defmodule Automata.DistributedCognition.EmergentSpecialization.SpecializationEmergence do
  @moduledoc """
  Provides mechanisms for enabling the emergence of specialization within distributed agent systems.
  
  This module enables specialization detection, reinforcement, and coordination, as well as
  the discovery of complementary specializations and the adaptation of the system to
  emerging specialization patterns.
  """
  
  alias Automata.DistributedCognition.EmergentSpecialization.CapabilityProfiling
  alias Automata.DistributedCognition.EmergentSpecialization.RoleAdaptation
  alias Automata.DistributedCognition.BeliefArchitecture.DecentralizedBeliefSystem
  
  defmodule SpecializationPattern do
    @moduledoc """
    Represents an emerging specialization pattern within the agent system.
    
    A specialization pattern is a collection of complementary capabilities and roles
    that emerge through agent interactions and adaptations over time.
    """
    
    @type capability_id :: atom() | String.t()
    @type role_id :: atom() | String.t()
    @type agent_id :: term()
    
    @type t :: %__MODULE__{
      id: String.t(),
      name: String.t(),
      description: String.t(),
      core_capabilities: list(capability_id),
      related_roles: list(role_id),
      exemplar_agents: list(agent_id),
      complementary_patterns: list(String.t()),
      emergence_score: float(),
      stability: float(),
      detected_at: DateTime.t(),
      updated_at: DateTime.t()
    }
    
    defstruct [
      :id,
      :name,
      :description,
      :core_capabilities,
      :related_roles,
      :exemplar_agents,
      :complementary_patterns,
      :emergence_score,
      :stability,
      :detected_at,
      :updated_at
    ]
    
    @doc """
    Creates a new specialization pattern.
    """
    def new(id, name, description, core_capabilities, opts \\ []) do
      now = DateTime.utc_now()
      
      %__MODULE__{
        id: id,
        name: name,
        description: description,
        core_capabilities: core_capabilities,
        related_roles: Keyword.get(opts, :related_roles, []),
        exemplar_agents: Keyword.get(opts, :exemplar_agents, []),
        complementary_patterns: Keyword.get(opts, :complementary_patterns, []),
        emergence_score: Keyword.get(opts, :emergence_score, 0.5),
        stability: Keyword.get(opts, :stability, 0.5),
        detected_at: now,
        updated_at: now
      }
    end
    
    @doc """
    Updates a specialization pattern with new information.
    """
    def update(pattern, updates) do
      # Apply updates
      updated_pattern = Enum.reduce(updates, pattern, fn {key, value}, acc ->
        apply_update(acc, key, value)
      end)
      
      # Update timestamp
      %{updated_pattern | updated_at: DateTime.utc_now()}
    end
    
    @doc """
    Merges two related specialization patterns.
    """
    def merge(pattern1, pattern2) do
      now = DateTime.utc_now()
      
      # Create a merged pattern
      %__MODULE__{
        id: "merged_#{pattern1.id}_#{pattern2.id}",
        name: "#{pattern1.name} + #{pattern2.name}",
        description: "Merged pattern from #{pattern1.name} and #{pattern2.name}",
        core_capabilities: Enum.uniq(pattern1.core_capabilities ++ pattern2.core_capabilities),
        related_roles: Enum.uniq(pattern1.related_roles ++ pattern2.related_roles),
        exemplar_agents: Enum.uniq(pattern1.exemplar_agents ++ pattern2.exemplar_agents),
        complementary_patterns: Enum.uniq((pattern1.complementary_patterns ++ pattern2.complementary_patterns) -- [pattern1.id, pattern2.id]),
        emergence_score: (pattern1.emergence_score + pattern2.emergence_score) / 2,
        stability: min(pattern1.stability, pattern2.stability),
        detected_at: now,
        updated_at: now
      }
    end
    
    @doc """
    Calculates the similarity between two specialization patterns.
    """
    def similarity(pattern1, pattern2) do
      # Calculate Jaccard similarity for capabilities
      capability_similarity = jaccard_similarity(
        pattern1.core_capabilities,
        pattern2.core_capabilities
      )
      
      # Calculate Jaccard similarity for roles
      role_similarity = jaccard_similarity(
        pattern1.related_roles,
        pattern2.related_roles
      )
      
      # Calculate Jaccard similarity for exemplar agents
      agent_similarity = jaccard_similarity(
        pattern1.exemplar_agents,
        pattern2.exemplar_agents
      )
      
      # Weighted combination
      (capability_similarity * 0.5) +
      (role_similarity * 0.3) +
      (agent_similarity * 0.2)
    end
    
    # Private functions
    
    defp apply_update(pattern, :core_capabilities, value) do
      %{pattern | core_capabilities: value}
    end
    
    defp apply_update(pattern, :related_roles, value) do
      %{pattern | related_roles: value}
    end
    
    defp apply_update(pattern, :exemplar_agents, value) do
      %{pattern | exemplar_agents: value}
    end
    
    defp apply_update(pattern, :complementary_patterns, value) do
      %{pattern | complementary_patterns: value}
    end
    
    defp apply_update(pattern, :emergence_score, value) do
      %{pattern | emergence_score: value}
    end
    
    defp apply_update(pattern, :stability, value) do
      %{pattern | stability: value}
    end
    
    defp apply_update(pattern, :name, value) do
      %{pattern | name: value}
    end
    
    defp apply_update(pattern, :description, value) do
      %{pattern | description: value}
    end
    
    defp apply_update(pattern, _key, _value) do
      # Ignore unknown keys
      pattern
    end
    
    defp jaccard_similarity(set1, set2) do
      # Handle empty sets
      if Enum.empty?(set1) && Enum.empty?(set2) do
        1.0
      else
        # Calculate intersection and union
        intersection = MapSet.intersection(MapSet.new(set1), MapSet.new(set2))
        union = MapSet.union(MapSet.new(set1), MapSet.new(set2))
        
        # Jaccard similarity: |intersection| / |union|
        MapSet.size(intersection) / MapSet.size(union)
      end
    end
  end
  
  defmodule SpecializationDetection do
    @moduledoc """
    Provides mechanisms for detecting emerging specialization patterns.
    
