defmodule Automata.Reasoning.Cognitive.Metacognition.ReflectionFramework do
  @moduledoc """
  Reflection Framework for Meta-Cognitive System

  This module provides mechanisms for introspective monitoring and adaptation of
  cognitive processes:
  - Performance introspection with causal analysis
  - Reasoning strategy selection based on problem characteristics
  - Execution trace analysis for bottleneck identification
  """

  alias Automata.Reasoning.Cognitive.Metacognition.ReflectionFramework.{
    Introspection,
    StrategySelection,
    TraceAnalysis
  }

  defmodule Introspection do
    @moduledoc """
    Provides mechanisms for analyzing and understanding the system's own performance.
    """

    @type performance_data :: %{
            task_id: String.t(),
            execution_time: non_neg_integer(),
            resource_usage: map(),
            outcomes: map(),
            metadata: map()
          }

    @type introspection_result :: %{
            performance_summary: map(),
            causal_factors: list(map()),
            bottlenecks: list(map()),
            improvement_opportunities: list(map()),
            confidence: float()
          }

    @doc """
    Analyzes performance data to identify causal factors and improvement opportunities.
    """
    @spec analyze_performance(list(performance_data()), keyword()) :: introspection_result()
    def analyze_performance(performance_data, options \\ []) do
      # Extract key metrics from performance data
      metrics = extract_performance_metrics(performance_data)
      
      # Identify causal factors affecting performance
      causal_factors = identify_causal_factors(performance_data, metrics)
      
      # Detect performance bottlenecks
      bottlenecks = detect_bottlenecks(performance_data, metrics, options)
      
      # Generate improvement opportunities
      improvements = generate_improvement_opportunities(causal_factors, bottlenecks)
      
      # Calculate confidence in the analysis
      confidence = calculate_analysis_confidence(
        performance_data, 
        causal_factors,
        bottlenecks
      )
      
      # Return the introspection result
      %{
        performance_summary: summarize_performance(metrics),
        causal_factors: causal_factors,
        bottlenecks: bottlenecks,
        improvement_opportunities: improvements,
        confidence: confidence
      }
    end

    @doc """
    Extracts key performance metrics from raw performance data.
    """
    @spec extract_performance_metrics(list(performance_data())) :: map()
    defp extract_performance_metrics(performance_data) do
      # Calculate basic statistics on execution time
      execution_times = Enum.map(performance_data, & &1.execution_time)
      avg_execution_time = mean(execution_times)
      max_execution_time = Enum.max(execution_times, fn -> 0 end)
      min_execution_time = Enum.min(execution_times, fn -> 0 end)
      
      # Calculate resource usage patterns
      resource_usage = aggregate_resource_usage(performance_data)
      
      # Analyze outcome patterns
      outcome_analysis = analyze_outcomes(performance_data)
      
      # Return compiled metrics
      %{
        execution_time: %{
          average: avg_execution_time,
          max: max_execution_time,
          min: min_execution_time,
          std_dev: std_dev(execution_times, avg_execution_time)
        },
        resource_usage: resource_usage,
        outcome_analysis: outcome_analysis,
        sample_size: length(performance_data)
      }
    end

    @doc """
    Aggregates resource usage across performance data.
    """
    @spec aggregate_resource_usage(list(performance_data())) :: map()
    defp aggregate_resource_usage(performance_data) do
      # Extract all resource types first
      resource_types = performance_data
      |> Enum.flat_map(fn data -> Map.keys(data.resource_usage) end)
      |> Enum.uniq()
      
      # For each resource type, calculate usage statistics
      Enum.reduce(resource_types, %{}, fn resource_type, acc ->
        usage_values = Enum.map(performance_data, fn data -> 
          Map.get(data.resource_usage, resource_type, 0)
        end)
        
        avg_usage = mean(usage_values)
        
        Map.put(acc, resource_type, %{
          average: avg_usage,
          max: Enum.max(usage_values, fn -> 0 end),
          min: Enum.min(usage_values, fn -> 0 end),
          std_dev: std_dev(usage_values, avg_usage)
        })
      end)
    end

    @doc """
    Analyzes outcome patterns across performance data.
    """
    @spec analyze_outcomes(list(performance_data())) :: map()
    defp analyze_outcomes(performance_data) do
      # Count outcome frequencies
      outcome_counts = Enum.reduce(performance_data, %{}, fn data, acc ->
        outcome_type = Map.get(data.outcomes, :type, :unknown)
        Map.update(acc, outcome_type, 1, &(&1 + 1))
      end)
      
      # Calculate success rate if success/failure outcomes exist
      success_count = Map.get(outcome_counts, :success, 0)
      failure_count = Map.get(outcome_counts, :failure, 0)
      total_count = success_count + failure_count
      
      success_rate = if total_count > 0, do: success_count / total_count, else: 0
      
      # Return outcome analysis
      %{
        counts: outcome_counts,
        success_rate: success_rate,
        outcome_distribution: outcome_distribution(performance_data)
      }
    end

    @doc """
    Analyzes the distribution of outcomes by various factors.
    """
    @spec outcome_distribution(list(performance_data())) :: map()
    defp outcome_distribution(performance_data) do
      # Group outcomes by common metadata factors
      # In a real implementation this would analyze outcomes by various dimensions
      %{
        by_complexity: %{}, # Placeholder
        by_resource_level: %{}, # Placeholder
        by_time_of_day: %{} # Placeholder
      }
    end

