Claude Code subagent imported from lizTheDeveloper/ai_game_theory_simulation (.claude/agents/task_roy_climate_deployment_delays_20251121.md). Copyright stays with the author.

Task: Implement Climate Deployment Timescales (Phase 1)

Date: November 21, 2025 Priority: TIER 1 CRITICAL Agent: simulation-maintainer (Roy) Spawned by: orchestrator-1 Task ID: climate-deployment-timescales-phase1

Research Foundation (Quality Gate 1: ✅ PASSED)

Research Document: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/research/climate_tech_deployment_timescales_20251112.md

• Grade: A- (Sylvia validated Nov 12, 2025)
• Sources: 15+ peer-reviewed (2024-2025)
• Size: 35KB, 4,700 words
• Status: COMPLETE, ready for implementation

Problem Statement

God mode testing deployed all 9 climate technologies at month 0 but achieved only 5.5% effectiveness for Climate Change planetary boundary.

Research Finding: This is REALISTIC due to three compounding delays:

1. Activation delay (2-15 years): Construction/manufacturing before first operation
2. Scaling delay (5-50 years): S-curve adoption, pilot → gigatonne capacity
3. Physical response delay (<1 to 100 years): Atmospheric CO2 equilibration

Expected Outcome: Month 60 effectiveness should increase from 5.5% → ~15% when realistic delays are modeled.

Implementation Tasks

Task 1: Add Deployment Tracking to GameState

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/types/game.ts

Add new field to GameState interface:

climateDeploymentTracking: {
  [techId: string]: {
    deploymentStartMonth: number;     // When tech was deployed (0 if not deployed)
    monthsSinceDeployment: number;    // Current age (0 if not deployed)
    activationProgress: number;       // 0.0 to 1.0 (0% before T_activate, 100% after)
    scalingProgress: number;          // 0.0 to 1.0 (0% at deployment, 100% at maturity)
    physicalResponseProgress: number; // 0.0 to 1.0 (CO2 removal → climate effect delay)
    effectiveCapacity: number;        // Current effective capacity (0.0 to 1.0)
  }
}

Initialization: Add to src/simulation/initialization/initialize.ts with default values (all zeros).

Task 2: Create ClimateDeploymentDelayPhase

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/phases/ClimateDeploymentDelayPhase.ts

Phase Responsibilities:

1. Track which climate technologies are deployed
2. Update monthsSinceDeployment for each deployed tech
3. Calculate activationProgress, scalingProgress, physicalResponseProgress
4. Compute effectiveCapacity = activationProgress × scalingProgress × physicalResponseProgress
5. Update planetary boundary effectiveness based on effectiveCapacity

Technology-Specific Parameters (from research):

Progress Calculation (Simplified S-curve - Piecewise Linear):

// Activation progress (0% before T_activate, 100% after)
function calculateActivationProgress(monthsSince: number, T_activate_months: number): number {
  if (monthsSince < T_activate_months) {
    return 0.0;
  }
  return 1.0;
}

// Scaling progress (linear ramp from activation to T_50, then asymptotic)
function calculateScalingProgress(monthsSince: number, T_activate_months: number, T_scale_months: number): number {
  if (monthsSince < T_activate_months) {
    return 0.0;
  }

  const monthsSinceActivation = monthsSince - T_activate_months;
  const T_50_months = T_scale_months / 2;  // Time to reach 50% effectiveness

  if (monthsSinceActivation < T_50_months) {
    // Linear ramp to 50%
    return 0.5 * (monthsSinceActivation / T_50_months);
  } else {
    // Asymptotic approach to 100% (exponential)
    const tau = T_scale_months / 3;  // Time constant
    const progress = 1.0 - 0.5 * Math.exp(-(monthsSinceActivation - T_50_months) / tau);
    return Math.min(progress, 1.0);
  }
}

// Physical response progress (exponential approach for CO2 removal)
function calculatePhysicalResponseProgress(monthsSince: number, T_physical_months: number): number {
  if (T_physical_months === 0) {
    return 1.0;  // Immediate effect (energy efficiency, not CO2 removal)
  }

  const progress = 1.0 - Math.exp(-monthsSince / T_physical_months);
  return Math.min(progress, 1.0);
}

// Effective capacity (multiplicative combination)
function calculateEffectiveCapacity(activation: number, scaling: number, physical: number): number {
  return activation * scaling * physical;
}

Phase Structure:

export function ClimateDeploymentDelayPhase(state: GameState, rng: () => number): void {
  // Validate RNG (CRITICAL: no Math.random fallback)
  if (!rng || typeof rng !== 'function') {
    throw new Error('❌ CRITICAL: RNG required for deterministic simulation');
  }

  console.log('\n=== 🌍 Climate Deployment Delay Phase ===');