    This module enables the identification of specialization patterns through
    capability clustering, role analysis, and behavioral pattern recognition.
    """
    
    @doc """
    Detects potential specialization patterns by analyzing agent capabilities.
    """
    def detect_from_capabilities(agent_profiles, opts \\ []) do
      # Extract agent capabilities
      agent_capabilities = extract_agent_capabilities(agent_profiles)
      
      # Cluster capabilities to identify specialization patterns
      capability_clusters = cluster_capabilities(agent_capabilities, opts)
      
      # Convert clusters to specialization patterns
      patterns = capability_clusters_to_patterns(capability_clusters)
      
      {:ok, patterns}
    end
    
    @doc """
    Detects potential specialization patterns by analyzing role assignments.
    """
    def detect_from_roles(role_assignments, roles, opts \\ []) do
      # Group agents by their assigned roles
      agents_by_role = group_agents_by_role(role_assignments)
      
      # Analyze roles to identify specialization patterns
      role_patterns = analyze_role_patterns(agents_by_role, roles, opts)
      
      # Convert role patterns to specialization patterns
      patterns = role_patterns_to_specialization_patterns(role_patterns)
      
      {:ok, patterns}
    end
    
    @doc """
    Detects potential specialization patterns by analyzing agent behaviors.
    """
    def detect_from_behaviors(agent_behaviors, opts \\ []) do
      # Identify behavioral patterns
      behavior_patterns = identify_behavior_patterns(agent_behaviors, opts)
      
      # Convert behavioral patterns to specialization patterns
      patterns = behavior_patterns_to_specialization_patterns(behavior_patterns)
      
      {:ok, patterns}
    end
    
    @doc """
    Combines multiple detection methods to identify robust specialization patterns.
    """
    def comprehensive_detection(agent_ids, opts \\ []) do
      # Get capability profiles
      agent_profiles = get_agent_profiles(agent_ids)
      
      # Get role assignments
      role_assignments = get_role_assignments(agent_ids)
      
      # Get roles
      roles = get_available_roles()
      
      # Get agent behaviors
      agent_behaviors = get_agent_behaviors(agent_ids)
      
      # Detect patterns using each method
      {:ok, capability_patterns} = detect_from_capabilities(agent_profiles, opts)
      {:ok, role_patterns} = detect_from_roles(role_assignments, roles, opts)
      {:ok, behavior_patterns} = detect_from_behaviors(agent_behaviors, opts)
      
      # Combine and reconcile patterns
      combined_patterns = combine_patterns([
        capability_patterns,
        role_patterns,
        behavior_patterns
      ])
      
      # Filter by emergence score
      min_emergence = Keyword.get(opts, :min_emergence, 0.6)
      
      filtered_patterns = Enum.filter(combined_patterns, fn pattern ->
        pattern.emergence_score >= min_emergence
      end)
      
      {:ok, filtered_patterns}
    end
    
    # Private functions
    
    defp extract_agent_capabilities(agent_profiles) do
      # Extract capabilities for each agent
      Enum.map(agent_profiles, fn {agent_id, profile} ->
        # Get capabilities with high performance
        capabilities = profile.capabilities
        |> Enum.filter(fn {_capability, metrics} ->
          CapabilityProfiling.CapabilityProfile.calculate_overall_performance(metrics) >= 0.7
        end)
        |> Enum.map(fn {capability, _metrics} -> capability end)
        
        {agent_id, capabilities}
      end)
      |> Enum.into(%{})
    end
    
    defp cluster_capabilities(agent_capabilities, opts) do
      # Extract all unique capabilities
      all_capabilities = agent_capabilities
      |> Enum.flat_map(fn {_agent_id, capabilities} -> capabilities end)
      |> Enum.uniq()
      
      # Create co-occurrence matrix: how often capabilities appear together in agents
      co_occurrence = create_co_occurrence_matrix(agent_capabilities, all_capabilities)
      
      # Cluster capabilities based on co-occurrence
      min_similarity = Keyword.get(opts, :min_similarity, 0.5)
      
      cluster_by_similarity(co_occurrence, min_similarity)
    end
    
    defp create_co_occurrence_matrix(agent_capabilities, all_capabilities) do
      # Initialize empty matrix
      empty_matrix = Enum.reduce(all_capabilities, %{}, fn cap1, acc ->
        inner_map = Enum.reduce(all_capabilities, %{}, fn cap2, inner_acc ->
          Map.put(inner_acc, cap2, 0)
        end)
        
        Map.put(acc, cap1, inner_map)
      end)
      
      # Fill matrix with co-occurrence counts
      Enum.reduce(agent_capabilities, empty_matrix, fn {_agent_id, capabilities}, matrix ->
        # For each pair of capabilities in this agent
        Enum.reduce(capabilities, matrix, fn cap1, outer_acc ->
          Enum.reduce(capabilities, outer_acc, fn cap2, inner_acc ->
            # Increment co-occurrence count
            inner_map = Map.get(inner_acc, cap1)
            updated_inner = Map.update!(inner_map, cap2, &(&1 + 1))
            
            Map.put(inner_acc, cap1, updated_inner)
          end)
        end)
      end)
    end
    
    defp cluster_by_similarity(co_occurrence, min_similarity) do
      # Convert co-occurrence to similarity
      similarity_matrix = Enum.map(co_occurrence, fn {cap1, inner_map} ->
        similarities = Enum.map(inner_map, fn {cap2, count} ->
          # Skip self-similarity
          if cap1 == cap2 do
            {cap2, 1.0}
          else
            # Normalize by max possible co-occurrence
            max_possible = min(
              Map.get(co_occurrence[cap1], cap1),
              Map.get(co_occurrence[cap2], cap2)
            )
            
            similarity = if max_possible > 0, do: count / max_possible, else: 0
            
            {cap2, similarity}
          end
        end)
        |> Enum.into(%{})
        
        {cap1, similarities}
      end)
      |> Enum.into(%{})
      
      # Use a simple clustering algorithm:
      # 1. Start with each capability as its own cluster
      # 2. Merge clusters if similarity between any members exceeds threshold
      # 3. Repeat until no more merges can be made
      
      # Initialize with singleton clusters
      initial_clusters = Map.keys(co_occurrence)
      |> Enum.map(fn cap -> [cap] end)
      
      # Merge clusters
      cluster_until_stable(initial_clusters, similarity_matrix, min_similarity)
    end
    
    defp cluster_until_stable(clusters, similarity_matrix, min_similarity) do
      # Try to merge any pair of clusters
      {new_clusters, merged} = merge_similar_clusters(clusters, similarity_matrix, min_similarity)
      
