Category: Risk & Decisions
Practical frameworks for making decisions under uncertainty, evaluating risk, checking evidence, and understanding where models and metrics can mislead.
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AI Has Started Learning From AI
The fight over Claude distillation looks like another U.S.–China technology dispute. Underneath it is something stranger: AI systems…
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The AI That Predicts Your Future Will Have a Problem: You Can Read the Prediction
An AI could know us statistically well enough to anticipate our decisions and probable futures. The problem begins…
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Seismic Risk in the AI Era: What Has Actually Improved (and What Hasn’t)
AI improved the speed of earthquake early warning and parametric insurance, but did not solve earthquake prediction. What…
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CIES Nearly Nailed Neymar’s €222M Price. The Lesson Is Better Than “Algorithm vs. Intuition”
PSG paid €222 million for Neymar in 2017. Contrary to this article’s previous claim, CIES did not value…
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AI Sports Predictions: How to Tell Whether an Accuracy Number Means Anything
An 80% or 90% hit rate can sound impressive and still tell you very little. Evaluate the baseline,…
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What We Mean by “Scalar Valuation” When Comparing Tools
ScalarPivot uses “scalar valuation” as shorthand for making weights explicit and reducing several criteria to a comparable score.…
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How to Evaluate the Risk of Automating a Decision With AI
Before turning an AI workflow into a score, separate impact, reversibility, scale, and legal requirements. A score can…
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AI Reputation Risk: When Should You Automate?
A practical four-factor framework for deciding when AI drafting is useful, when verification becomes the real cost, and…
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How to Make Better Tool and Workflow Decisions Under Incomplete Information
How to make high-stakes tool and workflow decisions under incomplete information using structured decision matrices.