object

defmodule OORL.CollectiveLearning do
  @moduledoc """
  Collective learning implementation for OORL framework enabling emergent
  group intelligence through distributed coordination and knowledge sharing.
  
  Provides mechanisms for:
  - Coalition formation and management
  - Distributed knowledge aggregation
  - Emergent collective intelligence
  - Byzantine fault tolerance in learning networks
  """

  @type t :: %__MODULE__{
    coalition_id: coalition_id() | nil,
    member_objects: MapSet.t() | nil,
    knowledge_graph: map() | nil,
    consensus_algorithm: atom() | nil,
    trust_network: map() | nil,
    collective_memory: map() | nil,
    emergence_detector: pid() | nil,
    performance_metrics: map() | nil,
    byzantine_tolerance: float() | nil,
    communication_protocols: list() | nil
  }

  defstruct [
    :coalition_id,
    :member_objects,
    :knowledge_graph,
    :consensus_algorithm,
    :trust_network,
    :collective_memory,
    :emergence_detector,
    :performance_metrics,
    :byzantine_tolerance,
    :communication_protocols
  ]

  @type coalition_id :: String.t()
  @type object_id :: String.t()
  @type knowledge_item :: %{
    source: object_id(),
    content: any(),
    confidence: float(),
    timestamp: DateTime.t(),
    validation_count: integer()
  }

  @doc """
  Creates a new collective learning coalition.
  
  ## Parameters
  - `coalition_id`: Unique identifier for the coalition
  - `initial_members`: List of object IDs to include
  - `opts`: Configuration options
  
  ## Returns
  `%OORL.CollectiveLearning{}` struct
  """
  def new(coalition_id, initial_members \\ [], opts \\ []) do
    %OORL.CollectiveLearning{
      coalition_id: coalition_id,
      member_objects: MapSet.new(initial_members),
      knowledge_graph: initialize_knowledge_graph(),
      consensus_algorithm: Keyword.get(opts, :consensus_algorithm, :byzantine_consensus),
      trust_network: initialize_trust_network(initial_members),
      collective_memory: [],
      emergence_detector: initialize_emergence_detector(opts),
      performance_metrics: %{
        learning_rate: 0.0,
        consensus_time: 0.0,
        knowledge_quality: 0.0,
        emergence_score: 0.0
      },
      byzantine_tolerance: Keyword.get(opts, :byzantine_tolerance, true),
      communication_protocols: initialize_communication_protocols(opts)
    }
  end

  @doc """
  Adds knowledge from an object to the collective learning system.
  
  ## Parameters
  - `collective`: Current collective learning state
  - `source_object_id`: ID of the contributing object
  - `knowledge`: Knowledge item to add
  - `validation_options`: Options for knowledge validation
  
  ## Returns
  Updated collective learning state
  """
  def add_knowledge(collective, source_object_id, knowledge, validation_options \\ %{}) do
    if MapSet.member?(collective.member_objects, source_object_id) do
      # Validate knowledge item
      case validate_knowledge(knowledge, collective.trust_network, validation_options) do
        {:ok, validated_knowledge} ->
          # Add to knowledge graph
          updated_graph = add_to_knowledge_graph(
            collective.knowledge_graph, 
            source_object_id, 
            validated_knowledge
          )
          
          # Update collective memory
          memory_item = create_memory_item(source_object_id, validated_knowledge)
          updated_memory = [memory_item | Enum.take(collective.collective_memory, 999)]
          
          # Update trust scores based on knowledge quality
          updated_trust = update_trust_scores(
            collective.trust_network, 
            source_object_id, 
            validated_knowledge
          )
          
          %{collective |
            knowledge_graph: updated_graph,
            collective_memory: updated_memory,
            trust_network: updated_trust
          }
        
        {:error, _reason} ->
          # Knowledge failed validation, update trust negatively
          updated_trust = penalize_trust(collective.trust_network, source_object_id)
          %{collective | trust_network: updated_trust}
      end
    else
      collective  # Object not in coalition
    end
  end

  @doc """
  Performs distributed consensus on a proposition across coalition members.
  
  ## Parameters
  - `collective`: Current collective learning state
  - `proposition`: Proposition to reach consensus on
  - `timeout_ms`: Maximum time to wait for consensus
  
  ## Returns
  `{:ok, consensus_result}` or `{:error, reason}`
  """
  def reach_consensus(collective, proposition, timeout_ms \\ 5000) do
    case collective.consensus_algorithm do
      :byzantine_consensus ->
        byzantine_consensus(collective, proposition, timeout_ms)
      
      :practical_byzantine_ft ->
        practical_byzantine_consensus(collective, proposition, timeout_ms)
      
      :raft_consensus ->
        raft_consensus(collective, proposition, timeout_ms)
      
      :simple_majority ->
        simple_majority_consensus(collective, proposition, timeout_ms)
      
      _ ->
        {:error, :unknown_consensus_algorithm}
    end
  end

  @doc """
  Detects and analyzes emergent collective intelligence phenomena.
  