      # If no merges made, we're done
      if merged do
        # Continue merging
        cluster_until_stable(new_clusters, similarity_matrix, min_similarity)
      else
        # Stable clustering achieved
        new_clusters
      end
    end
    
    defp merge_similar_clusters(clusters, similarity_matrix, min_similarity) do
      # Check all pairs of clusters
      cluster_pairs = for i <- 0..(length(clusters) - 1),
                          j <- (i + 1)..(length(clusters) - 1),
                          do: {Enum.at(clusters, i), Enum.at(clusters, j)}
      
      # Try to find a pair to merge
      case Enum.find(cluster_pairs, fn {cluster1, cluster2} ->
        # Check if any pair of capabilities (one from each cluster) exceeds similarity threshold
        Enum.any?(cluster1, fn cap1 ->
          Enum.any?(cluster2, fn cap2 ->
            Map.get(similarity_matrix[cap1], cap2, 0) >= min_similarity
          end)
        end)
      end) do
        nil ->
          # No pair found, clustering is stable
          {clusters, false}
          
        {cluster1, cluster2} ->
          # Merge the clusters
          merged_cluster = cluster1 ++ cluster2
          remaining_clusters = clusters -- [cluster1, cluster2]
          
          {[merged_cluster | remaining_clusters], true}
      end
    end
    
    defp capability_clusters_to_patterns(clusters) do
      # Convert each cluster to a specialization pattern
      Enum.map(clusters, fn cluster ->
        # Only consider clusters with multiple capabilities
        if length(cluster) > 1 do
          pattern_id = "capability_pattern_#{:erlang.phash2(cluster)}"
          
          SpecializationPattern.new(
            pattern_id,
            "Capability Pattern #{length(cluster)} capabilities",
            "Automatically detected specialization pattern based on capability clustering",
            cluster,
            emergence_score: calculate_emergence_score(cluster),
            stability: 0.5  # Initial stability estimate
          )
        else
          nil
        end
      end)
      |> Enum.reject(&is_nil/1)
    end
    
    defp calculate_emergence_score(cluster) do
      # For capability-based patterns, base score on cluster size and coherence
      # This is a placeholder implementation
      base_score = min(1.0, length(cluster) / 5.0)
      coherence = 0.7  # Placeholder coherence score
      
      base_score * coherence
    end
    
    defp group_agents_by_role(role_assignments) do
      # Group agents by their assigned roles
      Enum.reduce(role_assignments, %{}, fn assignment, acc ->
        agents = Map.get(acc, assignment.role_id, [])
        updated_agents = [assignment.agent_id | agents]
        
        Map.put(acc, assignment.role_id, updated_agents)
      end)
    end
    
    defp analyze_role_patterns(agents_by_role, roles, _opts) do
      # Find roles that commonly appear together
      role_patterns = []
      
      # For each role, find other roles that agents frequently have in their history
      Enum.reduce(roles, role_patterns, fn role, patterns ->
        # Get agents with this role
        agents = Map.get(agents_by_role, role.id, [])
        
        if length(agents) >= 2 do
          # Get historical roles for these agents
          agent_histories = get_agent_role_histories(agents)
          
          # Find common role progressions
          common_progressions = find_common_role_progressions(agent_histories)
          
          # Add to patterns
          if Enum.empty?(common_progressions) do
            patterns
          else
            pattern = %{
              core_role: role.id,
              agent_count: length(agents),
              common_progressions: common_progressions
            }
            
            [pattern | patterns]
          end
        else
          patterns
        end
      end)
    end
    
    defp get_agent_role_histories(agents) do
      # In a real implementation, this would retrieve the role history for each agent
      
      # For now, return placeholder histories
      Enum.map(agents, fn agent ->
        {agent, [:role1, :role2, :role3]}
      end)
      |> Enum.into(%{})
    end
    
    defp find_common_role_progressions(_agent_histories) do
      # In a real implementation, this would analyze role histories to find common progressions
      
      # For now, return placeholder progressions
      [
        [:role1, :role2],
        [:role2, :role3]
      ]
    end
    
    defp role_patterns_to_specialization_patterns(role_patterns) do
      # Convert role patterns to specialization patterns
      Enum.map(role_patterns, fn pattern ->
        # Create a pattern for each core role
        pattern_id = "role_pattern_#{pattern.core_role}"
        
        # Collect related roles from progressions
        related_roles = pattern.common_progressions
        |> Enum.flat_map(& &1)
        |> Enum.uniq()
        |> Enum.reject(fn role -> role == pattern.core_role end)
        
        SpecializationPattern.new(
          pattern_id,
          "Role Pattern for #{pattern.core_role}",
          "Automatically detected specialization pattern based on role analysis",
          [],  # No core capabilities yet
          related_roles: [pattern.core_role | related_roles],
          emergence_score: 0.6,  # Placeholder score
          stability: 0.5  # Initial stability estimate
        )
      end)
    end
    
    defp identify_behavior_patterns(_agent_behaviors, _opts) do
      # In a real implementation, this would identify patterns in agent behaviors
      
      # For now, return placeholder patterns
      [
        %{
          behavior_type: :collaborative,
          agents: [:agent1, :agent2, :agent3],
          characteristics: [:high_cooperation, :low_competition]
        },
        %{
          behavior_type: :autonomous,
          agents: [:agent4, :agent5],
          characteristics: [:high_initiative, :low_cooperation]
        }
      ]
    end
    
    defp behavior_patterns_to_specialization_patterns(behavior_patterns) do
      # Convert behavioral patterns to specialization patterns
      Enum.map(behavior_patterns, fn pattern ->
        pattern_id = "behavior_pattern_#{pattern.behavior_type}"
        
        SpecializationPattern.new(
          pattern_id,
          "#{String.capitalize(to_string(pattern.behavior_type))} Behavior Pattern",
          "Automatically detected specialization pattern based on agent behavior",
          [],  # No core capabilities yet
          exemplar_agents: pattern.agents,
          emergence_score: 0.7,  # Placeholder score
          stability: 0.6  # Initial stability estimate
        )
      end)
    end
    
    defp get_agent_profiles(agent_ids) do
      # In a real implementation, this would retrieve capability profiles for all agents
      
