."""
        recommendations = []

        # Stakeholder governance
        if len(context.stakeholders) > 3:
            recommendations.append(
                "Establish stakeholder advisory committee for ongoing input"
            )

        # Resource governance
        if context.constraints.max_information_requests > 25:
            recommendations.append(
                "Implement resource oversight and monitoring controls"
            )

        # Decision governance
        recommendations.extend(
            [
                "Document decision rationale and stakeholder considerations",
                "Establish regular review checkpoints for major decisions",
                "Create clear escalation procedures for ethical concerns",
                "Implement transparent communication protocols",
            ]
        )

        # Compliance governance
        recommendations.extend(
            [
                "Establish compliance monitoring and reporting procedures",
                "Create audit trail for all major decisions",
                "Implement regular compliance reviews and updates",
            ]
        )

        return recommendations[:8]  # Return top recommendations

    def _calculate_stakeholder_score(
        self, stakeholder_impacts: Dict[str, Any]
    ) -> float:
        """Calculate overall stakeholder satisfaction score."""
        if not stakeholder_impacts:
            return 0.0

        scores = [impact["net_impact_score"] for impact in stakeholder_impacts.values()]
        return sum(scores) / len(scores)

    def _calculate_net_impact_score(self, impact_assessment: Dict[str, Any]) -> float:
        """Calculate net impact score for a stakeholder."""
        # Simple scoring based on benefits vs impacts
        benefits = len(impact_assessment.get("direct_benefits", []))
        impacts = len(impact_assessment.get("direct_impacts", []))
        risks = len(impact_assessment.get("risk_exposure", []))

        # Base score from benefits minus impacts and risks
        net_score = benefits - impacts - (risks * 0.5)

        # Normalize to 0-1 scale
        return max(0.0, min(1.0, (net_score + 3) / 6))

    def _assess_direct_benefits(
        self, hypothesis: Hypothesis, stakeholder: str
    ) -> List[str]:
        """Assess direct benefits for a stakeholder from a hypothesis."""
        benefits = []

        # Generic benefits based on hypothesis success
        if hypothesis.probability > 0.7:
            benefits.append("High likelihood of successful outcome")

        # Stakeholder-specific benefits
        if "customer" in stakeholder:
            benefits.extend(["Improved service quality", "Better value proposition"])
        elif "employee" in stakeholder:
            benefits.extend(["Enhanced capabilities", "Process improvements"])
        elif "investor" in stakeholder:
            benefits.extend(["Potential return on investment", "Strategic advancement"])

        return benefits

    def _assess_direct_impacts(
        self, hypothesis: Hypothesis, stakeholder: str
    ) -> List[str]:
        """Assess direct impacts for a stakeholder from a hypothesis."""
        impacts = []

        # Resource-based impacts
        if hypothesis.resource_requirements:
            high_resource_items = [
                k
                for k, v in hypothesis.resource_requirements.items()
                if isinstance(v, str) and "high" in v.lower()
            ]
            if high_resource_items:
                impacts.append(
                    f"High resource requirements: {', '.join(high_resource_items)}"
                )

        # Stakeholder-specific impacts
        if "employee" in stakeholder:
            impacts.extend(["Change management effort", "Training requirements"])
        elif "customer" in stakeholder:
            impacts.extend(["Potential service disruption", "Adaptation requirements"])

        return impacts

    def _assess_indirect_effects(
        self, hypothesis: Hypothesis, stakeholder: str
    ) -> List[str]:
        """Assess indirect effects for a stakeholder."""
        effects = []

        # Generic indirect effects
        effects.extend(
            [
                "Organizational culture changes",
                "Process efficiency improvements",
                "Reputation impact",
            ]
        )

        return effects[:3]  # Return top effects

    def _assess_risk_exposure(
        self, hypothesis: Hypothesis, stakeholder: str
    ) -> List[str]:
        """Assess risk exposure for a stakeholder."""
        risks = []

        # Low probability hypothesis risk
        if hypothesis.probability < 0.5:
            risks.append("Risk of hypothesis failure")

        # Resource risks
        if hypothesis.resource_requirements:
            risks.append("Resource allocation risks")

        # Stakeholder-specific risks
        if "customer" in stakeholder:
            risks.extend(["Service quality risks", "Relationship impact risks"])
        elif "employee" in stakeholder:
            risks.extend(["Job impact risks", "Skill obsolescence risks"])

        return risks

    def _assess_opportunity_impact(
        self, hypothesis: Hypothesis, stakeholder: str
    ) -> List[str]:
        """Assess opportunity impact for a stakeholder."""
        opportunities = []

        if hypothesis.probability > 0.6:
            opportunities.extend(
                [
                    "Strategic positioning improvement",
                    "Capability enhancement",
                    "Future opportunity creation",
                ]
            )

        return opportunities

    # Additional helper methods for completeness
    def _assess_economic_sustainability(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess economic sustainability factors."""
        return {
            "resource_adequacy": "adequate"
            if context.constraints.max_information_requests > 10
            else "limited",
            "efficiency_potential": "medium",  # Would be calculated based on specific analysis
            "resource_structure_sustainability": "viable",
        }

    def _assess_environmental_impact(self, context: DecisionContext) -> Dict[str, Any]:
        """Assess environmental impact factors."""
        return {
            "resource_consumption": "standard",
            "waste_generation": "minimal",
            "carbon_footprint": "low",
        }

    def _assess_social_sustainability(self, context: DecisionContext) -> Dict[str, Any]:
        """Assess social sustainability factors."""
        return {
            "stakeholder_welfare": "positive",
            "community_impact": "neutral",
            "social_equity": "fair",
        }

    def _assess_organizational_sustainability(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Assess organizational sustainability factors."""
        return {
            "capability_building": "enhanced",
            "knowledge_retention": "maintained",
            "culture_alignment": "positive",
        }

    def _calculate_sustainability_score(self, dimensions: Dict[str, Any]) -> float:
        """Calculate overall sustainability score."""
        # Simplified scoring - in practice would be more sophisticated
        return 0.75  # Placeholder score

    def _generate_sustainability_recommendations(
        self, context: DecisionContext
    ) -> List[str]:
        """Generate sustainability recommendations."""
        return [
            "Monitor long-term stakeholder satisfaction",
            "Establish sustainability metrics and KPIs",
            "Create regular sustainability review processes",
            "Build adaptive capacity for changing conditions",
        ]