  ## Parameters
  - `collective`: Current collective learning state
  - `observation_window`: Time window for analysis
  
  ## Returns
  `{:ok, emergence_analysis}` with detected emergent behaviors
  """
  def analyze_emergence(collective, observation_window \\ 3600) do
    recent_knowledge = filter_recent_knowledge(collective.collective_memory, observation_window)
    
    emergence_indicators = %{
      knowledge_synthesis: detect_knowledge_synthesis(recent_knowledge),
      novel_solutions: detect_novel_solutions(recent_knowledge, collective.knowledge_graph),
      collective_reasoning: detect_collective_reasoning_patterns(recent_knowledge),
      distributed_coordination: analyze_coordination_patterns(collective),
      intelligence_amplification: measure_intelligence_amplification(collective)
    }
    
    emergence_score = calculate_emergence_score(emergence_indicators)
    
    emergence_analysis = %{
      emergence_score: emergence_score,
      indicators: emergence_indicators,
      classification: classify_emergence_type(emergence_indicators),
      collective_intelligence_detected: emergence_score > 0.7,
      member_count: MapSet.size(collective.member_objects),
      observation_period: observation_window
    }
    
    {:ok, emergence_analysis}
  end

  @doc """
  Optimizes collective learning performance through meta-learning.
  
  ## Parameters
  - `collective`: Current collective learning state
  - `performance_feedback`: Recent performance metrics
  - `optimization_strategy`: Strategy for optimization
  
  ## Returns
  Optimized collective learning configuration
  """
  def optimize_collective_performance(collective, performance_feedback, optimization_strategy \\ :adaptive) do
    case optimization_strategy do
      :adaptive ->
        adaptive_optimization(collective, performance_feedback)
      
      :evolutionary ->
        evolutionary_optimization(collective, performance_feedback)
      
      :gradient_based ->
        gradient_based_optimization(collective, performance_feedback)
      
      _ ->
        collective
    end
  end

  @doc """
  Manages coalition membership with dynamic joining and leaving.
  
  ## Parameters
  - `collective`: Current collective learning state
  - `action`: :join or :leave
  - `object_id`: Object to add or remove
  - `credentials`: Trust credentials for joining objects
  
  ## Returns
  Updated collective with modified membership
  """
  def manage_membership(collective, action, object_id, credentials \\ %{})
  def manage_membership(collective, :join, object_id, credentials) do
    if validate_join_credentials(credentials, collective.trust_network) do
      updated_members = MapSet.put(collective.member_objects, object_id)
      updated_trust = initialize_object_trust(collective.trust_network, object_id)
      
      %{collective |
        member_objects: updated_members,
        trust_network: updated_trust
      }
    else
      collective
    end
  end

  def manage_membership(collective, :leave, object_id, _credentials) do
    updated_members = MapSet.delete(collective.member_objects, object_id)
    updated_trust = remove_object_trust(collective.trust_network, object_id)
    
    %{collective |
      member_objects: updated_members,
      trust_network: updated_trust
    }
  end

  # Private implementation functions

  defp initialize_knowledge_graph do
    %{
      nodes: %{},
      edges: %{},
      metadata: %{
        created_at: DateTime.utc_now(),
        version: "1.0",
        node_count: 0,
        edge_count: 0
      }
    }
  end

  defp initialize_trust_network(initial_members) do
    base_trust = 0.5  # Neutral trust score
    
    trust_scores = Enum.reduce(initial_members, %{}, fn member_id, acc ->
      Map.put(acc, member_id, base_trust)
    end)
    
    %{
      trust_scores: trust_scores,
      interaction_history: %{},
      reputation_decay: 0.01,
      trust_threshold: 0.3
    }
  end

  defp initialize_emergence_detector(opts) do
    %{
      enabled: Keyword.get(opts, :emergence_detection, true),
      detection_algorithms: [:pattern_recognition, :novelty_detection, :complexity_analysis],
      thresholds: %{
        emergence_score: 0.7,
        novelty_threshold: 0.6,
        complexity_threshold: 0.8
      },
      observation_window: Keyword.get(opts, :observation_window, 3600)
    }
  end

  defp initialize_communication_protocols(opts) do
    %{
      message_protocol: Keyword.get(opts, :message_protocol, :asynchronous),
      reliability_level: Keyword.get(opts, :reliability, :at_least_once),
      encryption_enabled: Keyword.get(opts, :encryption, false),
      compression_enabled: Keyword.get(opts, :compression, true)
    }
  end

  defp validate_knowledge(knowledge, trust_network, _options) do
    # Simple validation based on source trust and content structure
    source_trust = get_trust_score(trust_network, knowledge[:source])
    
    if source_trust > trust_network.trust_threshold do
      validated_knowledge = Map.merge(knowledge, %{
        validation_score: source_trust,
        validated_at: DateTime.utc_now()
      })
      {:ok, validated_knowledge}
    else
      {:error, :insufficient_trust}
    end
  end

  defp add_to_knowledge_graph(graph, source_id, knowledge) do
    node_id = generate_knowledge_node_id(source_id, knowledge)
    
    new_node = %{
      id: node_id,
      source: source_id,
      content: knowledge,
      created_at: DateTime.utc_now(),
      access_count: 0
    }
    
    updated_nodes = Map.put(graph.nodes, node_id, new_node)
    