      # For now, create placeholder profiles
      Enum.map(agent_ids, fn agent_id ->
        profile = CapabilityProfiling.CapabilityProfile.new(agent_id, %{
          computation: %{
            efficiency: 0.8,
            quality: 0.7,
            reliability: 0.9,
            latency: 0.2
          },
          planning: %{
            efficiency: 0.7,
            quality: 0.7,
            reliability: 0.8,
            latency: 0.3
          }
        })
        
        {agent_id, profile}
      end)
      |> Enum.into(%{})
    end
    
    defp get_role_assignments(agent_ids) do
      # In a real implementation, this would retrieve role assignments for all agents
      
      # For now, create placeholder assignments
      Enum.map(agent_ids, fn agent_id ->
        %{
          agent_id: agent_id,
          role_id: :placeholder_role,
          fit_score: 0.8,
          assigned_at: DateTime.utc_now(),
          assigned_until: nil,
          status: :active
        }
      end)
    end
    
    defp get_available_roles do
      # In a real implementation, this would retrieve all available roles
      
      # For now, return placeholder roles
      [
        RoleAdaptation.Role.new(:role1, "Role 1", "Placeholder role 1"),
        RoleAdaptation.Role.new(:role2, "Role 2", "Placeholder role 2"),
        RoleAdaptation.Role.new(:role3, "Role 3", "Placeholder role 3")
      ]
    end
    
    defp get_agent_behaviors(agent_ids) do
      # In a real implementation, this would retrieve behavior parameters for all agents
      
      # For now, return placeholder behaviors
      Enum.map(agent_ids, fn agent_id ->
        {agent_id, %{
          reactivity: 0.7,
          proactivity: 0.6,
          cooperation: 0.8,
          risk_tolerance: 0.4,
          exploration: 0.5,
          exploitation: 0.7
        }}
      end)
      |> Enum.into(%{})
    end
    
    defp combine_patterns(pattern_lists) do
      # Flatten the lists
      all_patterns = List.flatten(pattern_lists)
      
      # Group similar patterns
      grouped = group_similar_patterns(all_patterns)
      
      # Merge each group into a single pattern
      Enum.map(grouped, fn patterns ->
        if length(patterns) == 1 do
          # No need to merge
          hd(patterns)
        else
          # Merge all patterns in the group
          Enum.reduce(tl(patterns), hd(patterns), fn pattern, acc ->
            SpecializationPattern.merge(acc, pattern)
          end)
        end
      end)
    end
    
    defp group_similar_patterns(patterns) do
      # Initialize with each pattern in its own group
      initial_groups = Enum.map(patterns, fn pattern -> [pattern] end)
      
      # Merge groups with similar patterns
      merge_similar_pattern_groups(initial_groups)
    end
    
    defp merge_similar_pattern_groups(groups) do
      # Try to merge any pair of groups
      {new_groups, merged} = merge_similar_groups(groups)
      
      # If no merges made, we're done
      if merged do
        # Continue merging
        merge_similar_pattern_groups(new_groups)
      else
        # Stable grouping achieved
        new_groups
      end
    end
    
    defp merge_similar_groups(groups) do
      # Check all pairs of groups
      group_pairs = for i <- 0..(length(groups) - 1),
                        j <- (i + 1)..(length(groups) - 1),
                        do: {Enum.at(groups, i), Enum.at(groups, j)}
      
      # Try to find a pair to merge
      case Enum.find(group_pairs, fn {group1, group2} ->
        # Check if any pair of patterns (one from each group) exceeds similarity threshold
        Enum.any?(group1, fn pattern1 ->
          Enum.any?(group2, fn pattern2 ->
            SpecializationPattern.similarity(pattern1, pattern2) >= 0.7
          end)
        end)
      end) do
        nil ->
          # No pair found, grouping is stable
          {groups, false}
          
        {group1, group2} ->
          # Merge the groups
          merged_group = group1 ++ group2
          remaining_groups = groups -- [group1, group2]
          
          {[merged_group | remaining_groups], true}
      end
    end
  end
  
  defmodule SpecializationReinforcement do
    @moduledoc """
    Provides mechanisms for reinforcing and stabilizing emerging specialization patterns.
    
    This module enables selective pressure application, incremental reinforcement, and
    integration with the role adaptation system to solidify emerging specializations.
    """
    
    @doc """
    Applies selective pressure to reinforce an emerging specialization pattern.
    """
    def apply_selective_pressure(pattern, agents, pressure_level \\ 0.5) do
      # Get exemplar agents for the pattern
      exemplars = pattern.exemplar_agents
      
      # For each agent that matches the pattern:
      # 1. Reinforce the core capabilities
      # 2. Adjust role assignments
      # 3. Modify behavioral parameters
      
      Enum.map(agents, fn agent_id ->
        # Check if agent matches the pattern
        {:ok, match_score} = calculate_pattern_match(agent_id, pattern)
        
        if match_score > 0.7 do
          # Agent matches pattern, apply pressure
          
          # Reinforce core capabilities
          {:ok, _} = reinforce_capabilities(agent_id, pattern.core_capabilities, pressure_level)
          
          # Adjust roles if needed
          {:ok, _} = align_roles_with_pattern(agent_id, pattern, pressure_level)
          
          # Modify behavior if exemplars exist
          if !Enum.empty?(exemplars) do
            {:ok, _} = RoleAdaptation.BehavioralAdaptation.learn_from_exemplars(
              agent_id,
              nil,  # No specific role
              exemplars
            )
          end
          
          # Track the reinforcement
          track_specialization_reinforcement(agent_id, pattern, pressure_level)
          
          {:reinforced, agent_id}
        else
          # Agent doesn't match pattern
          {:no_match, agent_id}
        end
      end)
    end
    
    @doc """
    Incrementally reinforces a specialization pattern over time.
    """
    def incremental_reinforcement(pattern, agents, stages, current_stage \\ 0) do
      # If already at max stage, return complete
      if current_stage >= length(stages) do
        {:complete, pattern}
      else
        # Get current stage config
        stage_config = Enum.at(stages, current_stage)
        
        # Apply selective pressure for this stage
        results = apply_selective_pressure(
          pattern,
          agents,
          stage_config.pressure_level
        )
        
        # Check if ready for next stage
        reinforced_count = Enum.count(results, fn result ->
          elem(result, 0) == :reinforced
        end)
        
        if reinforced_count >= stage_config.min_agents && stage_config.duration_complete do
          # Advance to next stage
          {:advanced, current_stage + 1, results}
        else
          # Continue current stage
          {:in_progress, current_stage, results}
        end
      end
    end
    
