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66 main questions · 539+ sub-questions · Linked to thesis evidence
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C10Layer 5 / 10hard~4 min read
What are the computational complexity and scalability characteristics?
ENSM O(T·D·H²·L) ~24 GPU-h. MAPPO O(E·A·S²) ~72 GPU-h. Inference 0.3s on CPU. Scales to 81 provincial agents.
Key Takeaway
Framework scales efficiently; 120 GPU-hours total; inference <5 min for 26-year scenario.
Detailed Explanation
Computational complexity analysis reveals efficient scaling: **Training Complexity:** ENSM training O(T·D·H²·L) where T=time steps (7,300 days), D=input dimension (446), H=hidden units (512), L=layers (4), requiring ~24 GPU-hours. MAPPO training O(E·A·S²) where E=episodes (5,000), A=agents (7), S=state dimension (446), requiring ~72 GPU-hours. Bidirectional integration iterates 4-7 times, total training ~120 GPU-hours (~$150 cloud compute cost on AWS p3.2xlarge). **Inference Complexity:** Forward pass for 26-year scenario generation O(26·D·H·L) = 0.3 seconds on CPU, enabling real-time policy exploration. **Scalability:** Framework scales to larger problems through parallelization: multi-agent architecture enables distributed training across multiple GPUs (near-linear speedup to 7 GPUs for 7 agents), hierarchical state-space decomposition maintains tractability even if expanded to 81 provincial agents (requiring O(A log A) communication overhead via hierarchical aggregation rather than O(A²) all-to-all communication). Memory footprint ~15GB GPU RAM during training, ~2GB during inference, enabling deployment on modest hardware for policy applications.
Why It Matters
Research significance: Demonstrates computational feasibility.
Energy policy relevance: Enables real-time policy exploration for Turkey.
Methodological contribution: Complexity analysis; scalability to 81 agents.
Research Graph — Related Questions
C10 What are the computational complexity an…
Sub-questions (10)
C10.1Layer 5 / 10hard~2 min read
What is ENSM training complexity?
O(T·D·H²·L), ~24 GPU-hours.
Key Takeaway
T=7300, D=446, H=512.
Detailed Explanation
ENSM: O(T·D·H²·L) with T=7300 days, D=446, H=512, L=4 → ~24 GPU-h.
Why It Matters
Research significance: Complexity.
Energy policy relevance: Cost.
Methodological contribution: C10.
C10.2Layer 5 / 10hard~2 min read
What is MAPPO training complexity?
O(E·A·S²), ~72 GPU-hours.
Key Takeaway
Optimization cost.
Detailed Explanation
MAPPO: O(E·A·S²) with E=5000, A=7, S=446 → ~72 GPU-h.
Why It Matters
Research significance: Complexity.
Energy policy relevance: Cost.
Methodological contribution: C10.
C10.3Layer 5 / 10hard~2 min read
What is inference 0.3s?
26-year scenario on CPU.
Key Takeaway
O(26·D·H·L).
Detailed Explanation
Forward pass for 26-year scenario: 0.3 seconds on CPU.
Why It Matters
Research significance: Speed.
Energy policy relevance: Exploration.
Methodological contribution: C10.
C10.4Layer 5 / 10hard~2 min read
What is ~$150 cloud cost?
120 GPU-hours on AWS p3.2xlarge.
Key Takeaway
Economically feasible.
Detailed Explanation
Total training ~120 GPU-h ≈ $150 on AWS.
Why It Matters
Research significance: Cost.
Energy policy relevance: Feasibility.
Methodological contribution: C10.
C10.5Layer 5 / 10hard~2 min read
What is near-linear speedup to 7 GPUs?
Multi-agent enables distributed training.
Key Takeaway
7 agents → 7 GPUs.
Detailed Explanation
Near-linear speedup when distributing 7 agents across 7 GPUs.
Why It Matters
Research significance: Scalability.
Energy policy relevance: N/A.
Methodological contribution: C10.
C10.6Layer 5 / 10hard~2 min read
What is scalability to 81 agents?
Provincial-level expansion possible.
Key Takeaway
Hierarchical aggregation.
Detailed Explanation
Framework scales to 81 provincial agents with O(A log A) communication.
Why It Matters
Research significance: Scalability.
Energy policy relevance: Provincial policy.
Methodological contribution: C10.
C10.7Layer 5 / 10hard~2 min read
What is 15GB GPU RAM training?
Memory footprint during training.
Key Takeaway
A100 40GB sufficient.
Detailed Explanation
Training requires ~15GB GPU RAM.
Why It Matters
Research significance: Hardware.
Energy policy relevance: N/A.
Methodological contribution: C10.
C10.8Layer 5 / 10hard~2 min read
What is 2GB inference memory?
Memory during policy generation.
Key Takeaway
CPU deployment.
Detailed Explanation
Inference requires ~2GB, enabling CPU deployment.
Why It Matters
Research significance: Deployment.
Energy policy relevance: Accessibility.
Methodological contribution: C10.
C10.9Layer 5 / 10hard~2 min read
What is O(A²) vs O(A log A)?
All-to-all vs hierarchical communication.
Key Takeaway
81 agents tractable.
Detailed Explanation
Hierarchical aggregation: O(A log A) vs all-to-all O(A²).
Why It Matters
Research significance: Algorithm.
Energy policy relevance: N/A.
Methodological contribution: C10.
C10.10Layer 5 / 10hard~2 min read
What enables real-time policy exploration?
Inference <5 min for 26-year scenario.
Key Takeaway
Stakeholder demos.
Detailed Explanation
Fast inference enables real-time policy exploration and stakeholder demos.
Why It Matters
Research significance: Usability.
Energy policy relevance: Policy exploration.
Methodological contribution: C10.