    @doc """
    Identifies causal factors affecting performance based on data analysis.
    """
    @spec identify_causal_factors(list(performance_data()), map()) :: list(map())
    defp identify_causal_factors(performance_data, metrics) do
      # This would implement causal analysis techniques such as:
      # - Correlation analysis
      # - Structural equation modeling
      # - Bayesian network inference
      # - A/B testing analysis
      
      # For now, return placeholder causal factors
      [
        %{
          factor: :memory_usage,
          impact_level: 0.8,
          confidence: 0.75,
          description: "Memory consumption strongly correlates with execution time",
          evidence: "Pearson correlation r=0.87 between memory usage and execution time"
        },
        %{
          factor: :input_complexity,
          impact_level: 0.6,
          confidence: 0.65,
          description: "Input complexity affects processing time",
          evidence: "Tasks with complexity > 7 take 2.5x longer on average"
        }
      ]
    end

    @doc """
    Detects bottlenecks in system performance.
    """
    @spec detect_bottlenecks(list(performance_data()), map(), keyword()) :: list(map())
    defp detect_bottlenecks(performance_data, metrics, options) do
      # Threshold configuration
      time_threshold = Keyword.get(options, :time_threshold, 0.8)
      resource_threshold = Keyword.get(options, :resource_threshold, 0.7)
      
      # Analyze execution time patterns
      time_bottlenecks = detect_time_bottlenecks(
        performance_data, 
        metrics.execution_time, 
        time_threshold
      )
      
      # Analyze resource usage patterns
      resource_bottlenecks = detect_resource_bottlenecks(
        performance_data,
        metrics.resource_usage,
        resource_threshold
      )
      
      # Combine and prioritize bottlenecks
      time_bottlenecks ++ resource_bottlenecks
      |> Enum.sort_by(fn b -> b.severity end, :desc)
    end

    @doc """
    Detects bottlenecks related to execution time.
    """
    @spec detect_time_bottlenecks(list(performance_data()), map(), float()) :: list(map())
    defp detect_time_bottlenecks(performance_data, time_metrics, threshold) do
      # Identify execution phases that take disproportionately long
      # In a real implementation, this would analyze task subtimings
      
      # For now, return placeholder time bottlenecks
      [
        %{
          type: :execution_time,
          component: :neural_translation,
          severity: 0.85,
          description: "Neural translation takes 45% of total execution time",
          recommendation: "Optimize vector operations in neural translation"
        }
      ]
    end

    @doc """
    Detects bottlenecks related to resource usage.
    """
    @spec detect_resource_bottlenecks(list(performance_data()), map(), float()) :: list(map())
    defp detect_resource_bottlenecks(performance_data, resource_metrics, threshold) do
      # Identify resources that are constraining performance
      # In a real implementation, this would analyze resource consumption patterns
      
      # For now, return placeholder resource bottlenecks
      [
        %{
          type: :resource_usage,
          resource: :memory,
          severity: 0.75,
          description: "Memory usage at 82% capacity during peak operation",
          recommendation: "Implement incremental garbage collection"
        }
      ]
    end

    @doc """
    Generates improvement opportunities based on causal factors and bottlenecks.
    """
    @spec generate_improvement_opportunities(list(map()), list(map())) :: list(map())
    defp generate_improvement_opportunities(causal_factors, bottlenecks) do
      # Combine insights from causal analysis and bottleneck detection
      # to generate actionable improvement opportunities
      
      # For now, return placeholder improvement opportunities
      [
        %{
          target: :neural_translation,
          action: :optimize_algorithm,
          expected_benefit: 0.35,
          confidence: 0.8,
          description: "Optimize vector operations in neural translation module",
          rationale: "Based on execution profiling showing 45% time spent in vector calculations"
        },
        %{
          target: :memory_management,
          action: :implement_incremental_gc,
          expected_benefit: 0.25,
          confidence: 0.7,
          description: "Implement incremental garbage collection for memory-intensive operations",
          rationale: "Memory pressure correlates with performance degradation during extended operation"
        }
      ]
    end

    @doc """
    Summarizes overall performance based on metrics.
    """
    @spec summarize_performance(map()) :: map()
    defp summarize_performance(metrics) do
      # Create a concise summary of performance characteristics
      %{
        overall_efficiency: calculate_efficiency_score(metrics),
        primary_bottlenecks: identify_primary_bottlenecks(metrics),
        stability: calculate_stability_score(metrics),
        resource_efficiency: calculate_resource_efficiency(metrics)
      }
    end

    @doc """
    Calculates an overall efficiency score.
    """
    @spec calculate_efficiency_score(map()) :: float()
    defp calculate_efficiency_score(metrics) do
      # In a real implementation, this would compute a weighted score
      # based on multiple performance dimensions
      
      # For now, return a placeholder efficiency score
      0.75
    end

    @doc """
    Identifies the primary bottlenecks from metrics.
    """
    @spec identify_primary_bottlenecks(map()) :: list(atom())
    defp identify_primary_bottlenecks(metrics) do
      # In a real implementation, this would identify the most
      # significant limiting factors in performance
      
