Applied research at the intersection of strategic foresight, AI system design, and quantitative risk modeling. The goal is publishable work that holds up in both academic and operational settings.
Three-scenario portfolio stress-testing U.S. CBDC deployment through 2030. Capstone for Georgetown's Certificate in AI Scenarios for Strategic Decision-Making (Dr. Frederic Lemieux, instructor). Methodology: diagnostic scenarios designed to expose load-bearing assumptions, not predict outcomes.
Artifacts:
- 📄 Capstone report (25 pages, PDF)
- 🎬 Pathway Bravo video — 2035 retrospective on the 2028 Digital Bank Run (1:44, MP4)
- 🗂 Storyboard — Seven-panel scenario sequence (PPTX)
→ Full project: /ai-scenarios-strategic-decision-making/
/ai-scenarios-strategic-decision-making/— Georgetown capstone (2026)/strategy/— Working notes on risk management, scenario design, game theory/research-engineering/— Reference implementations, math models, system design/papers/— Drafts and submitted manuscripts
Open to collaboration on applied research in foresight, AI architecture, and quantitative risk. Pull requests welcome for substantive improvements; discussion via Issues for higher-level questions.
Code: MIT. Written work and figures: CC BY-NC-ND 4.0.
Maintained by Bledar Blake Zenuni.