    @doc """
    Creates or adapts roles to match emerging specialization patterns.
    """
    def create_roles_from_patterns(patterns, min_emergence \\ 0.7) do
      # Filter patterns by emergence score
      stable_patterns = Enum.filter(patterns, fn pattern ->
        pattern.emergence_score >= min_emergence
      end)
      
      # Create roles for each stable pattern
      Enum.map(stable_patterns, fn pattern ->
        # Check if a matching role already exists
        case find_matching_role(pattern) do
          {:ok, existing_role} ->
            # Adapt existing role
            updated_role = adapt_role_to_pattern(existing_role, pattern)
            {:adapted, updated_role}
            
          :not_found ->
            # Create new role
            new_role = create_role_from_pattern(pattern)
            {:created, new_role}
        end
      end)
    end
    
    @doc """
    Reinforces patterns through belief propagation.
    """
    def reinforce_through_beliefs(pattern, agents) do
      # Create beliefs about the specialization pattern
      pattern_beliefs = create_pattern_beliefs(pattern)
      
      # Propagate beliefs to relevant agents
      Enum.each(agents, fn agent_id ->
        propagate_pattern_beliefs(agent_id, pattern, pattern_beliefs)
      end)
      
      # Create shared beliefs among pattern exemplars
      if length(pattern.exemplar_agents) >= 2 do
        create_shared_exemplar_beliefs(pattern)
      end
      
      {:ok, :beliefs_propagated}
    end
    
    # Private functions
    
    defp calculate_pattern_match(agent_id, pattern) do
      # Get agent's capability profile
      {:ok, profile} = get_capability_profile(agent_id)
      
      # Calculate match score for core capabilities
      capability_match = calculate_capability_match(profile, pattern.core_capabilities)
      
      # Check current role alignment
      role_match = calculate_role_match(agent_id, pattern.related_roles)
      
      # Combined match score
      match_score = (capability_match * 0.7) + (role_match * 0.3)
      
      {:ok, match_score}
    end
    
    defp get_capability_profile(agent_id) do
      # In a real implementation, this would retrieve the agent's capability profile
      
      # For now, create a placeholder profile
      profile = CapabilityProfiling.CapabilityProfile.new(agent_id, %{
        computation: %{
          efficiency: 0.8,
          quality: 0.7,
          reliability: 0.9,
          latency: 0.2
        },
        planning: %{
          efficiency: 0.7,
          quality: 0.7,
          reliability: 0.8,
          latency: 0.3
        }
      })
      
      {:ok, profile}
    end
    
    defp calculate_capability_match(profile, core_capabilities) do
      # If no core capabilities, return perfect match
      if Enum.empty?(core_capabilities) do
        1.0
      else
        # Calculate match for each core capability
        matches = Enum.map(core_capabilities, fn capability ->
          performance = CapabilityProfiling.CapabilityProfile.capability_performance(
            profile,
            capability
          )
          
          performance
        end)
        
        # Average match across all capabilities
        Enum.sum(matches) / length(matches)
      end
    end
    
    defp calculate_role_match(agent_id, related_roles) do
      # If no related roles, return perfect match
      if Enum.empty?(related_roles) do
        1.0
      else
        # Get agent's current role
        {:ok, current_assignment} = RoleAdaptation.RoleAssignment.get_agent_role(agent_id)
        
        # Check if current role is in related roles
        if Enum.member?(related_roles, current_assignment.role_id) do
          # Role matches, return fit score
          current_assignment.fit_score
        else
          # Role doesn't match
          0.0
        end
      end
    end
    
    defp reinforce_capabilities(agent_id, capabilities, pressure_level) do
      # In a real implementation, this would apply targeted training or
      # resource allocation to reinforce specific capabilities
      
      # For now, do nothing
      {:ok, :capabilities_reinforced}
    end
    
    defp align_roles_with_pattern(agent_id, pattern, pressure_level) do
      # Get available roles related to the pattern
      available_roles = get_roles_for_pattern(pattern)
      
      if Enum.empty?(available_roles) do
        # No related roles available
        {:ok, :no_roles_available}
      else
        # Find best role match
        {:ok, best_role, fit_score} = RoleAdaptation.RoleAssignment.find_best_role(
          agent_id,
          available_roles
        )
        
        # Get current role
        {:ok, current_assignment} = RoleAdaptation.RoleAssignment.get_agent_role(agent_id)
        
        # Check if role should be updated
        if current_assignment.role_id != best_role.id && fit_score > current_assignment.fit_score do
          # Apply pressure-based probability
          if :rand.uniform() < pressure_level do
            # Assign new role
            {:ok, new_assignment} = RoleAdaptation.RoleAssignment.assign_role(
              agent_id,
              best_role,
              fit_score
            )
            
            {:ok, :role_updated}
          else
            # Don't change role yet
            {:ok, :role_change_deferred}
          end
        else
          # Current role is appropriate
          {:ok, :role_appropriate}
        end
      end
    end
    
    defp get_roles_for_pattern(pattern) do
      # In a real implementation, this would retrieve roles related to the pattern
      
      # For now, create placeholder roles
      Enum.map(pattern.related_roles, fn role_id ->
        RoleAdaptation.Role.new(
          role_id,
          "Role #{role_id}",
          "Placeholder role for pattern #{pattern.id}"
        )
      end)
    end
    
    defp track_specialization_reinforcement(agent_id, pattern, pressure_level) do
      # In a real implementation, this would track reinforcement actions
      # for monitoring and analysis
      
      # For now, do nothing
      :ok
    end
    
    defp find_matching_role(pattern) do
      # In a real implementation, this would search for existing roles
      # that match the specialization pattern
      
      # For now, always create new roles
      :not_found
    end
    
    defp adapt_role_to_pattern(role, pattern) do
      # In a real implementation, this would adapt an existing role
      # to better match the specialization pattern
      
      # For now, return the original role
      role
    end
    
    defp create_role_from_pattern(pattern) do
      # Create capability requirements based on core capabilities
      capability_requirements = Enum.map(pattern.core_capabilities, fn capability ->
        %{
          capability_id: capability,
          min_performance: 0.7,
          weight: 1.0
        }
      end)
      
