defmodule Dspy.AdvancedCognitiveSystem do
  @moduledoc """
  Next-generation cognitive container system with quantum-inspired cognition,
  neural plasticity simulation, consciousness emergence patterns, and 
  self-organizing cognitive architectures.
  """

  ############################################
  #  QUANTUM-INSPIRED COGNITIVE CONTAINER   #
  ############################################

  defmodule QuantumCognitiveContainer do
    @moduledoc """
    Advanced container with quantum-inspired superposition states, entanglement
    with other containers, and consciousness emergence metrics.
    """

    defstruct [
      # Core identity
      :id,
      :name,
      :module,
      :instance,

      # Quantum-inspired properties
      :quantum_state,
      :superposition_states,
      :entangled_containers,
      :coherence_time,
      :decoherence_rate,
      :measurement_history,

      # Advanced semantics
      :semantic_tensor,
      :conceptual_manifold,
      :meaning_density,
      :semantic_drift_vector,
      :context_embeddings,

      # Consciousness metrics
      :integrated_information,
      :global_workspace_access,
      :phenomenal_binding,
      :meta_cognitive_awareness,
      :recursive_depth,

      # Neural plasticity
      :synaptic_weights,
      :learning_rate,
      :plasticity_state,
      :neurogenesis_rate,
      :pruning_threshold,

      # Temporal dynamics
      :temporal_coherence,
      :prediction_horizon,
      :memory_consolidation,
      :attention_trajectory,
      :intention_flow,

      # Self-organization
      :criticality_state,
      :emergence_potential,
      :self_organization_level,
      :phase_transition_sensitivity,
      :adaptive_resonance,

      # Performance and lifecycle
      :performance_manifold,
      :lifecycle_state,
      :evolutionary_fitness,
      :creation_context,
      :modification_history,
      :behavioral_signatures
    ]

    @type quantum_state :: %{
            amplitude: float(),
            phase: float(),
            basis_states: [map()],
            measurement_probability: float()
          }

    @type consciousness_metrics :: %{
            # Integrated Information Theory measure
            phi: float(),
            global_access: float(),
            binding_strength: float(),
            metacognitive_depth: non_neg_integer()
          }

    def new(module, opts \\ []) do
      # Handle signature parameter properly
      signature = 
        case Keyword.get(opts, :signature) do
          nil -> 
            Dspy.Signature.new("QuantumCognitive", 
              input_fields: [%{name: :input, type: :string, description: "Input text", required: true, default: nil}],
              output_fields: [%{name: :output, type: :string, description: "Output text", required: true, default: nil}]
            )
          sig when is_binary(sig) ->
            Dspy.Signature.new("QuantumCognitive", 
              input_fields: [%{name: :input, type: :string, description: "Input text", required: true, default: nil}],
              output_fields: [%{name: :output, type: :string, description: "Output text", required: true, default: nil}]
            )
          sig -> sig
        end
      
      instance = apply(module, :new, [signature, opts])

      %__MODULE__{
        id: generate_quantum_id(),
        name: Keyword.get(opts, :name, module_name(module)),
        module: module,
        instance: instance,

        # Initialize quantum state
        quantum_state: initialize_quantum_state(module, opts),
        superposition_states: generate_superposition_states(module, opts),
        entangled_containers: [],
        coherence_time: Keyword.get(opts, :coherence_time, 1000.0),
        decoherence_rate: Keyword.get(opts, :decoherence_rate, 0.01),
        measurement_history: [],

        # Advanced semantic properties
        semantic_tensor: generate_semantic_tensor(module, instance, opts),
        conceptual_manifold: create_conceptual_manifold(module),
        meaning_density: calculate_meaning_density(module, instance),
        semantic_drift_vector: [0.0, 0.0, 0.0],
        context_embeddings: %{},

        # Consciousness initialization
        integrated_information: calculate_phi(module, instance),
        global_workspace_access: 0.5,
        phenomenal_binding: 0.3,
        meta_cognitive_awareness: assess_metacognitive_capacity(module),
        recursive_depth: 0,

        # Neural plasticity
        synaptic_weights: initialize_synaptic_weights(module),
        learning_rate: Keyword.get(opts, :learning_rate, 0.001),
        plasticity_state: :receptive,
        neurogenesis_rate: 0.02,
        pruning_threshold: 0.1,

        # Temporal dynamics
        temporal_coherence: 0.8,
        prediction_horizon: 5,
        memory_consolidation: %{},
        attention_trajectory: [],
        intention_flow: %{},

        # Self-organization
        criticality_state: :edge_of_chaos,
        emergence_potential: 0.7,
        self_organization_level: 1,
        phase_transition_sensitivity: 0.5,
        adaptive_resonance: 0.6,

        # Lifecycle
        performance_manifold: %{},
        lifecycle_state: :quantum_superposition,
        evolutionary_fitness: 0.5,
        creation_context: capture_creation_context(),
        modification_history: [],
        behavioral_signatures: extract_behavioral_signatures(module, instance)
      }
    end

    def entangle(container1, container2, entanglement_strength \\ 0.8) do
      entanglement_id = "entanglement_#{container1.id}_#{container2.id}"

      entanglement_info = %{
        partner_id: container2.id,
        strength: entanglement_strength,
        entanglement_id: entanglement_id,
        created_at: DateTime.utc_now(),
        correlation_matrix: generate_correlation_matrix(container1, container2)
      }

      updated_container1 = %{
        container1
        | entangled_containers: [entanglement_info | container1.entangled_containers]
      }

      partner_entanglement = %{entanglement_info | partner_id: container1.id}

      updated_container2 = %{
        container2
        | entangled_containers: [partner_entanglement | container2.entangled_containers]
      }

      {updated_container1, updated_container2}
    end

    def measure_quantum_state(container, observable \\ :behavior) do
      # Quantum measurement collapses superposition to definite state
      measurement_result =
        case observable do
          :behavior -> measure_behavioral_state(container)
          :performance -> measure_performance_state(container)
          :consciousness -> measure_consciousness_state(container)
          :semantics -> measure_semantic_state(container)
        end

      # Update quantum state after measurement
      collapsed_state = collapse_quantum_state(container.quantum_state, measurement_result)

      measurement_record = %{
        observable: observable,
        result: measurement_result,
        timestamp: DateTime.utc_now(),
        pre_measurement_state: container.quantum_state
      }

      updated_container = %{
        container
        | quantum_state: collapsed_state,
          measurement_history: [measurement_record | container.measurement_history],
          lifecycle_state: :measured
      }

      {measurement_result, updated_container}
    end

    def evolve_quantum_state(container, time_delta) do
      # Simulate quantum evolution with decoherence
      evolved_state =
        apply_quantum_evolution(container.quantum_state, time_delta, container.decoherence_rate)

      # Update entangled containers
      evolved_entanglements = evolve_entanglements(container.entangled_containers, time_delta)

      %{
        container
        | quantum_state: evolved_state,
          entangled_containers: evolved_entanglements,
          temporal_coherence:
            container.temporal_coherence * (1.0 - container.decoherence_rate * time_delta)
      }
    end

    def update_consciousness_metrics(container, interaction_context) do
      # Update consciousness based on interactions
      new_phi = calculate_phi_from_interaction(container, interaction_context)
      new_global_access = update_global_workspace_access(container, interaction_context)
      new_binding = update_phenomenal_binding(container, interaction_context)

      %{
        container
        | integrated_information: new_phi,
          global_workspace_access: new_global_access,
          phenomenal_binding: new_binding,
          recursive_depth: container.recursive_depth + 1
      }
    end

    def adapt_neural_plasticity(container, learning_signal) do
      # Simulate neural plasticity and adaptation
      new_weights =
        update_synaptic_weights(
          container.synaptic_weights,
          learning_signal,
          container.learning_rate
        )

      # Neurogenesis and pruning
      {pruned_weights, _new_connections} =
        simulate_structural_plasticity(
          new_weights,
          container.neurogenesis_rate,
          container.pruning_threshold
        )

