defmodule Foundation.MABEAM.Coordination.Auction do
  @moduledoc """
  Auction-based coordination where agents bid for resources or parameter values.
  """
  
  @doc """
  Run an auction for variable value selection.
  
  ## Examples
      
      # Agents bid for their preferred temperature setting
      bids = [
        {coder_agent, 0.1, bid: 5.0, reason: "Need precision for code"},
        {creative_agent, 1.2, bid: 8.0, reason: "Need creativity for design"},  
        {reviewer_agent, 0.3, bid: 3.0, reason: "Need balance for review"}
      ]
      
      {:ok, result} = Auction.run_auction(:temperature, bids, 
        auction_type: :sealed_bid,
        payment_rule: :second_price
      )
  """
  @spec run_auction(atom(), [bid()], keyword()) :: {:ok, auction_result()} | {:error, term()}
  def run_auction(variable_id, bids, opts \\ []) do
    auction_type = Keyword.get(opts, :auction_type, :sealed_bid)
    payment_rule = Keyword.get(opts, :payment_rule, :first_price)
    
    case auction_type do
      :sealed_bid -> run_sealed_bid_auction(variable_id, bids, payment_rule)
      :english -> run_english_auction(variable_id, bids, opts)
      :dutch -> run_dutch_auction(variable_id, bids, opts)
      :combinatorial -> run_combinatorial_auction(variable_id, bids, opts)
    end
  end
  
  defp run_sealed_bid_auction(variable_id, bids, payment_rule) do
    # Sort bids by amount (highest first)
    sorted_bids = Enum.sort_by(bids, fn {_agent, _value, opts} -> 
      Keyword.get(opts, :bid, 0.0) 
    end, :desc)
    
    case sorted_bids do
      [] -> {:error, :no_bids}
      [{winner_agent, winner_value, winner_opts} | rest] ->
        # Determine payment based on rule
        payment = case payment_rule do
          :first_price -> Keyword.get(winner_opts, :bid, 0.0)
          :second_price -> 
            case rest do
              [] -> Keyword.get(winner_opts, :bid, 0.0)
              [{_second_agent, _second_value, second_opts} | _] ->
                Keyword.get(second_opts, :bid, 0.0)
            end
        end
        
        result = %{
          winner: winner_agent,
          winning_value: winner_value,
          payment: payment,
          auction_type: :sealed_bid,
          participants: length(bids),
          efficiency: calculate_auction_efficiency(sorted_bids)
        }
        
        {:ok, result}
    end
  end
  
  defp run_english_auction(variable_id, bids, opts) do
    # Ascending price auction with multiple rounds
    starting_price = Keyword.get(opts, :starting_price, 0.0)
    increment = Keyword.get(opts, :increment, 0.1)
    max_rounds = Keyword.get(opts, :max_rounds, 10)
    
    # Simulate ascending auction rounds
    final_result = Enum.reduce(1..max_rounds, {bids, starting_price}, fn round, {active_bids, current_price} ->
      # Filter agents willing to bid at current price
      willing_bidders = Enum.filter(active_bids, fn {agent, value, opts} ->
        max_bid = Keyword.get(opts, :max_bid, Keyword.get(opts, :bid, 0.0))
        max_bid >= current_price
      end)
      
      case length(willing_bidders) do
        0 -> {[], current_price - increment}  # No bidders, auction ends
        1 -> {willing_bidders, current_price}  # One bidder left, they win
        _ -> {willing_bidders, current_price + increment}  # Continue auction
      end
    end)
    
    case final_result do
      {[], final_price} -> {:error, :no_winning_bid}
      {[{winner_agent, winner_value, _opts}], final_price} ->
        result = %{
          winner: winner_agent,
          winning_value: winner_value,
          payment: final_price,
          auction_type: :english,
          rounds: max_rounds,
          participants: length(bids)
        }
        {:ok, result}
      _ -> {:error, :auction_timeout}
    end
  end
end

defmodule Foundation.MABEAM.Coordination.Consensus do
  @moduledoc """
  Consensus mechanisms for multi-agent decision making.
  """
  
  @doc """
  Achieve consensus using various algorithms.
  