  // Get climate tech IDs from deployed technologies
  const climateTechIds = [
    'direct_air_capture',
    'enhanced_weathering',
    'ocean_alkalinization',
    'biochar_carbon_sequestration',
    'beccs',
    'stratospheric_aerosol_injection',
    'smart_grid',
    'green_hydrogen',
    'heat_pumps'
  ];

  // Update tracking for each deployed climate tech
  for (const techId of climateTechIds) {
    const isDeployed = state.technologyTree.has(techId) && state.technologyTree.get(techId)!.researched;

    if (isDeployed) {
      if (!state.climateDeploymentTracking[techId]) {
        // Initialize tracking on first deployment
        state.climateDeploymentTracking[techId] = {
          deploymentStartMonth: state.currentMonth,
          monthsSinceDeployment: 0,
          activationProgress: 0.0,
          scalingProgress: 0.0,
          physicalResponseProgress: 0.0,
          effectiveCapacity: 0.0
        };
        console.log(`  🌍💡 DEPLOYED: ${techId} at month ${state.currentMonth}`);
      }

      const tracking = state.climateDeploymentTracking[techId];
      tracking.monthsSinceDeployment = state.currentMonth - tracking.deploymentStartMonth;

      // Get tech-specific parameters
      const params = getTechDelayParameters(techId);

      // Calculate progress curves
      tracking.activationProgress = calculateActivationProgress(
        tracking.monthsSinceDeployment,
        params.T_activate_months
      );
      tracking.scalingProgress = calculateScalingProgress(
        tracking.monthsSinceDeployment,
        params.T_activate_months,
        params.T_scale_months
      );
      tracking.physicalResponseProgress = calculatePhysicalResponseProgress(
        tracking.monthsSinceDeployment,
        params.T_physical_months
      );

      // Calculate effective capacity
      tracking.effectiveCapacity = calculateEffectiveCapacity(
        tracking.activationProgress,
        tracking.scalingProgress,
        tracking.physicalResponseProgress
      );

      if (tracking.monthsSinceDeployment % 12 === 0 && tracking.monthsSinceDeployment > 0) {
        console.log(`  🌍 ${techId}: ${(tracking.effectiveCapacity * 100).toFixed(1)}% effective (Year ${Math.floor(tracking.monthsSinceDeployment / 12)})`);
      }
    }
  }

  // Calculate aggregate climate effectiveness
  const deployedTechs = climateTechIds.filter(id => state.climateDeploymentTracking[id]);
  const totalEffectiveCapacity = deployedTechs.reduce((sum, id) =>
    sum + state.climateDeploymentTracking[id].effectiveCapacity, 0
  );
  const aggregateEffectiveness = deployedTechs.length > 0
    ? totalEffectiveCapacity / deployedTechs.length
    : 0.0;

  console.log(`  🌍 Aggregate Climate Effectiveness: ${(aggregateEffectiveness * 100).toFixed(1)}% (${deployedTechs.length} techs deployed)`);
}

function getTechDelayParameters(techId: string): { T_activate_months: number, T_scale_months: number, T_physical_months: number } {
  const params: Record<string, { T_activate_months: number, T_scale_months: number, T_physical_months: number }> = {
    'direct_air_capture': { T_activate_months: 7 * 12, T_scale_months: 35 * 12, T_physical_months: 20 * 12 },
    'enhanced_weathering': { T_activate_months: 3 * 12, T_scale_months: 45 * 12, T_physical_months: 50 * 12 },
    'ocean_alkalinization': { T_activate_months: 5 * 12, T_scale_months: 25 * 12, T_physical_months: 2 * 12 },
    'biochar_carbon_sequestration': { T_activate_months: 3 * 12, T_scale_months: 12 * 12, T_physical_months: 0 },
    'beccs': { T_activate_months: 10 * 12, T_scale_months: 25 * 12, T_physical_months: 5 * 12 },
    'stratospheric_aerosol_injection': { T_activate_months: 3 * 12, T_scale_months: 7 * 12, T_physical_months: 1.5 * 12 },
    'smart_grid': { T_activate_months: 7 * 12, T_scale_months: 17 * 12, T_physical_months: 0 },
    'green_hydrogen': { T_activate_months: 7 * 12, T_scale_months: 22 * 12, T_physical_months: 0 },
    'heat_pumps': { T_activate_months: 3 * 12, T_scale_months: 10 * 12, T_physical_months: 0 }
  };

  const param = params[techId];
  if (!param) {
    throw new Error(`❌ CRITICAL: No delay parameters for climate tech: ${techId}`);
  }
  return param;
}

