Random topics, anything that interests me
Google's Systematic Exploitation
Google’s Systematic Exploitation of Everyone: “Don’t Be Evil” Became a Perversely Egalitarian “Be Evil”
Random topics, anything that interests me
Random topics, anything that interests me
Microsoft's Extortion Through Manufactured Captivity
Microsoft’s dominance is no accident; it is a calculated masterpiece of commercialized deprivation. By reframing technical regression as innovation and dependency as a feature, the company has perfected the art of selling stagnation at a premium.
The Greedy Algorithm Is Not the Problem
The Greedy Algorithm Is Not the Problem
Insights on CMS
Insights on CMS
The Epistemological Void: Why Predictive Models Fail at Strategic Insight
The Epistemological Void: Why Predictive Models Fail at Strategic Insight
The Deceiver's Paradox: Why Deception is an Unreliable Strategic Foundation
The Deceiver’s Paradox: Why Deception is an Unreliable Strategic Foundation
PERT: Logical Paradox and Epistemological Futility in Estimation
PERT: Logical Paradox and Epistemological Futility in Estimation
Overfit
Overfitting Reconsidered: An Epistemic Framework for Strategy Evaluation Introduction In quantitative finance, overfitting is usually defined in statistical terms. A model is said to overfit when it captures noise in historical data rather than persistent structure. This definition assumes that the past contains reliable information about the future and that the main risk lies in extracting too much of it. This essay adopts a stricter and more rigorous definition. Overfitting is treated as an epistemic failure rather than a statistical one. A model is overfit whenever it relies on information that is not known ex-ante or not known with certainty. This shift in definition changes which models are admissible and reverses many common intuitions about realism and sophistication. ...