      # For now, return placeholder primary bottlenecks
      [:memory_usage, :algorithm_complexity]
    end

    @doc """
    Calculates a stability score from performance metrics.
    """
    @spec calculate_stability_score(map()) :: float()
    defp calculate_stability_score(metrics) do
      # In a real implementation, this would compute stability based on
      # variance in performance across similar conditions
      
      # For now, return a placeholder stability score
      0.8
    end

    @doc """
    Calculates resource efficiency from metrics.
    """
    @spec calculate_resource_efficiency(map()) :: float()
    defp calculate_resource_efficiency(metrics) do
      # In a real implementation, this would compute efficiency of
      # resource utilization across tasks
      
      # For now, return a placeholder resource efficiency score
      0.7
    end

    @doc """
    Calculates confidence in the analysis based on data quality and consistency.
    """
    @spec calculate_analysis_confidence(list(performance_data()), list(map()), list(map())) :: float()
    defp calculate_analysis_confidence(performance_data, causal_factors, bottlenecks) do
      # Factors that affect confidence:
      # - Sample size
      # - Data consistency
      # - Agreement between different analysis methods
      # - Coverage of factors analyzed
      
      # For a quick approximation:
      sample_factor = min(1.0, length(performance_data) / 30)
      causal_confidence = Enum.reduce(causal_factors, 0, fn f, acc -> acc + f.confidence end) / 
                         max(1, length(causal_factors))
      
      # Combine factors with appropriate weights
      0.6 * sample_factor + 0.4 * causal_confidence
    end

    @doc """
    Calculates the mean of a list of numbers.
    """
    @spec mean(list(number())) :: float()
    defp mean([]), do: 0.0
    defp mean(values), do: Enum.sum(values) / length(values)

    @doc """
    Calculates the standard deviation of a list of numbers.
    """
    @spec std_dev(list(number()), float()) :: float()
    defp std_dev([], _), do: 0.0
    defp std_dev([_], _), do: 0.0
    defp std_dev(values, mean) do
      variance = Enum.reduce(values, 0, fn x, acc ->
        diff = x - mean
        acc + diff * diff
      end) / length(values)
      
      :math.sqrt(variance)
    end
  end

  defmodule StrategySelection do
    @moduledoc """
    Provides mechanisms for selecting optimal reasoning strategies based on problem characteristics.
    """

    @type problem_description :: %{
            type: atom(),
            characteristics: map(),
            constraints: map(),
            metadata: map()
          }

    @type strategy :: %{
            id: atom() | String.t(),
            name: String.t(),
            characteristics: map(),
            resource_profile: map(),
            success_patterns: map()
          }

    @type selection_result :: %{
            selected_strategy: strategy(),
            alternatives: list(strategy()),
            rationale: map(),
            confidence: float()
          }

    @doc """
    Selects the optimal reasoning strategy for a given problem.
    """
    @spec select_strategy(problem_description(), list(strategy()), keyword()) :: selection_result()
    def select_strategy(problem, available_strategies, options \\ []) do
      # Extract problem characteristics
      characteristics = problem.characteristics
      constraints = problem.constraints
      
      # Calculate strategy suitability scores
      strategy_scores = calculate_strategy_scores(problem, available_strategies)
      
      # Apply constraints to filter strategies
      viable_strategies = filter_strategies_by_constraints(
        available_strategies,
        constraints
      )
      
      # Select the highest scoring viable strategy
      {selected, score} = select_highest_scoring_strategy(
        viable_strategies,
        strategy_scores
      )
      
      # Sort remaining strategies by score
      alternatives = sort_strategies_by_score(
        viable_strategies -- [selected],
        strategy_scores
      )
      
      # Generate rationale for selection
      rationale = generate_selection_rationale(
        selected, 
        score,
        problem, 
        alternatives
      )
      
      # Calculate confidence in selection
      confidence = calculate_selection_confidence(
        selected,
        score,
        problem,
        alternatives
      )
      
      # Return selection result
      %{
        selected_strategy: selected,
        alternatives: alternatives,
        rationale: rationale,
        confidence: confidence
      }
    end

    @doc """
    Calculates suitability scores for strategies against a problem.
    """
    @spec calculate_strategy_scores(problem_description(), list(strategy())) :: %{required(atom() | String.t()) => float()}
    defp calculate_strategy_scores(problem, strategies) do
      # Calculate a suitability score for each strategy
      Enum.reduce(strategies, %{}, fn strategy, acc ->
        score = calculate_strategy_score(strategy, problem)
        Map.put(acc, strategy.id, score)
      end)
    end

    @doc """
    Calculates a single strategy's suitability score for a problem.
    """
    @spec calculate_strategy_score(strategy(), problem_description()) :: float()
    defp calculate_strategy_score(strategy, problem) do
      # In a real implementation, this would:
      # - Match problem characteristics against strategy strengths
      # - Consider historical performance on similar problems
      # - Account for resource constraints
      # - Consider domain-specific factors
      
      # For now, use a simple heuristic based on type matching
      type_match_score = if strategy.characteristics[:ideal_problem_types] && 
                           Enum.member?(strategy.characteristics[:ideal_problem_types], problem.type) do
        0.8
      else
        0.3
      end
      
      # Add some randomness for demonstration (this would be more sophisticated in reality)
      type_match_score + :rand.uniform() * 0.2
    end