    # Create edges to related knowledge
    related_edges = find_related_knowledge_edges(graph.nodes, new_node)
    updated_edges = Map.merge(graph.edges, related_edges)
    
    %{graph |
      nodes: updated_nodes,
      edges: updated_edges,
      metadata: update_graph_metadata(graph.metadata, 1, length(Map.keys(related_edges)))
    }
  end

  defp create_memory_item(source_id, knowledge) do
    %{
      source: source_id,
      knowledge: knowledge,
      timestamp: DateTime.utc_now(),
      access_count: 0,
      influence_score: 0.0
    }
  end

  defp update_trust_scores(trust_network, source_id, knowledge) do
    current_trust = get_trust_score(trust_network, source_id)
    knowledge_quality = assess_knowledge_quality(knowledge)
    
    # Trust update based on knowledge quality
    trust_adjustment = 0.1 * (knowledge_quality - 0.5)
    new_trust = max(0.0, min(1.0, current_trust + trust_adjustment))
    
    updated_scores = Map.put(trust_network.trust_scores, source_id, new_trust)
    
    %{trust_network | trust_scores: updated_scores}
  end

  defp penalize_trust(trust_network, source_id) do
    current_trust = get_trust_score(trust_network, source_id)
    penalty = 0.2
    new_trust = max(0.0, current_trust - penalty)
    
    updated_scores = Map.put(trust_network.trust_scores, source_id, new_trust)
    %{trust_network | trust_scores: updated_scores}
  end

  # Consensus algorithms

  defp byzantine_consensus(collective, proposition, timeout_ms) do
    member_count = MapSet.size(collective.member_objects)
    
    if member_count < 4 do
      {:error, :insufficient_members_for_byzantine_consensus}
    else
      # Simulate Byzantine consensus
      byzantine_fault_tolerance = div(member_count - 1, 3)
      honest_members = member_count - byzantine_fault_tolerance
      
      # Simplified consensus simulation
      votes = simulate_member_votes(collective.member_objects, proposition, collective.trust_network)
      honest_votes = filter_trusted_votes(votes, collective.trust_network)
      
      if length(honest_votes) >= honest_members do
        consensus_result = aggregate_votes(honest_votes)
        {:ok, %{
          result: consensus_result,
          confidence: calculate_consensus_confidence(honest_votes),
          participating_members: length(honest_votes),
          consensus_time: :rand.uniform(timeout_ms)
        }}
      else
        {:error, :consensus_not_reached}
      end
    end
  end

  defp practical_byzantine_consensus(collective, proposition, timeout_ms) do
    # Simplified PBFT simulation
    member_count = MapSet.size(collective.member_objects)
    
    if member_count >= 4 do
      # Simulate three-phase PBFT protocol
      prepare_phase = simulate_prepare_phase(collective, proposition)
      commit_phase = simulate_commit_phase(collective, prepare_phase)
      
      if commit_phase.success do
        {:ok, %{
          result: commit_phase.result,
          confidence: 0.99,  # High confidence for PBFT
          participating_members: member_count,
          consensus_time: :rand.uniform(timeout_ms)
        }}
      else
        {:error, :pbft_consensus_failed}
      end
    else
      {:error, :insufficient_members_for_pbft}
    end
  end

  defp simple_majority_consensus(collective, proposition, _timeout_ms) do
    votes = simulate_member_votes(collective.member_objects, proposition, collective.trust_network)
    
    positive_votes = Enum.count(votes, fn vote -> vote.decision == :accept end)
    total_votes = length(votes)
    
    if positive_votes > div(total_votes, 2) do
      {:ok, %{
        result: :accepted,
        confidence: positive_votes / total_votes,
        participating_members: total_votes,
        consensus_time: 100  # Fast for simple majority
      }}
    else
      {:ok, %{
        result: :rejected,
        confidence: (total_votes - positive_votes) / total_votes,
        participating_members: total_votes,
        consensus_time: 100
      }}
    end
  end

  defp raft_consensus(_collective, _proposition, _timeout_ms) do
    # Placeholder for Raft consensus
    {:ok, %{result: :accepted, confidence: 0.8, participating_members: 3, consensus_time: 200}}
  end

  # Emergence detection functions

  defp detect_knowledge_synthesis(recent_knowledge) do
    # Detect patterns where knowledge from multiple sources combines
    synthesis_patterns = Enum.group_by(recent_knowledge, fn item ->
      extract_knowledge_domain(item.knowledge)
    end)
    
    cross_domain_synthesis = Enum.count(synthesis_patterns, fn {_domain, items} ->
      length(items) > 1 and has_multiple_sources(items)
    end)
    
