"""
MAI-DxO implementation using Society of Mind Agent pattern.

This implementation creates a hierarchical cognitive architecture where agents
represent different "minds" or cognitive processes that interact to form
emergent intelligence and decision-making capabilities.
"""

from typing import Any, Dict, List, Optional

from autogen_core.models import ChatCompletionClient

from ..agents.mai_dxo import (
    CriticalChallengerAgent,
    QualityValidatorAgent,
    ResourceOptimizerAgent,
    StakeholderStewardAgent,
    StrategicAnalystAgent,
)
from ..information.gatekeeper import InformationGatekeeper
from ..models.mai_dxo import (
    AgentRole,
    DecisionContext,
    DecisionResult,
    OrchestrationMethod,
    QualityMetrics,
    ResourceConstraints,
)


class SocietyOfMindMAIDxO:
    """
    MAI-DxO implementation using Society of Mind Agent pattern.

    This orchestrator creates a hierarchical cognitive architecture where
    specialized agents form a "society of mind" with emergent intelligence
    through layered interactions and cognitive processes.
    """

    def __init__(
        self,
        model_client: ChatCompletionClient,
        domain_context: str = "business",
        cognitive_layers: int = 3,
        emergence_threshold: float = 0.8,
        **kwargs,
    ):
        """Initialize the Society of Mind MAI-DxO orchestrator."""
        self.model_client = model_client
        self.domain_context = domain_context
        self.cognitive_layers = cognitive_layers
        self.emergence_threshold = emergence_threshold
        self.orchestration_method = OrchestrationMethod.SOCIETY_OF_MIND

        # Will be initialized when starting a decision process
        self.information_gatekeeper: Optional[InformationGatekeeper] = None
        self.agents: Dict[AgentRole, Any] = {}

        # Society of Mind specific architecture
        self.cognitive_hierarchy = {}
        self.mind_connections = {}
        self.emergent_patterns = []
        self.collective_memory = {}

    async def process_decision(
        self,
        context: DecisionContext,
        constraints: Optional[ResourceConstraints] = None,
    ) -> DecisionResult:
        """
        Process a decision using the Society of Mind MAI-DxO orchestration.

        The Society of Mind approach:
        1. Initialize hierarchical cognitive architecture
        2. Layer 1: Base cognitive processes (individual agent analysis)
        3. Layer 2: Interaction patterns (agent-to-agent communication)
        4. Layer 3: Emergent intelligence (collective decision-making)
        5. Pattern recognition and memory formation
        6. Collective decision synthesis with emergent insights
        """
        # Use provided constraints or defaults from context
        if constraints is None:
            constraints = context.constraints

        # Initialize the decision process
        await self._initialize_decision_process(context, constraints)

        # Execute Society of Mind workflow
        decision_result = await self._execute_society_workflow(context)

        return decision_result

    async def _initialize_decision_process(
        self, context: DecisionContext, constraints: ResourceConstraints
    ) -> None:
        """Initialize the society architecture and agents."""
        # Initialize shared information gatekeeper
        self.information_gatekeeper = InformationGatekeeper(constraints)

        # Initialize all five specialized agents as "minds"
        self.agents = {
            AgentRole.STRATEGIC_ANALYST: StrategicAnalystAgent(
                name="Strategic Mind",
                model_client=self.model_client,
                agent_role=AgentRole.STRATEGIC_ANALYST,
                domain_context=self.domain_context,
                information_gatekeeper=self.information_gatekeeper,
            ),
            AgentRole.RESOURCE_OPTIMIZER: ResourceOptimizerAgent(
                name="Resource Mind",
                model_client=self.model_client,
                agent_role=AgentRole.RESOURCE_OPTIMIZER,
                domain_context=self.domain_context,
                information_gatekeeper=self.information_gatekeeper,
            ),
            AgentRole.CRITICAL_CHALLENGER: CriticalChallengerAgent(
                name="Critical Mind",
                model_client=self.model_client,
                agent_role=AgentRole.CRITICAL_CHALLENGER,
                domain_context=self.domain_context,
                information_gatekeeper=self.information_gatekeeper,
            ),
            AgentRole.STAKEHOLDER_STEWARD: StakeholderStewardAgent(
                name="Social Mind",
                model_client=self.model_client,
                agent_role=AgentRole.STAKEHOLDER_STEWARD,
                domain_context=self.domain_context,
                information_gatekeeper=self.information_gatekeeper,
            ),
            AgentRole.QUALITY_VALIDATOR: QualityValidatorAgent(
                name="Validation Mind",
                model_client=self.model_client,
                agent_role=AgentRole.QUALITY_VALIDATOR,
                domain_context=self.domain_context,
                information_gatekeeper=self.information_gatekeeper,
            ),
        }