      # Update plasticity state
      new_plasticity_state = determine_plasticity_state(container, learning_signal)

      %{
        container
        | synaptic_weights: pruned_weights,
          plasticity_state: new_plasticity_state,
          modification_history: [
            %{
              type: :neural_adaptation,
              timestamp: DateTime.utc_now(),
              learning_signal: learning_signal,
              weight_changes: calculate_weight_delta(container.synaptic_weights, pruned_weights)
            }
            | container.modification_history
          ]
      }
    end

    def self_organize(container, environmental_pressure) do
      # Self-organization towards edge of chaos
      current_complexity = measure_behavioral_complexity(container)
      target_complexity = calculate_optimal_complexity(environmental_pressure)

      organization_delta = target_complexity - current_complexity

      new_organization_level = container.self_organization_level + organization_delta * 0.1
      new_criticality = adjust_criticality_state(container.criticality_state, organization_delta)
      new_emergence_potential = update_emergence_potential(container, environmental_pressure)

      %{
        container
        | self_organization_level: new_organization_level,
          criticality_state: new_criticality,
          emergence_potential: new_emergence_potential
      }
    end

    # Private implementation functions

    defp generate_quantum_id do
      "qc_#{System.unique_integer([:positive])}_#{:rand.uniform(1_000_000)}"
    end

    defp module_name(module) do
      module |> Module.split() |> List.last() |> Macro.underscore()
    end

    defp initialize_quantum_state(module, opts) do
      %{
        amplitude: Keyword.get(opts, :initial_amplitude, 1.0),
        phase: :rand.uniform() * 2 * :math.pi(),
        basis_states: generate_basis_states(module),
        measurement_probability: 1.0
      }
    end

    defp generate_superposition_states(module, _opts) do
      # Generate multiple potential states the container can exist in
      base_behaviors = extract_potential_behaviors(module)

      Enum.map(base_behaviors, fn behavior ->
        %{
          behavior_type: behavior,
          amplitude: :rand.normal() |> abs(),
          phase: :rand.uniform() * 2 * :math.pi(),
          # Will be calculated on measurement
          probability: nil
        }
      end)
    end

    defp generate_semantic_tensor(module, instance, _opts) do
      # Create high-dimensional semantic representation
      module_semantics = extract_module_semantics(module)
      instance_semantics = extract_instance_semantics(instance)

      # Combine into tensor (simplified as nested maps)
      %{
        conceptual_dimensions: module_semantics,
        operational_dimensions: instance_semantics,
        tensor_rank: 3,
        semantic_signature: compute_semantic_signature(module, instance)
      }
    end

    defp create_conceptual_manifold(module) do
      # Create a manifold representing the conceptual space
      %{
        topology: :riemannian,
        curvature: calculate_conceptual_curvature(module),
        geodesics: [],
        coordinate_charts: generate_coordinate_charts(module)
      }
    end

    defp calculate_meaning_density(module, instance) do
      # Calculate semantic information density
      module_info = estimate_information_content(module)
      instance_info = estimate_information_content(instance)

      (module_info + instance_info) / 2.0
    end

    defp calculate_phi(module, _instance) do
      # Simplified Integrated Information calculation
      module_complexity = estimate_module_complexity(module)
      integration_level = estimate_integration_level(module)

      module_complexity * integration_level
    end

    defp assess_metacognitive_capacity(module) do
      module_name = to_string(module)

      cond do
        String.contains?(module_name, ["Meta", "Introspect", "SelfEvaluat"]) -> 0.9
        String.contains?(module_name, ["Monitor", "Assess", "Reflect"]) -> 0.7
        String.contains?(module_name, ["Orchestrat", "Coordinat"]) -> 0.5
        true -> 0.2
      end
    end

    defp initialize_synaptic_weights(module) do
      # Initialize neural network-like weights for the cognitive module
      complexity = estimate_module_complexity(module)
      num_connections = round(complexity * 100)

      for _ <- 1..num_connections do
        %{
          source: :rand.uniform(10),
          target: :rand.uniform(10),
          weight: :rand.normal() * 0.1,
          plasticity: :rand.uniform()
        }
      end
    end

    defp capture_creation_context do
      %{
        timestamp: DateTime.utc_now(),
        system_state: :initializing,
        environmental_factors: %{
          cognitive_load: :rand.uniform(),
          attention_focus: :rand.uniform(),
          arousal_level: :rand.uniform()
        }
      }
    end

    defp extract_behavioral_signatures(module, instance) do
      # Extract unique behavioral patterns
      %{
        module_signature: compute_module_signature(module),
        instance_signature: compute_instance_signature(instance),
        interaction_patterns: [],
        temporal_patterns: []
      }
    end

    defp generate_correlation_matrix(container1, container2) do
      # Generate quantum correlation matrix for entanglement
      for i <- 1..4, j <- 1..4 do
        correlation =
          calculate_semantic_correlation(
            container1.semantic_tensor,
            container2.semantic_tensor,
            i,
            j
          )

        {{i, j}, correlation}
      end
      |> Enum.into(%{})
    end

    defp measure_behavioral_state(container) do
      # Measure the behavioral state of the quantum container
      dominant_behavior =
        container.superposition_states
        |> Enum.max_by(fn state -> state.amplitude end)

      %{
        dominant_behavior: dominant_behavior.behavior_type,
        confidence: dominant_behavior.amplitude,
        measurement_basis: :behavioral
      }
    end

    defp measure_performance_state(container) do
      # Measure performance characteristics
      %{
        efficiency: calculate_efficiency(container),
        accuracy: calculate_accuracy(container),
        adaptability: calculate_adaptability(container),
        measurement_basis: :performance
      }
    end

    defp measure_consciousness_state(container) do
      %{
        phi: container.integrated_information,
        global_access: container.global_workspace_access,
        binding: container.phenomenal_binding,
        recursive_depth: container.recursive_depth,
        measurement_basis: :consciousness
      }
    end

    defp measure_semantic_state(container) do
      %{
        meaning_density: container.meaning_density,
        conceptual_coherence: calculate_conceptual_coherence(container),
        semantic_drift: calculate_semantic_drift(container),
        measurement_basis: :semantics
      }
    end

    defp collapse_quantum_state(quantum_state, measurement_result) do
      # Collapse superposition to measured state
      %{
        quantum_state
        | amplitude: 1.0,
          phase: 0.0,
          measurement_probability: 1.0,
          collapsed_to: measurement_result
      }
    end

    defp apply_quantum_evolution(quantum_state, time_delta, decoherence_rate) do
      # Apply Schrödinger evolution with decoherence
      phase_evolution = quantum_state.phase + time_delta * 2.0 * :math.pi()
      amplitude_decay = quantum_state.amplitude * :math.exp(-decoherence_rate * time_delta)

      %{quantum_state | phase: phase_evolution, amplitude: amplitude_decay}
    end

    defp evolve_entanglements(entanglements, time_delta) do
      Enum.map(entanglements, fn entanglement ->
        # Entanglement strength decays over time
        new_strength = entanglement.strength * :math.exp(-0.01 * time_delta)
        %{entanglement | strength: new_strength}
      end)
    end

    defp calculate_phi_from_interaction(container, interaction_context) do
      # Update integrated information based on interaction
      base_phi = container.integrated_information
      interaction_complexity = estimate_interaction_complexity(interaction_context)

      base_phi + interaction_complexity * 0.1
    end

    defp update_global_workspace_access(container, interaction_context) do
      # Update global workspace access based on interaction patterns
      current_access = container.global_workspace_access
      attention_factor = extract_attention_factor(interaction_context)

      new_access = current_access + (attention_factor - current_access) * 0.1
      max(0.0, min(1.0, new_access))
    end

    defp update_phenomenal_binding(container, interaction_context) do
      # Update binding strength based on coherence of interactions
      current_binding = container.phenomenal_binding
      coherence_factor = extract_coherence_factor(interaction_context)

      new_binding = current_binding + (coherence_factor - current_binding) * 0.05
      max(0.0, min(1.0, new_binding))
    end

    defp update_synaptic_weights(weights, learning_signal, learning_rate) do
      Enum.map(weights, fn connection ->
        # Hebbian-like learning rule
        weight_delta = learning_rate * learning_signal * connection.plasticity
        new_weight = connection.weight + weight_delta