  ## Examples
      
      # Simple majority voting
      {:ok, result} = Consensus.achieve_consensus(
        [agent1, agent2, agent3, agent4, agent5],
        %{temperature: 0.7, max_tokens: 1000},
        algorithm: :simple_majority
      )
      
      # Weighted voting based on agent expertise
      {:ok, result} = Consensus.achieve_consensus(
        agents_with_weights,
        proposed_changes,
        algorithm: :weighted_voting,
        weights: %{expert_agent: 3.0, novice_agent: 1.0}
      )
  """
  @spec achieve_consensus([atom()], map(), keyword()) :: {:ok, consensus_result()} | {:error, term()}
  def achieve_consensus(agents, proposed_changes, opts \\ []) do
    algorithm = Keyword.get(opts, :algorithm, :simple_majority)
    timeout = Keyword.get(opts, :timeout, 30_000)
    
    case algorithm do
      :simple_majority -> simple_majority_consensus(agents, proposed_changes, opts)
      :weighted_voting -> weighted_voting_consensus(agents, proposed_changes, opts)
      :unanimous -> unanimous_consensus(agents, proposed_changes, opts)
      :raft -> raft_consensus(agents, proposed_changes, opts)
      :pbft -> pbft_consensus(agents, proposed_changes, opts)
      :blockchain -> blockchain_consensus(agents, proposed_changes, opts)
    end
  end
  
  defp simple_majority_consensus(agents, proposed_changes, opts) do
    # Collect votes from all agents
    votes = collect_votes(agents, proposed_changes, opts)
    
    # Count votes for each proposal
    vote_counts = Enum.reduce(votes, %{}, fn {_agent, vote}, acc ->
      Map.update(acc, vote, 1, &(&1 + 1))
    end)
    
    # Find majority winner
    total_votes = length(votes)
    majority_threshold = div(total_votes, 2) + 1
    
    winner = vote_counts
    |> Enum.filter(fn {_proposal, count} -> count >= majority_threshold end)
    |> Enum.max_by(fn {_proposal, count} -> count end, fn -> nil end)
    
    case winner do
      nil -> {:error, :no_majority}
      {winning_proposal, vote_count} ->
        result = %{
          consensus: winning_proposal,
          algorithm: :simple_majority,
          vote_count: vote_count,
          total_voters: total_votes,
          confidence: vote_count / total_votes
        }
        {:ok, result}
    end
  end
  
  defp weighted_voting_consensus(agents, proposed_changes, opts) do
    weights = Keyword.get(opts, :weights, %{})
    votes = collect_weighted_votes(agents, proposed_changes, weights, opts)
    
    # Calculate weighted vote totals
    weighted_totals = Enum.reduce(votes, %{}, fn {agent, vote, weight}, acc ->
      Map.update(acc, vote, weight, &(&1 + weight))
    end)
    
    # Find weighted majority
    total_weight = Enum.sum(Map.values(weights))
    majority_threshold = total_weight / 2.0
    
    winner = weighted_totals
    |> Enum.filter(fn {_proposal, weight} -> weight > majority_threshold end)
    |> Enum.max_by(fn {_proposal, weight} -> weight end, fn -> nil end)
    
    case winner do
      nil -> {:error, :no_weighted_majority}
      {winning_proposal, total_weight} ->
        result = %{
          consensus: winning_proposal,
          algorithm: :weighted_voting,
          total_weight: total_weight,
          confidence: total_weight / Enum.sum(Map.values(weighted_totals))
        }
        {:ok, result}
    end
  end
  
  defp raft_consensus(agents, proposed_changes, opts) do
    # Simplified Raft consensus implementation
    # 1. Leader election
    {:ok, leader} = elect_leader(agents, opts)
    
    # 2. Leader proposes changes
    proposal_id = generate_proposal_id()
    
    # 3. Collect follower acknowledgments
    acks = collect_raft_acknowledgments(leader, agents -- [leader], proposed_changes, proposal_id)
    
    # 4. Check for majority
    majority_threshold = div(length(agents), 2) + 1
    
    if length(acks) >= majority_threshold do
      # 5. Commit the changes
      commit_result = commit_raft_changes(leader, agents, proposed_changes, proposal_id)
      
      result = %{
        consensus: proposed_changes,
        algorithm: :raft,
        leader: leader,
        proposal_id: proposal_id,
        acknowledgments: length(acks),
        committed: commit_result == :ok
      }
      
      {:ok, result}
    else
      {:error, :insufficient_raft_majority}
    end
  end
end

defmodule Foundation.MABEAM.Coordination.ConflictResolution do
  @moduledoc """
  Sophisticated conflict resolution mechanisms for multi-agent systems.
  """
  
  @doc """
  Resolve conflicts when agents have incompatible requirements.
  