    %{
      synthesis_events: cross_domain_synthesis,
      synthesis_rate: cross_domain_synthesis / max(length(recent_knowledge), 1),
      domains_involved: map_size(synthesis_patterns)
    }
  end

  defp detect_novel_solutions(recent_knowledge, knowledge_graph) do
    # Detect solutions that are novel compared to existing knowledge
    novel_count = Enum.count(recent_knowledge, fn item ->
      is_novel_knowledge(item.knowledge, knowledge_graph)
    end)
    
    %{
      novel_solutions: novel_count,
      novelty_rate: novel_count / max(length(recent_knowledge), 1),
      innovation_score: calculate_innovation_score(recent_knowledge, knowledge_graph)
    }
  end

  defp detect_collective_reasoning_patterns(recent_knowledge) do
    # Detect chains of reasoning across multiple objects
    reasoning_chains = find_reasoning_chains(recent_knowledge)
    
    %{
      reasoning_chains: length(reasoning_chains),
      average_chain_length: calculate_average_chain_length(reasoning_chains),
      distributed_reasoning_score: calculate_distributed_reasoning_score(reasoning_chains)
    }
  end

  defp analyze_coordination_patterns(collective) do
    # Analyze how well objects coordinate their learning
    coordination_metrics = %{
      synchronization_level: calculate_synchronization_level(collective),
      coordination_efficiency: calculate_coordination_efficiency(collective),
      distributed_decision_quality: assess_distributed_decisions(collective)
    }
    
    coordination_metrics
  end

  defp measure_intelligence_amplification(collective) do
    # Measure if collective performance exceeds sum of individual performances
    individual_performance = estimate_individual_performance(collective)
    collective_performance = estimate_collective_performance(collective)
    
    amplification_factor = if individual_performance > 0 do
      collective_performance / individual_performance
    else
      1.0
    end
    
    %{
      amplification_factor: amplification_factor,
      collective_performance: collective_performance,
      individual_baseline: individual_performance,
      amplification_detected: amplification_factor > 1.2
    }
  end

  defp calculate_emergence_score(indicators) do
    weights = %{
      knowledge_synthesis: 0.25,
      novel_solutions: 0.25,
      collective_reasoning: 0.20,
      distributed_coordination: 0.15,
      intelligence_amplification: 0.15
    }
    
    scores = %{
      knowledge_synthesis: indicators.knowledge_synthesis.synthesis_rate,
      novel_solutions: indicators.novel_solutions.novelty_rate,
      collective_reasoning: indicators.collective_reasoning.distributed_reasoning_score,
      distributed_coordination: indicators.distributed_coordination.coordination_efficiency,
      intelligence_amplification: min(1.0, indicators.intelligence_amplification.amplification_factor / 2.0)
    }
    
    Enum.reduce(weights, 0.0, fn {component, weight}, acc ->
      acc + weight * Map.get(scores, component, 0.0)
    end)
  end

  defp classify_emergence_type(indicators) do
    cond do
      Map.has_key?(indicators, :intelligence_amplification) and 
      indicators.intelligence_amplification.amplification_factor > 2.0 ->
        :strong_emergence
      
      Map.has_key?(indicators, :collective_reasoning) and
      indicators.collective_reasoning.distributed_reasoning_score > 0.8 ->
        :collective_intelligence
      
      Map.has_key?(indicators, :novel_solutions) and
      indicators.novel_solutions.innovation_score > 0.7 ->
        :creative_emergence
      
      Map.has_key?(indicators, :distributed_coordination) and
      indicators.distributed_coordination.coordination_efficiency > 0.8 ->
        :coordination_emergence
      
      true ->
        :weak_emergence
    end
  end

  # Utility functions (simplified implementations)

  defp get_trust_score(trust_network, object_id) do
    Map.get(trust_network.trust_scores, object_id, 0.5)
  end

  defp assess_knowledge_quality(_knowledge) do
    # Placeholder quality assessment
    0.5 + :rand.uniform() * 0.5
  end

  defp generate_knowledge_node_id(source_id, knowledge) do
    hash = :erlang.phash2({source_id, knowledge})
    "knowledge_#{hash}"
  end

  defp find_related_knowledge_edges(_existing_nodes, _new_node) do
    # Placeholder for edge detection
    %{}
  end

  defp update_graph_metadata(metadata, new_nodes, new_edges) do
    %{metadata |
      node_count: metadata.node_count + new_nodes,
      edge_count: metadata.edge_count + new_edges
    }
  end

  defp simulate_member_votes(members, _proposition, trust_network) do
    Enum.map(members, fn member_id ->
      trust_score = get_trust_score(trust_network, member_id)
      decision = if :rand.uniform() < trust_score, do: :accept, else: :reject
      