    @doc """
    Filters strategies based on problem constraints.
    """
    @spec filter_strategies_by_constraints(list(strategy()), map()) :: list(strategy())
    defp filter_strategies_by_constraints(strategies, constraints) do
      # Filter out strategies that violate hard constraints
      Enum.filter(strategies, fn strategy ->
        strategy_satisfies_constraints(strategy, constraints)
      end)
    end

    @doc """
    Checks if a strategy satisfies all constraints.
    """
    @spec strategy_satisfies_constraints(strategy(), map()) :: boolean()
    defp strategy_satisfies_constraints(strategy, constraints) do
      # Check resource constraints
      resource_compatible = check_resource_compatibility(
        strategy.resource_profile,
        constraints[:resources]
      )
      
      # Check time constraints
      time_compatible = check_time_compatibility(
        strategy.characteristics[:typical_duration],
        constraints[:max_time]
      )
      
      # Check any other constraints
      other_constraints_met = true # Placeholder
      
      # Strategy must satisfy all constraints
      resource_compatible and time_compatible and other_constraints_met
    end

    @doc """
    Checks if strategy's resource profile is compatible with constraints.
    """
    @spec check_resource_compatibility(map(), map() | nil) :: boolean()
    defp check_resource_compatibility(_profile, nil), do: true
    defp check_resource_compatibility(profile, constraints) do
      # Check that each required resource is within constraints
      Enum.all?(Map.keys(profile), fn resource ->
        strategy_requirement = Map.get(profile, resource, 0)
        constraint_limit = Map.get(constraints, resource, :infinity)
        
        case constraint_limit do
          :infinity -> true
          limit when is_number(limit) -> strategy_requirement <= limit
          _ -> true
        end
      end)
    end

    @doc """
    Checks if strategy's typical duration is compatible with time constraints.
    """
    @spec check_time_compatibility(number() | nil, number() | nil) :: boolean()
    defp check_time_compatibility(nil, _), do: true
    defp check_time_compatibility(_, nil), do: true
    defp check_time_compatibility(duration, max_time), do: duration <= max_time

    @doc """
    Selects the highest scoring strategy from a list.
    """
    @spec select_highest_scoring_strategy(list(strategy()), %{required(atom() | String.t()) => float()}) :: {strategy(), float()}
    defp select_highest_scoring_strategy(strategies, scores) do
      # Find the strategy with the highest score
      Enum.reduce(strategies, {nil, 0.0}, fn strategy, {best_strategy, best_score} ->
        score = Map.get(scores, strategy.id, 0.0)
        if score > best_score do
          {strategy, score}
        else
          {best_strategy, best_score}
        end
      end)
    end

    @doc """
    Sorts strategies by their scores.
    """
    @spec sort_strategies_by_score(list(strategy()), %{required(atom() | String.t()) => float()}) :: list(strategy())
    defp sort_strategies_by_score(strategies, scores) do
      Enum.sort_by(strategies, fn strategy -> 
        Map.get(scores, strategy.id, 0.0)
      end, :desc)
    end

    @doc """
    Generates rationale for strategy selection.
    """
    @spec generate_selection_rationale(strategy(), float(), problem_description(), list(strategy())) :: map()
    defp generate_selection_rationale(selected, score, problem, alternatives) do
      # In a real implementation, this would generate detailed
      # explanations of why this strategy was selected over others
      
      # For now, return a simple rationale
      %{
        primary_factors: [
          "Strategy #{selected.name} is well-suited for #{problem.type} problems",
          "Resource requirements are within constraints",
          "Historical success rate of 78% on similar problems"
        ],
        score_breakdown: %{
          problem_match: 0.75,
          resource_efficiency: 0.65,
          historical_performance: 0.78
        },
        comparison: generate_alternative_comparison(selected, alternatives)
      }
    end

    @doc """
    Generates comparison with alternative strategies.
    """
    @spec generate_alternative_comparison(strategy(), list(strategy())) :: map()
    defp generate_alternative_comparison(selected, alternatives) do
      # Generate comparison with top alternatives
      top_alternatives = Enum.take(alternatives, 2)
      
      comparisons = Enum.map(top_alternatives, fn alt ->
        %{
          strategy: alt.name,
          relative_strengths: ["Lower resource usage", "Faster execution"],
          relative_weaknesses: ["Lower accuracy", "Less robust to noise"],
          tradeoff: "Sacrifices accuracy for speed"
        }
      end)
      
      %{
        better_than: comparisons,
        key_differentiators: ["Higher accuracy", "Better noise tolerance"]
      }
    end

    @doc """
    Calculates confidence in strategy selection.
    """
    @spec calculate_selection_confidence(strategy(), float(), problem_description(), list(strategy())) :: float()
    defp calculate_selection_confidence(selected, score, problem, alternatives) do
      # Factors affecting confidence:
      # - Gap between top strategy and alternatives
      # - Historical performance data availability
      # - Problem similarity to known scenarios
      # - Strategy score quality
      
      # For simplicity, use score directly as a confidence measure
      # In practice, this would be more sophisticated
      min(score, 0.95)
    end

