defmodule Automata.DistributedCognition.EmergentSpecialization.RoleAdaptation do
  @moduledoc """
  Provides mechanisms for adaptive role assignment and evolution within distributed
  agent systems.
  
  This module enables dynamic role mapping, automatic adaptation to changing conditions,
  behavioral modification for role fulfillment, and feedback-driven role evolution.
  """
  
  alias Automata.DistributedCognition.EmergentSpecialization.CapabilityProfiling
  alias Automata.DistributedCognition.BeliefArchitecture.DecentralizedBeliefSystem
  
  defmodule Role do
    @moduledoc """
    Represents a role that can be assigned to agents in the system.
    
    A role defines a set of capabilities, responsibilities, priorities, and constraints
    that guide an agent's behavior when assigned to the role.
    """
    
    @type capability_requirement :: %{
      capability_id: atom() | String.t(),
      min_performance: float(),
      weight: float()
    }
    
    @type responsibility :: %{
      action: atom(),
      priority: integer(),
      conditions: list(term())
    }
    
    @type t :: %__MODULE__{
      id: atom() | String.t(),
      name: String.t(),
      description: String.t(),
      capability_requirements: list(capability_requirement),
      responsibilities: list(responsibility),
      authority_level: integer(),
      resource_allowances: map(),
      constraints: map(),
      adaptability: float(),
      created_at: DateTime.t(),
      updated_at: DateTime.t()
    }
    
    defstruct [
      :id,
      :name,
      :description,
      :capability_requirements,
      :responsibilities,
      :authority_level,
      :resource_allowances,
      :constraints,
      :adaptability,
      :created_at,
      :updated_at
    ]
    
    @doc """
    Creates a new role with the given attributes.
    """
    def new(id, name, description, opts \\ []) do
      now = DateTime.utc_now()
      
      %__MODULE__{
        id: id,
        name: name,
        description: description,
        capability_requirements: Keyword.get(opts, :capability_requirements, []),
        responsibilities: Keyword.get(opts, :responsibilities, []),
        authority_level: Keyword.get(opts, :authority_level, 1),
        resource_allowances: Keyword.get(opts, :resource_allowances, %{}),
        constraints: Keyword.get(opts, :constraints, %{}),
        adaptability: Keyword.get(opts, :adaptability, 0.5),
        created_at: now,
        updated_at: now
      }
    end
    
    @doc """
    Calculates how well an agent fits a role based on their capability profile.
    """
    def calculate_fit(role, capability_profile) do
      # Calculate fit for each capability requirement
      capability_fits = Enum.map(role.capability_requirements, fn requirement ->
        # Get the agent's performance for this capability
        performance = CapabilityProfiling.CapabilityProfile.capability_performance(
          capability_profile,
          requirement.capability_id
        )
        
        # Calculate fit (0-1 scale)
        fit = if performance >= requirement.min_performance do
          # Above minimum, scale to 0-1 based on how much above minimum
          min(1.0, performance / requirement.min_performance)
        else
          # Below minimum, scale to 0-1 based on how close to minimum
          performance / requirement.min_performance
        end
        
        # Apply weight
        fit * requirement.weight
      end)
      
      # Calculate overall fit
      if Enum.empty?(capability_fits) do
        0.0
      else
        Enum.sum(capability_fits) / Enum.sum(Enum.map(role.capability_requirements, & &1.weight))
      end
    end
    
    @doc """
    Updates a role's capability requirements based on performance data.
    """
    def update_requirements(role, performance_data) do
      # Update each capability requirement based on performance data
      updated_requirements = Enum.map(role.capability_requirements, fn requirement ->
        # Get performance data for this capability
        capability_performance = Map.get(
          performance_data,
          requirement.capability_id,
          nil
        )
        
        if capability_performance do
          # Adjust minimum performance based on actual performance
          # and the role's adaptability
          new_min = requirement.min_performance * (1 - role.adaptability) +
                    capability_performance * role.adaptability
          
          # Update the requirement
          %{requirement | min_performance: new_min}
        else
          # No performance data, keep the same
          requirement
        end
      end)
      
      # Update the role
      %{role |
        capability_requirements: updated_requirements,
        updated_at: DateTime.utc_now()
      }
    end
    
