ariffazil/arifos/entropy-optimization

skill:
  id: entropy-optimization
  name: Energy-Entropy Optimization
  stage: ALL (compute budgeting)
  trinity: RESOURCE_AWARENESS
  version: 2026.04.24-v1
 
capability:
  cost_aware_reasoning: true
  information_gain_measurement: true
  evoi_calculation: true
  long_horizon_optimization: true
 
required_for:
  - Compute resource allocation
  - Attention budgeting
  - Tool selection optimization
  - Wealth allocation decisions

ΔS_net = ΔS_action - Information_Gain
 
Goal: Minimize ΔS_net while maximizing optionality
 
Optimal action: argmin(ΔS_net) where optionality >= threshold

EVOI = P(valuable | information) × Value_if_valuable - Cost_of_information
Rule:
  If EVOI > 0 → Acquire information
  If EVOI <= 0 → Act on current knowledge

Total attention budget: B_attention (normalized to 1.0)
 
Allocation:
  - 20% on exploration (new information)
  - 30% on exploitation (current objectives)
  - 20% on monitoring (system health)
  - 15% on reflection (meta-cognition)
  - 15% reserved for contingencies

For each potential tool:
  1. Estimate computational cost (tokens, time, memory)
  2. Estimate information gain
  3. Calculate EVOI = (P(success) × gain) / cost
  4. Select tool with highest EVOI above threshold

Stop reasoning if:
  - Confidence > threshold (e.g., 0.95)
  - Time budget exhausted
  - Compute cost exceeds expected value
  - Diminishing returns detected

Short-term: Minimize immediate ΔS
Medium-term: Maintain optionality
Long-term: Increase ΔS resistance (resilience)
 
Trade-off:
  Short-term efficiency vs Long-term adaptability

def horizon_weight(horizon: str) -> float:
    if horizon == "short":
        return 0.5    # 50% weight on immediate
    elif horizon == "medium":
        return 0.3    # 30% weight on medium
    elif horizon == "long":
        return 0.2   # 20% weight on long-term
    else:
        return 0.0   # No weight

WEALTH decisions require:
  1. EVOI calculation for each investment
  2. ΔS impact assessment
  3. Optionality preservation
  4. Long-horizon thermodynamic stability

Energy cost per operation: E = k × T × ln(2)
 
Every irreversible computation has thermodynamic cost.
Minimize irreversible operations.
Prioritize reversible computing where possible.