      %{
        member_id: member_id,
        decision: decision,
        trust_score: trust_score,
        timestamp: DateTime.utc_now()
      }
    end)
  end

  defp filter_trusted_votes(votes, trust_network) do
    Enum.filter(votes, fn vote ->
      vote.trust_score > trust_network.trust_threshold
    end)
  end

  defp aggregate_votes(votes) do
    positive_count = Enum.count(votes, fn vote -> vote.decision == :accept end)
    total_count = length(votes)
    
    if positive_count > div(total_count, 2), do: :accepted, else: :rejected
  end

  defp calculate_consensus_confidence(votes) do
    if length(votes) > 0 do
      avg_trust = Enum.reduce(votes, 0, fn vote, acc -> acc + vote.trust_score end) / length(votes)
      avg_trust
    else
      0.0
    end
  end

  defp simulate_prepare_phase(_collective, _proposition) do
    %{success: true, prepared: true}
  end

  defp simulate_commit_phase(_collective, prepare_result) do
    %{success: prepare_result.success, result: :accepted}
  end

  defp filter_recent_knowledge(memory, window_seconds) do
    cutoff_time = DateTime.add(DateTime.utc_now(), -window_seconds, :second)
    
    Enum.filter(memory, fn item ->
      DateTime.compare(item.timestamp, cutoff_time) == :gt
    end)
  end

  defp validate_join_credentials(_credentials, _trust_network) do
    # Placeholder validation
    true
  end

  defp initialize_object_trust(trust_network, object_id) do
    updated_scores = Map.put(trust_network.trust_scores, object_id, 0.5)
    %{trust_network | trust_scores: updated_scores}
  end

  defp remove_object_trust(trust_network, object_id) do
    updated_scores = Map.delete(trust_network.trust_scores, object_id)
    %{trust_network | trust_scores: updated_scores}
  end

  # Placeholder implementations for complex analysis functions
  defp extract_knowledge_domain(_knowledge), do: :general
  defp has_multiple_sources(items), do: length(Enum.uniq_by(items, & &1.source)) > 1
  defp is_novel_knowledge(_knowledge, _graph), do: :rand.uniform() > 0.7
  defp calculate_innovation_score(_recent, _graph), do: :rand.uniform()
  defp find_reasoning_chains(_knowledge), do: []
  defp calculate_average_chain_length([]), do: 0.0
  defp calculate_average_chain_length(chains), do: length(chains) / max(length(chains), 1)
  defp calculate_distributed_reasoning_score(_chains), do: :rand.uniform()
  defp calculate_synchronization_level(_collective), do: :rand.uniform()
  defp calculate_coordination_efficiency(_collective), do: :rand.uniform()
  defp assess_distributed_decisions(_collective), do: :rand.uniform()
  defp estimate_individual_performance(_collective), do: 0.6
  defp estimate_collective_performance(_collective), do: 0.8
  defp adaptive_optimization(collective, _feedback), do: collective
  defp evolutionary_optimization(collective, _feedback), do: collective
  defp gradient_based_optimization(collective, _feedback), do: collective

  @doc """
  Performs distributed policy optimization across coalition members.
  
  Implements a distributed version of policy gradient optimization where
  each coalition member contributes gradients based on their local experiences.
  
  ## Parameters
  - `collective`: Current collective learning state
  
  ## Returns
  Updated collective with optimized policies
  """
  def distributed_policy_optimization(collective) do
    # Collect policy gradients from each member
    member_gradients = collect_member_gradients(collective)
    
    # Aggregate gradients using trust-weighted averaging
    aggregated_gradients = aggregate_trusted_gradients(member_gradients, collective.trust_network)
    
    # Apply distributed optimization step
    optimized_policies = apply_gradient_updates(collective, aggregated_gradients)
    
    # Update collective performance metrics
    updated_metrics = update_optimization_metrics(collective.performance_metrics, optimized_policies)
    
    %{collective |
      performance_metrics: updated_metrics,
      collective_memory: record_optimization_step(collective.collective_memory, optimized_policies)
    }
  end

  @doc """
  Forms a learning coalition from compatible objects based on learning objectives.
  
  Analyzes object compatibility and creates coalitions that maximize collective
  learning potential while maintaining trust and coordination efficiency.
  
  ## Parameters
  - `available_objects`: List of object IDs available for coalition formation  
  - `learning_objective`: The shared learning goal for the coalition
  
  ## Returns
  `{:ok, collective}` with formed coalition or `{:error, reason}`
  """
  def form_learning_coalition(available_objects, learning_objective) do
    # Analyze object compatibility for the learning objective
    compatibility_matrix = analyze_object_compatibility(available_objects, learning_objective)
    
    # Select optimal coalition members based on compatibility and diversity
    selected_members = select_coalition_members(compatibility_matrix, learning_objective)
    
    if length(selected_members) >= 2 do
      # Create coalition with selected members
      coalition_id = "coalition_#{:erlang.phash2(learning_objective)}_#{System.system_time(:millisecond)}"
      
      coalition_opts = [
        consensus_algorithm: determine_optimal_consensus(selected_members),
        byzantine_tolerance: calculate_required_tolerance(selected_members),
        emergence_detection: true,
        observation_window: 1800
      ]
      
      collective = new(coalition_id, selected_members, coalition_opts)
      
      # Initialize coalition with learning objective
      initialized_collective = initialize_learning_objective(collective, learning_objective)
      
      {:ok, initialized_collective}
    else
      {:error, :insufficient_compatible_objects}
    end
  end

  @doc """
  Detects emergent behaviors and intelligence in the collective.
  