    @doc """
    Evolves a role based on system feedback and performance data.
    """
    def evolve(role, feedback, performance_data) do
      # Update requirements based on performance
      role_with_updated_reqs = update_requirements(role, performance_data)
      
      # Adjust responsibilities based on feedback
      updated_responsibilities = adjust_responsibilities(role.responsibilities, feedback)
      
      # Update adaptability based on feedback
      new_adaptability = adjust_adaptability(role.adaptability, feedback)
      
      # Update the role
      %{role_with_updated_reqs |
        responsibilities: updated_responsibilities,
        adaptability: new_adaptability,
        updated_at: DateTime.utc_now()
      }
    end
    
    # Private functions
    
    defp adjust_responsibilities(responsibilities, feedback) do
      # In a real implementation, this would adjust responsibilities based on feedback
      
      # For now, return unchanged
      responsibilities
    end
    
    defp adjust_adaptability(adaptability, feedback) do
      # In a real implementation, this would adjust adaptability based on feedback
      
      # For now, return slightly increased adaptability (capped at 0.9)
      min(0.9, adaptability + 0.05)
    end
  end
  
  defmodule RoleAssignment do
    @moduledoc """
    Manages the assignment of roles to agents based on capability matching and system needs.
    
    This module enables optimal role assignment, dynamic role reassignment, and
    tracking of role assignments over time.
    """
    
    @type assignment :: %{
      agent_id: term(),
      role_id: atom() | String.t(),
      fit_score: float(),
      assigned_at: DateTime.t(),
      assigned_until: DateTime.t() | nil,
      status: atom()
    }
    
    @doc """
    Assigns a role to an agent based on capability matching.
    """
    def assign_role(agent_id, role, fit_score, opts \\ []) do
      now = DateTime.utc_now()
      
      # Create assignment
      assignment = %{
        agent_id: agent_id,
        role_id: role.id,
        fit_score: fit_score,
        assigned_at: now,
        assigned_until: Keyword.get(opts, :duration) && DateTime.add(now, Keyword.get(opts, :duration), :second),
        status: :active
      }
      
      # Store the assignment
      store_role_assignment(assignment)
      
      # Configure agent for the role
      configure_agent_for_role(agent_id, role)
      
      {:ok, assignment}
    end
    
    @doc """
    Finds the best role for an agent based on their capability profile.
    """
    def find_best_role(agent_id, available_roles) do
      # Get the agent's capability profile
      {:ok, capability_profile} = get_capability_profile(agent_id)
      
      # Calculate fit for each role
      role_fits = Enum.map(available_roles, fn role ->
        fit = Role.calculate_fit(role, capability_profile)
        {role, fit}
      end)
      
      # Find the best fit
      {best_role, best_fit} = Enum.max_by(role_fits, fn {_role, fit} -> fit end, fn -> {nil, 0.0} end)
      
      if best_role && best_fit > 0.7 do  # Threshold for acceptable fit
        {:ok, best_role, best_fit}
      else
        {:error, :no_suitable_role}
      end
    end
    
    @doc """
    Finds the best agent for a role among available agents.
    """
    def find_best_agent(role, available_agents) do
      # Get capability profiles for all available agents
      agent_profiles = Enum.map(available_agents, fn agent_id ->
        {:ok, profile} = get_capability_profile(agent_id)
        {agent_id, profile}
      end)
      |> Enum.into(%{})
      
      # Calculate fit for each agent
      agent_fits = Enum.map(available_agents, fn agent_id ->
        profile = agent_profiles[agent_id]
        fit = Role.calculate_fit(role, profile)
        {agent_id, fit}
      end)
      
      # Find the best fit
      {best_agent, best_fit} = Enum.max_by(agent_fits, fn {_agent, fit} -> fit end, fn -> {nil, 0.0} end)
      
      if best_agent && best_fit > 0.7 do  # Threshold for acceptable fit
        {:ok, best_agent, best_fit}
      else
        {:error, :no_suitable_agent}
      end
    end
    
    @doc """
    Gets the current role assignment for an agent.
    """
    def get_agent_role(agent_id) do
      # In a real implementation, this would retrieve the agent's current role assignment
      
      # For now, return a placeholder assignment
      {:ok, %{
        agent_id: agent_id,
        role_id: :placeholder_role,
        fit_score: 0.8,
        assigned_at: DateTime.utc_now(),
        assigned_until: nil,
        status: :active
      }}
    end
    