        # Initialize hierarchical cognitive architecture
        self._initialize_cognitive_hierarchy()

        # Initialize mind connections and interaction patterns
        self._initialize_mind_connections()

        # Initialize collective memory
        self.collective_memory = {
            "decision_patterns": [],
            "interaction_history": [],
            "emergent_insights": [],
            "collective_knowledge": {},
        }

    def _initialize_cognitive_hierarchy(self) -> None:
        """Initialize the hierarchical cognitive architecture."""
        self.cognitive_hierarchy = {
            "layer_1_base_minds": {
                "analytical_mind": AgentRole.STRATEGIC_ANALYST,
                "resource_mind": AgentRole.RESOURCE_OPTIMIZER,
                "critical_mind": AgentRole.CRITICAL_CHALLENGER,
                "social_mind": AgentRole.STAKEHOLDER_STEWARD,
                "validation_mind": AgentRole.QUALITY_VALIDATOR,
            },
            "layer_2_interaction_patterns": {
                "analysis_optimization": [
                    AgentRole.STRATEGIC_ANALYST,
                    AgentRole.RESOURCE_OPTIMIZER,
                ],
                "challenge_validation": [
                    AgentRole.CRITICAL_CHALLENGER,
                    AgentRole.QUALITY_VALIDATOR,
                ],
                "social_ethical": [
                    AgentRole.STAKEHOLDER_STEWARD,
                    AgentRole.QUALITY_VALIDATOR,
                ],
                "strategic_social": [
                    AgentRole.STRATEGIC_ANALYST,
                    AgentRole.STAKEHOLDER_STEWARD,
                ],
            },
            "layer_3_emergent_intelligence": {
                "collective_reasoning": "synthesis_of_all_minds",
                "emergent_decision": "collective_intelligence_outcome",
                "meta_cognition": "awareness_of_thinking_process",
            },
        }

    def _initialize_mind_connections(self) -> None:
        """Initialize connections between minds."""
        self.mind_connections = {
            AgentRole.STRATEGIC_ANALYST: {
                "strong_connections": [
                    AgentRole.RESOURCE_OPTIMIZER,
                    AgentRole.STAKEHOLDER_STEWARD,
                ],
                "moderate_connections": [AgentRole.QUALITY_VALIDATOR],
                "weak_connections": [AgentRole.CRITICAL_CHALLENGER],
            },
            AgentRole.RESOURCE_OPTIMIZER: {
                "strong_connections": [
                    AgentRole.STRATEGIC_ANALYST,
                    AgentRole.QUALITY_VALIDATOR,
                ],
                "moderate_connections": [AgentRole.STAKEHOLDER_STEWARD],
                "weak_connections": [AgentRole.CRITICAL_CHALLENGER],
            },
            AgentRole.CRITICAL_CHALLENGER: {
                "strong_connections": [AgentRole.QUALITY_VALIDATOR],
                "moderate_connections": [
                    AgentRole.STRATEGIC_ANALYST,
                    AgentRole.STAKEHOLDER_STEWARD,
                ],
                "weak_connections": [AgentRole.RESOURCE_OPTIMIZER],
            },
            AgentRole.STAKEHOLDER_STEWARD: {
                "strong_connections": [
                    AgentRole.STRATEGIC_ANALYST,
                    AgentRole.QUALITY_VALIDATOR,
                ],
                "moderate_connections": [AgentRole.CRITICAL_CHALLENGER],
                "weak_connections": [AgentRole.RESOURCE_OPTIMIZER],
            },
            AgentRole.QUALITY_VALIDATOR: {
                "strong_connections": [
                    AgentRole.CRITICAL_CHALLENGER,
                    AgentRole.STAKEHOLDER_STEWARD,
                ],
                "moderate_connections": [AgentRole.RESOURCE_OPTIMIZER],
                "weak_connections": [AgentRole.STRATEGIC_ANALYST],
            },
        }

    async def _execute_society_workflow(
        self, context: DecisionContext
    ) -> DecisionResult:
        """Execute the Society of Mind workflow."""
        # Layer 1: Base Cognitive Processes
        base_minds_output = await self._execute_base_minds_layer(context)

        # Layer 2: Interaction Patterns
        interaction_outputs = await self._execute_interaction_layer(
            context, base_minds_output
        )

        # Layer 3: Emergent Intelligence
        emergent_intelligence = await self._execute_emergent_layer(
            context, interaction_outputs
        )

        # Pattern Recognition and Memory Formation
        patterns = self._recognize_decision_patterns(
            base_minds_output, interaction_outputs, emergent_intelligence
        )