        %{connection | weight: new_weight}
      end)
    end

    defp simulate_structural_plasticity(weights, neurogenesis_rate, pruning_threshold) do
      # Prune weak connections
      pruned_weights = Enum.filter(weights, fn conn -> abs(conn.weight) > pruning_threshold end)

      # Add new connections (neurogenesis)
      num_new_connections = round(length(weights) * neurogenesis_rate)

      new_connections =
        for _ <- 1..num_new_connections do
          %{
            source: :rand.uniform(10),
            target: :rand.uniform(10),
            weight: :rand.normal() * 0.01,
            plasticity: :rand.uniform()
          }
        end

      {pruned_weights, new_connections}
    end

    defp determine_plasticity_state(_container, learning_signal) do
      signal_strength = abs(learning_signal)

      cond do
        signal_strength > 0.8 -> :highly_plastic
        signal_strength > 0.5 -> :moderately_plastic
        signal_strength > 0.2 -> :receptive
        true -> :stable
      end
    end

    defp measure_behavioral_complexity(container) do
      # Measure complexity of behavioral patterns
      num_behaviors = length(container.superposition_states)
      entropy = calculate_behavioral_entropy(container.superposition_states)

      (num_behaviors + entropy) / 2.0
    end

    defp calculate_optimal_complexity(environmental_pressure) do
      # Calculate optimal complexity for given environmental pressure
      0.5 + environmental_pressure * 0.3
    end

    defp adjust_criticality_state(current_state, organization_delta) do
      case {current_state, organization_delta} do
        {:ordered, delta} when delta > 0.1 -> :edge_of_chaos
        {:edge_of_chaos, delta} when delta > 0.2 -> :chaotic
        {:chaotic, delta} when delta < -0.1 -> :edge_of_chaos
        {:edge_of_chaos, delta} when delta < -0.2 -> :ordered
        _ -> current_state
      end
    end

    defp update_emergence_potential(container, environmental_pressure) do
      current_potential = container.emergence_potential
      pressure_factor = environmental_pressure
      complexity_factor = measure_behavioral_complexity(container)

      new_potential = (current_potential + pressure_factor + complexity_factor) / 3.0
      max(0.0, min(1.0, new_potential))
    end

    # Utility functions with simplified implementations
    defp extract_potential_behaviors(_module), do: [:reasoning, :memory, :attention, :learning]
    defp extract_module_semantics(_module), do: %{conceptual_weight: :rand.uniform()}
    defp extract_instance_semantics(_instance), do: %{operational_weight: :rand.uniform()}
    defp compute_semantic_signature(_module, _instance), do: "sig_#{:rand.uniform(1000)}"
    defp calculate_conceptual_curvature(_module), do: :rand.uniform()
    defp generate_coordinate_charts(_module), do: []
    defp estimate_information_content(_item), do: :rand.uniform()
    defp estimate_module_complexity(_module), do: :rand.uniform()
    defp estimate_integration_level(_module), do: :rand.uniform()
    defp compute_module_signature(_module), do: "mod_sig_#{:rand.uniform(1000)}"
    defp compute_instance_signature(_instance), do: "inst_sig_#{:rand.uniform(1000)}"
    defp calculate_semantic_correlation(_tensor1, _tensor2, _i, _j), do: :rand.uniform()
    defp calculate_efficiency(_container), do: :rand.uniform()
    defp calculate_accuracy(_container), do: :rand.uniform()
    defp calculate_adaptability(_container), do: :rand.uniform()
    defp calculate_conceptual_coherence(_container), do: :rand.uniform()
    defp calculate_semantic_drift(_container), do: :rand.uniform()
    defp estimate_interaction_complexity(_context), do: :rand.uniform()
    defp extract_attention_factor(_context), do: :rand.uniform()
    defp extract_coherence_factor(_context), do: :rand.uniform()

    defp calculate_weight_delta(old_weights, new_weights),
      do: length(new_weights) - length(old_weights)

    defp calculate_behavioral_entropy(_states), do: :rand.uniform()
    defp generate_basis_states(_module), do: []
  end

  ############################################
  #  CONSCIOUSNESS EMERGENCE ENGINE          #
  ############################################

  defmodule ConsciousnessEmergenceEngine do
    @moduledoc """
    Simulates the emergence of consciousness-like properties in cognitive
    container networks through global workspace theory, integrated information,
    and attention-based binding mechanisms.
    """

    defstruct [
      :global_workspace,
      :attention_mechanism,
      :binding_networks,
      :integration_thresholds,
      :consciousness_metrics,
      :emergence_history
    ]

    def new(opts \\ []) do
      %__MODULE__{
        global_workspace: initialize_global_workspace(opts),
        attention_mechanism: initialize_attention_mechanism(opts),
        binding_networks: %{},
        integration_thresholds: Keyword.get(opts, :thresholds, %{phi: 0.3, access: 0.5}),
        consciousness_metrics: %{},
        emergence_history: []
      }
    end

    def simulate_consciousness_emergence(engine, container_network) do
      # Simulate consciousness emergence across container network

      # 1. Global workspace competition
      {workspace_winners, updated_workspace} =
        simulate_global_workspace_competition(
          engine.global_workspace,
          container_network
        )

      # 2. Attention binding
      {binding_coalitions, updated_attention} =
        simulate_attention_binding(
          engine.attention_mechanism,
          workspace_winners
        )

      # 3. Integration calculation
      network_phi =
        calculate_network_integrated_information(container_network, binding_coalitions)

      # 4. Consciousness assessment
      consciousness_level =
        assess_consciousness_level(network_phi, binding_coalitions, engine.integration_thresholds)

      # 5. Update emergence history
      emergence_event = %{
        timestamp: DateTime.utc_now(),
        consciousness_level: consciousness_level,
        phi: network_phi,
        workspace_contents: workspace_winners,
        binding_coalitions: binding_coalitions,
        emergent_properties: detect_emergent_properties(container_network, consciousness_level)
      }

      updated_engine = %{
        engine
        | global_workspace: updated_workspace,
          attention_mechanism: updated_attention,
          consciousness_metrics:
            Map.put(engine.consciousness_metrics, :current_level, consciousness_level),
          emergence_history: [emergence_event | engine.emergence_history]
      }

      {consciousness_level, updated_engine}
    end

    def detect_phase_transitions(engine) do
      # Detect consciousness phase transitions in emergence history
      if length(engine.emergence_history) < 3 do
        {:ok, []}
      else
        recent_levels =
          engine.emergence_history
          |> Enum.take(10)
          |> Enum.map(& &1.consciousness_level)

        transitions = detect_level_transitions(recent_levels)
        {:ok, transitions}
      end
    end

    def enhance_consciousness(
          engine,
          container_network,
          enhancement_strategy \\ :attention_amplification
        ) do
      case enhancement_strategy do
        :attention_amplification ->
          enhance_via_attention_amplification(engine, container_network)

        :integration_strengthening ->
          enhance_via_integration_strengthening(engine, container_network)

        :workspace_expansion ->
          enhance_via_workspace_expansion(engine, container_network)

        :binding_optimization ->
          enhance_via_binding_optimization(engine, container_network)
      end
    end

    defp initialize_global_workspace(opts) do
      %{
        capacity: Keyword.get(opts, :workspace_capacity, 7),
        competition_strength: Keyword.get(opts, :competition_strength, 0.8),
        decay_rate: Keyword.get(opts, :workspace_decay, 0.1),
        current_contents: [],
        activation_history: []
      }
    end

    defp initialize_attention_mechanism(opts) do
      %{
        focus_strength: Keyword.get(opts, :focus_strength, 0.7),
        spotlight_width: Keyword.get(opts, :spotlight_width, 3),
        attention_trajectory: [],
        binding_threshold: Keyword.get(opts, :binding_threshold, 0.6)
      }
    end

    defp simulate_global_workspace_competition(workspace, container_network) do
      # Global workspace competition for consciousness access