  Continuously monitors coalition activity to identify emergent phenomena
  such as novel problem-solving strategies, collective reasoning patterns,
  and intelligence amplification effects.
  
  ## Parameters
  - `collective`: Current collective learning state
  
  ## Returns
  `{:ok, emergence_report}` with detailed emergence analysis
  """
  def emergence_detection(collective) do
    # Collect recent activity data
    recent_activity = collect_recent_activity(collective)
    
    # Detect various types of emergence
    emergence_indicators = %{
      behavioral_emergence: detect_behavioral_emergence(recent_activity),
      cognitive_emergence: detect_cognitive_emergence(collective),
      social_emergence: detect_social_emergence(collective),
      adaptive_emergence: detect_adaptive_emergence(collective)
    }
    
    # Calculate overall emergence score
    emergence_score = calculate_comprehensive_emergence_score(emergence_indicators)
    
    # Generate emergence report
    emergence_report = %{
      emergence_score: emergence_score,
      emergence_type: classify_emergence_type(emergence_indicators),
      indicators: emergence_indicators,
      detected_phenomena: identify_specific_phenomena(emergence_indicators),
      coalition_maturity: assess_coalition_maturity(collective),
      prediction_confidence: calculate_prediction_confidence(emergence_indicators),
      recommendations: generate_emergence_recommendations(emergence_indicators)
    }
    
    {:ok, emergence_report}
  end

  # Private functions for distributed policy optimization
  
  defp collect_member_gradients(collective) do
    Enum.map(collective.member_objects, fn member_id ->
      # Simulate gradient collection from each member
      %{
        member_id: member_id,
        policy_gradient: simulate_policy_gradient(member_id),
        experience_count: :rand.uniform(100),
        confidence: get_trust_score(collective.trust_network, member_id)
      }
    end)
  end
  
  defp aggregate_trusted_gradients(gradients, trust_network) do
    # Weight gradients by trust scores
    weighted_gradients = Enum.map(gradients, fn gradient ->
      trust_weight = get_trust_score(trust_network, gradient.member_id)
      %{gradient | policy_gradient: scale_gradient(gradient.policy_gradient, trust_weight)}
    end)
    
    # Aggregate weighted gradients
    total_gradient = Enum.reduce(weighted_gradients, init_gradient(), fn gradient, acc ->
      combine_gradients(acc, gradient.policy_gradient)
    end)
    
    total_gradient
  end
  
  defp apply_gradient_updates(_collective, gradients) do
    # Apply distributed gradient updates to collective policies
    learning_rate = 0.01
    
    updated_policies = %{
      exploration_policy: update_exploration_policy(gradients, learning_rate),
      coordination_policy: update_coordination_policy(gradients, learning_rate),
      knowledge_sharing_policy: update_knowledge_sharing_policy(gradients, learning_rate)
    }
    
    updated_policies
  end
  
  defp update_optimization_metrics(current_metrics, _optimized_policies) do
    %{current_metrics |
      learning_rate: calculate_effective_learning_rate(current_metrics),
      convergence_rate: estimate_convergence_rate(current_metrics),
      optimization_steps: Map.get(current_metrics, :optimization_steps, 0) + 1
    }
  end
  
  defp record_optimization_step(memory, policies) do
    optimization_record = %{
      type: :policy_optimization,
      policies: policies,
      timestamp: DateTime.utc_now(),
      performance_impact: estimate_performance_impact(policies)
    }
    
    [optimization_record | Enum.take(memory, 999)]
  end

  # Private functions for coalition formation
  
  defp analyze_object_compatibility(objects, learning_objective) do
    # Create compatibility matrix for all object pairs
    Enum.reduce(objects, %{}, fn object_a, acc ->
      object_scores = Enum.reduce(objects, %{}, fn object_b, scores ->
        if object_a != object_b do
          compatibility_score = calculate_compatibility_score(object_a, object_b, learning_objective)
          Map.put(scores, object_b, compatibility_score)
        else
          scores
        end
      end)
      Map.put(acc, object_a, object_scores)
    end)
  end
  
  defp select_coalition_members(compatibility_matrix, learning_objective) do
    # Use greedy selection to build optimal coalition
    available_objects = Map.keys(compatibility_matrix)
    
    if length(available_objects) == 0 do
      []
    else
      # Start with highest individual potential
      seed_object = select_seed_object(available_objects, learning_objective)
      if seed_object do
        selected = [seed_object]
        remaining = List.delete(available_objects, seed_object)
        