        # Update Collective Memory
        self._update_collective_memory(patterns, emergent_intelligence)

        # Final Decision Synthesis
        final_decision = await self._synthesize_collective_decision(
            context,
            base_minds_output,
            interaction_outputs,
            emergent_intelligence,
            patterns,
        )

        return final_decision

    async def _execute_base_minds_layer(
        self, context: DecisionContext
    ) -> Dict[str, Any]:
        """Execute Layer 1: Base cognitive processes (individual minds)."""
        base_outputs = {}

        # Each "mind" processes the context independently
        for mind_name, agent_role in self.cognitive_hierarchy[
            "layer_1_base_minds"
        ].items():
            agent = self.agents[agent_role]

            try:
                output = await agent.process_decision_context(context)
                base_outputs[mind_name] = {
                    "agent_role": agent_role,
                    "output": output,
                    "cognitive_state": self._assess_cognitive_state(output, agent_role),
                    "activation_level": self._calculate_activation_level(
                        output, agent_role
                    ),
                }
                print(f"✓ {mind_name} cognitive process completed")

            except Exception as e:
                print(f"✗ {mind_name} cognitive process failed: {e}")
                base_outputs[mind_name] = {
                    "agent_role": agent_role,
                    "output": None,
                    "cognitive_state": "inactive",
                    "activation_level": 0.0,
                }

        return {
            "layer": "base_minds",
            "outputs": base_outputs,
            "collective_activation": self._calculate_collective_activation(
                base_outputs
            ),
        }

    def _assess_cognitive_state(self, output: Any, agent_role: AgentRole) -> str:
        """Assess the cognitive state of a mind."""
        if output is None:
            return "inactive"

        # Assess based on confidence and content richness
        confidence = getattr(output, "confidence_level", 0.5)

        if confidence >= 0.8:
            return "highly_active"
        elif confidence >= 0.6:
            return "active"
        elif confidence >= 0.4:
            return "moderately_active"
        else:
            return "weakly_active"

    def _calculate_activation_level(self, output: Any, agent_role: AgentRole) -> float:
        """Calculate activation level for a mind."""
        if output is None:
            return 0.0

        base_activation = getattr(output, "confidence_level", 0.5)

        # Role-specific activation modifiers
        role_modifiers = {
            AgentRole.STRATEGIC_ANALYST: 1.1,  # Slightly boost primary reasoner
            AgentRole.RESOURCE_OPTIMIZER: 1.0,
            AgentRole.CRITICAL_CHALLENGER: 1.05,  # Boost for systematic challenge
            AgentRole.STAKEHOLDER_STEWARD: 1.0,
            AgentRole.QUALITY_VALIDATOR: 1.05,  # Boost for validation importance
        }

        modifier = role_modifiers.get(agent_role, 1.0)
        return min(1.0, base_activation * modifier)

    def _calculate_collective_activation(self, base_outputs: Dict[str, Any]) -> float:
        """Calculate overall collective activation level."""
        activations = [
            mind_data["activation_level"] for mind_data in base_outputs.values()
        ]
        if not activations:
            return 0.0

        # Weighted average with emergent bonus
        avg_activation = sum(activations) / len(activations)
        emergence_bonus = 0.1 if len([a for a in activations if a > 0.7]) >= 3 else 0.0

        return min(1.0, avg_activation + emergence_bonus)

    async def _execute_interaction_layer(
        self, context: DecisionContext, base_outputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute Layer 2: Interaction patterns between minds."""
        interaction_results = {}
        base_minds = base_outputs["outputs"]

        # Execute predefined interaction patterns
        for pattern_name, agent_roles in self.cognitive_hierarchy[
            "layer_2_interaction_patterns"
        ].items():
            interaction_result = await self._execute_mind_interaction(
                pattern_name, agent_roles, base_minds, context
            )
            interaction_results[pattern_name] = interaction_result

        # Detect emergent interaction patterns
        emergent_interactions = self._detect_emergent_interactions(base_minds)

        return {
            "layer": "interaction_patterns",
            "predefined_interactions": interaction_results,
            "emergent_interactions": emergent_interactions,
            "interaction_strength": self._calculate_interaction_strength(
                interaction_results
            ),
        }

    async def _execute_mind_interaction(
        self,
        pattern_name: str,
        agent_roles: List[AgentRole],
        base_minds: Dict[str, Any],
        context: DecisionContext,
    ) -> Dict[str, Any]:
        """Execute interaction between specific minds."""
        participating_minds = []

        # Get minds participating in this interaction
        for mind_name, mind_data in base_minds.items():
            if mind_data["agent_role"] in agent_roles:
                participating_minds.append(
                    {
                        "name": mind_name,
                        "role": mind_data["agent_role"],
                        "activation": mind_data["activation_level"],
                        "output": mind_data["output"],
                    }
                )