    @doc """
    Adapts strategy selection based on performance feedback.
    """
    @spec adapt_selection_model(performance_data :: map(), selection_history :: list(map())) :: :ok
    def adapt_selection_model(performance_data, selection_history) do
      # In a real implementation, this would:
      # - Update strategy success statistics
      # - Refine scoring heuristics based on actual outcomes
      # - Adjust weightings for different problem characteristics
      # - Update strategy profiles based on observed performance
      
      # For now, just return :ok as a placeholder
      :ok
    end
  end

  defmodule TraceAnalysis do
    @moduledoc """
    Provides mechanisms for analyzing execution traces to identify
    patterns, bottlenecks, and improvement opportunities.
    """

    @type trace_entry :: %{
            timestamp: integer(),
            component: atom() | String.t(),
            action: atom(),
            duration: integer(),
            input_info: map(),
            output_info: map(),
            metadata: map()
          }

    @type execution_trace :: list(trace_entry())

    @type analysis_result :: %{
            execution_flow: map(),
            critical_path: list(map()),
            bottlenecks: list(map()),
            anomalies: list(map()),
            optimization_opportunities: list(map()),
            confidence: float()
          }

    @doc """
    Analyzes an execution trace to identify patterns and bottlenecks.
    """
    @spec analyze_trace(execution_trace(), keyword()) :: analysis_result()
    def analyze_trace(trace, options \\ []) do
      # Sort trace by timestamp to ensure chronological order
      sorted_trace = Enum.sort_by(trace, & &1.timestamp)
      
      # Reconstruct execution flow
      execution_flow = reconstruct_execution_flow(sorted_trace)
      
      # Identify critical path
      critical_path = identify_critical_path(sorted_trace, execution_flow)
      
      # Detect bottlenecks
      bottlenecks = detect_bottlenecks(sorted_trace, critical_path, options)
      
      # Identify anomalies
      anomalies = identify_anomalies(sorted_trace, execution_flow)
      
      # Generate optimization opportunities
      optimization_opportunities = generate_optimization_opportunities(
        bottlenecks,
        anomalies,
        execution_flow
      )
      
      # Calculate confidence in analysis
      confidence = calculate_analysis_confidence(sorted_trace)
      
      # Return analysis result
      %{
        execution_flow: execution_flow,
        critical_path: critical_path,
        bottlenecks: bottlenecks,
        anomalies: anomalies,
        optimization_opportunities: optimization_opportunities,
        confidence: confidence
      }
    end

    @doc """
    Reconstructs the execution flow from a trace.
    """
    @spec reconstruct_execution_flow(execution_trace()) :: map()
    defp reconstruct_execution_flow(trace) do
      # Build a graph of component interactions
      component_graph = build_component_graph(trace)
      
      # Calculate component execution statistics
      component_stats = calculate_component_stats(trace)
      
      # Identify execution stages and transitions
      stages = identify_execution_stages(trace)
      
      %{
        component_graph: component_graph,
        component_stats: component_stats,
        stages: stages,
        total_duration: calculate_total_duration(trace),
        parallelism_degree: calculate_parallelism_degree(trace)
      }
    end

    @doc """
    Builds a graph of component interactions from the trace.
    """
    @spec build_component_graph(execution_trace()) :: map()
    defp build_component_graph(trace) do
      # Extract all unique components
      components = trace
      |> Enum.map(& &1.component)
      |> Enum.uniq()
      
      # Initialize graph with components as nodes
      initial_graph = Enum.reduce(components, %{}, fn component, graph ->
        Map.put(graph, component, %{
          outgoing: %{},
          incoming: %{},
          self_loops: 0
        })
      end)
      
      # Add edges based on sequential calls
      trace
      |> Enum.chunk_every(2, 1, :discard)
      |> Enum.reduce(initial_graph, fn [entry1, entry2], graph ->
        # Skip if from different parallelism branches
        if connected_execution?(entry1, entry2) do
          add_edge(graph, entry1.component, entry2.component)
        else
          graph
        end
      end)
    end

    @doc """
    Determines if two trace entries are connected in execution flow.
    """
    @spec connected_execution?(trace_entry(), trace_entry()) :: boolean()
    defp connected_execution?(entry1, entry2) do
      # In a real implementation, this would use thread ID, 
      # parent-child relationships, or explicit continuation markers
      
      # For now, assume sequential entries in same thread are connected
      abs(entry2.timestamp - (entry1.timestamp + entry1.duration)) < 10
    end

    @doc """
    Adds an edge to the component graph.
    """
    @spec add_edge(map(), atom() | String.t(), atom() | String.t()) :: map()
    defp add_edge(graph, from, to) do
      if from == to do
        # Self-loop
        update_in(graph, [from, :self_loops], &(&1 + 1))
      else
        # Regular edge
        graph
        |> update_in([from, :outgoing, to], fn
          nil -> 1
          count -> count + 1
        end)
        |> update_in([to, :incoming, from], fn
          nil -> 1
          count -> count + 1
        end)
      end
    end

    @doc """
    Calculates execution statistics for each component.
    """
    @spec calculate_component_stats(execution_trace()) :: map()
    defp calculate_component_stats(trace) do
      # Group trace entries by component
      component_entries = Enum.group_by(trace, & &1.component)
      
      # Calculate statistics for each component
      Enum.reduce(component_entries, %{}, fn {component, entries}, stats ->
        durations = Enum.map(entries, & &1.duration)
        total_time = Enum.sum(durations)
        avg_time = total_time / length(durations)
        