      # Create new role
      role_id = String.to_atom("pattern_role_#{:erlang.phash2(pattern.id)}")
      
      RoleAdaptation.Role.new(
        role_id,
        "Role for #{pattern.name}",
        "Role created from specialization pattern: #{pattern.description}",
        capability_requirements: capability_requirements,
        adaptability: 0.6
      )
    end
    
    defp create_pattern_beliefs(pattern) do
      # Create a set of beliefs about the specialization pattern
      [
        %{
          content: {:specialization_pattern, pattern.id, :valid},
          confidence: pattern.emergence_score,
          metadata: %{
            core_capabilities: pattern.core_capabilities,
            related_roles: pattern.related_roles
          }
        },
        %{
          content: {:specialization_exemplars, pattern.id, pattern.exemplar_agents},
          confidence: 0.9,
          metadata: %{
            updated_at: pattern.updated_at
          }
        }
      ]
    end
    
    defp propagate_pattern_beliefs(agent_id, pattern, beliefs) do
      # In a real implementation, this would propagate beliefs to the agent
      # using the DecentralizedBeliefSystem
      
      # For now, do nothing
      :ok
    end
    
    defp create_shared_exemplar_beliefs(pattern) do
      # In a real implementation, this would create shared beliefs among exemplars
      # to reinforce their specialization identity
      
      # For now, do nothing
      :ok
    end
  end
  
  defmodule ComplementarySpecialization do
    @moduledoc """
    Provides mechanisms for identifying and fostering complementary specialization patterns.
    
    This module enables the detection of complementary capabilities, encouragement of
    diversity, and coordination of specialization development across the agent system.
    """
    
    @doc """
    Identifies potentially complementary specialization patterns.
    """
    def identify_complementary_patterns(patterns) do
      # Calculate complementarity scores between all pattern pairs
      complementarity_scores = calculate_pattern_complementarity(patterns)
      
      # Filter for highly complementary pairs
      complementary_pairs = Enum.filter(complementarity_scores, fn {_pair, score} ->
        score > 0.7
      end)
      
      # Group patterns by complementarity
      complementary_groups = group_complementary_patterns(patterns, complementary_pairs)
      
      {:ok, complementary_groups}
    end
    
    @doc """
    Promotes the development of complementary specializations within a group.
    """
    def promote_complementary_development(agent_groups, complementary_patterns) do
      # For each group and its target complementary pattern
      Enum.map(Enum.zip(agent_groups, complementary_patterns), fn {agents, pattern} ->
        # Apply selective pressure to develop the complementary specialization
        SpecializationReinforcement.apply_selective_pressure(pattern, agents, 0.7)
      end)
    end
    
    @doc """
    Ensures diversity of specializations across the agent population.
    """
    def ensure_specialization_diversity(patterns, agents, min_coverage \\ 0.8) do
      # Analyze current specialization coverage
      {coverage, uncovered_patterns} = analyze_specialization_coverage(patterns, agents)
      
      if coverage >= min_coverage do
        # Sufficient diversity already exists
        {:ok, :sufficient_diversity}
      else
        # Need to promote underrepresented specializations
        promote_underrepresented_patterns(uncovered_patterns, agents)
        
        {:ok, :diversity_promoted}
      end
    end
    
    @doc """
    Coordinates specialization development across agent coalitions.
    """
    def coordinate_coalition_specialization(coalitions, specialization_strategy) do
      # For each coalition, determine optimal specialization strategy
      coordination_plans = Enum.map(coalitions, fn coalition ->
        # Based on strategy, assign specialization patterns to the coalition
        {specialization_pattern, agent_assignments} = plan_coalition_specialization(
          coalition,
          specialization_strategy
        )
        
        # Apply the specialization plan
        apply_coalition_specialization_plan(coalition, specialization_pattern, agent_assignments)
        
        # Return the coordination plan
        %{
          coalition_id: coalition.id,
          specialization_pattern: specialization_pattern,
          agent_assignments: agent_assignments
        }
      end)
      
      {:ok, coordination_plans}
    end
    
    # Private functions
    
    defp calculate_pattern_complementarity(patterns) do
      # Create all unique pattern pairs
      pairs = for i <- 0..(length(patterns) - 1),
                  j <- (i + 1)..(length(patterns) - 1),
                  do: {Enum.at(patterns, i), Enum.at(patterns, j)}
      
      # Calculate complementarity score for each pair
      Enum.map(pairs, fn {pattern1, pattern2} ->
        score = calculate_complementarity_score(pattern1, pattern2)
        {{pattern1, pattern2}, score}
      end)
      |> Enum.into(%{})
    end
    
    defp calculate_complementarity_score(pattern1, pattern2) do
      # Calculate capability complementarity
      capability_complementarity = calculate_capability_complementarity(
        pattern1.core_capabilities,
        pattern2.core_capabilities
      )
      
      # Calculate role complementarity
      role_complementarity = calculate_role_complementarity(
        pattern1.related_roles,
        pattern2.related_roles
      )
      
      # Combine scores
      (capability_complementarity * 0.7) + (role_complementarity * 0.3)
    end
    
    defp calculate_capability_complementarity(capabilities1, capabilities2) do
      # Calculate how well the capabilities complement each other
      
      # If either set is empty, no complementarity
      if Enum.empty?(capabilities1) || Enum.empty?(capabilities2) do
        0.0
      else
        # Convert to sets for set operations
        set1 = MapSet.new(capabilities1)
        set2 = MapSet.new(capabilities2)
        
        # Calculate overlap
        overlap = MapSet.intersection(set1, set2)
        overlap_size = MapSet.size(overlap)
        
        # Calculate unique capabilities
        unique1 = MapSet.difference(set1, set2)
        unique2 = MapSet.difference(set2, set1)
        
        # Complementarity is high when:
        # 1. Small overlap (some commonality but not too much)
        # 2. Both have unique capabilities
        
        overlap_score = 1.0 - (overlap_size / (MapSet.size(set1) + MapSet.size(set2) - overlap_size))
        unique_score = (MapSet.size(unique1) * MapSet.size(unique2)) / 
                        (MapSet.size(set1) * MapSet.size(set2))
        
        (overlap_score * 0.5) + (unique_score * 0.5)
      end
    end
    
    defp calculate_role_complementarity(roles1, roles2) do
      # Calculate how well the roles complement each other
      
      # If either set is empty, no complementarity
      if Enum.empty?(roles1) || Enum.empty?(roles2) do
        0.0
      else
        # Convert to sets for set operations
        set1 = MapSet.new(roles1)
        set2 = MapSet.new(roles2)
        
