How to avoid getting lost reading Scott Alexander and his 1500+ blog posts? This unaffiliated fan website lets you sort and search through the whole codex. Enjoy!

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Apr 09, 2024
acx

Highlights From The Comments On The Lab Leak Debate

141 min • 18,301 words • 761 comments • 120 likes • podcast
Scott Alexander responds to comments on his COVID-19 origins debate post, addressing criticisms and maintaining his support for the zoonosis theory. Longer summary
Scott Alexander provides a detailed response to comments and criticisms of his previous post on the COVID-19 origins debate. He addresses arguments against zoonosis, clarifies misunderstandings, and discusses the methodologies used by various parties in the debate. He maintains his position favoring zoonosis over lab leak, while acknowledging the complexity of the issue and the ongoing nature of the debate. Shorter summary
Mar 28, 2024
acx

Practically-A-Book Review: Rootclaim $100,000 Lab Leak Debate

142 min • 18,357 words • 905 comments • 369 likes • podcast
Scott Alexander reviews a $100,000 debate on COVID-19 origins, where the zoonotic hypothesis unexpectedly won against the lab leak theory. Longer summary
Scott Alexander reviews a debate on the origins of COVID-19 between Saar Wilf, who supports the lab leak hypothesis, and Peter Miller, who argues for zoonotic origin. The debate was part of a $100,000 challenge by Wilf's Rootclaim project. Miller won decisively, with both judges ruling in favor of zoonotic origin. Alexander analyzes the debate format, arguments, and aftermath, discussing issues with Bayesian reasoning, extreme probabilities, and the challenges of resolving complex scientific questions through debate. Shorter summary
Jan 16, 2024
acx

Against Learning From Dramatic Events

31 min • 3,906 words • 638 comments • 282 likes • podcast
Scott Alexander argues against significantly updating beliefs based on single dramatic events, advocating for consistent policies based on pre-existing probability distributions. Longer summary
Scott Alexander argues against dramatically updating one's beliefs based on single events, even if they are significant. He contends that a good Bayesian should have distributions for various events and only make small updates when they occur. The post covers several examples, including COVID-19 origin theories, 9/11, mass shootings, sexual harassment scandals, and crises in the effective altruism movement. Scott suggests that while dramatic events can be useful for coordination and activism, they shouldn't significantly alter our understanding of underlying probabilities. He advocates for predicting distributions beforehand and maintaining consistent policies rather than overreacting to individual incidents. Shorter summary
Apr 14, 2020
ssc

A Failure, But Not Of Prediction

33 min • 4,215 words • 863 comments • podcast
Scott Alexander argues that the media's failure in coronavirus coverage was not about prediction, but about poor probabilistic reasoning and decision-making under uncertainty. Longer summary
This post discusses the media's failure in covering the coronavirus pandemic, arguing that the issue was not primarily one of prediction but of probabilistic reasoning and decision-making under uncertainty. Scott Alexander argues that while predicting the exact course of the pandemic was difficult, the media and experts failed to properly convey and act on the potential risks even when the probability seemed low. He contrasts this with examples of good reasoning from individuals who took the threat seriously early on, not because they were certain it would be catastrophic, but because they understood the importance of preparing for low-probability, high-impact events. Shorter summary
Oct 08, 2018
ssc

Kavanaugh: A Probability Poll

10 min • 1,179 words • 533 comments • podcast
Scott Alexander analyzes a survey on readers' estimated probabilities of Kavanaugh's guilt, finding significant partisan differences and no clear consensus even with probabilistic thinking. Longer summary
Scott Alexander conducted a survey asking readers to estimate the probability of Judge Kavanaugh being guilty of sexually assaulting Dr. Ford. The post analyzes the results, breaking them down by political party, gender, and background knowledge. The average probability given was 52.64%, with significant partisan differences. The survey also explored whether respondents thought the accusations were sufficient to reject Kavanaugh's nomination. Scott notes that even when encouraged to think probabilistically, people's responses still showed strong partisan biases, and there was no clear consensus even among politically neutral respondents. Shorter summary