        Map.put(stats, component, %{
          call_count: length(entries),
          total_time: total_time,
          average_time: avg_time,
          min_time: Enum.min(durations),
          max_time: Enum.max(durations),
          std_dev: calculate_std_dev(durations, avg_time)
        })
      end)
    end

    @doc """
    Identifies distinct stages in execution flow.
    """
    @spec identify_execution_stages(execution_trace()) :: list(map())
    defp identify_execution_stages(trace) do
      # In a real implementation, this would use temporal clustering,
      # component type analysis, and data flow patterns to identify stages
      
      # For now, use a simple time-based approach
      trace
      |> Enum.chunk_by(fn entry ->
        # Categorize by rough time periods
        div(entry.timestamp, 1000)
      end)
      |> Enum.map(fn stage_entries ->
        start_time = Enum.min_by(stage_entries, & &1.timestamp).timestamp
        end_time = Enum.max_by(stage_entries, & &1.timestamp + &1.duration)
                  |> then(fn entry -> entry.timestamp + entry.duration end)
        
        components = stage_entries
                    |> Enum.map(& &1.component)
                    |> Enum.frequencies()
        
        %{
          start_time: start_time,
          end_time: end_time,
          duration: end_time - start_time,
          components: components,
          entry_count: length(stage_entries)
        }
      end)
    end

    @doc """
    Calculates the total execution duration.
    """
    @spec calculate_total_duration(execution_trace()) :: integer()
    defp calculate_total_duration([]), do: 0
    defp calculate_total_duration(trace) do
      start_time = Enum.min_by(trace, & &1.timestamp).timestamp
      end_time = Enum.max_by(trace, fn entry -> entry.timestamp + entry.duration end)
                |> then(fn entry -> entry.timestamp + entry.duration end)
      
      end_time - start_time
    end

    @doc """
    Calculates the average degree of parallelism in execution.
    """
    @spec calculate_parallelism_degree(execution_trace()) :: float()
    defp calculate_parallelism_degree(trace) do
      # In a real implementation, this would analyze overlapping execution
      # periods to determine average concurrent execution threads
      
      # Sum of individual durations
      total_component_time = Enum.reduce(trace, 0, fn entry, acc ->
        acc + entry.duration
      end)
      
      # Total wall-clock time
      wall_time = calculate_total_duration(trace)
      
      # Parallelism is the ratio of component time to wall time
      if wall_time > 0 do
        total_component_time / wall_time
      else
        0.0
      end
    end

    @doc """
    Identifies the critical path in execution.
    """
    @spec identify_critical_path(execution_trace(), map()) :: list(map())
    defp identify_critical_path(trace, execution_flow) do
      # In a real implementation, this would find the longest path
      # through the execution graph, considering dependencies
      
      # For now, identify components with highest contribution to total time
      component_stats = execution_flow.component_stats
      total_duration = execution_flow.total_duration
      
      # Sort components by total time
      component_stats
      |> Enum.map(fn {component, stats} ->
        {component, stats, stats.total_time / total_duration}
      end)
      |> Enum.sort_by(fn {_, _, ratio} -> ratio end, :desc)
      |> Enum.take(3)  # Take top 3 components
      |> Enum.map(fn {component, stats, ratio} ->
        %{
          component: component,
          time_contribution: stats.total_time,
          time_ratio: ratio,
          calls: stats.call_count,
          average_time: stats.average_time
        }
      end)
    end

    @doc """
    Detects bottlenecks in execution.
    """
    @spec detect_bottlenecks(execution_trace(), list(map()), keyword()) :: list(map())
    defp detect_bottlenecks(trace, critical_path, options) do
      # Time threshold configuration
      time_ratio_threshold = Keyword.get(options, :time_ratio_threshold, 0.2)
      
      # Identify bottlenecks from critical path
      critical_path_bottlenecks = Enum.filter(critical_path, fn component ->
        component.time_ratio >= time_ratio_threshold
      end)
      
      # Identify bottlenecks from waiting times
      waiting_bottlenecks = identify_waiting_bottlenecks(trace)
      
      # Identify contention bottlenecks
      contention_bottlenecks = identify_contention_bottlenecks(trace)
      
      # Combine and deduplicate bottlenecks
      (critical_path_bottlenecks ++ waiting_bottlenecks ++ contention_bottlenecks)
      |> deduplicate_bottlenecks()
      |> Enum.sort_by(fn b -> b.severity end, :desc)
    end

    @doc """
    Identifies waiting time bottlenecks.
    """
    @spec identify_waiting_bottlenecks(execution_trace()) :: list(map())
    defp identify_waiting_bottlenecks(trace) do
      # In a real implementation, this would analyze gaps between
      # dependent operations to identify waiting times
      
      # For now, return placeholder waiting bottlenecks
      [
        %{
          type: :waiting,
          component: :data_store,
          severity: 0.7,
          description: "Long waiting times for data store responses",
          recommendation: "Implement caching or connection pooling"
        }
      ]
    end

    @doc """
    Identifies contention bottlenecks.
    """
    @spec identify_contention_bottlenecks(execution_trace()) :: list(map())
    defp identify_contention_bottlenecks(trace) do
      # In a real implementation, this would analyze patterns
      # indicating resource contention
      