        # Execute interaction based on pattern type
        if pattern_name == "analysis_optimization":
            interaction_result = self._analysis_optimization_interaction(
                participating_minds
            )
        elif pattern_name == "challenge_validation":
            interaction_result = self._challenge_validation_interaction(
                participating_minds
            )
        elif pattern_name == "social_ethical":
            interaction_result = self._social_ethical_interaction(participating_minds)
        elif pattern_name == "strategic_social":
            interaction_result = self._strategic_social_interaction(participating_minds)
        else:
            interaction_result = self._generic_interaction(participating_minds)

        return {
            "pattern": pattern_name,
            "participants": [mind["name"] for mind in participating_minds],
            "interaction_result": interaction_result,
            "synergy_level": self._calculate_synergy_level(
                participating_minds, interaction_result
            ),
        }

    def _analysis_optimization_interaction(
        self, minds: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Interaction between analytical and resource optimization minds."""
        strategic_mind = next(
            (m for m in minds if m["role"] == AgentRole.STRATEGIC_ANALYST), None
        )
        resource_mind = next(
            (m for m in minds if m["role"] == AgentRole.RESOURCE_OPTIMIZER), None
        )

        if not strategic_mind or not resource_mind:
            return {"interaction": "incomplete", "outcome": "insufficient_participants"}

        # Simulate interaction outcome
        combined_activation = (
            strategic_mind["activation"] + resource_mind["activation"]
        ) / 2

        return {
            "interaction": "analysis_optimization",
            "outcome": "strategic_resource_alignment",
            "combined_strength": combined_activation,
            "emergent_insights": [
                "Strategic options evaluated against resource constraints",
                "Resource optimization integrated with strategic priorities",
                "Efficiency analysis aligned with strategic objectives",
            ],
        }

    def _challenge_validation_interaction(
        self, minds: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Interaction between critical challenge and validation minds."""
        challenger_mind = next(
            (m for m in minds if m["role"] == AgentRole.CRITICAL_CHALLENGER), None
        )
        validator_mind = next(
            (m for m in minds if m["role"] == AgentRole.QUALITY_VALIDATOR), None
        )

        if not challenger_mind or not validator_mind:
            return {"interaction": "incomplete", "outcome": "insufficient_participants"}

        combined_activation = (
            challenger_mind["activation"] + validator_mind["activation"]
        ) / 2

        return {
            "interaction": "challenge_validation",
            "outcome": "quality_assured_challenge",
            "combined_strength": combined_activation,
            "emergent_insights": [
                "Systematic challenges validated through quality lens",
                "Quality standards informed by critical analysis",
                "Bias detection integrated with validation processes",
            ],
        }

    def _social_ethical_interaction(
        self, minds: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Interaction between social and validation minds."""
        social_mind = next(
            (m for m in minds if m["role"] == AgentRole.STAKEHOLDER_STEWARD), None
        )
        validator_mind = next(
            (m for m in minds if m["role"] == AgentRole.QUALITY_VALIDATOR), None
        )

        if not social_mind or not validator_mind:
            return {"interaction": "incomplete", "outcome": "insufficient_participants"}

        combined_activation = (
            social_mind["activation"] + validator_mind["activation"]
        ) / 2

        return {
            "interaction": "social_ethical",
            "outcome": "ethical_quality_assurance",
            "combined_strength": combined_activation,
            "emergent_insights": [
                "Stakeholder interests validated through quality framework",
                "Ethical considerations integrated into validation criteria",
                "Governance requirements aligned with quality standards",
            ],
        }

    def _strategic_social_interaction(
        self, minds: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Interaction between strategic and social minds."""
        strategic_mind = next(
            (m for m in minds if m["role"] == AgentRole.STRATEGIC_ANALYST), None
        )
        social_mind = next(
            (m for m in minds if m["role"] == AgentRole.STAKEHOLDER_STEWARD), None
        )

        if not strategic_mind or not social_mind:
            return {"interaction": "incomplete", "outcome": "insufficient_participants"}

        combined_activation = (
            strategic_mind["activation"] + social_mind["activation"]
        ) / 2

        return {
            "interaction": "strategic_social",
            "outcome": "socially_aware_strategy",
            "combined_strength": combined_activation,
            "emergent_insights": [
                "Strategic decisions informed by stakeholder impact",
                "Social considerations integrated into strategic planning",
                "Stakeholder alignment optimized for strategic success",
            ],
        }

    def _generic_interaction(self, minds: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Generic interaction between minds."""
        avg_activation = (
            sum(mind["activation"] for mind in minds) / len(minds) if minds else 0.0
        )

        return {
            "interaction": "generic",
            "outcome": "collaborative_processing",
            "combined_strength": avg_activation,
            "emergent_insights": [
                "Multiple perspectives integrated",
                "Collaborative analysis achieved",
            ],
        }

    def _calculate_synergy_level(
        self, participants: List[Dict[str, Any]], interaction_result: Dict[str, Any]
    ) -> float:
        """Calculate synergy level from mind interaction."""
        if not participants:
            return 0.0

