I simulated closing the Strait of Hormuz on real oil trade data
OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade. The model: The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked. Insights: The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0…
In plain words
This visualization tool models how disruptions to global oil trade propagate through supply chains using real trade data. Designed for educators and policy analysts, it simulates scenarios like closing the Strait of Hormuz to show how crises spread across interconnected trading networks. The tool demonstrates second-order effects—such as how price increases affect countries indirectly, even when they don't directly depend on a blocked chokepoint—revealing the complex mechanics of oil market dynamics during geopolitical shocks.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade. The model: The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked. Insights: The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0 oil from Hormuz directly, yet their reserves are depleted faster because other countries reactively increase their safety oil stock, increasing oil price, making stockouts more expensive for everyone. The model also gives price dynamics which are interesting on their own: the price increase is not immediate, it follows sequentially as countries reserves deplete. Some caveats: 1. For producer nodes, depletion means their export slack is reduced/exhausted. 2. No sanctioned trade (UN Comtrade data) Technical Details: The visualization is 600 lines of flask plus js frontend (LLM assisted visualization with ground-truth matching the original numerical exercise of the paper) Paper with proofs/theory: https://arxiv.org/abs/2607.17491
Does the same job
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Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
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A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
AI · Jul 2026 · github.com
