Complexity Thinking

A few years ago, an ad on a search results page was clearly labeled and easy to ignore; that sponsor‑heavy layout used to be something you saw mostly on Chinese search engine Baidu, but now the same slick, indistinguishable promotions appear across global search engines, forcing extra scrolling and turning quick answers into guesswork. Enshittification describes a three‑stage pattern in which a platform first wins users by offering clear value, then privileges paying partners and advertisers to monetize that scale, and finally degrades the product so much that it harms both users and the businesses that rely on the platform. The phrase emerged in tech conversations as a blunt way to name the predictable harms that follow unchecked scale, extractive monetization, and weak competitive pressure. This post traces how platforms move from delight to monetization to destructive extraction, why concentration and lock‑in make that arc likely, and what could plausibly halt or reverse the proces...

Sometimes a tiny change flips an entire system — a spark that either fizzles or razes a forest.  Technology and complex systems share a pattern: long stretches of incremental change that abruptly yield new regimes. Understanding how and why those shifts happen helps explain phenomena as diverse as superconductivity, sudden ecosystem collapse, and the rapid adoption of new computing platforms. In this blog post,  I’ll use a simple agent‑based fire model to show how thresholds and randomness interact, then connect those lessons to S‑curves and platform shifts, and finish by assessing whether large language models might represent a genuine platform transition.

What if the famous warning “correlation does not imply causation” isn’t cautious enough? What if a strong correlation can still be a phantom? Consider rural homes in Denmark and the Netherlands: as the number of children in a household rises, so does the count of stork nests on the roof. At face value, the curves climb in lockstep. But that doesn’t mean children attract storks or storks deliver babies—both trends most likely stem from families living in spacious countryside houses. But we’ll take this puzzle even further. In this post, I’ll show you how completely random, independent events can sometimes produce striking correlations purely by chance. We’ll run simple simulations, dissect real-world phantom patterns, and reveal how sampling quirks and data-collection oddities can cook up misleading links. By the end, you’ll see why the strongest echoes in your data can be nothing more than statistical noise.

When I first started learning about finance, I thought it was all about talent, hard work, and making the right moves at the right time. The media loves stories of traders and investors who seem to have a “golden touch,” turning every decision into gold, and I found myself imagining a world where the smartest people magically climb to the top while everyone else scrambles below. I assumed that if I learned the right strategies, studied the market endlessly, and stayed disciplined, I could do the same. But after finishing  Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics , I started to see things differently. The book lays bare how easily data can mislead us and how much of what we celebrate as “success” might really be random luck in disguise. One idea in particular stuck with me:  luck matters—a lot more than we usually admit . Suddenly, those stories of “financial geniuses” felt a little less magical and a lot more like the dice r...