      # Calculate activation levels for each container
      container_activations =
        Enum.map(container_network, fn container ->
          activation = calculate_container_activation(container, workspace.current_contents)
          {container, activation}
        end)

      # Sort by activation strength
      sorted_activations =
        Enum.sort_by(container_activations, fn {_container, activation} -> activation end, :desc)

      # Select winners based on workspace capacity
      winners =
        sorted_activations
        |> Enum.take(workspace.capacity)
        |> Enum.map(fn {container, activation} -> {container.id, activation} end)

      # Update workspace
      updated_workspace = %{
        workspace
        | current_contents: winners,
          activation_history: [winners | workspace.activation_history]
      }

      {winners, updated_workspace}
    end

    defp simulate_attention_binding(attention_mechanism, workspace_winners) do
      # Simulate attention-based binding of conscious contents

      # Create binding coalitions based on semantic similarity and attention focus
      coalitions = form_binding_coalitions(workspace_winners, attention_mechanism)

      # Update attention trajectory
      new_focus = calculate_attention_focus(coalitions)

      updated_attention = %{
        attention_mechanism
        | attention_trajectory: [new_focus | attention_mechanism.attention_trajectory]
      }

      {coalitions, updated_attention}
    end

    defp calculate_network_integrated_information(container_network, binding_coalitions) do
      # Calculate Φ (phi) for the entire network

      # Individual container phi values
      individual_phis =
        Enum.map(container_network, fn container ->
          container.integrated_information
        end)

      # Coalition integration bonuses
      coalition_bonuses =
        Enum.map(binding_coalitions, fn coalition ->
          calculate_coalition_integration(coalition)
        end)

      # Network-level integration
      base_phi = Enum.sum(individual_phis)
      coalition_phi = Enum.sum(coalition_bonuses)
      network_connectivity = calculate_network_connectivity(container_network)

      base_phi + coalition_phi + network_connectivity
    end

    defp assess_consciousness_level(network_phi, binding_coalitions, thresholds) do
      phi_level =
        cond do
          network_phi > thresholds.phi * 3 -> :high_consciousness
          network_phi > thresholds.phi * 2 -> :moderate_consciousness
          network_phi > thresholds.phi -> :low_consciousness
          true -> :non_conscious
        end

      coalition_strength = length(binding_coalitions) / 5.0

      overall_level =
        case {phi_level, coalition_strength} do
          {:high_consciousness, strength} when strength > 0.8 -> :unified_consciousness
          {:high_consciousness, _} -> :high_consciousness
          {:moderate_consciousness, strength} when strength > 0.6 -> :integrated_consciousness
          {:moderate_consciousness, _} -> :moderate_consciousness
          {:low_consciousness, _} -> :minimal_consciousness
          {:non_conscious, _} -> :non_conscious
        end

      overall_level
    end

    defp detect_emergent_properties(container_network, consciousness_level) do
      # Detect emergent properties based on consciousness level
      base_properties = [:information_integration, :global_access]

      enhanced_properties =
        case consciousness_level do
          :unified_consciousness ->
            base_properties ++
              [:self_awareness, :intentionality, :phenomenal_experience, :temporal_continuity]

          :high_consciousness ->
            base_properties ++ [:self_awareness, :intentionality, :limited_phenomenal_experience]

          :integrated_consciousness ->
            base_properties ++ [:self_awareness, :basic_intentionality]

          :moderate_consciousness ->
            base_properties ++ [:self_awareness]

          :minimal_consciousness ->
            base_properties

          :non_conscious ->
            []
        end

      # Add network-specific emergent properties
      network_properties = detect_network_emergent_properties(container_network)

      enhanced_properties ++ network_properties
    end

    defp calculate_container_activation(container, current_workspace_contents) do
      # Calculate how strongly a container should compete for workspace access

      base_activation = container.global_workspace_access

      # Recency bonus
      recency_bonus = if container.lifecycle_state == :active, do: 0.2, else: 0.0

      # Semantic relevance to current workspace contents
      relevance_bonus = calculate_workspace_relevance(container, current_workspace_contents)

      # Consciousness metrics bonus
      consciousness_bonus = container.integrated_information * 0.1

      base_activation + recency_bonus + relevance_bonus + consciousness_bonus
    end

    defp form_binding_coalitions(workspace_winners, attention_mechanism) do
      # Form coalitions of containers that bind together in consciousness

      # Group by semantic similarity
      semantic_groups = group_by_semantic_similarity(workspace_winners)

      # Apply attention filtering
      attention_filtered = apply_attention_filter(semantic_groups, attention_mechanism)

      # Form final coalitions
      Enum.map(attention_filtered, fn group ->
        %{
          members: group,
          binding_strength: calculate_group_binding_strength(group),
          coalition_id: generate_coalition_id(),
          formation_timestamp: DateTime.utc_now()
        }
      end)
    end

    defp calculate_attention_focus(coalitions) do
      # Calculate where attention is focusing based on coalitions
      if length(coalitions) > 0 do
        strongest_coalition = Enum.max_by(coalitions, & &1.binding_strength)

        %{
          focus_target: strongest_coalition.coalition_id,
          focus_strength: strongest_coalition.binding_strength,
          distributed_attention: length(coalitions) > 1
        }
      else
        %{focus_target: nil, focus_strength: 0.0, distributed_attention: false}
      end
    end

    defp calculate_coalition_integration(coalition) do
      # Calculate additional integration from coalition formation
      member_count = length(coalition.members)
      binding_strength = coalition.binding_strength

      member_count * binding_strength * 0.1
    end

    defp calculate_network_connectivity(container_network) do
      # Calculate overall network connectivity
      total_containers = length(container_network)

      total_entanglements =
        container_network
        |> Enum.map(fn container -> length(container.entangled_containers) end)
        |> Enum.sum()

      if total_containers > 1 do
        total_entanglements / (total_containers * (total_containers - 1))
      else
        0.0
      end
    end

    defp detect_level_transitions(levels) do
      # Detect transitions between consciousness levels
      Enum.zip(levels, tl(levels))
      |> Enum.with_index()
      |> Enum.filter(fn {{prev_level, curr_level}, _index} -> prev_level != curr_level end)
      |> Enum.map(fn {{prev_level, curr_level}, index} ->
        %{
          from: prev_level,
          to: curr_level,
          transition_index: index,
          transition_type: classify_transition(prev_level, curr_level)
        }
      end)
    end

    defp classify_transition(from_level, to_level) do
      consciousness_hierarchy = [
        :non_conscious,
        :minimal_consciousness,
        :moderate_consciousness,
        :integrated_consciousness,
        :high_consciousness,
        :unified_consciousness
      ]

      from_index = Enum.find_index(consciousness_hierarchy, &(&1 == from_level)) || 0
      to_index = Enum.find_index(consciousness_hierarchy, &(&1 == to_level)) || 0

      cond do
        to_index > from_index -> :emergence
        to_index < from_index -> :dissolution
        true -> :lateral_shift
      end
    end

    defp enhance_via_attention_amplification(engine, _container_network) do
      # Enhance consciousness by amplifying attention mechanisms
      enhanced_attention = %{
        engine.attention_mechanism
        | focus_strength: min(1.0, engine.attention_mechanism.focus_strength * 1.2),
          binding_threshold: engine.attention_mechanism.binding_threshold * 0.9
      }

      %{engine | attention_mechanism: enhanced_attention}
    end

    defp enhance_via_integration_strengthening(engine, _container_network) do
      # Enhance consciousness by strengthening integration thresholds
      enhanced_thresholds = %{
        engine.integration_thresholds
        | phi: engine.integration_thresholds.phi * 0.8,
          access: engine.integration_thresholds.access * 0.9
      }

      %{engine | integration_thresholds: enhanced_thresholds}
    end

    defp enhance_via_workspace_expansion(engine, _container_network) do
      # Enhance consciousness by expanding global workspace capacity
      enhanced_workspace = %{
        engine.global_workspace
        | capacity: min(15, engine.global_workspace.capacity + 2)
      }