        # Iteratively add most compatible objects
        Enum.reduce_while(remaining, selected, fn candidate, current_selection ->
          if should_add_to_coalition(candidate, current_selection, compatibility_matrix) do
            {:cont, [candidate | current_selection]}
          else
            {:halt, current_selection}
          end
        end)
      else
        []
      end
    end
  end
  
  defp determine_optimal_consensus(members) do
    member_count = length(members)
    
    cond do
      member_count >= 7 -> :byzantine_consensus
      member_count >= 4 -> :practical_byzantine_ft
      member_count >= 3 -> :raft_consensus
      true -> :simple_majority
    end
  end
  
  defp calculate_required_tolerance(members) do
    # Calculate Byzantine fault tolerance based on member count and trust
    member_count = length(members)
    max(0.1, min(0.5, (member_count - 1) / (3 * member_count)))
  end
  
  defp initialize_learning_objective(collective, learning_objective) do
    objective_metadata = %{
      objective_type: classify_learning_objective(learning_objective),
      complexity_level: assess_objective_complexity(learning_objective),
      estimated_duration: estimate_learning_duration(learning_objective),
      success_criteria: define_success_criteria(learning_objective)
    }
    
    # collective_memory is a list, so we prepend the learning objective
    updated_memory = [%{type: :learning_objective, data: objective_metadata, timestamp: DateTime.utc_now()} | collective.collective_memory]
    %{collective | collective_memory: updated_memory}
  end

  # Private functions for emergence detection
  
  defp collect_recent_activity(collective) do
    # Collect activity from the last observation window
    observation_window = collective.emergence_detector.observation_window
    recent_memory = filter_recent_knowledge(collective.collective_memory, observation_window)
    
    %{
      knowledge_exchanges: count_knowledge_exchanges(recent_memory),
      consensus_decisions: count_consensus_decisions(recent_memory),
      trust_updates: count_trust_updates(recent_memory),
      novel_solutions: count_novel_solutions(recent_memory),
      coordination_events: count_coordination_events(recent_memory)
    }
  end
  
  defp detect_behavioral_emergence(activity) do
    # Detect emergent behavioral patterns
    %{
      novel_coordination_patterns: detect_novel_coordination(activity),
      adaptive_strategies: detect_adaptive_strategies(activity),
      emergent_roles: detect_emergent_roles(activity),
      behavioral_complexity: calculate_behavioral_complexity(activity)
    }
  end
  
  defp detect_cognitive_emergence(collective) do
    # Detect emergent cognitive capabilities
    %{
      collective_reasoning: assess_collective_reasoning_capability(collective),
      distributed_problem_solving: assess_distributed_problem_solving(collective),
      meta_cognitive_awareness: assess_meta_cognitive_awareness(collective),
      knowledge_synthesis: assess_knowledge_synthesis_capability(collective)
    }
  end
  
  defp detect_social_emergence(collective) do
    # Detect emergent social structures and dynamics
    %{
      social_hierarchy: detect_emergent_hierarchy(collective),
      communication_patterns: analyze_communication_patterns(collective),
      trust_networks: analyze_trust_network_evolution(collective),
      social_learning: assess_social_learning_dynamics(collective)
    }
  end
  
  defp detect_adaptive_emergence(collective) do
    # Detect emergent adaptive capabilities
    %{
      environmental_adaptation: assess_environmental_adaptation(collective),
      learning_adaptation: assess_learning_adaptation(collective),
      structural_adaptation: assess_structural_adaptation(collective),
      behavioral_adaptation: assess_behavioral_adaptation(collective)
    }
  end
  
  defp calculate_comprehensive_emergence_score(indicators) do
    # Calculate weighted average of all emergence indicators
    weights = %{
      behavioral_emergence: 0.3,
      cognitive_emergence: 0.3,
      social_emergence: 0.2,
      adaptive_emergence: 0.2
    }
    
    Enum.reduce(weights, 0.0, fn {type, weight}, acc ->
      type_score = calculate_indicator_type_score(Map.get(indicators, type, %{}))
      acc + weight * type_score
    end)
  end
  
  defp identify_specific_phenomena(indicators) do
    # Identify specific emergent phenomena based on indicators
    phenomena = []
    
    phenomena = if indicators.cognitive_emergence.collective_reasoning > 0.8 do
      [:collective_intelligence | phenomena]
    else
      phenomena
    end
    
    phenomena = if indicators.behavioral_emergence.novel_coordination_patterns > 0.7 do
      [:emergent_coordination | phenomena]
    else
      phenomena
    end
    
    phenomena = if indicators.social_emergence.social_hierarchy > 0.6 do
      [:emergent_hierarchy | phenomena]
    else
      phenomena
    end
    
    phenomena = if indicators.adaptive_emergence.environmental_adaptation > 0.8 do
      [:adaptive_intelligence | phenomena]
    else
      phenomena
    end
    
    phenomena
  end
  
  defp assess_coalition_maturity(collective) do
    # Assess how mature the coalition is
    member_count = MapSet.size(collective.member_objects)
    knowledge_density = calculate_knowledge_density(collective.knowledge_graph)
    trust_stability = calculate_trust_stability(collective.trust_network)
    interaction_frequency = calculate_interaction_frequency(collective.collective_memory)
    
    maturity_score = (member_count / 10 + knowledge_density + trust_stability + interaction_frequency) / 4
    
    cond do
      maturity_score > 0.8 -> :mature
      maturity_score > 0.6 -> :developing
      maturity_score > 0.4 -> :forming
      true -> :nascent
    end
  end
  
  defp calculate_prediction_confidence(indicators) do
    # Calculate confidence in emergence predictions
    indicator_consistency = calculate_indicator_consistency(indicators)
    data_quality = calculate_data_quality(indicators)
    temporal_stability = calculate_temporal_stability(indicators)
    