    @doc """
    Lists all agents currently assigned to a specific role.
    """
    def list_agents_with_role(role_id) do
      # In a real implementation, this would retrieve all agents assigned to the role
      
      # For now, return placeholder agents
      {:ok, [:agent1, :agent2]}
    end
    
    @doc """
    Unassigns a role from an agent.
    """
    def unassign_role(agent_id, role_id) do
      # Get the current assignment
      {:ok, assignment} = get_role_assignment(agent_id, role_id)
      
      # Update the assignment
      updated_assignment = %{
        assignment |
        status: :inactive,
        assigned_until: DateTime.utc_now()
      }
      
      # Store the updated assignment
      store_role_assignment(updated_assignment)
      
      # Reset agent configuration
      reset_agent_configuration(agent_id)
      
      {:ok, updated_assignment}
    end
    
    @doc """
    Reassigns roles across a group of agents to optimize system performance.
    """
    def optimize_role_assignments(agent_ids, available_roles, current_assignments) do
      # Get capability profiles for all agents
      agent_profiles = Enum.map(agent_ids, fn agent_id ->
        {:ok, profile} = get_capability_profile(agent_id)
        {agent_id, profile}
      end)
      |> Enum.into(%{})
      
      # Calculate fit matrix (agent -> role -> fit)
      fit_matrix = Enum.map(agent_ids, fn agent_id ->
        profile = agent_profiles[agent_id]
        
        role_fits = Enum.map(available_roles, fn role ->
          fit = Role.calculate_fit(role, profile)
          {role.id, fit}
        end)
        |> Enum.into(%{})
        
        {agent_id, role_fits}
      end)
      |> Enum.into(%{})
      
      # Use a simple greedy algorithm for now
      # In a real implementation, this would use a more sophisticated algorithm
      # like the Hungarian algorithm for optimal assignment
      
      # Sort roles by importance
      sorted_roles = Enum.sort_by(available_roles, & &1.authority_level, :desc)
      
      # Assign roles in order of importance
      {assignments, remaining_agents} = Enum.reduce(sorted_roles, {[], agent_ids}, fn role, {assignments, available_agents} ->
        if Enum.empty?(available_agents) do
          # No more agents available
          {assignments, []}
        else
          # Find the best agent for this role
          agent_fits = Enum.map(available_agents, fn agent_id ->
            fit = fit_matrix[agent_id][role.id]
            {agent_id, fit}
          end)
          
          {best_agent, best_fit} = Enum.max_by(agent_fits, fn {_agent, fit} -> fit end)
          
          if best_fit > 0.7 do  # Threshold for acceptable fit
            # Assign the role to the best agent
            new_assignment = %{
              agent_id: best_agent,
              role_id: role.id,
              fit_score: best_fit,
              assigned_at: DateTime.utc_now(),
              assigned_until: nil,
              status: :active
            }
            
            # Remove the agent from available agents
            remaining = available_agents -- [best_agent]
            
            {[new_assignment | assignments], remaining}
          else
            # No suitable agent for this role
            {assignments, available_agents}
          end
        end
      end)
      
      # Return the new assignments
      {:ok, assignments, remaining_agents}
    end
    
    # Private functions
    
    defp get_capability_profile(agent_id) do
      # In a real implementation, this would retrieve the agent's capability profile
      # from a data store
      
      # 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 store_role_assignment(assignment) do
      # In a real implementation, this would store the role assignment
      # in a data store
      
      # For now, do nothing
      :ok
    end
    
    defp get_role_assignment(agent_id, role_id) do
      # In a real implementation, this would retrieve the role assignment
      # from a data store
      
      # For now, return a placeholder assignment
      {:ok, %{
        agent_id: agent_id,
        role_id: role_id,
        fit_score: 0.8,
        assigned_at: DateTime.add(DateTime.utc_now(), -3600, :second),  # 1 hour ago
        assigned_until: nil,
        status: :active
      }}
    end
    
    defp configure_agent_for_role(agent_id, role) do
      # In a real implementation, this would configure the agent for the role
      # by setting appropriate parameters, permissions, etc.
      
      # For now, do nothing
      :ok
    end
    
    defp reset_agent_configuration(agent_id) do
      # In a real implementation, this would reset the agent's configuration
      # to its default state
      
      # For now, do nothing
      :ok
    end
  end
  
  defmodule BehavioralAdaptation do
    @moduledoc """
    Provides mechanisms for adapting agent behavior based on assigned roles.
    