  ## Examples
      
      conflicts = [
        %{
          variable_id: :temperature,
          conflicting_agents: [:coder, :creative_writer],
          requested_values: [0.1, 1.5],
          conflict_type: :value_incompatible
        }
      ]
      
      {:ok, resolutions} = ConflictResolution.resolve_conflicts(
        conflicts,
        strategy: :compromise,
        fallback_strategy: :priority_override
      )
  """
  @spec resolve_conflicts([conflict()], keyword()) :: {:ok, [resolution()]} | {:error, term()}
  def resolve_conflicts(conflicts, opts \\ []) do
    strategy = Keyword.get(opts, :strategy, :compromise)
    fallback_strategy = Keyword.get(opts, :fallback_strategy, :priority_override)
    
    resolutions = Enum.map(conflicts, fn conflict ->
      case resolve_single_conflict(conflict, strategy, opts) do
        {:ok, resolution} -> resolution
        {:error, _reason} -> 
          # Try fallback strategy
          case resolve_single_conflict(conflict, fallback_strategy, opts) do
            {:ok, fallback_resolution} -> fallback_resolution
            {:error, fallback_reason} -> 
              %{
                variable_id: conflict.variable_id,
                resolution_type: :failed,
                error: fallback_reason
              }
          end
      end
    end)
    
    {:ok, resolutions}
  end
  
  defp resolve_single_conflict(conflict, strategy, opts) do
    case strategy do
      :compromise -> resolve_by_compromise(conflict, opts)
      :priority_override -> resolve_by_priority(conflict, opts)
      :time_sharing -> resolve_by_time_sharing(conflict, opts)
      :resource_partitioning -> resolve_by_resource_partitioning(conflict, opts)
      :negotiated_settlement -> resolve_by_negotiation(conflict, opts)
      :leader_decision -> resolve_by_leader_decision(conflict, opts)
    end
  end
  
  defp resolve_by_compromise(conflict, opts) do
    case conflict.conflict_type do
      :value_incompatible ->
        # Find a compromise value between conflicting requests
        values = conflict.requested_values
        compromise_value = calculate_compromise_value(values, conflict.conflicting_agents, opts)
        
        resolution = %{
          variable_id: conflict.variable_id,
          resolution_type: :compromise,
          resolved_value: compromise_value,
          affected_agents: conflict.conflicting_agents,
          compromise_method: determine_compromise_method(values)
        }
        
        {:ok, resolution}
      
      :resource_competition ->
        # Allocate resources proportionally
        total_requested = Enum.sum(conflict.requested_values)
        available = Keyword.get(opts, :available_resources, total_requested)
        
        proportional_allocations = conflict.requested_values
        |> Enum.zip(conflict.conflicting_agents)
        |> Enum.map(fn {requested, agent} ->
          allocated = (requested / total_requested) * available
          {agent, allocated}
        end)
        
        resolution = %{
          variable_id: conflict.variable_id,
          resolution_type: :resource_compromise,
          allocations: proportional_allocations,
          efficiency: available / total_requested
        }
        
        {:ok, resolution}
      
      :temporal_constraint ->
        # Create time-based sharing schedule
        time_slots = create_time_sharing_schedule(conflict.conflicting_agents, conflict.requested_values, opts)
        
        resolution = %{
          variable_id: conflict.variable_id,
          resolution_type: :temporal_compromise,
          schedule: time_slots,
          rotation_period: Keyword.get(opts, :rotation_period, 3600_000)  # 1 hour default
        }
        
        {:ok, resolution}
    end
  end
  
  defp resolve_by_priority(conflict, opts) do
    # Get agent priorities
    priorities = Keyword.get(opts, :agent_priorities, %{})
    
    # Find highest priority agent
    highest_priority_agent = conflict.conflicting_agents
    |> Enum.max_by(fn agent -> Map.get(priorities, agent, 0) end)
    
    # Find the value requested by highest priority agent
    agent_index = Enum.find_index(conflict.conflicting_agents, &(&1 == highest_priority_agent))
    winning_value = Enum.at(conflict.requested_values, agent_index)
    
    resolution = %{
      variable_id: conflict.variable_id,
      resolution_type: :priority_override,
      resolved_value: winning_value,
      winning_agent: highest_priority_agent,
      overridden_agents: conflict.conflicting_agents -- [highest_priority_agent]
    }
    
    {:ok, resolution}
  end
  
  defp resolve_by_time_sharing(conflict, opts) do
    # Create fair time-sharing schedule
    time_slot_duration = Keyword.get(opts, :time_slot_duration, 600_000)  # 10 minutes
    
    schedule = conflict.conflicting_agents
    |> Enum.zip(conflict.requested_values)
    |> Enum.with_index()
    |> Enum.map(fn {{agent, value}, index} ->
      start_time = index * time_slot_duration
      end_time = start_time + time_slot_duration
      