        # Calculate overlap
        overlap = MapSet.intersection(set1, set2)
        overlap_size = MapSet.size(overlap)
        
        # Calculate unique roles
        unique1 = MapSet.difference(set1, set2)
        unique2 = MapSet.difference(set2, set1)
        
        # Complementarity is high when:
        # 1. Small overlap (some commonality but not too much)
        # 2. Both have unique roles
        
        overlap_score = 1.0 - (overlap_size / (MapSet.size(set1) + MapSet.size(set2) - overlap_size))
        unique_score = (MapSet.size(unique1) * MapSet.size(unique2)) / 
                        (MapSet.size(set1) * MapSet.size(set2))
        
        (overlap_score * 0.5) + (unique_score * 0.5)
      end
    end
    
    defp group_complementary_patterns(patterns, complementary_pairs) do
      # Create a graph where patterns are nodes and complementary relationships are edges
      # Then find connected components (groups of complementary patterns)
      
      # Initialize graph with all patterns as isolated nodes
      graph = Enum.reduce(patterns, %{}, fn pattern, acc ->
        Map.put(acc, pattern.id, [])
      end)
      
      # Add edges for complementary pairs
      graph = Enum.reduce(complementary_pairs, graph, fn {{pattern1, pattern2}, _score}, acc ->
        # Add edge pattern1 -> pattern2
        p1_edges = Map.get(acc, pattern1.id, [])
        acc = Map.put(acc, pattern1.id, [pattern2.id | p1_edges])
        
        # Add edge pattern2 -> pattern1
        p2_edges = Map.get(acc, pattern2.id, [])
        Map.put(acc, pattern2.id, [pattern1.id | p2_edges])
      end)
      
      # Find connected components
      find_connected_components(graph, patterns)
    end
    
    defp find_connected_components(graph, patterns) do
      # Convert patterns to map for easier lookup
      pattern_map = Enum.reduce(patterns, %{}, fn pattern, acc ->
        Map.put(acc, pattern.id, pattern)
      end)
      
      # Initialize visited set
      visited = MapSet.new()
      
      # Find components
      pattern_ids = Map.keys(graph)
      
      Enum.reduce(pattern_ids, {[], visited}, fn pattern_id, {components, visited} ->
        if MapSet.member?(visited, pattern_id) do
          # Already visited, skip
          {components, visited}
        else
          # Find component starting from this pattern
          {component, new_visited} = find_component(graph, pattern_id, visited)
          
          # Convert pattern IDs to patterns
          component_patterns = Enum.map(component, fn id -> pattern_map[id] end)
          
          {[component_patterns | components], new_visited}
        end
      end)
      |> elem(0)
    end
    
    defp find_component(graph, start, visited) do
      # Simple DFS to find connected component
      component = []
      visited = MapSet.put(visited, start)
      component = [start | component]
      
      # Visit neighbors
      neighbors = Map.get(graph, start, [])
      
      Enum.reduce(neighbors, {component, visited}, fn neighbor, {component, visited} ->
        if MapSet.member?(visited, neighbor) do
          # Already visited, skip
          {component, visited}
        else
          # Visit neighbor
          {new_component, new_visited} = find_component(graph, neighbor, visited)
          
          # Combine components
          {new_component ++ component, new_visited}
        end
      end)
    end
    
    defp analyze_specialization_coverage(patterns, agents) do
      # Calculate how many patterns have sufficient agent coverage
      covered_count = Enum.count(patterns, fn pattern ->
        # Count agents that match this pattern
        matching_agents = Enum.count(agents, fn agent_id ->
          {:ok, match_score} = SpecializationReinforcement.calculate_pattern_match(agent_id, pattern)
          match_score > 0.7
        end)
        
        # Consider covered if at least 2 agents match
        matching_agents >= 2
      end)
      
      # Calculate coverage percentage
      coverage = covered_count / length(patterns)
      
      # Identify uncovered patterns
      uncovered = Enum.filter(patterns, fn pattern ->
        # Count agents that match this pattern
        matching_agents = Enum.count(agents, fn agent_id ->
          {:ok, match_score} = SpecializationReinforcement.calculate_pattern_match(agent_id, pattern)
          match_score > 0.7
        end)
        
        # Consider uncovered if fewer than 2 agents match
        matching_agents < 2
      end)
      
      {coverage, uncovered}
    end
    
    defp promote_underrepresented_patterns(patterns, agents) do
      # For each underrepresented pattern, find agents that could develop it
      Enum.each(patterns, fn pattern ->
        # Find agents with partial match
        potential_agents = Enum.filter(agents, fn agent_id ->
          {:ok, match_score} = SpecializationReinforcement.calculate_pattern_match(agent_id, pattern)
          match_score > 0.4 && match_score < 0.7
        end)
        
        # Take up to 3 agents
        target_agents = Enum.take(potential_agents, 3)
        
        # Apply high pressure to develop the pattern
        if !Enum.empty?(target_agents) do
          SpecializationReinforcement.apply_selective_pressure(pattern, target_agents, 0.9)
        end
      end)
    end
    
    defp plan_coalition_specialization(coalition, strategy) do
      # In a real implementation, this would create a specialization plan
      # for the coalition based on the strategy
      
      # For now, return placeholder data
      pattern = %SpecializationPattern{
        id: "placeholder",
        name: "Placeholder Pattern",
        description: "Placeholder specialization pattern",
        core_capabilities: [:computation, :planning],
        related_roles: [:role1, :role2],
        exemplar_agents: [],
        complementary_patterns: [],
        emergence_score: 0.7,
        stability: 0.6,
        detected_at: DateTime.utc_now(),
        updated_at: DateTime.utc_now()
      }
      
      agent_assignments = Enum.map(coalition.active_members, fn agent ->
        {agent, :role1}
      end)
      |> Enum.into(%{})
      
      {pattern, agent_assignments}
    end
    
    defp apply_coalition_specialization_plan(coalition, pattern, agent_assignments) do
      # In a real implementation, this would apply the specialization plan
      # to the coalition
      
      # For now, do nothing
      :ok
    end
  end
  
  defmodule SystemAdaptation do
    @moduledoc """
    Provides mechanisms for adapting the overall system to emerging specialization patterns.
    