    This module enables automatic behavior modification, learning from role models,
    and progressive adaptation to new roles.
    """
    
    @doc """
    Adapts an agent's behavior to match a role's requirements.
    """
    def adapt_behavior(agent_id, role) do
      # Get the agent's current behavior parameters
      current_behavior = get_agent_behavior(agent_id)
      
      # Calculate target behavior based on role
      target_behavior = calculate_target_behavior(role)
      
      # Gradually adapt behavior towards target
      adapted_behavior = adapt_towards_target(current_behavior, target_behavior)
      
      # Apply the adapted behavior
      apply_adapted_behavior(agent_id, adapted_behavior)
      
      {:ok, adapted_behavior}
    end
    
    @doc """
    Learns role behavior from exemplar agents.
    """
    def learn_from_exemplars(agent_id, role_id, exemplar_ids) do
      # Get behavior parameters from exemplars
      exemplar_behaviors = Enum.map(exemplar_ids, fn exemplar_id ->
        get_agent_behavior(exemplar_id)
      end)
      
      # Calculate aggregated exemplar behavior
      aggregate_behavior = aggregate_exemplar_behaviors(exemplar_behaviors)
      
      # Adapt agent behavior towards exemplar behavior
      adapt_behavior_towards_exemplar(agent_id, aggregate_behavior)
      
      {:ok, :learning_complete}
    end
    
    @doc """
    Progressively adapts an agent to a new role over time.
    """
    def progressive_adaptation(agent_id, role, stages) do
      # Get current stage or initialize to first stage
      current_stage = get_adaptation_stage(agent_id, role.id) || 0
      
      # If already at max stage, return complete
      if current_stage >= length(stages) do
        {:ok, :adaptation_complete}
      else
        # Get the current stage config
        stage_config = Enum.at(stages, current_stage)
        
        # Adapt behavior for this stage
        {:ok, _} = adapt_behavior_for_stage(agent_id, role, stage_config)
        
        # Check if ready for next stage
        if adaptation_stage_complete?(agent_id, role.id, stage_config) do
          # Advance to next stage
          set_adaptation_stage(agent_id, role.id, current_stage + 1)
          
          if current_stage + 1 >= length(stages) do
            {:ok, :adaptation_complete}
          else
            {:ok, :stage_advanced}
          end
        else
          {:ok, :in_progress}
        end
      end
    end
    
    @doc """
    Monitors and corrects behavioral drift from role expectations.
    """
    def monitor_behavioral_drift(agent_id, role, threshold \\ 0.2) do
      # Get the agent's current behavior
      current_behavior = get_agent_behavior(agent_id)
      
      # Get expected behavior for the role
      expected_behavior = calculate_target_behavior(role)
      
      # Calculate drift
      drift = calculate_behavioral_drift(current_behavior, expected_behavior)
      
      if drift > threshold do
        # Significant drift detected, correct behavior
        corrected_behavior = correct_behavioral_drift(current_behavior, expected_behavior, drift)
        
        # Apply corrected behavior
        apply_adapted_behavior(agent_id, corrected_behavior)
        
        {:drift_detected, %{drift: drift, corrected: true}}
      else
        {:ok, %{drift: drift, corrected: false}}
      end
    end
    
    # Private functions
    
    defp get_agent_behavior(agent_id) do
      # In a real implementation, this would retrieve the agent's behavior parameters
      # from its state or configuration
      
      # For now, return placeholder behavior
      %{
        reactivity: 0.7,
        proactivity: 0.6,
        cooperation: 0.8,
        risk_tolerance: 0.4,
        exploration: 0.5,
        exploitation: 0.7
      }
    end
    
    defp calculate_target_behavior(role) do
      # In a real implementation, this would calculate target behavior parameters
      # based on the role's requirements and responsibilities
      
      # For now, return placeholder target behavior
      %{
        reactivity: 0.5 + role.authority_level * 0.1,
        proactivity: 0.3 + role.authority_level * 0.2,
        cooperation: 0.7,
        risk_tolerance: 0.3 + role.adaptability * 0.2,
        exploration: role.adaptability,
        exploitation: 1.0 - role.adaptability
      }
    end
    
    defp adapt_towards_target(current, target, rate \\ 0.3) do
      # Gradually adapt each parameter towards target
      Map.new(target, fn {param, target_value} ->
        current_value = Map.get(current, param, 0.5)  # Default if not present
        