      # For now, return placeholder contention bottlenecks
      [
        %{
          type: :contention,
          component: :shared_memory,
          severity: 0.6,
          description: "Contention on shared memory access",
          recommendation: "Implement finer-grained locking or lock-free algorithms"
        }
      ]
    end

    @doc """
    Deduplicates bottlenecks referring to the same component.
    """
    @spec deduplicate_bottlenecks(list(map())) :: list(map())
    defp deduplicate_bottlenecks(bottlenecks) do
      # Group bottlenecks by component
      grouped = Enum.group_by(bottlenecks, & &1.component)
      
      # For each component, keep the bottleneck with highest severity
      Enum.map(grouped, fn {_component, group} ->
        Enum.max_by(group, & &1.severity)
      end)
    end

    @doc """
    Identifies anomalies in execution traces.
    """
    @spec identify_anomalies(execution_trace(), map()) :: list(map())
    defp identify_anomalies(trace, execution_flow) do
      # Identify timing anomalies
      timing_anomalies = identify_timing_anomalies(trace, execution_flow)
      
      # Identify sequence anomalies
      sequence_anomalies = identify_sequence_anomalies(trace, execution_flow)
      
      # Identify behavioral anomalies
      behavioral_anomalies = identify_behavioral_anomalies(trace, execution_flow)
      
      # Combine all anomalies
      timing_anomalies ++ sequence_anomalies ++ behavioral_anomalies
    end

    @doc """
    Identifies timing anomalies in execution.
    """
    @spec identify_timing_anomalies(execution_trace(), map()) :: list(map())
    defp identify_timing_anomalies(trace, execution_flow) do
      # In a real implementation, this would use statistical analysis
      # to identify execution times that deviate significantly from norms
      
      # For now, return placeholder timing anomalies
      [
        %{
          type: :timing,
          component: :neural_encoder,
          severity: 0.8,
          description: "Neural encoder execution time spiked to 3x normal at timestamp 15200",
          possible_causes: ["Input size anomaly", "Resource contention", "Cache miss"]
        }
      ]
    end

    @doc """
    Identifies sequence anomalies in execution.
    """
    @spec identify_sequence_anomalies(execution_trace(), map()) :: list(map())
    defp identify_sequence_anomalies(trace, execution_flow) do
      # In a real implementation, this would identify unexpected
      # or irregular execution sequences
      
      # For now, return placeholder sequence anomalies
      [
        %{
          type: :sequence,
          components: [:context_manager, :symbolic_reasoning],
          severity: 0.6,
          description: "Unexpected repeated context switches between components",
          possible_causes: ["Race condition", "Improper synchronization"]
        }
      ]
    end

    @doc """
    Identifies behavioral anomalies in execution.
    """
    @spec identify_behavioral_anomalies(execution_trace(), map()) :: list(map())
    defp identify_behavioral_anomalies(trace, execution_flow) do
      # In a real implementation, this would identify components
      # behaving in unexpected ways (e.g., unusual input/output patterns)
      
      # For now, return placeholder behavioral anomalies
      [
        %{
          type: :behavioral,
          component: :grounding_system,
          severity: 0.5,
          description: "Grounding system produced unusually low confidence scores",
          possible_causes: ["Novel input domain", "Configuration issue"]
        }
      ]
    end

    @doc """
    Generates optimization opportunities from analysis results.
    """
    @spec generate_optimization_opportunities(list(map()), list(map()), map()) :: list(map())
    defp generate_optimization_opportunities(bottlenecks, anomalies, execution_flow) do
      # Generate opportunities from bottlenecks
      bottleneck_opportunities = bottlenecks
      |> Enum.map(fn bottleneck ->
        %{
          target: bottleneck.component,
          type: :bottleneck_resolution,
          severity: bottleneck.severity,
          description: bottleneck.recommendation || "Optimize #{bottleneck.component} performance",
          expected_impact: estimate_bottleneck_impact(bottleneck, execution_flow)
        }
      end)
      
      # Generate opportunities from anomalies
      anomaly_opportunities = anomalies
      |> Enum.filter(fn anomaly -> Map.has_key?(anomaly, :component) end)
      |> Enum.map(fn anomaly ->
        %{
          target: anomaly.component,
          type: :anomaly_resolution,
          severity: anomaly.severity * 0.8,  # Slightly lower priority than bottlenecks
          description: "Investigate and address #{anomaly.type} anomaly in #{anomaly.component}",
          expected_impact: estimate_anomaly_impact(anomaly, execution_flow)
        }
      end)
      
      # Generate architectural optimization opportunities
      architectural_opportunities = generate_architectural_opportunities(execution_flow)
      
      # Combine, deduplicate, and sort optimization opportunities
      (bottleneck_opportunities ++ anomaly_opportunities ++ architectural_opportunities)
      |> deduplicate_opportunities()
      |> Enum.sort_by(fn opp -> opp.expected_impact * opp.severity end, :desc)
    end

    @doc """
    Estimates the impact of resolving a bottleneck.
    """
    @spec estimate_bottleneck_impact(map(), map()) :: float()
    defp estimate_bottleneck_impact(bottleneck, execution_flow) do
      # In a real implementation, this would use sophisticated models
      # to estimate performance improvement potential
      
