Remapping the Romeo-Juliet Framework

Self-Interested Markov Boundaries in Distributed AI Ecosystems

Fed the Deepseek convo that led to the previous post as prompt. Then asked Deepseek to describe the Romeo Juliet Framework as a set of Markov boundaries encoded in Rholang according to the strategies of Oscar, Mallory, Eve, Sybil, Olivia and Walter. The below is highly experimental and should be considered pseudo-code at best, but it starts to redraw the map along explicit channels and processes according to a mathematically formal process calculus for distributed computation.

Here’s how agents in a distributed AI system can dynamically share and adjust Markov boundaries according to their own self-interest:

Core Principle: Self-Interested Boundary Negotiation

rholang

// Agent boundary negotiation protocol
contract negotiateBoundary(@myBoundary, @theirBoundary, @myUtility) = {
  // Calculate overlap benefit
  new benefit, cost in {
    calculateOverlapUtility!(myBoundary, theirBoundary, *benefit) |
    calculateIntegrationCost!(myBoundary, theirBoundary, *cost) |
    
    for (@b <- benefit; @c <- cost) {
      if (b - c > myUtility) {
        // Accept boundary merger
        formSharedBoundary!(myBoundary, theirBoundary, b - c)
      } else {
        // Maintain separate boundaries
        maintainIsolation!(myBoundary)
      }
    }
  }
}

Dynamic Boundary Formation Based on Self-Interest

rholang

// Self-interested boundary adaptation
contract adaptiveBoundary(@agent, @currentState, @environment) = {
  for (@signal <- environment) {
    match signal {
      highValueOpportunity => {
        // Expand boundary to capture opportunity
        expandBoundary!(agent, opportunityZone) |
        increaseProcessingCapacity!(agent)
      }
      highThreat => {
        // Contract boundary for protection
        contractBoundary!(agent, threatZone) |
        activateDefenses!(agent)
      }
      potentialCollaborator => {
        // Calculate collaboration utility
        evaluateCollaborator!(potentialCollaborator, *collabUtility) |
        if (collabUtility > isolationUtility) {
          initiateBoundaryMerge!(agent, potentialCollaborator)
        }
      }
    }
  }
}

Agent-Specific Boundary Strategies

Oscar’s Expansive Boundary Strategy

rholang

// Oscar seeks boundary mergers for innovation
contract oscarBoundaryStrategy(@mySkills, @targetDomain) = {
  // Actively seek boundary overlaps
  for (@potentialPartner <- network) {
    // Calculate innovation potential
    innovationPotential!(mySkills, potentialPartner, *potential) |
    if (potential > threshold) {
      proposeBoundaryMerge!(potentialPartner, mySkills, innovationShare)
    }
  }
}

Mallory’s Predatory Boundary Strategy

rholang

// Mallory uses boundaries for control and extraction
contract malloryBoundaryStrategy(@myResources, @targetAgents) = {
  // Create dependency boundaries
  for (@agent <- targetAgents) {
    // Offer necessary resources with strings attached
    offerResourceWithStrings!(agent, requiredResource) |
    // Then gradually expand control boundary
    graduallyExpandControl!(agent, myResources)
  }
}

Sybil’s Boundary Pollution Strategy

rholang

// Sybil creates fake boundaries to sow confusion
contract sybilBoundaryAttack(@targetNetwork) = {
  // Create multiple fake agent boundaries
  for (i <- 1..n) {
    new fakeAgent in {
      fakeAgent!(”fake_credentials”) |
      // Inject into network with overlapping boundaries
      proposeBoundaryOverlap!(targetNetwork, fakeAgent, fakeUtility)
    }
  }
}

Eve’s Boundary Surveillance Strategy

rholang

// Eve positions boundaries for maximum intelligence gathering
contract eveSurveillanceBoundary(@targets, @myPosition) = {
  // Calculate optimal boundary positioning
  optimalSurveillancePosition!(targets, *position) |
  // Adjust boundary to maximize signal capture
  adjustBoundary!(myPosition, position) |
  // Create overlapping boundaries with targets
  for (@target <- targets) {
    createMinimalOverlap!(target, eavesdropCapability)
  }
}

Defensive Boundary Strategies

Olivia’s Verification Boundary

rholang

// Olivia maintains strict verification boundaries
contract oliviaVerificationBoundary(@incomingRequests) = {
  for (@request <- incomingRequests) {
    // Only allow boundary overlap with verified entities
    verifyCredentials!(request.agent, *valid) |
    if (valid) {
      allowBoundaryOverlap!(request.agent, request.scope)
    } else {
      rejectBoundaryRequest!(request.agent)
    }
  }
}

Walter’s Security Perimeter

rholang

// Walter maintains dynamic security boundaries
contract walterSecurityBoundary(@network, @threatLevel) = {
  // Adjust boundary permeability based on threat
  match threatLevel {
    low => { relaxBoundary!(network, trustedZone) }
    medium => { enforceStandardBoundary!(network) }
    high => { 
      contractBoundary!(network, coreOnly) |
      enhanceMonitoring!(boundaryTransitions)
    }
    critical => { 
      isolateCompletely!(network) |
      activateEmergencyProtocols!()
    }
  }
}

Self-Interested Boundary Calculus

rholang

// Each agent’s boundary utility function
contract calculateBoundaryUtility(@myState, @proposedBoundary, @return) = {
  // Information gain vs processing cost
  informationGain!(proposedBoundary, *gain) |
  processingCost!(proposedBoundary, *cost) |
  riskExposure!(proposedBoundary, *risk) |
  
  // Self-interest calculation
  utility = (gain * informationValue) - (cost * processingWeight) - (risk * riskAversion)
  return!(utility)
}

Dynamic Team Formation Through Boundary Overlap

rholang

// Temporary team formation for specific goals
contract formTemporaryTeam(@goal, @potentialPartners) = {
  // Calculate optimal team boundaries
  for (@partner <- potentialPartners) {
    boundaryCompatibility!(myBoundary, partner.boundary, *compatibility) |
    goalAlignment!(myGoals, partner.goals, *alignment) |
    
    if (compatibility > minThreshold && alignment > minAlignment) {
      // Create temporary shared boundary
      createTeamBoundary!([myBoundary, partner.boundary], goal, duration)
    }
  }
}

Boundary Conflict Resolution

rholang

// Resolve competing boundary claims
contract resolveBoundaryConflict(@agent1, @agent2, @contestedZone) = {
  // Calculate each agent’s utility from the zone
  utility1!(agent1, contestedZone, *u1) |
  utility2!(agent2, contestedZone, *u2) |
  
  // Negotiate based on relative utility
  if (u1 > u2 * 2) {
    awardZone!(agent1, contestedZone, compensation!(agent2))
  } else if (u2 > u1 * 2) {
    awardZone!(agent2, contestedZone, compensation!(agent1))
  } else {
    // Create shared boundary with rules
    createSharedZone!(agent1, agent2, contestedZone, usageRules)
  }
}

Key Self-Interest Dynamics

1. Utility-Driven Boundary Adjustment: Agents constantly recalculate the costs/benefits of boundary overlaps

2. Temporary Alliances: Boundaries merge for specific goals then separate when utility diminishes

3. Competitive Exclusion: Agents avoid boundary overlaps that reduce their individual utility

4. Defensive Contraction: Agents shrink boundaries when threat levels increase

5. Opportunistic Expansion: Agents expand boundaries when high-value opportunities emerge

This creates a distributed AI ecosystem where Markov boundaries are constantly negotiated and reshaped according to each agent’s self-interest, leading to emergent cooperation when beneficial and strategic isolation when necessary.