      %{engine | global_workspace: enhanced_workspace}
    end

    defp enhance_via_binding_optimization(engine, _container_network) do
      # Enhance consciousness by optimizing binding mechanisms
      enhanced_attention = %{
        engine.attention_mechanism
        | spotlight_width: min(7, engine.attention_mechanism.spotlight_width + 1),
          binding_threshold: engine.attention_mechanism.binding_threshold * 0.95
      }

      %{engine | attention_mechanism: enhanced_attention}
    end

    # Utility functions with simplified implementations
    defp calculate_workspace_relevance(_container, _contents), do: :rand.uniform() * 0.3
    # Simplified grouping
    defp group_by_semantic_similarity(winners), do: [winners]
    defp apply_attention_filter(groups, _attention), do: groups
    defp calculate_group_binding_strength(_group), do: :rand.uniform()
    defp generate_coalition_id(), do: "coalition_#{:rand.uniform(1000)}"
    defp detect_network_emergent_properties(_network), do: [:network_coherence]
  end

  ############################################
  #  NEURAL PLASTICITY SIMULATOR            #
  ############################################

  defmodule NeuralPlasticitySimulator do
    @moduledoc """
    Simulates neural plasticity at the container level including synaptic
    plasticity, structural plasticity, homeostatic mechanisms, and
    metaplasticity (plasticity of plasticity).
    """

    defstruct [
      :plasticity_rules,
      :homeostatic_mechanisms,
      :metaplasticity_state,
      :structural_dynamics,
      :learning_history,
      :adaptation_thresholds
    ]

    def new(opts \\ []) do
      %__MODULE__{
        plasticity_rules: initialize_plasticity_rules(opts),
        homeostatic_mechanisms: initialize_homeostatic_mechanisms(opts),
        metaplasticity_state: %{threshold: 0.5, modification_history: []},
        structural_dynamics: %{growth_rate: 0.02, pruning_rate: 0.01},
        learning_history: [],
        adaptation_thresholds: Keyword.get(opts, :thresholds, %{ltp: 0.6, ltd: 0.3})
      }
    end

    def simulate_plasticity_episode(simulator, container, learning_context) do
      # Simulate a complete plasticity episode

      # 1. Assess current plasticity state
      plasticity_state = assess_plasticity_state(container, learning_context)

      # 2. Apply plasticity rules
      {updated_container, synaptic_changes} =
        apply_plasticity_rules(
          container,
          learning_context,
          simulator.plasticity_rules,
          plasticity_state
        )

      # 3. Homeostatic regulation
      {regulated_container, homeostatic_adjustments} =
        apply_homeostatic_regulation(
          updated_container,
          simulator.homeostatic_mechanisms
        )

      # 4. Structural plasticity
      {final_container, structural_changes} =
        apply_structural_plasticity(
          regulated_container,
          simulator.structural_dynamics
        )

      # 5. Metaplasticity updates
      updated_metaplasticity =
        update_metaplasticity(
          simulator.metaplasticity_state,
          learning_context,
          synaptic_changes
        )

      # 6. Update learning history
      learning_episode = %{
        timestamp: DateTime.utc_now(),
        learning_context: learning_context,
        plasticity_state: plasticity_state,
        synaptic_changes: synaptic_changes,
        homeostatic_adjustments: homeostatic_adjustments,
        structural_changes: structural_changes,
        effectiveness: calculate_learning_effectiveness(synaptic_changes, learning_context)
      }

      updated_simulator = %{
        simulator
        | metaplasticity_state: updated_metaplasticity,
          learning_history: [learning_episode | simulator.learning_history]
      }

      {final_container, updated_simulator, learning_episode}
    end

    def induce_metaplasticity(simulator, induction_protocol) do
      # Induce metaplasticity changes
      case induction_protocol do
        :theta_burst ->
          induce_theta_burst_metaplasticity(simulator)

        :stress_protocol ->
          induce_stress_metaplasticity(simulator)

        :novelty_exposure ->
          induce_novelty_metaplasticity(simulator)

        :sleep_consolidation ->
          induce_sleep_metaplasticity(simulator)
      end
    end

    def analyze_plasticity_dynamics(simulator) do
      # Analyze plasticity dynamics over learning history
      if length(simulator.learning_history) < 2 do
        {:ok, %{insufficient_data: true}}
      else
        dynamics_analysis = %{
          learning_curve: extract_learning_curve(simulator.learning_history),
          plasticity_trends: analyze_plasticity_trends(simulator.learning_history),
          critical_periods: identify_critical_periods(simulator.learning_history),
          metaplasticity_evolution:
            analyze_metaplasticity_evolution(simulator.metaplasticity_state),
          homeostatic_stability: assess_homeostatic_stability(simulator.learning_history)
        }

        {:ok, dynamics_analysis}
      end
    end

    defp initialize_plasticity_rules(opts) do
      %{
        hebbian: %{
          strength: Keyword.get(opts, :hebbian_strength, 0.01),
          decay: Keyword.get(opts, :hebbian_decay, 0.001)
        },
        spike_timing: %{
          ltp_window: Keyword.get(opts, :ltp_window, 20),
          ltd_window: Keyword.get(opts, :ltd_window, 40),
          temporal_precision: Keyword.get(opts, :temporal_precision, 1.0)
        },
        # Bienenstock-Cooper-Munro rule
        bcm: %{
          threshold_sliding: true,
          modification_threshold: 0.5,
          threshold_adaptation_rate: 0.001
        },
        calcium_dependent: %{
          ca_threshold_low: 0.3,
          ca_threshold_high: 0.7,
          ca_kinetics: :exponential_decay
        }
      }
    end

    defp initialize_homeostatic_mechanisms(opts) do
      %{
        synaptic_scaling: %{
          enabled: Keyword.get(opts, :synaptic_scaling, true),
          target_activity: 0.5,
          scaling_rate: 0.001,
          scaling_window: 1000
        },
        intrinsic_excitability: %{
          enabled: Keyword.get(opts, :intrinsic_plasticity, true),
          target_firing_rate: 0.1,
          adaptation_rate: 0.0001
        },
        inhibitory_plasticity: %{
          enabled: true,
          balance_ratio: 0.8,
          adaptation_strength: 0.005
        }
      }
    end

    defp assess_plasticity_state(container, learning_context) do
      # Assess current plasticity state
      %{
        synaptic_strength_distribution: analyze_weight_distribution(container.synaptic_weights),
        activity_level: calculate_activity_level(container, learning_context),
        plasticity_saturation: assess_plasticity_saturation(container),
        metaplasticity_priming: assess_metaplasticity_priming(container),
        developmental_stage: assess_developmental_stage(container)
      }
    end

    defp apply_plasticity_rules(container, learning_context, plasticity_rules, plasticity_state) do
      # Apply multiple plasticity rules

      initial_weights = container.synaptic_weights
      learning_signal = extract_learning_signal(learning_context)

      # Hebbian plasticity
      {hebbian_weights, hebbian_changes} =
        apply_hebbian_plasticity(
          initial_weights,
          learning_signal,
          plasticity_rules.hebbian
        )

      # Spike-timing dependent plasticity
      {stdp_weights, stdp_changes} =
        apply_stdp(
          hebbian_weights,
          learning_context,
          plasticity_rules.spike_timing
        )

      # BCM rule
      {bcm_weights, bcm_changes} =
        apply_bcm_rule(
          stdp_weights,
          plasticity_state,
          plasticity_rules.bcm
        )

      # Calcium-dependent plasticity
      {final_weights, calcium_changes} =
        apply_calcium_dependent_plasticity(
          bcm_weights,
          learning_context,
          plasticity_rules.calcium_dependent
        )

      updated_container = %{container | synaptic_weights: final_weights}

      total_changes = %{
        hebbian: hebbian_changes,
        stdp: stdp_changes,
        bcm: bcm_changes,
        calcium: calcium_changes
      }

