An independent open-source research lab producing institutional-grade strategic analysis using purely public, verifiable data (OSINT). 专注于网络-物理风险量化与开源数据科学建模的独立研究实验室，输出对标机构研报质量的战略分析。
| Metric | Status |
|---|---|
| Frameworks | HEI 5.3 | AIPI 2.0 | SINT 1.0 | SSR-OSINT V4.0 |
| Red-Team Audits | 11 rounds completed |
| Active Case Studies | 5 (Hormuz | Power Battery | AI Supply Chain | Cross-Border Logistics | Digital Infrastructure) |
| Last Updated | 2026-05-25 |
| Verification Standard | All predictions include falsification conditions + post-hoc audit trails |
AI infrastructure does not scale linearly; it migrates across sequential physical bottlenecks.
| Level | Constraint Layer | Current Focus | Status |
|---|---|---|---|
| L1 | Compute (HBM / Packaging) | Samsung Union / HBM3e Supply | Active |
| L2 | Contract / Finance | Anthropic-SpaceX Timing Structure | Ready |
| L3 | Deployment (Power / Grid / Permitting) | Western China Water EIA | Monitoring |
This repository maintains an independent research framework for quantifying cyber-physical and supply-chain risks using purely public, verifiable data (OSINT).
All outputs are developed as structured strategic analyses to test the precision of constraint-based modeling under open-source information limits. We do not provide investment advice, trade recommendations, or non-public material non-public information (MNPI).
本仓库维护一个独立研究框架，利用100%公开、可验证的数据（OSINT），对网络-物理与供应链风险进行量化分析。
所有产出均定位为结构化战略分析，用于测试基于约束的建模在开源信息条件下的精准度。不提供投资建议、交易推荐或任何未公开信息（Non-MNPI）。
A structured expert judgment (SEJ) framework for quantifying crisis escalation under open-source intelligence constraints. Current case study: Front A (Hormuz Strait institutionalized passage control).
Status: Active validation. HEI = 0.67 ± 0.08 (S3: Institutionalized Rule Competition).
AIPI 2.0 — Seven-Layer Strategic Pressure Architecture
A structured expert judgment (SEJ) framework for quantifying AI supply-chain strategic pressure and demand-validation endgames under open-source intelligence constraints. Integrates seven-layer bottleneck cartography, multi-polar bidirectional game-theoretic equilibrium analysis, and falsifiable scenario planning.
Current case study: Front D (AI Investment Paradox) — Full-chain inflation vs. efficiency deflation strategic传导 (May 2026).
Status: v2.0 baseline delivered. Active monthly calibration. AIPI = 0.64 ± 0.08 (Moderate Pressure). 20+ falsifiable conditions under observation.
SINT 1.0 — Five-Layer Bottleneck-Conduction Chain A structured expert judgment (SEJ) framework for industrial chain strategic decomposition and resilience quantification under open-source intelligence constraints. Integrates five-layer bottleneck cartography (L1–L5), three-horizon growth matrix (H1–H3), three-track parallel logical audit, and falsifiable verification protocols with upward/downward conduction-chain dynamics. Current case study: General Industrial Chain Application — Full-stack bottleneck identification, congestion transfer law validation, and competitive species classification across upstream resources to policy/compliance layers. Status: v1.0 baseline delivered. Active quarterly applicability review. Four-layer closed-loop OSINT architecture operational: Input (source hierarchy) → Processing (logical audit) → Output (argument grading) → Feedback (falsification & backtracking). Resilience quantification: 3D weighted scoring (Supply Stability 40%, Substitution Speed 30%, Cost Absorbability 30%).
| Tier | Nature | Distribution |
|---|---|---|
| L1 | Public Methodology & Framework / 公开逻辑层 | This repository (GitHub) |
| L2 | De-sensitized Case Study Samples / 战略审计脱敏版 | Academic trust circle / Smartkarma |
| L3 | Institutional Customized Worksheets / 核心决策对账单 | Bilateral NDA only / 不公开 |
A structured expert judgment (SEJ) framework for quantifying industrial supply-chain strategic pressure and demand-validation endgames under open-source intelligence constraints. Integrates five-layer bottleneck cartography (L1–L5), multi-polar competitive speciation analysis, three-horizon growth matrix (H1/H2/H3), and falsifiable scenario planning with replicable verification protocols.
Current case study: Global EV battery supply chain (May 2026) — Full-chain bottleneck migration analysis from manufacturing capacity (L3) to resource pricing (L1) and regulatory compliance (L5).
Status: v1.0 baseline delivered. Active quarterly calibration. Framework valid through 30 September 2026. 11 falsifiable conditions under observation. Base-case probability distribution: Alpha (Steady Evolution) 45% / Beta (Systemic Rupture) 35% / Gamma (Black Swan) 20%.
- 100% Public Data: All anchor facts sourced from publicly verifiable channels (Reuters, EIA, official gazettes, industry databases). No proprietary or classified intelligence subscriptions.
- No MNPI: We do not handle, request, or infer any material non-public information.
- No Financial Positions: The maintainer holds no financial positions in any analyzed entities.
- Structured Expert Judgment (SEJ): All quantitative outputs reflect structured subjective judgment under uncertainty, not statistical predictions.
- Falsifiable Track Record: Every prediction includes explicit falsification conditions and post-hoc audit trails. Errors are preserved in version history with attribution.
| Case Study | Framework | Status | Last Updated |
|---|---|---|---|
| Hormuz Strait Crisis (Front A) | HEI 5.3 + S0-S4 State Machine | Active monitoring | 2026-05-22 |
| Power Battery Supply Chain | Five-Layer Bottleneck Model | Validation phase | 2026-05-22 |
| Cross-Border Logistics Policy | PCFI (Policy-Capacity Friction Index) | Methodology design | 2026-05-20 |
- Hormuz Crisis V15 (L2) — De-sensitized case study sample / 霍尔木兹危机战略审计脱敏版
- 动力电池产业链v1.0 -动力电池产业链OSINT战略审计报告（机构版）
- SGR 1.4 战略审计报告 —SGR 1.4 战略审计报告（脱敏版 / Level 2）
- 数字基础设施的地缘韧性 -数字基础设施的地缘韧性——从代码漏洞到变压器断供的完整风险光谱
- AI 投资悖论 -全链通胀、需求验证与战略传导推演
- 2026 Judgement Log — Falsifiable prediction records & intuition narratives / 可证伪预测记录与判断日志
- Track Record — Post-hoc audit, error preservation & version history / 事后审计、错误保留与版本历史
- Front B Experiment — Cross-domain methodology migration test (AI supply chain) / 跨领域方法论迁移实验（AI供应链）
This laboratory operates as a non-profit academic exploration. We accept research grants and knowledge subscriptions from global institutional researchers to support our independent open-source methodology research.
Contact: scanwang@gmail.com (Non-urgent matters, 48-72h response cycle)
Disclaimer: All outputs are methodological case studies for academic validation purposes only. They do not constitute legal, compliance, or investment advice.