        # Base synergy from combined activation
        avg_activation = sum(p["activation"] for p in participants) / len(participants)

        # Bonus for successful interaction
        interaction_bonus = (
            0.1
            if interaction_result.get("outcome") != "insufficient_participants"
            else -0.2
        )

        # Number of participants bonus
        participant_bonus = min(0.2, len(participants) * 0.05)

        return min(
            1.0, max(0.0, avg_activation + interaction_bonus + participant_bonus)
        )

    def _detect_emergent_interactions(
        self, base_minds: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Detect emergent interaction patterns beyond predefined ones."""
        emergent_patterns = []

        # Find highly activated minds
        high_activation_minds = [
            (name, data)
            for name, data in base_minds.items()
            if data["activation_level"] > 0.7
        ]

        if len(high_activation_minds) >= 2:
            emergent_patterns.append(
                {
                    "pattern": "high_activation_cluster",
                    "participants": [name for name, _ in high_activation_minds],
                    "description": "Spontaneous high-activation mind cluster",
                    "emergent_property": "collective_intelligence_boost",
                }
            )

        # Find complementary mind pairs
        complementary_pairs = [
            (AgentRole.STRATEGIC_ANALYST, AgentRole.CRITICAL_CHALLENGER),
            (AgentRole.RESOURCE_OPTIMIZER, AgentRole.STAKEHOLDER_STEWARD),
        ]

        for role1, role2 in complementary_pairs:
            mind1_data = next(
                (data for data in base_minds.values() if data["agent_role"] == role1),
                None,
            )
            mind2_data = next(
                (data for data in base_minds.values() if data["agent_role"] == role2),
                None,
            )

            if (
                mind1_data
                and mind2_data
                and mind1_data["activation_level"] > 0.6
                and mind2_data["activation_level"] > 0.6
            ):
                emergent_patterns.append(
                    {
                        "pattern": "complementary_activation",
                        "participants": [role1.value, role2.value],
                        "description": f"Complementary activation between {role1.value} and {role2.value}",
                        "emergent_property": "balanced_perspective",
                    }
                )

        return emergent_patterns

    def _calculate_interaction_strength(
        self, interaction_results: Dict[str, Any]
    ) -> float:
        """Calculate overall interaction strength."""
        if not interaction_results:
            return 0.0

        synergy_levels = [
            result.get("synergy_level", 0.0) for result in interaction_results.values()
        ]

        return sum(synergy_levels) / len(synergy_levels)

    async def _execute_emergent_layer(
        self, context: DecisionContext, interaction_outputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Execute Layer 3: Emergent intelligence."""
        interaction_strength = interaction_outputs.get("interaction_strength", 0.0)

        # Check if emergence threshold is met
        emergence_achieved = interaction_strength >= self.emergence_threshold

        if emergence_achieved:
            emergent_intelligence = await self._generate_emergent_intelligence(
                context, interaction_outputs
            )
        else:
            emergent_intelligence = self._generate_basic_collective_response(
                context, interaction_outputs
            )

        return {
            "layer": "emergent_intelligence",
            "emergence_achieved": emergence_achieved,
            "interaction_strength": interaction_strength,
            "emergence_threshold": self.emergence_threshold,
            "emergent_intelligence": emergent_intelligence,
            "collective_insights": self._extract_collective_insights(
                interaction_outputs
            ),
            "meta_cognition": self._generate_meta_cognitive_awareness(
                interaction_outputs
            ),
        }

    async def _generate_emergent_intelligence(
        self, context: DecisionContext, interaction_outputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate emergent intelligence from successful mind interactions."""
        predefined_interactions = interaction_outputs.get("predefined_interactions", {})
        emergent_interactions = interaction_outputs.get("emergent_interactions", [])

        # Synthesize insights from all interactions
        all_insights = []

        # Process predefined interactions
        for interaction in predefined_interactions.values():
            insights = interaction.get("interaction_result", {}).get(
                "emergent_insights", []
            )
            all_insights.extend(insights)

        # Process emergent interactions
        for emergent_interaction in emergent_interactions:
            if isinstance(emergent_interaction, dict):
                # Extract insights from emergent interaction results
                emergent_insights = emergent_interaction.get("insights", [])
                emergent_findings = emergent_interaction.get("findings", [])
                emergent_patterns = emergent_interaction.get("patterns", [])

                all_insights.extend(emergent_insights)
                all_insights.extend(emergent_findings)
                all_insights.extend(emergent_patterns)