      %{
        agent: agent,
        value: value,
        start_time: start_time,
        end_time: end_time,
        active: index == 0  # First agent starts active
      }
    end)
    
    resolution = %{
      variable_id: conflict.variable_id,
      resolution_type: :time_sharing,
      schedule: schedule,
      rotation_period: length(conflict.conflicting_agents) * time_slot_duration
    }
    
    {:ok, resolution}
  end
  
  defp resolve_by_negotiation(conflict, opts) do
    # Use auction or bargaining to resolve conflict
    negotiation_type = Keyword.get(opts, :negotiation_type, :auction)
    
    case negotiation_type do
      :auction ->
        # Convert conflict to auction format
        bids = conflict.conflicting_agents
        |> Enum.zip(conflict.requested_values)
        |> Enum.map(fn {agent, value} ->
          # Generate bid based on agent's urgency/importance
          bid_amount = calculate_agent_bid(agent, value, conflict, opts)
          {agent, value, [bid: bid_amount]}
        end)
        
        case Foundation.MABEAM.Coordination.Auction.run_auction(conflict.variable_id, bids, opts) do
          {:ok, auction_result} ->
            resolution = %{
              variable_id: conflict.variable_id,
              resolution_type: :negotiated_auction,
              resolved_value: auction_result.winning_value,
              winning_agent: auction_result.winner,
              payment: auction_result.payment,
              auction_efficiency: auction_result.efficiency
            }
            {:ok, resolution}
          
          {:error, reason} ->
            {:error, reason}
        end
      
      :bargaining ->
        # Implement multi-party bargaining
        {:ok, bargaining_result} = run_multi_party_bargaining(conflict, opts)
        
        resolution = %{
          variable_id: conflict.variable_id,
          resolution_type: :negotiated_bargaining,
          resolved_value: bargaining_result.agreed_value,
          concessions: bargaining_result.concessions,
          satisfaction_scores: bargaining_result.satisfaction_scores
        }
        
        {:ok, resolution}
    end
  end
  
  ## Helper Functions
  
  defp calculate_compromise_value(values, agents, opts) when is_list(values) do
    compromise_method = Keyword.get(opts, :compromise_method, :weighted_average)
    
    case compromise_method do
      :simple_average -> Enum.sum(values) / length(values)
      :median -> calculate_median(values)
      :weighted_average ->
        weights = get_agent_weights(agents, opts)
        weighted_sum = values
        |> Enum.zip(weights)
        |> Enum.map(fn {value, weight} -> value * weight end)
        |> Enum.sum()
        
        weighted_sum / Enum.sum(weights)
    end
  end
  
  defp determine_compromise_method(values) do
    # Analyze the distribution of values to suggest best compromise method
    range = Enum.max(values) - Enum.min(values)
    variance = calculate_variance(values)
    
    cond do
      range < 0.1 -> :simple_average  # Values are close
      variance > 0.5 -> :median      # High variance, median is more robust
      true -> :weighted_average      # Default to weighted average
    end
  end
  
  defp calculate_agent_bid(agent, value, conflict, opts) do
    # Calculate how much an agent should bid based on various factors
    base_bid = Keyword.get(opts, :base_bid, 1.0)
    urgency_factor = get_agent_urgency(agent, opts)
    importance_factor = get_variable_importance(conflict.variable_id, agent, opts)
    resource_factor = get_agent_resources(agent, opts)
    
    base_bid * urgency_factor * importance_factor * resource_factor
  end
end

defmodule Foundation.MABEAM.Coordination.Market do
  @moduledoc """
  Market-based coordination mechanisms using economic principles.
  """
  
  @doc """
  Create a market for resource allocation and parameter coordination.
  
  ## Examples
      
      # Create a computational resource market
      {:ok, market} = Market.create_market(:compute_resources,
        participants: [:coder, :reviewer, :tester],
        resources: %{cpu: 100, memory: 1000, network: 50},
        pricing_mechanism: :double_auction
      )
      
      # Run market clearing
      {:ok, allocations} = Market.clear_market(market)
  """
  @spec create_market(atom(), keyword()) :: {:ok, market()} | {:error, term()}
  def create_market(market_id, opts) do
    participants = Keyword.get(opts, :participants, [])
    resources = Keyword.get(opts, :resources, %{})
    pricing_mechanism = Keyword.get(opts, :pricing_mechanism, :double_auction)
    
    market = %{
      id: market_id,
      participants: participants,
      available_resources: resources,
      pricing_mechanism: pricing_mechanism,
      buy_orders: [],
      sell_orders: [],
      transaction_history: [],
      market_state: :open
    }
    