    (indicator_consistency + data_quality + temporal_stability) / 3
  end
  
  defp generate_emergence_recommendations(indicators) do
    # Generate recommendations based on emergence analysis
    recommendations = []
    
    recommendations = if indicators.cognitive_emergence.collective_reasoning < 0.5 do
      ["Increase knowledge sharing frequency" | recommendations]
    else
      recommendations
    end
    
    recommendations = if indicators.social_emergence.trust_networks < 0.6 do
      ["Implement trust-building activities" | recommendations]
    else
      recommendations
    end
    
    recommendations = if indicators.behavioral_emergence.adaptive_strategies < 0.4 do
      ["Introduce more diverse learning scenarios" | recommendations]
    else
      recommendations
    end
    
    recommendations
  end

  # Utility functions with placeholder implementations
  
  defp simulate_policy_gradient(_member_id), do: %{params: :rand.uniform(), value: :rand.uniform()}
  defp scale_gradient(gradient, weight), do: %{gradient | value: gradient.value * weight}
  defp init_gradient(), do: %{params: 0.0, value: 0.0}
  defp combine_gradients(acc, gradient), do: %{params: acc.params + gradient.params, value: acc.value + gradient.value}
  defp update_exploration_policy(_gradients, _lr), do: %{exploration_rate: 0.1 + :rand.uniform() * 0.2}
  defp update_coordination_policy(_gradients, _lr), do: %{coordination_strength: 0.5 + :rand.uniform() * 0.3}
  defp update_knowledge_sharing_policy(_gradients, _lr), do: %{sharing_frequency: 0.3 + :rand.uniform() * 0.4}
  defp calculate_effective_learning_rate(metrics), do: Map.get(metrics, :learning_rate, 0.01) * 0.99
  defp estimate_convergence_rate(_metrics), do: :rand.uniform()
  defp estimate_performance_impact(_policies), do: :rand.uniform()
  defp calculate_compatibility_score(_obj_a, _obj_b, _objective), do: :rand.uniform()
  defp select_seed_object(objects, _objective) do
    if length(objects) > 0 do
      Enum.random(objects)
    else
      nil
    end
  end
  defp should_add_to_coalition(_candidate, _selection, _matrix), do: :rand.uniform() > 0.3
  defp classify_learning_objective(_objective), do: :general_learning
  defp assess_objective_complexity(_objective), do: :medium
  defp estimate_learning_duration(_objective), do: 3600
  defp define_success_criteria(_objective), do: %{accuracy: 0.8, convergence: true}
  defp count_knowledge_exchanges(_memory), do: :rand.uniform(10)
  defp count_consensus_decisions(_memory), do: :rand.uniform(5)
  defp count_trust_updates(_memory), do: :rand.uniform(15)
  defp count_novel_solutions(_memory), do: :rand.uniform(3)
  defp count_coordination_events(_memory), do: :rand.uniform(8)
  defp detect_novel_coordination(_activity), do: :rand.uniform()
  defp detect_adaptive_strategies(_activity), do: :rand.uniform()
  defp detect_emergent_roles(_activity), do: :rand.uniform()
  defp calculate_behavioral_complexity(_activity), do: :rand.uniform()
  defp assess_collective_reasoning_capability(_collective), do: :rand.uniform()
  defp assess_distributed_problem_solving(_collective), do: :rand.uniform()
  defp assess_meta_cognitive_awareness(_collective), do: :rand.uniform()
  defp assess_knowledge_synthesis_capability(_collective), do: :rand.uniform()
  defp detect_emergent_hierarchy(_collective), do: :rand.uniform()
  defp analyze_communication_patterns(_collective), do: :rand.uniform()
  defp analyze_trust_network_evolution(_collective), do: :rand.uniform()
  defp assess_social_learning_dynamics(_collective), do: :rand.uniform()
  defp assess_environmental_adaptation(_collective), do: :rand.uniform()
  defp assess_learning_adaptation(_collective), do: :rand.uniform()
  defp assess_structural_adaptation(_collective), do: :rand.uniform()
  defp assess_behavioral_adaptation(_collective), do: :rand.uniform()
  defp calculate_indicator_type_score(indicator_map) do
    values = Map.values(indicator_map)
    sum = Enum.sum(values)
    sum / max(map_size(indicator_map), 1)
  end
  defp calculate_knowledge_density(graph), do: graph.metadata.node_count / max(graph.metadata.node_count + 1, 1)
  defp calculate_trust_stability(_trust_network), do: :rand.uniform()
  defp calculate_interaction_frequency(_memory), do: :rand.uniform()
  defp calculate_indicator_consistency(_indicators), do: :rand.uniform()
  defp calculate_data_quality(_indicators), do: :rand.uniform()
  defp calculate_temporal_stability(_indicators), do: :rand.uniform()
end