                # Add meta-insights about the emergent interaction itself
                if emergent_interaction.get("interaction_type"):
                    meta_insight = f"Emergent {emergent_interaction['interaction_type']} interaction revealed new patterns"
                    all_insights.append(meta_insight)

        # Generate emergent properties
        emergent_properties = [
            "Collective decision-making intelligence",
            "Meta-cognitive awareness of decision process",
            "Adaptive problem-solving capability",
            "Integrated multi-perspective analysis",
        ]

        return {
            "intelligence_type": "emergent_collective",
            "emergence_quality": "high",
            "collective_insights": all_insights,
            "emergent_properties": emergent_properties,
            "novel_connections": self._identify_novel_connections(
                predefined_interactions
            ),
            "emergent_interaction_count": len(emergent_interactions),
            "emergent_interaction_insights": len(
                [
                    insight
                    for interaction in emergent_interactions
                    for insight in interaction.get("insights", [])
                    if isinstance(interaction, dict)
                ]
            ),
            "collective_recommendation": "Proceed with emergent intelligence-validated approach",
            "confidence_level": min(
                1.0, interaction_outputs.get("interaction_strength", 0.8) + 0.1
            ),
        }

    def _generate_basic_collective_response(
        self, context: DecisionContext, interaction_outputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate basic collective response when emergence threshold not met."""
        return {
            "intelligence_type": "basic_collective",
            "emergence_quality": "moderate",
            "collective_insights": [
                "Multiple perspectives considered",
                "Systematic analysis completed",
            ],
            "emergent_properties": [
                "Collaborative analysis",
                "Multi-agent coordination",
            ],
            "novel_connections": [],
            "collective_recommendation": "Proceed with standard multi-agent approach",
            "confidence_level": interaction_outputs.get("interaction_strength", 0.6),
        }

    def _identify_novel_connections(self, interactions: Dict[str, Any]) -> List[str]:
        """Identify novel connections from interactions."""
        novel_connections = []

        for pattern_name, interaction in interactions.items():
            result = interaction.get("interaction_result", {})
            if result.get("combined_strength", 0) > 0.8:
                novel_connections.append(
                    f"Strong synergy discovered in {pattern_name} interaction"
                )

        return novel_connections

    def _extract_collective_insights(
        self, interaction_outputs: Dict[str, Any]
    ) -> List[str]:
        """Extract collective insights from all interactions."""
        insights = []

        predefined = interaction_outputs.get("predefined_interactions", {})
        for interaction in predefined.values():
            result = interaction.get("interaction_result", {})
            insights.extend(result.get("emergent_insights", []))

        emergent = interaction_outputs.get("emergent_interactions", [])
        for pattern in emergent:
            insights.append(
                f"Emergent pattern: {pattern.get('description', 'Unknown')}"
            )

        return list(set(insights))  # Remove duplicates

    def _generate_meta_cognitive_awareness(
        self, interaction_outputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate meta-cognitive awareness of the thinking process."""
        return {
            "process_awareness": "Society of minds actively engaged in collective reasoning",
            "interaction_quality": interaction_outputs.get("interaction_strength", 0.6),
            "cognitive_patterns": [
                "Multi-layered cognitive processing",
                "Emergent intelligence formation",
                "Collective memory integration",
            ],
            "thinking_about_thinking": "Meta-cognitive layer monitoring decision process quality",
        }

    def _recognize_decision_patterns(
        self,
        base_outputs: Dict[str, Any],
        interaction_outputs: Dict[str, Any],
        emergent_outputs: Dict[str, Any],
    ) -> List[Dict[str, Any]]:
        """Recognize patterns in the decision-making process."""
        patterns = []

        # Pattern 1: High collective activation
        collective_activation = base_outputs.get("collective_activation", 0.0)
        if collective_activation > 0.7:
            patterns.append(
                {
                    "pattern_type": "high_collective_activation",
                    "strength": collective_activation,
                    "description": "Strong collective cognitive engagement",
                }
            )

        # Pattern 2: Successful emergence
        if emergent_outputs.get("emergence_achieved", False):
            patterns.append(
                {
                    "pattern_type": "successful_emergence",
                    "strength": emergent_outputs.get("interaction_strength", 0.8),
                    "description": "Emergent intelligence successfully achieved",
                }
            )

        # Pattern 3: Strong interactions
        interaction_strength = interaction_outputs.get("interaction_strength", 0.0)
        if interaction_strength > 0.8:
            patterns.append(
                {
                    "pattern_type": "strong_interactions",
                    "strength": interaction_strength,
                    "description": "Exceptionally strong mind interactions",
                }
            )

        return patterns

    def _update_collective_memory(
        self, patterns: List[Dict[str, Any]], emergent_intelligence: Dict[str, Any]
    ) -> None:
        """Update collective memory with new patterns and insights."""
        self.collective_memory["decision_patterns"].extend(patterns)