    {:ok, market}
  end
  
  @spec clear_market(market()) :: {:ok, [allocation()]} | {:error, term()}
  def clear_market(market) do
    case market.pricing_mechanism do
      :double_auction -> clear_double_auction(market)
      :posted_price -> clear_posted_price_market(market)
      :combinatorial_auction -> clear_combinatorial_auction(market)
      :continuous_trading -> clear_continuous_trading(market)
    end
  end
  
  defp clear_double_auction(market) do
    # Sort buy orders by price (highest first)
    sorted_buy_orders = Enum.sort_by(market.buy_orders, & &1.price, :desc)
    
    # Sort sell orders by price (lowest first)  
    sorted_sell_orders = Enum.sort_by(market.sell_orders, & &1.price, :asc)
    
    # Find matching orders
    {transactions, remaining_buy, remaining_sell} = match_orders(sorted_buy_orders, sorted_sell_orders)
    
    # Convert transactions to allocations
    allocations = Enum.map(transactions, fn transaction ->
      %{
        buyer: transaction.buyer,
        seller: transaction.seller,
        resource: transaction.resource,
        quantity: transaction.quantity,
        price: transaction.clearing_price,
        timestamp: DateTime.utc_now()
      }
    end)
    
    {:ok, allocations}
  end
  
  defp match_orders(buy_orders, sell_orders) do
    match_orders_recursive(buy_orders, sell_orders, [])
  end
  
  defp match_orders_recursive([], sell_orders, transactions) do
    {transactions, [], sell_orders}
  end
  
  defp match_orders_recursive(buy_orders, [], transactions) do
    {transactions, buy_orders, []}
  end
  
  defp match_orders_recursive([buy_order | rest_buy], [sell_order | rest_sell], transactions) do
    if buy_order.price >= sell_order.price do
      # Match found - create transaction
      clearing_price = (buy_order.price + sell_order.price) / 2
      quantity = min(buy_order.quantity, sell_order.quantity)
      
      transaction = %{
        buyer: buy_order.agent,
        seller: sell_order.agent,
        resource: buy_order.resource,
        quantity: quantity,
        clearing_price: clearing_price
      }
      
      # Update order quantities
      updated_buy = %{buy_order | quantity: buy_order.quantity - quantity}
      updated_sell = %{sell_order | quantity: sell_order.quantity - quantity}
      
      # Continue matching with remaining quantities
      remaining_buy = if updated_buy.quantity > 0, do: [updated_buy | rest_buy], else: rest_buy
      remaining_sell = if updated_sell.quantity > 0, do: [updated_sell | rest_sell], else: rest_sell
      
      match_orders_recursive(remaining_buy, remaining_sell, [transaction | transactions])
    else
      # No match possible - prices don't cross
      {transactions, [buy_order | rest_buy], [sell_order | rest_sell]}
    end
  end
end

defmodule Foundation.MABEAM.Coordination.Hierarchical do
  @moduledoc """
  Hierarchical coordination with coordinator agents managing subordinates.
  """
  
  @doc """
  Set up hierarchical coordination structure.
  """
  @spec setup_hierarchy([atom()], keyword()) :: {:ok, hierarchy()} | {:error, term()}
  def setup_hierarchy(agents, opts) do
    coordinator = Keyword.get(opts, :coordinator)
    hierarchy_type = Keyword.get(opts, :type, :tree)
    
    case hierarchy_type do
      :tree -> setup_tree_hierarchy(agents, coordinator, opts)
      :star -> setup_star_hierarchy(agents, coordinator, opts)
      :layered -> setup_layered_hierarchy(agents, opts)
    end
  end
  
  defp setup_tree_hierarchy(agents, root_coordinator, opts) do
    branching_factor = Keyword.get(opts, :branching_factor, 3)
    
    # Build tree structure
    tree = build_coordination_tree(agents, root_coordinator, branching_factor)
    
    hierarchy = %{
      type: :tree,
      root: root_coordinator,
      structure: tree,
      coordination_protocol: :top_down_with_feedback
    }
    
    {:ok, hierarchy}
  end
  
  @doc """
  Coordinate agents through hierarchical structure.
  """
  @spec coordinate_hierarchy(hierarchy(), coordination_request()) :: 
    {:ok, [coordination_result()]} | {:error, term()}
  def coordinate_hierarchy(hierarchy, request) do
    case hierarchy.type do
      :tree -> coordinate_tree_hierarchy(hierarchy, request)
      :star -> coordinate_star_hierarchy(hierarchy, request)
      :layered -> coordinate_layered_hierarchy(hierarchy, request)
    end
  end
end