    This module enables the creation of new roles, adjustment of resource allocation, and
    evolution of system structures to support and leverage emergent specializations.
    """
    
    @doc """
    Adapts the system's organizational structure to support emerging specializations.
    """
    def adapt_organization(patterns, current_structure) do
      # Analyze current structure suitability
      structure_analysis = analyze_structure_suitability(current_structure, patterns)
      
      # Generate recommended adaptations
      adaptations = generate_structure_adaptations(structure_analysis)
      
      # Apply organizational adaptations
      apply_organizational_adaptations(adaptations)
      
      {:ok, adaptations}
    end
    
    @doc """
    Adjusts resource allocation based on specialization patterns.
    """
    def adjust_resource_allocation(patterns, current_allocation) do
      # Calculate resource needs for each specialization pattern
      pattern_resource_needs = calculate_pattern_resource_needs(patterns)
      
      # Optimize resource allocation
      new_allocation = optimize_resource_allocation(current_allocation, pattern_resource_needs)
      
      # Apply allocation changes
      apply_allocation_changes(current_allocation, new_allocation)
      
      {:ok, new_allocation}
    end
    
    @doc """
    Creates new system components to support emerging specializations.
    """
    def create_supporting_components(patterns) do
      # Identify required system components
      required_components = identify_required_components(patterns)
      
      # Filter for components that don't exist yet
      new_components = filter_new_components(required_components)
      
      # Create new components
      created_components = create_components(new_components)
      
      {:ok, created_components}
    end
    
    @doc """
    Evolves communication pathways based on specialization patterns.
    """
    def evolve_communication_pathways(patterns, current_pathways) do
      # Analyze current communication patterns
      current_analysis = analyze_communication_patterns(current_pathways)
      
      # Identify optimal pathways for specialization patterns
      optimal_pathways = identify_optimal_pathways(patterns)
      
      # Generate pathway adaptations
      adaptations = generate_pathway_adaptations(current_analysis, optimal_pathways)
      
      # Apply pathway adaptations
      apply_pathway_adaptations(adaptations)
      
      {:ok, adaptations}
    end
    
    # Private functions
    
    defp analyze_structure_suitability(current_structure, patterns) do
      # In a real implementation, this would analyze how well the current
      # organizational structure supports the specialization patterns
      
      # For now, return placeholder analysis
      %{
        fit_score: 0.7,
        gaps: [:coordination_mechanism, :role_hierarchy],
        strengths: [:flexible_teams, :clear_responsibilities]
      }
    end
    
    defp generate_structure_adaptations(analysis) do
      # In a real implementation, this would generate specific adaptations
      # to address gaps in the organizational structure
      
      # For now, return placeholder adaptations
      [
        %{
          type: :add_component,
          component: :coordination_hub,
          purpose: "Facilitate coordination between specialized teams"
        },
        %{
          type: :modify_hierarchy,
          changes: [
            {add_role_level: :specialist_lead}
          ]
        }
      ]
    end
    
    defp apply_organizational_adaptations(adaptations) do
      # In a real implementation, this would apply the adaptations
      # to the organizational structure
      
      # For now, do nothing
      :ok
    end
    
    defp calculate_pattern_resource_needs(patterns) do
      # In a real implementation, this would calculate resource needs
      # for each specialization pattern
      
      # For now, return placeholder needs
      Enum.map(patterns, fn pattern ->
        {pattern.id, %{
          computation: 100 * length(pattern.core_capabilities),
          memory: 200 * length(pattern.core_capabilities),
          energy: 50 * length(pattern.core_capabilities)
        }}
      end)
      |> Enum.into(%{})
    end
    
    defp optimize_resource_allocation(current_allocation, pattern_resource_needs) do
      # In a real implementation, this would optimize resource allocation
      # based on pattern needs and available resources
      
      # For now, return placeholder allocation
      %{
        "pattern1" => %{
          computation: 300,
          memory: 600,
          energy: 150
        },
        "pattern2" => %{
          computation: 200,
          memory: 400,
          energy: 100
        }
      }
    end
    
    defp apply_allocation_changes(current_allocation, new_allocation) do
      # In a real implementation, this would apply the allocation changes
      # to the system
      
      # For now, do nothing
      :ok
    end
    
    defp identify_required_components(patterns) do
      # In a real implementation, this would identify system components
      # required to support the specialization patterns
      
      # For now, return placeholder components
      [
        %{
          type: :service,
          name: "SpecializationTrainingService",
          purpose: "Provide training for specialized capabilities"
        },
        %{
          type: :coordinator,
          name: "SpecializationCoordinator",
          purpose: "Coordinate activities between specialized agents"
        }
      ]
    end
    
    defp filter_new_components(required_components) do
      # In a real implementation, this would filter out components
      # that already exist in the system
      
      # For now, return all components as new
      required_components
    end
    
    defp create_components(new_components) do
      # In a real implementation, this would create the new system components
      
      # For now, return placeholder created components
      Enum.map(new_components, fn component ->
        %{component | status: :created}
      end)
    end
    
    defp analyze_communication_patterns(current_pathways) do
      # In a real implementation, this would analyze current communication patterns
      
      # For now, return placeholder analysis
      %{
        density: 0.6,
        bottlenecks: [:agent3],
        underutilized: [:agent7]
      }
    end
    
    defp identify_optimal_pathways(patterns) do
      # In a real implementation, this would identify optimal communication
      # pathways for the specialization patterns
      
      # For now, return placeholder pathways
      Enum.map(patterns, fn pattern ->
        exemplars = pattern.exemplar_agents
        
        if length(exemplars) >= 2 do
          # Create a fully connected pathway between exemplars
          pathways = for i <- 0..(length(exemplars) - 1),
                        j <- (i + 1)..(length(exemplars) - 1),
                        do: {Enum.at(exemplars, i), Enum.at(exemplars, j)}
          
          {pattern.id, pathways}
        else
          {pattern.id, []}
        end
      end)
      |> Enum.into(%{})
    end
    
    defp generate_pathway_adaptations(current_analysis, optimal_pathways) do
      # In a real implementation, this would generate adaptations to
      # communication pathways
      
      # For now, return placeholder adaptations
      [
        %{
          type: :add_pathway,
          source: :agent1,
          target: :agent5,
          priority: :high
        },
        %{
          type: :remove_pathway,
          source: :agent3,
          target: :agent7,
          reason: :underutilized
        }
      ]
    end
    
    defp apply_pathway_adaptations(adaptations) do
      # In a real implementation, this would apply the adaptations
      # to communication pathways
      
      # For now, do nothing
      :ok
    end
  end
end