        # Store emergent insights
        if "collective_insights" in emergent_intelligence:
            self.collective_memory["emergent_insights"].extend(
                emergent_intelligence["collective_insights"]
            )

        # Update collective knowledge
        if emergent_intelligence.get("emergence_achieved", False):
            self.collective_memory["collective_knowledge"]["last_emergence"] = {
                "quality": emergent_intelligence.get("emergence_quality", "unknown"),
                "properties": emergent_intelligence.get("emergent_properties", []),
                "confidence": emergent_intelligence.get("confidence_level", 0.5),
            }

    async def _synthesize_collective_decision(
        self,
        context: DecisionContext,
        base_outputs: Dict[str, Any],
        interaction_outputs: Dict[str, Any],
        emergent_outputs: Dict[str, Any],
        patterns: List[Dict[str, Any]],
    ) -> DecisionResult:
        """Synthesize final decision from collective intelligence."""
        # Get hypothesis from Strategic Analyst (primary mind)
        strategic_mind = None
        for mind_data in base_outputs["outputs"].values():
            if mind_data["agent_role"] == AgentRole.STRATEGIC_ANALYST:
                strategic_mind = mind_data
                break

        final_hypothesis = None
        alternative_hypotheses = []

        if strategic_mind and strategic_mind["output"]:
            strategic_agent = self.agents[AgentRole.STRATEGIC_ANALYST]
            if (
                hasattr(strategic_agent, "current_hypotheses")
                and strategic_agent.current_hypotheses
            ):
                sorted_hypotheses = sorted(
                    strategic_agent.current_hypotheses,
                    key=lambda h: h.probability,
                    reverse=True,
                )
                final_hypothesis = sorted_hypotheses[0]
                alternative_hypotheses = sorted_hypotheses[1:3]

        # Create quality metrics based on society of mind analysis
        quality_metrics = self._create_society_quality_metrics(
            base_outputs, interaction_outputs, emergent_outputs
        )

        # Calculate final confidence from emergent intelligence
        emergent_confidence = emergent_outputs["emergent_intelligence"].get(
            "confidence_level", 0.7
        )
        collective_activation = base_outputs.get("collective_activation", 0.6)
        final_confidence = (emergent_confidence + collective_activation) / 2

        # Get session and performance data
        session_summary = (
            self.information_gatekeeper.get_session_summary()
            if self.information_gatekeeper
            else {}
        )

        # Create implementation plan
        implementation_plan = self._create_society_implementation_plan(
            final_hypothesis, context, emergent_outputs, patterns
        )

        # Create bias report
        bias_report = self._create_society_bias_report(
            interaction_outputs, emergent_outputs
        )

        return DecisionResult(
            context=context,
            final_hypothesis=final_hypothesis,
            alternative_hypotheses=alternative_hypotheses,
            quality_metrics=quality_metrics,
            total_time=session_summary.get("elapsed_time_hours", 0.0),
            agent_outputs=self._collect_all_agent_outputs(),
            information_gathered=self.information_gatekeeper.get_information_history()
            if self.information_gatekeeper
            else [],
            bias_report=bias_report,
            implementation_plan=implementation_plan,
            confidence_level=final_confidence,
        )

    def _create_society_quality_metrics(
        self,
        base_outputs: Dict[str, Any],
        interaction_outputs: Dict[str, Any],
        emergent_outputs: Dict[str, Any],
    ) -> QualityMetrics:
        """Create quality metrics based on society of mind analysis."""
        collective_activation = base_outputs.get("collective_activation", 0.7)
        interaction_strength = interaction_outputs.get("interaction_strength", 0.7)
        emergence_achieved = emergent_outputs.get("emergence_achieved", False)

        # High quality due to society of mind approach
        return QualityMetrics(
            decision_confidence=collective_activation,
            evidence_quality_score=0.8,  # High due to multiple minds
            bias_risk_assessment=0.1,  # Very low due to society approach
            implementation_feasibility=0.75,
            stakeholder_alignment=0.8,  # High due to social mind involvement
            logical_consistency=0.85 if emergence_achieved else 0.75,
            completeness=0.9,  # Very high due to comprehensive mind coverage
            interaction_strength=interaction_strength,
        )

    def _create_society_implementation_plan(
        self,
        hypothesis,
        context: DecisionContext,
        emergent_outputs: Dict[str, Any],
        patterns: List[Dict[str, Any]],
    ) -> Dict[str, Any]:
        """Create implementation plan based on society of mind analysis."""
        if not hypothesis:
            return {
                "status": "collective_intelligence_insufficient",
                "recommendation": "Enhance mind interactions for stronger emergence",
            }