Task 3: Register Phase in PhaseOrchestrator

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/engine/PhaseOrchestrator.ts

Add ClimateDeploymentDelayPhase:

• After TechnologyPhase (tech unlock)
• Before PlanetaryBoundariesPhase (boundary calculation)
• Position: ~Phase 15-20 (environmental systems section)

Task 4: Update Climate Effectiveness Calculation

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/phases/PlanetaryBoundariesPhase.ts (or wherever climate effectiveness is calculated)

Replace instant effectiveness with deployment-scaled effectiveness:

// OLD (instant effectiveness):
const climateTechCount = getDeployedClimateTechCount(state);
const effectiveness = climateTechCount * 0.15;  // Assume 15% per tech

// NEW (deployment-scaled effectiveness):
const climateTechIds = [/* ... list of 9 tech IDs ... */];
const totalEffectiveness = climateTechIds.reduce((sum, techId) => {
  const tracking = state.climateDeploymentTracking[techId];
  if (!tracking) return sum;

  const baseEffectiveness = 0.15;  // 15% max per tech
  return sum + (baseEffectiveness * tracking.effectiveCapacity);
}, 0.0);

Task 5: Create Unit Tests

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/tests/phases/ClimateDeploymentDelayPhase.test.ts

Test cases:

1. Test activation delay: Verify 0% effectiveness before T_activate
2. Test scaling progress: Verify linear ramp then asymptotic approach
3. Test physical response: Verify exponential approach for CO2 removal
4. Test effective capacity: Verify multiplicative combination
5. Test determinism: Same seed → same results
6. Test god mode: All 9 techs at month 0 → 5.5% at month 36, ~15% at month 60

Task 6: Create God Mode Validation Script

File: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/scripts/validateClimateDeployment.ts

Script to validate god mode behavior:

1. Deploy all 9 climate technologies at month 0
2. Run simulation to month 60 (5 years)
3. Measure climate effectiveness at month 36 and month 60
4. Expected: ~5-8% at month 36, ~12-18% at month 60
5. Output: Effectiveness over time plot data

Defensive Coding Requirements

CRITICAL - No exceptions:

1. RNG REQUIRED: No Math.random fallback

   if (!rng || typeof rng !== 'function') {
     throw new Error('❌ CRITICAL: RNG required for deterministic simulation');
   }

2. No silent fallbacks: Use assertion utilities

   import { assertFinite, assertStateProperty } from '@/simulation/utils/assertions';
   
   const progress = assertFinite(calculatedProgress, {
     location: 'calculateScalingProgress',
     valueName: 'progress',
     month: state.currentMonth
   });

3. Emoji conventions:

   • 🌍 (planetary/climate)
   • 💡 (breakthrough/progress)
   • ⚠️ (warning/threshold)
   • ❌ (error/critical)

4. Type safety: npx tsc --noEmit must pass

Quality Gates

• ✅ Research validation: Already complete (Grade A-, Nov 12)
• ⏳ Architecture review: REQUIRED after implementation (architecture-skeptic)
• ⏳ Monte Carlo validation: REQUIRED (priya, N≥10 runs)

Success Criteria

• Phase implemented with 3-delay model
• GameState tracking added and initialized
• All 9 technologies parameterized (correct tech IDs, delay values from research)
• Phase registered in PhaseOrchestrator
• Climate effectiveness calculation updated
• Unit tests pass (6 test cases minimum)
• God mode validation shows 5.5% → ~15% at month 60
• Type safety (npx tsc --noEmit passes)
• Deterministic (same seed = same results)
• Ready for architecture review

Resources

• Research report: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/research/climate_tech_deployment_timescales_20251112.md
• Assertion utilities: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/utils/assertions.ts
• Example phase: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/phases/NovelEntitiesPhase.ts (recent good example)
• Tech tree: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/data/comprehensiveTechTree.ts
• Initialization: /home/lizthedeveloper_gmail_com/ai_game_theory_simulation/src/simulation/initialization/initialize.ts

Next Steps After Completion

Orchestrator will invoke:

1. architecture-skeptic (Quality Gate 2) - Review architecture, performance, state propagation
2. priya - Monte Carlo validation (N≥10 runs, verify effectiveness increases, check determinism)
3. wiki-documentation-updater - Sync documentation
4. architect - Archive plan, update roadmap

Roy's Notes Section (for you to update as you work):

[Add your notes here as you implement - blocking issues, decisions made, test results, etc.]