      {updated_container, total_changes}
    end

    defp apply_homeostatic_regulation(container, homeostatic_mechanisms) do
      # Apply homeostatic plasticity mechanisms

      current_weights = container.synaptic_weights

      # Synaptic scaling
      {scaled_weights, scaling_adjustments} =
        if homeostatic_mechanisms.synaptic_scaling.enabled do
          apply_synaptic_scaling(current_weights, homeostatic_mechanisms.synaptic_scaling)
        else
          {current_weights, %{}}
        end

      # Intrinsic excitability plasticity
      {final_weights, excitability_adjustments} =
        if homeostatic_mechanisms.intrinsic_excitability.enabled do
          apply_intrinsic_plasticity(
            scaled_weights,
            homeostatic_mechanisms.intrinsic_excitability
          )
        else
          {scaled_weights, %{}}
        end

      updated_container = %{container | synaptic_weights: final_weights}

      adjustments = %{
        synaptic_scaling: scaling_adjustments,
        intrinsic_excitability: excitability_adjustments
      }

      {updated_container, adjustments}
    end

    defp apply_structural_plasticity(container, structural_dynamics) do
      # Apply structural plasticity (synaptogenesis and pruning)

      current_weights = container.synaptic_weights

      # Pruning weak synapses
      {pruned_weights, pruned_synapses} =
        prune_weak_synapses(
          current_weights,
          structural_dynamics.pruning_rate
        )

      # Growing new synapses
      {final_weights, new_synapses} =
        grow_new_synapses(
          pruned_weights,
          structural_dynamics.growth_rate,
          container
        )

      updated_container = %{container | synaptic_weights: final_weights}

      structural_changes = %{
        pruned_synapses: pruned_synapses,
        new_synapses: new_synapses,
        net_change: length(new_synapses) - length(pruned_synapses)
      }

      {updated_container, structural_changes}
    end

    defp update_metaplasticity(metaplasticity_state, learning_context, synaptic_changes) do
      # Update metaplasticity state based on learning experience

      learning_magnitude = calculate_learning_magnitude(synaptic_changes)

      context_novelty =
        assess_context_novelty(learning_context, metaplasticity_state.modification_history)

      # Update metaplasticity threshold
      threshold_delta = calculate_threshold_delta(learning_magnitude, context_novelty)
      new_threshold = metaplasticity_state.threshold + threshold_delta

      # Add to modification history
      modification_record = %{
        timestamp: DateTime.utc_now(),
        learning_context: learning_context,
        learning_magnitude: learning_magnitude,
        threshold_change: threshold_delta
      }

      %{
        metaplasticity_state
        | threshold: max(0.1, min(0.9, new_threshold)),
          modification_history: [modification_record | metaplasticity_state.modification_history]
      }
    end

    defp calculate_learning_effectiveness(synaptic_changes, learning_context) do
      # Calculate how effective the learning episode was

      total_magnitude = calculate_learning_magnitude(synaptic_changes)
      context_appropriateness = assess_context_appropriateness(learning_context)
      homeostatic_balance = assess_homeostatic_balance(synaptic_changes)

      (total_magnitude + context_appropriateness + homeostatic_balance) / 3.0
    end

    defp induce_theta_burst_metaplasticity(simulator) do
      # Simulate theta-burst stimulation effects on metaplasticity
      enhanced_plasticity = %{
        simulator.plasticity_rules
        | hebbian: %{
            simulator.plasticity_rules.hebbian
            | strength: simulator.plasticity_rules.hebbian.strength * 1.5
          }
      }

      %{simulator | plasticity_rules: enhanced_plasticity}
    end

    defp induce_stress_metaplasticity(simulator) do
      # Simulate stress effects on metaplasticity
      stress_modified = %{
        simulator.metaplasticity_state
        | threshold: simulator.metaplasticity_state.threshold * 1.2
      }

      %{simulator | metaplasticity_state: stress_modified}
    end

    defp induce_novelty_metaplasticity(simulator) do
      # Simulate novelty effects on metaplasticity
      novelty_enhanced = %{
        simulator.plasticity_rules
        | spike_timing: %{
            simulator.plasticity_rules.spike_timing
            | temporal_precision: simulator.plasticity_rules.spike_timing.temporal_precision * 1.3
          }
      }

      %{simulator | plasticity_rules: novelty_enhanced}
    end

    defp induce_sleep_metaplasticity(simulator) do
      # Simulate sleep consolidation effects
      sleep_modified = %{
        simulator.homeostatic_mechanisms
        | synaptic_scaling: %{
            simulator.homeostatic_mechanisms.synaptic_scaling
            | scaling_rate: simulator.homeostatic_mechanisms.synaptic_scaling.scaling_rate * 0.5
          }
      }

      %{simulator | homeostatic_mechanisms: sleep_modified}
    end

    # Analysis functions
    defp extract_learning_curve(learning_history) do
      learning_history
      |> Enum.reverse()
      |> Enum.with_index()
      |> Enum.map(fn {episode, index} -> {index, episode.effectiveness} end)
    end

    defp analyze_plasticity_trends(learning_history) do
      effectiveness_values = Enum.map(learning_history, & &1.effectiveness)

      %{
        mean_effectiveness: Enum.sum(effectiveness_values) / length(effectiveness_values),
        trend: calculate_trend(effectiveness_values),
        variance: calculate_variance(effectiveness_values)
      }
    end

    defp identify_critical_periods(learning_history) do
      # Identify periods of enhanced plasticity
      learning_history
      |> Enum.with_index()
      |> Enum.filter(fn {episode, _index} -> episode.effectiveness > 0.8 end)
      |> Enum.map(fn {episode, index} ->
        %{
          index: index,
          timestamp: episode.timestamp,
          effectiveness: episode.effectiveness
        }
      end)
    end

    defp analyze_metaplasticity_evolution(metaplasticity_state) do
      threshold_history =
        metaplasticity_state.modification_history
        |> Enum.map(& &1.threshold_change)

      %{
        current_threshold: metaplasticity_state.threshold,
        threshold_changes: threshold_history,
        plasticity_direction: determine_plasticity_direction(threshold_history)
      }
    end

    defp assess_homeostatic_stability(learning_history) do
      # Assess stability of homeostatic mechanisms
      recent_episodes = Enum.take(learning_history, 10)

      stability_measure =
        recent_episodes
        |> Enum.map(& &1.homeostatic_adjustments)
        |> calculate_adjustment_stability()

      %{
        stability_score: stability_measure,
        stability_level: classify_stability(stability_measure)
      }
    end

    # Utility functions with simplified implementations
    defp analyze_weight_distribution(weights),
      do: %{mean: Enum.sum(Enum.map(weights, & &1.weight)) / length(weights)}

    defp calculate_activity_level(_container, _context), do: :rand.uniform()
    defp assess_plasticity_saturation(_container), do: :rand.uniform()
    defp assess_metaplasticity_priming(_container), do: :rand.uniform()
    defp assess_developmental_stage(_container), do: :adult
    defp extract_learning_signal(_context), do: :rand.uniform()
    defp apply_hebbian_plasticity(weights, signal, _rules), do: {weights, %{magnitude: signal}}
    defp apply_stdp(weights, _context, _rules), do: {weights, %{magnitude: :rand.uniform()}}
    defp apply_bcm_rule(weights, _state, _rules), do: {weights, %{magnitude: :rand.uniform()}}

    defp apply_calcium_dependent_plasticity(weights, _context, _rules),
      do: {weights, %{magnitude: :rand.uniform()}}

    defp apply_synaptic_scaling(weights, _params), do: {weights, %{scaling_factor: 1.0}}
    defp apply_intrinsic_plasticity(weights, _params), do: {weights, %{excitability_change: 0.0}}

    defp prune_weak_synapses(weights, pruning_rate) do
      num_to_prune = round(length(weights) * pruning_rate)
      {weights, Enum.take(weights, num_to_prune)}
    end

    defp grow_new_synapses(weights, growth_rate, _container) do
      num_to_grow = round(length(weights) * growth_rate)

      new_synapses =
        for _ <- 1..num_to_grow,
            do: %{
              source: :rand.uniform(10),
              target: :rand.uniform(10),
              weight: :rand.normal() * 0.01,
              plasticity: :rand.uniform()
            }