        # Calculate new value (move a portion of the way towards target)
        new_value = current_value + (target_value - current_value) * rate
        
        {param, new_value}
      end)
    end
    
    defp apply_adapted_behavior(agent_id, behavior) do
      # In a real implementation, this would apply the adapted behavior
      # to the agent's state or configuration
      
      # For now, do nothing
      :ok
    end
    
    defp aggregate_exemplar_behaviors(behaviors) do
      # Get all behavior parameters
      all_params = behaviors
      |> Enum.flat_map(&Map.keys/1)
      |> Enum.uniq()
      
      # Calculate average for each parameter
      Map.new(all_params, fn param ->
        values = Enum.map(behaviors, &Map.get(&1, param, 0.5))
        avg = Enum.sum(values) / length(values)
        
        {param, avg}
      end)
    end
    
    defp adapt_behavior_towards_exemplar(agent_id, exemplar_behavior) do
      # Get the agent's current behavior
      current_behavior = get_agent_behavior(agent_id)
      
      # Adapt towards exemplar behavior
      adapted_behavior = adapt_towards_target(current_behavior, exemplar_behavior, 0.5)
      
      # Apply the adapted behavior
      apply_adapted_behavior(agent_id, adapted_behavior)
      
      {:ok, adapted_behavior}
    end
    
    defp get_adaptation_stage(agent_id, role_id) do
      # In a real implementation, this would retrieve the agent's adaptation stage
      # for the given role
      
      # For now, return placeholder stage
      0
    end
    
    defp set_adaptation_stage(agent_id, role_id, stage) do
      # In a real implementation, this would set the agent's adaptation stage
      # for the given role
      
      # For now, do nothing
      :ok
    end
    
    defp adapt_behavior_for_stage(agent_id, role, stage_config) do
      # Get the agent's current behavior
      current_behavior = get_agent_behavior(agent_id)
      
      # Calculate target behavior for this stage
      target_behavior = calculate_stage_target_behavior(role, stage_config)
      
      # Adapt towards target for this stage
      adapted_behavior = adapt_towards_target(current_behavior, target_behavior, stage_config.rate)
      
      # Apply the adapted behavior
      apply_adapted_behavior(agent_id, adapted_behavior)
      
      {:ok, adapted_behavior}
    end
    
    defp calculate_stage_target_behavior(role, stage_config) do
      # Calculate full target behavior
      full_target = calculate_target_behavior(role)
      
      # Apply stage-specific modifications
      Map.new(full_target, fn {param, value} ->
        # Get stage-specific modifier for this parameter
        modifier = Map.get(stage_config.modifiers, param, 1.0)
        
        # Apply modifier
        {param, value * modifier}
      end)
    end
    
    defp adaptation_stage_complete?(agent_id, role_id, stage_config) do
      # In a real implementation, this would check if the agent has completed
      # the current adaptation stage based on performance metrics, time spent, etc.
      
      # For now, return true (stage complete) with 30% probability
      :rand.uniform() < 0.3
    end
    
    defp calculate_behavioral_drift(current, expected) do
      # Calculate average squared difference
      diffs = Enum.map(expected, fn {param, expected_value} ->
        current_value = Map.get(current, param, 0.5)
        :math.pow(current_value - expected_value, 2)
      end)
      
      # Root mean squared difference
      :math.sqrt(Enum.sum(diffs) / length(diffs))
    end
    
    defp correct_behavioral_drift(current, expected, drift) do
      # Calculate correction rate based on drift magnitude
      correction_rate = min(0.8, drift * 2)
      
      # Adapt towards expected behavior
      adapt_towards_target(current, expected, correction_rate)
    end
  end
  
  defmodule FeedbackSystem do
    @moduledoc """
    Provides mechanisms for collecting and processing feedback on role assignments.
    