      # For now, use a simple heuristic based on time contribution
      component_stats = Map.get(execution_flow.component_stats, bottleneck.component)
      
      if component_stats do
        # Estimate potential time savings (assuming 50% improvement in component)
        potential_savings = component_stats.total_time * 0.5
        
        # Impact is ratio of savings to total execution time
        potential_savings / execution_flow.total_duration
      else
        # If no stats, use severity as a proxy
        bottleneck.severity * 0.3
      end
    end

    @doc """
    Estimates the impact of resolving an anomaly.
    """
    @spec estimate_anomaly_impact(map(), map()) :: float()
    defp estimate_anomaly_impact(anomaly, execution_flow) do
      # For simplicity, use severity as a proxy for impact potential
      # In practice, this would be more sophisticated
      anomaly.severity * 0.25
    end

    @doc """
    Generates architectural optimization opportunities.
    """
    @spec generate_architectural_opportunities(map()) :: list(map())
    defp generate_architectural_opportunities(execution_flow) do
      # In a real implementation, this would analyze execution patterns
      # to identify higher-level architectural improvements
      
      # For now, return placeholder architectural opportunities
      [
        %{
          target: :execution_model,
          type: :architectural,
          severity: 0.7,
          description: "Increase parallelism in data processing pipeline",
          expected_impact: 0.3
        },
        %{
          target: :component_interaction,
          type: :architectural,
          severity: 0.65,
          description: "Reduce inter-component communication overhead",
          expected_impact: 0.25
        }
      ]
    end

    @doc """
    Deduplicates optimization opportunities.
    """
    @spec deduplicate_opportunities(list(map())) :: list(map())
    defp deduplicate_opportunities(opportunities) do
      # Group by target component
      grouped = Enum.group_by(opportunities, & &1.target)
      
      # For targets with multiple opportunities, merge or select best
      Enum.flat_map(grouped, fn {_target, group} ->
        if length(group) > 1 do
          # If opportunities are of same type, keep highest impact
          by_type = Enum.group_by(group, & &1.type)
          
          Enum.flat_map(by_type, fn {_type, type_group} ->
            [Enum.max_by(type_group, fn opp -> opp.expected_impact * opp.severity end)]
          end)
        else
          group
        end
      end)
    end

    @doc """
    Calculates confidence in the trace analysis.
    """
    @spec calculate_analysis_confidence(execution_trace()) :: float()
    defp calculate_analysis_confidence(trace) do
      # Factors affecting confidence:
      # - Trace completeness
      # - Sample size
      # - Trace consistency
      # - Coverage of execution
      
      # For simplicity, use trace length as a proxy for confidence
      # In practice, this would be more sophisticated
      trace_length = length(trace)
      
      min(0.5 + trace_length / 100, 0.95)
    end

    @doc """
    Calculates the standard deviation of a list of numbers.
    """
    @spec calculate_std_dev(list(number()), float()) :: float()
    defp calculate_std_dev([], _), do: 0.0
    defp calculate_std_dev([_], _), do: 0.0
    defp calculate_std_dev(values, mean) do
      variance = Enum.reduce(values, 0, fn x, acc ->
        diff = x - mean
        acc + diff * diff
      end) / length(values)
      
      :math.sqrt(variance)
    end
  end

  @doc """
  Analyzes performance data to identify improvement opportunities.
  """
  @spec analyze_performance(list(map()), keyword()) :: map()
  def analyze_performance(performance_data, options \\ []) do
    Introspection.analyze_performance(performance_data, options)
  end

  @doc """
  Selects the optimal reasoning strategy for a given problem.
  """
  @spec select_strategy(map(), list(map()), keyword()) :: map()
  def select_strategy(problem, available_strategies, options \\ []) do
    StrategySelection.select_strategy(problem, available_strategies, options)
  end

  @doc """
  Analyzes an execution trace to identify bottlenecks and opportunities.
  """
  @spec analyze_trace(list(map()), keyword()) :: map()
  def analyze_trace(trace, options \\ []) do
    TraceAnalysis.analyze_trace(trace, options)
  end

  @doc """
  Updates strategy selection model based on performance feedback.
  """
  @spec adapt_strategy_selection(map(), list(map())) :: :ok
  def adapt_strategy_selection(performance_data, selection_history) do
    StrategySelection.adapt_selection_model(performance_data, selection_history)
  end

  @doc """
  Provides a unified interface to meta-cognitive functions.
  """
  @spec reflect(map(), keyword()) :: map()
  def reflect(data, options \\ []) do
    reflect_type = Keyword.get(options, :type, :auto)
    
    case reflect_type do
      :performance ->
        analyze_performance(data.performance_data, options)
        
      :strategy ->
        select_strategy(data.problem, data.available_strategies, options)
        
      :trace ->
        analyze_trace(data.execution_trace, options)
        
      :auto ->
        # Determine type based on data content
        cond do
          Map.has_key?(data, :execution_trace) ->
            analyze_trace(data.execution_trace, options)
            
          Map.has_key?(data, :performance_data) ->
            analyze_performance(data.performance_data, options)
            
          Map.has_key?(data, :problem) and Map.has_key?(data, :available_strategies) ->
            select_strategy(data.problem, data.available_strategies, options)
            
          true ->
            {:error, :unknown_reflection_type}
        end
        
      unknown ->
        {:error, {:invalid_reflection_type, unknown}}
    end
  end
end