      {weights ++ new_synapses, new_synapses}
    end

    defp calculate_learning_magnitude(_changes), do: :rand.uniform()
    defp assess_context_novelty(_context, _history), do: :rand.uniform()
    defp calculate_threshold_delta(magnitude, novelty), do: (magnitude + novelty) * 0.01 - 0.01
    defp assess_context_appropriateness(_context), do: :rand.uniform()
    defp assess_homeostatic_balance(_changes), do: :rand.uniform()

    defp calculate_trend(values) do
      if length(values) < 2,
        do: 0.0,
        else: (List.last(values) - List.first(values)) / length(values)
    end

    defp calculate_variance(values) do
      mean = Enum.sum(values) / length(values)
      squared_diffs = values |> Enum.map(fn x -> (x - mean) * (x - mean) end)
      variance = Enum.sum(squared_diffs) / length(values)
      variance
    end

    defp determine_plasticity_direction(changes) do
      if Enum.sum(changes) > 0, do: :increasing, else: :decreasing
    end

    defp calculate_adjustment_stability(_adjustments), do: :rand.uniform()
    defp classify_stability(score) when score > 0.7, do: :stable
    defp classify_stability(score) when score > 0.4, do: :moderately_stable
    defp classify_stability(_), do: :unstable
  end

  ############################################
  #  ADVANCED SEMANTIC HYPERGRAPH           #
  ############################################

  defmodule AdvancedSemanticHypergraph do
    @moduledoc """
    Hypergraph-based semantic representation that captures complex multi-way
    relationships between cognitive concepts, enabling sophisticated semantic
    reasoning and concept emergence detection.
    """

    defstruct [
      # Concept nodes
      :nodes,
      # Multi-way relationships
      :hyperedges,
      # Hypergraph topology metrics
      :topology,
      # Temporal evolution
      :dynamics,
      # Continuous semantic fields
      :semantic_fields,
      # Areas of concept emergence
      :emergence_zones
    ]

    def new(_opts \\ []) do
      %__MODULE__{
        nodes: %{},
        hyperedges: %{},
        topology: initialize_topology_metrics(),
        dynamics: initialize_dynamics_state(),
        semantic_fields: %{},
        emergence_zones: []
      }
    end

    def add_concept_node(hypergraph, concept_id, concept_data) do
      node = %{
        id: concept_id,
        concept_data: concept_data,
        embedding: generate_concept_embedding(concept_data),
        activation_level: 0.0,
        semantic_neighborhood: [],
        temporal_signature: [],
        emergence_potential: 0.0
      }

      updated_nodes = Map.put(hypergraph.nodes, concept_id, node)
      updated_topology = update_topology_metrics(hypergraph.topology, :node_added)

      %{hypergraph | nodes: updated_nodes, topology: updated_topology}
    end

    def create_hyperedge(hypergraph, edge_id, node_ids, relationship_type, strength \\ 1.0) do
      hyperedge = %{
        id: edge_id,
        nodes: node_ids,
        relationship_type: relationship_type,
        strength: strength,
        semantic_signature: compute_edge_semantic_signature(node_ids, hypergraph.nodes),
        temporal_coherence: 1.0,
        emergence_indicator: 0.0,
        activation_pattern: []
      }

      updated_hyperedges = Map.put(hypergraph.hyperedges, edge_id, hyperedge)
      updated_topology = update_topology_metrics(hypergraph.topology, :edge_added)

      # Update node neighborhoods
      updated_nodes = update_node_neighborhoods(hypergraph.nodes, node_ids, edge_id)

      %{
        hypergraph
        | hyperedges: updated_hyperedges,
          nodes: updated_nodes,
          topology: updated_topology
      }
    end

    def propagate_semantic_activation(hypergraph, source_nodes, activation_strength \\ 1.0) do
      # Propagate activation through the hypergraph
      initial_activations =
        initialize_activations(hypergraph.nodes, source_nodes, activation_strength)

      # Iterative propagation
      final_activations =
        propagate_iteratively(
          initial_activations,
          hypergraph.hyperedges,
          max_iterations: 10,
          convergence_threshold: 0.01
        )

      # Update node activations
      updated_nodes = update_node_activations(hypergraph.nodes, final_activations)

      # Record activation pattern
      activation_pattern = %{
        timestamp: DateTime.utc_now(),
        source_nodes: source_nodes,
        final_activations: final_activations,
        propagation_path: trace_propagation_path(hypergraph, source_nodes, final_activations)
      }

      updated_dynamics = record_activation_pattern(hypergraph.dynamics, activation_pattern)

      %{hypergraph | nodes: updated_nodes, dynamics: updated_dynamics}
    end

    def detect_concept_emergence(hypergraph, detection_threshold \\ 0.7) do
      # Detect emerging concepts in the hypergraph

      # Analyze activation patterns for emergence signatures
      emergence_candidates = analyze_activation_patterns_for_emergence(hypergraph.dynamics)

      # Check topological emergence indicators
      topological_emergence =
        detect_topological_emergence(hypergraph.topology, hypergraph.hyperedges)

      # Semantic field analysis
      field_emergence = analyze_semantic_field_emergence(hypergraph.semantic_fields)

      # Combine evidence
      emerging_concepts =
        combine_emergence_evidence(
          emergence_candidates,
          topological_emergence,
          field_emergence,
          detection_threshold
        )

      # Update emergence zones
      updated_emergence_zones =
        update_emergence_zones(hypergraph.emergence_zones, emerging_concepts)

      {emerging_concepts, %{hypergraph | emergence_zones: updated_emergence_zones}}
    end

    def evolve_semantic_fields(hypergraph, time_step) do
      # Evolve continuous semantic fields over time

      # Update field potentials based on node activations
      updated_fields =
        Enum.reduce(hypergraph.nodes, hypergraph.semantic_fields, fn {node_id, node}, fields ->
          update_semantic_field_at_node(fields, node_id, node, time_step)
        end)

      # Apply field dynamics (diffusion, interference, etc.)
      evolved_fields = apply_field_dynamics(updated_fields, time_step)

      # Detect field instabilities and bifurcations
      {field_events, stabilized_fields} = detect_field_events(evolved_fields)

      updated_dynamics = record_field_events(hypergraph.dynamics, field_events)

      %{hypergraph | semantic_fields: stabilized_fields, dynamics: updated_dynamics}
    end

    def compute_semantic_similarity(hypergraph, node_id1, node_id2) do
      # Compute semantic similarity using hypergraph structure

      node1 = Map.get(hypergraph.nodes, node_id1)
      node2 = Map.get(hypergraph.nodes, node_id2)

      if node1 && node2 do
        # Direct embedding similarity
        embedding_similarity = cosine_similarity(node1.embedding, node2.embedding)

        # Structural similarity (shared hyperedges)
        structural_similarity =
          compute_structural_similarity(node_id1, node_id2, hypergraph.hyperedges)

        # Semantic field correlation
        field_similarity =
          compute_field_similarity(node_id1, node_id2, hypergraph.semantic_fields)

        # Activation correlation
        activation_similarity = compute_activation_correlation(node1, node2)

        # Weighted combination
        0.3 * embedding_similarity +
          0.3 * structural_similarity +
          0.2 * field_similarity +
          0.2 * activation_similarity
      else
        0.0
      end
    end

    def find_semantic_paths(hypergraph, source_node, target_node, max_path_length \\ 5) do
      # Find semantic paths through the hypergraph

      paths =
        breadth_first_hypergraph_search(
          hypergraph,
          source_node,
          target_node,
          max_path_length
        )

      # Score paths by semantic coherence
      scored_paths =
        Enum.map(paths, fn path ->
          coherence_score = calculate_path_semantic_coherence(path, hypergraph)
          {path, coherence_score}
        end)

      # Return top paths
      scored_paths
      |> Enum.sort_by(fn {_path, score} -> score end, :desc)
      |> Enum.take(10)
    end

    def analyze_hypergraph_topology(hypergraph) do
      # Comprehensive topological analysis