    This module enables performance evaluation, role effectiveness assessment, and
    continuous improvement of roles and assignments.
    """
    
    @doc """
    Records feedback on an agent's performance in a role.
    """
    def record_feedback(agent_id, role_id, source, feedback_data) do
      # Process and validate feedback
      processed_feedback = process_feedback(feedback_data)
      
      # Create feedback record
      feedback_record = %{
        agent_id: agent_id,
        role_id: role_id,
        source: source,
        feedback: processed_feedback,
        timestamp: DateTime.utc_now()
      }
      
      # Store feedback
      store_feedback(feedback_record)
      
      # Update performance metrics based on feedback
      update_performance_metrics(agent_id, role_id, processed_feedback)
      
      {:ok, feedback_record}
    end
    
    @doc """
    Analyzes role effectiveness based on feedback.
    """
    def analyze_role_effectiveness(role_id, time_period \\ nil) do
      # Get feedback for the role
      feedback = get_role_feedback(role_id, time_period)
      
      # Calculate effectiveness metrics
      effectiveness = calculate_role_effectiveness(feedback)
      
      # Identify strengths and weaknesses
      {strengths, weaknesses} = identify_strengths_and_weaknesses(feedback)
      
      {:ok, %{
        effectiveness: effectiveness,
        strengths: strengths,
        weaknesses: weaknesses,
        sample_size: length(feedback)
      }}
    end
    
    @doc """
    Evaluates agent performance in a role.
    """
    def evaluate_agent_performance(agent_id, role_id, time_period \\ nil) do
      # Get feedback for the agent in the role
      feedback = get_agent_role_feedback(agent_id, role_id, time_period)
      
      # Calculate performance metrics
      performance = calculate_agent_performance(feedback)
      
      # Identify areas for improvement
      improvement_areas = identify_improvement_areas(feedback)
      
      {:ok, %{
        performance: performance,
        improvement_areas: improvement_areas,
        sample_size: length(feedback)
      }}
    end
    
    @doc """
    Recommends role adjustments based on feedback.
    """
    def recommend_role_adjustments(role_id) do
      # Get feedback for the role
      feedback = get_role_feedback(role_id)
      
      # Analyze feedback to identify potential adjustments
      adjustments = analyze_for_adjustments(feedback)
      
      # Prioritize adjustments
      prioritized = prioritize_adjustments(adjustments)
      
      {:ok, prioritized}
    end
    
    # Private functions
    
    defp process_feedback(feedback_data) do
      # In a real implementation, this would validate and normalize feedback data
      
      # For now, ensure all required fields are present with default values
      required_fields = [:performance, :satisfaction, :fit]
      
      Enum.reduce(required_fields, feedback_data, fn field, acc ->
        Map.put_new(acc, field, 0.5)  # Default value
      end)
    end
    
    defp store_feedback(feedback_record) do
      # In a real implementation, this would store the feedback record
      # in a data store
      
      # For now, do nothing
      :ok
    end
    
    defp update_performance_metrics(agent_id, role_id, feedback) do
      # In a real implementation, this would update performance metrics
      # based on the feedback
      
      # For now, do nothing
      :ok
    end
    
    defp get_role_feedback(role_id, time_period \\ nil) do
      # In a real implementation, this would retrieve feedback records
      # for the role within the specified time period
      
      # For now, return placeholder feedback
      [
        %{
          agent_id: :agent1,
          performance: 0.8,
          satisfaction: 0.7,
          fit: 0.9,
          aspects: %{
            responsiveness: 0.8,
            initiative: 0.7,
            teamwork: 0.9
          }
        },
        %{
          agent_id: :agent2,
          performance: 0.6,
          satisfaction: 0.5,
          fit: 0.7,
          aspects: %{
            responsiveness: 0.7,
            initiative: 0.5,
            teamwork: 0.6
          }
        }
      ]
    end
    
    defp calculate_role_effectiveness(feedback) do
      # Calculate average performance and satisfaction across all feedback
      performances = Enum.map(feedback, & &1.performance)
      satisfactions = Enum.map(feedback, & &1.satisfaction)
      fits = Enum.map(feedback, & &1.fit)
      
      avg_performance = Enum.sum(performances) / length(performances)
      avg_satisfaction = Enum.sum(satisfactions) / length(satisfactions)
      avg_fit = Enum.sum(fits) / length(fits)
      
      # Calculate overall effectiveness
      (avg_performance * 0.5) + (avg_satisfaction * 0.3) + (avg_fit * 0.2)
    end
    
    defp identify_strengths_and_weaknesses(feedback) do
      # Collect all aspect keys
      all_aspects = feedback
      |> Enum.flat_map(fn f -> Map.keys(f.aspects) end)
      |> Enum.uniq()
      