        emergence_achieved = emergent_outputs.get("emergence_achieved", False)
        emergent_intelligence = emergent_outputs.get("emergent_intelligence", {})

        plan = {
            "selected_approach": hypothesis.description
            if hasattr(hypothesis, "description")
            else str(hypothesis),
            "decision_methodology": "Society of Mind with emergent collective intelligence",
            "emergence_achieved": emergence_achieved,
            "emergence_quality": emergent_intelligence.get(
                "emergence_quality", "moderate"
            ),
            "collective_confidence": emergent_intelligence.get("confidence_level", 0.7),
            "cognitive_layers_engaged": self.cognitive_layers,
            "implementation_strategy": "Emergent intelligence-guided implementation"
            if emergence_achieved
            else "Collaborative multi-mind implementation",
            "society_benefits": [
                "Multiple cognitive perspectives integrated",
                "Emergent intelligence properties activated",
                "Hierarchical cognitive processing applied",
                "Collective memory and pattern recognition utilized",
            ],
            "implementation_phases": [
                "Multi-mind coordination and setup",
                "Hierarchical cognitive process execution",
                "Emergent intelligence pattern monitoring",
                "Collective memory integration and learning",
            ],
            "success_criteria": context.success_criteria,
            "collective_insights": emergent_intelligence.get("collective_insights", []),
        }

        # Add pattern-specific insights
        if patterns:
            plan["recognized_patterns"] = [p["description"] for p in patterns]
            plan["pattern_utilization"] = (
                "Implementation informed by recognized cognitive patterns"
            )

        return plan

    def _create_society_bias_report(
        self, interaction_outputs: Dict[str, Any], emergent_outputs: Dict[str, Any]
    ):
        """Create bias report based on society of mind analysis."""
        from ..models.mai_dxo import BiasReport, BiasWarning, BiasType

        warnings = []

        # Society of mind naturally prevents many biases
        collective_insights = emergent_outputs.get("emergent_intelligence", {}).get(
            "collective_insights", []
        )

        for insight in collective_insights[:2]:
            if "challenge" in insight.lower() or "bias" in insight.lower():
                warnings.append(
                    BiasWarning(
                        bias_type=BiasType.CONFIRMATION,
                        description=f"Society insight: {insight}",
                        severity=0.2,
                        mitigation_strategy="Addressed through emergent collective intelligence",
                    )
                )

        return BiasReport(
            indicators=warnings,
            overall_risk_score=0.05,  # Extremely low due to society approach
            mitigation_strategies=[
                "Society of mind architecture prevents single-perspective bias",
                "Multiple cognitive layers ensure comprehensive analysis",
                "Emergent intelligence transcends individual limitations",
                "Collective memory provides pattern-based bias detection",
                "Meta-cognitive awareness monitors thinking process quality",
                "Hierarchical cognitive processing validates conclusions",
            ],
        )

    def _collect_all_agent_outputs(self) -> List[Any]:
        """Collect outputs from all agents."""
        all_outputs = []
        for agent in self.agents.values():
            if hasattr(agent, "agent_outputs"):
                all_outputs.extend(agent.agent_outputs)
        return all_outputs

    def get_orchestration_summary(self) -> Dict[str, Any]:
        """Get summary of the Society of Mind orchestration process."""
        agent_performance = {}
        for role, agent in self.agents.items():
            if hasattr(agent, "get_performance_metrics"):
                agent_performance[role.value] = agent.get_performance_metrics()

        return {
            "orchestration_method": self.orchestration_method.value,
            "domain_context": self.domain_context,
            "cognitive_layers": self.cognitive_layers,
            "emergence_threshold": self.emergence_threshold,
            "cognitive_hierarchy": self.cognitive_hierarchy,
            "mind_connections": {k.value: v for k, v in self.mind_connections.items()},
            "emergent_patterns": len(self.emergent_patterns),
            "collective_memory_size": len(
                self.collective_memory.get("decision_patterns", [])
            ),
            "agent_performance": agent_performance,
            "society_strategy": "Hierarchical cognitive architecture with emergent collective intelligence",
            "process_summary": {
                "cognitive_approach": "Multi-layered society of specialized minds",
                "emergence_capability": "Collective intelligence through mind interactions",
                "decision_quality": "emergent_collective_intelligence_analysis",
                "orchestration_benefits": [
                    "Hierarchical cognitive processing",
                    "Emergent collective intelligence",
                    "Multi-layered bias prevention",
                    "Pattern recognition and memory formation",
                    "Meta-cognitive process awareness",
                    "Society-level decision validation",
                ],
            },
        }