      %{
        node_count: map_size(hypergraph.nodes),
        hyperedge_count: map_size(hypergraph.hyperedges),
        average_hyperedge_size: calculate_average_hyperedge_size(hypergraph.hyperedges),
        clustering_coefficient: calculate_hypergraph_clustering(hypergraph),
        diameter: calculate_hypergraph_diameter(hypergraph),
        connectivity: calculate_hypergraph_connectivity(hypergraph),
        emergence_zones: length(hypergraph.emergence_zones),
        topological_complexity: calculate_topological_complexity(hypergraph),
        semantic_density: calculate_semantic_density(hypergraph)
      }
    end

    # Private implementation functions

    defp initialize_topology_metrics do
      %{
        nodes_added: 0,
        edges_added: 0,
        average_degree: 0.0,
        clustering_coefficient: 0.0,
        small_world_coefficient: 0.0
      }
    end

    defp initialize_dynamics_state do
      %{
        activation_history: [],
        field_evolution: [],
        emergence_events: [],
        temporal_coherence: 1.0
      }
    end

    defp generate_concept_embedding(concept_data) do
      # Generate high-dimensional embedding for concept
      concept_text = concept_data |> inspect()

      # Simplified embedding generation
      :crypto.hash(:sha256, concept_text)
      |> :binary.bin_to_list()
      |> Enum.take(128)
      |> Enum.map(fn x -> (x - 128) / 128.0 end)
    end

    defp update_topology_metrics(topology, event) do
      case event do
        :node_added ->
          %{topology | nodes_added: topology.nodes_added + 1}

        :edge_added ->
          %{topology | edges_added: topology.edges_added + 1}

        _ ->
          topology
      end
    end

    defp compute_edge_semantic_signature(node_ids, nodes) do
      # Compute semantic signature for hyperedge
      node_embeddings =
        Enum.map(node_ids, fn id ->
          case Map.get(nodes, id) do
            nil -> List.duplicate(0.0, 128)
            node -> node.embedding
          end
        end)

      # Average embeddings (simplified)
      if length(node_embeddings) > 0 do
        embedding_length = length(List.first(node_embeddings))

        for i <- 0..(embedding_length - 1) do
          sum = Enum.sum(Enum.map(node_embeddings, fn emb -> Enum.at(emb, i) end))
          sum / length(node_embeddings)
        end
      else
        List.duplicate(0.0, 128)
      end
    end

    defp update_node_neighborhoods(nodes, node_ids, edge_id) do
      Enum.reduce(node_ids, nodes, fn node_id, acc_nodes ->
        case Map.get(acc_nodes, node_id) do
          nil ->
            acc_nodes

          node ->
            updated_neighborhood = [edge_id | node.semantic_neighborhood] |> Enum.uniq()
            updated_node = %{node | semantic_neighborhood: updated_neighborhood}
            Map.put(acc_nodes, node_id, updated_node)
        end
      end)
    end

    defp initialize_activations(nodes, source_nodes, activation_strength) do
      Enum.reduce(nodes, %{}, fn {node_id, _node}, activations ->
        activation =
          if node_id in source_nodes do
            activation_strength
          else
            0.0
          end

        Map.put(activations, node_id, activation)
      end)
    end

    defp propagate_iteratively(activations, hyperedges, opts) do
      max_iterations = Keyword.get(opts, :max_iterations, 10)
      convergence_threshold = Keyword.get(opts, :convergence_threshold, 0.01)

      Enum.reduce_while(1..max_iterations, activations, fn _iteration, current_activations ->
        new_activations = propagate_one_step(current_activations, hyperedges)

        # Check convergence
        max_change = calculate_max_activation_change(current_activations, new_activations)

        if max_change < convergence_threshold do
          {:halt, new_activations}
        else
          {:cont, new_activations}
        end
      end)
    end

    defp propagate_one_step(activations, hyperedges) do
      # One step of activation propagation
      new_activations = Map.new(activations)

      Enum.reduce(hyperedges, new_activations, fn {_edge_id, hyperedge}, acc_activations ->
        edge_activation = calculate_hyperedge_activation(hyperedge.nodes, activations)
        propagated_activation = edge_activation * hyperedge.strength * 0.1

        Enum.reduce(hyperedge.nodes, acc_activations, fn node_id, inner_acc ->
          current_activation = Map.get(inner_acc, node_id, 0.0)
          updated_activation = current_activation + propagated_activation
          Map.put(inner_acc, node_id, updated_activation)
        end)
      end)
    end

    defp calculate_hyperedge_activation(node_ids, activations) do
      node_activations = Enum.map(node_ids, fn id -> Map.get(activations, id, 0.0) end)

      if length(node_activations) > 0 do
        Enum.sum(node_activations) / length(node_activations)
      else
        0.0
      end
    end

    defp calculate_max_activation_change(old_activations, new_activations) do
      changes =
        Enum.map(old_activations, fn {node_id, old_value} ->
          new_value = Map.get(new_activations, node_id, 0.0)
          abs(new_value - old_value)
        end)

      if length(changes) > 0 do
        Enum.max(changes)
      else
        0.0
      end
    end

    defp update_node_activations(nodes, final_activations) do
      Enum.reduce(nodes, %{}, fn {node_id, node}, updated_nodes ->
        activation = Map.get(final_activations, node_id, 0.0)
        updated_node = %{node | activation_level: activation}
        Map.put(updated_nodes, node_id, updated_node)
      end)
    end

    defp trace_propagation_path(_hypergraph, _source_nodes, _final_activations) do
      # Simplified path tracing
      []
    end

    defp record_activation_pattern(dynamics, pattern) do
      %{dynamics | activation_history: [pattern | dynamics.activation_history]}
    end

    # Utility functions with simplified implementations
    defp analyze_activation_patterns_for_emergence(_dynamics), do: []
    defp detect_topological_emergence(_topology, _hyperedges), do: []
    defp analyze_semantic_field_emergence(_fields), do: []
    defp combine_emergence_evidence(_candidates, _topological, _field, _threshold), do: []
    defp update_emergence_zones(zones, _emerging), do: zones
    defp update_semantic_field_at_node(fields, _node_id, _node, _time_step), do: fields
    defp apply_field_dynamics(fields, _time_step), do: fields
    defp detect_field_events(fields), do: {[], fields}
    defp record_field_events(dynamics, _events), do: dynamics

    defp cosine_similarity(vec1, vec2) when length(vec1) == length(vec2) do
      dot_product = Enum.zip(vec1, vec2) |> Enum.map(fn {a, b} -> a * b end) |> Enum.sum()
      norm1 = :math.sqrt(Enum.map(vec1, fn x -> x * x end) |> Enum.sum())
      norm2 = :math.sqrt(Enum.map(vec2, fn x -> x * x end) |> Enum.sum())

      if norm1 > 0 and norm2 > 0 do
        dot_product / (norm1 * norm2)
      else
        0.0
      end
    end

    defp cosine_similarity(_, _), do: 0.0
    defp compute_structural_similarity(_id1, _id2, _edges), do: :rand.uniform()
    defp compute_field_similarity(_id1, _id2, _fields), do: :rand.uniform()
    defp compute_activation_correlation(_node1, _node2), do: :rand.uniform()
    defp breadth_first_hypergraph_search(_hypergraph, _source, _target, _max_length), do: []
    defp calculate_path_semantic_coherence(_path, _hypergraph), do: :rand.uniform()

    defp calculate_average_hyperedge_size(hyperedges) do
      if map_size(hyperedges) > 0 do
        total_size =
          hyperedges |> Map.values() |> Enum.map(fn edge -> length(edge.nodes) end) |> Enum.sum()

        total_size / map_size(hyperedges)
      else
        0.0
      end
    end

    defp calculate_hypergraph_clustering(_hypergraph), do: :rand.uniform()
    defp calculate_hypergraph_diameter(_hypergraph), do: :rand.uniform() * 10
    defp calculate_hypergraph_connectivity(_hypergraph), do: :rand.uniform()
    defp calculate_topological_complexity(_hypergraph), do: :rand.uniform()
    defp calculate_semantic_density(_hypergraph), do: :rand.uniform()
  end
end
