Consensus Mechanisms

The swarm employs multiple consensus strategies:

Weighted Voting

// Weighted voting consensus
function weightedVotingConsensus(signals, weights) {
  const tally = {};
  
  // Initialize tally for each asset and direction
  signals.forEach(signal => {
    const key = `${signal.asset}-${signal.direction}`;
    
    if (!tally[key]) {
      tally[key] = {
        asset: signal.asset,
        direction: signal.direction,
        weightedConfidence: 0,
        votes: 0,
        signals: []
      };
    }
  });
  
  // Add weighted votes
  signals.forEach(signal => {
    const key = `${signal.asset}-${signal.direction}`;
    const agentWeight = weights[signal.agentId] || 1;
    
    tally[key].weightedConfidence += signal.confidence * agentWeight;
    tally[key].votes += 1;
    tally[key].signals.push(signal);
  });
  
  // Calculate final confidence scores and sort by strength
  const results = Object.values(tally).map(result => {
    return {
      ...result,
      consensusConfidence: result.weightedConfidence / 
        result.signals.reduce((sum, signal) => sum + weights[signal.agentId], 0)
    };
  });
  
  // Sort by consensus confidence
  return results.sort((a, b) => b.consensusConfidence - a.consensusConfidence);
}

// Example usage
const consensusResults = weightedVotingConsensus(allAgentSignals, agentWeights);
console.log('Consensus results:', consensusResults);

Bayesian Aggregation

// Bayesian signal aggregation
function bayesianAggregation(signals) {
  const aggregatedResults = {};
  
  // Group signals by asset
  signals.forEach(signal => {
    if (!aggregatedResults[signal.asset]) {
      aggregatedResults[signal.asset] = {
        asset: signal.asset,
        priorUp: 0.5,  // Initial prior probability (neutral)
        priorDown: 0.5,
        posteriorUp: 0.5,
        posteriorDown: 0.5,
        signals: []
      };
    }
    
    aggregatedResults[signal.asset].signals.push(signal);
  });
  
  // Apply Bayesian update for each asset
  Object.values(aggregatedResults).forEach(result => {
    result.signals.forEach(signal => {
      // Adjust signal confidence based on historical accuracy
      const adjustedConfidence = signal.confidence * signal.agentAccuracy;
      
      if (signal.direction === 'up') {
        // Update posterior probability for upward movement
        result.posteriorUp = updateBayesianProbability(
          result.posteriorUp,
          adjustedConfidence
        );
        result.posteriorDown = 1 - result.posteriorUp;
      } else {
        // Update posterior probability for downward movement
        result.posteriorDown = updateBayesianProbability(
          result.posteriorDown,
          adjustedConfidence
        );
        result.posteriorUp = 1 - result.posteriorDown;
      }
    });
    
    // Determine consensus direction and confidence
    if (result.posteriorUp > result.posteriorDown) {
      result.consensusDirection = 'up';
      result.consensusConfidence = result.posteriorUp;
    } else {
      result.consensusDirection = 'down';
      result.consensusConfidence = result.posteriorDown;
    }
  });
  
  return Object.values(aggregatedResults);
}

// Bayesian probability update
function updateBayesianProbability(prior, likelihood) {
  // P(A|B) = (P(B|A) * P(A)) / P(B)
  // Simplified Bayesian update for binary hypothesis
  const posterior = (likelihood * prior) / 
                   (likelihood * prior + (1 - likelihood) * (1 - prior));
  
  return posterior;
}

Collective Decision Making

Signal Composition

// Compose signals from multiple agents
async function composeSwarmSignals(swarm, marketData) {
  // Collect signals from all agents
  const agentSignals = [];
  
  for (const agent of swarm.getAgents()) {
    const signals = await agent.process(marketData);
    
    // Annotate signals with agent information
    const annotatedSignals = signals.map(signal => ({
      ...signal,
      agentId: agent.getId(),
      agentType: agent.getType(),
      agentAccuracy: agent.getHistoricalAccuracy()
    }));
    
    agentSignals.push(...annotatedSignals);
  }
  
  // Group signals by asset
  const signalsByAsset = groupBy(agentSignals, 'asset');
  
  // Apply consensus mechanism to each asset group
  const consensusResults = {};
  
  for (const [asset, signals] of Object.entries(signalsByAsset)) {
    consensusResults[asset] = swarm.applyConsensus(signals);
  }
  
  return {
    rawSignals: agentSignals,
    consensusResults
  };
}

// Example usage
const swarmAnalysis = await composeSwarmSignals(marketSwarm, currentMarketData);
console.log('Swarm consensus:', swarmAnalysis.consensusResults);

Decision Refinement

// Refine decisions through agent interaction
async function refineSwarmDecision(swarm, initialConsensus, refinementRounds = 3) {
  let currentConsensus = initialConsensus;
  let round;
  
  // Perform multiple rounds of refinement
  for (round = 0; round < refinementRounds; round++) {
    console.log(`Refinement round ${round + 1}`);
    
    // Each agent reviews the current consensus
    const refinementOpinions = [];
    
    for (const agent of swarm.getAgents()) {
      const opinion = await agent.evaluateConsensus(currentConsensus);
      refinementOpinions.push(opinion);
    }
    
    // Aggregate refinement opinions
    currentConsensus = swarm.aggregateRefinements(
      currentConsensus,
      refinementOpinions
    );
    
    // Check for convergence
    if (swarm.hasConverged(currentConsensus)) {
      console.log(`Refinement converged after ${round + 1} rounds`);
      break;
    }
  }
  
  return {
    finalConsensus: currentConsensus,
    refinementRounds: Math.min(refinementRounds, round + 1),
    hasConverged: swarm.hasConverged(currentConsensus)
  };
}

// Example usage
const refinedDecision = await refineSwarmDecision(
  marketSwarm,
  swarmAnalysis.consensusResults
);