      # Calculate average score for each aspect
      aspect_scores = Enum.map(all_aspects, fn aspect ->
        scores = Enum.map(feedback, fn f -> Map.get(f.aspects, aspect, 0.5) end)
        avg = Enum.sum(scores) / length(scores)
        {aspect, avg}
      end)
      |> Enum.into(%{})
      
      # Identify strengths (top 3 aspects)
      strengths = aspect_scores
      |> Enum.sort_by(fn {_aspect, score} -> score end, :desc)
      |> Enum.take(3)
      |> Enum.map(fn {aspect, _score} -> aspect end)
      
      # Identify weaknesses (bottom 3 aspects)
      weaknesses = aspect_scores
      |> Enum.sort_by(fn {_aspect, score} -> score end)
      |> Enum.take(3)
      |> Enum.map(fn {aspect, _score} -> aspect end)
      
      {strengths, weaknesses}
    end
    
    defp get_agent_role_feedback(agent_id, role_id, time_period \\ nil) do
      # In a real implementation, this would retrieve feedback records
      # for the agent in the role within the specified time period
      
      # For now, return placeholder feedback
      [
        %{
          source: :peer,
          performance: 0.8,
          satisfaction: 0.7,
          fit: 0.9,
          aspects: %{
            responsiveness: 0.8,
            initiative: 0.7,
            teamwork: 0.9
          },
          timestamp: DateTime.add(DateTime.utc_now(), -86400, :second)  # 1 day ago
        },
        %{
          source: :supervisor,
          performance: 0.7,
          satisfaction: 0.6,
          fit: 0.8,
          aspects: %{
            responsiveness: 0.7,
            initiative: 0.6,
            teamwork: 0.8
          },
          timestamp: DateTime.add(DateTime.utc_now(), -43200, :second)  # 12 hours ago
        }
      ]
    end
    
    defp calculate_agent_performance(feedback) do
      # Calculate weighted average performance based on source
      total_weight = 0
      weighted_sum = 0
      
      {weighted_sum, total_weight} = Enum.reduce(feedback, {0, 0}, fn f, {sum, weight} ->
        # Assign weight based on source
        source_weight = case f.source do
          :supervisor -> 2.0
          :peer -> 1.0
          :self -> 0.5
          _ -> 1.0
        end
        
        {sum + f.performance * source_weight, weight + source_weight}
      end)
      
      if total_weight > 0 do
        weighted_sum / total_weight
      else
        0.0
      end
    end
    
    defp identify_improvement_areas(feedback) do
      # Collect all aspect keys
      all_aspects = feedback
      |> Enum.flat_map(fn f -> Map.keys(f.aspects) end)
      |> Enum.uniq()
      
      # Calculate average score for each aspect
      aspect_scores = Enum.map(all_aspects, fn aspect ->
        scores = Enum.map(feedback, fn f -> Map.get(f.aspects, aspect, 0.5) end)
        avg = Enum.sum(scores) / length(scores)
        {aspect, avg}
      end)
      |> Enum.into(%{})
      
      # Identify improvement areas (aspects with scores below 0.7)
      aspect_scores
      |> Enum.filter(fn {_aspect, score} -> score < 0.7 end)
      |> Enum.sort_by(fn {_aspect, score} -> score end)
      |> Enum.map(fn {aspect, _score} -> aspect end)
    end
    
    defp analyze_for_adjustments(feedback) do
      # Identify common issues across feedback
      # For now, focus on aspects with low scores
      {_strengths, weaknesses} = identify_strengths_and_weaknesses(feedback)
      
      # Generate adjustment recommendations for each weakness
      Enum.map(weaknesses, fn weakness ->
        adjustment = case weakness do
          :responsiveness ->
            "Increase emphasis on quick response times in role requirements"
            
          :initiative ->
            "Add more autonomous decision-making responsibilities to the role"
            
          :teamwork ->
            "Enhance collaboration requirements and provide more structured team interactions"
            
          _ ->
            "Review and improve #{weakness} aspects of the role"
        end
        
        {weakness, adjustment}
      end)
    end
    
    defp prioritize_adjustments(adjustments) do
      # For now, just return the adjustments in the same order
      # In a real implementation, this would prioritize based on impact, difficulty, etc.
      adjustments
    end
  end
end