Tag: probabilistic reasoning · Read Scott Alexander

Apr 09, 2024

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Highlights From The Comments On The Lab Leak Debate

131 min • 18,301 words • 761 comments • 120 likes • podcast (99 min)

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

Recurring tags: scientific debate (6), probabilistic reasoning (5), evidence analysis (4), COVID-19 origins (4), zoonosis (2), Rootclaim (2), lab leak theory (2)

Mar 28, 2024

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

132 min • 18,357 words • 905 comments • 369 likes • podcast (95 min)

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

Recurring tags: book review (191), debate (62), Bayesian reasoning (18), probabilistic reasoning (5), COVID-19 origins (4), evidence analysis (4), scientific uncertainty (3), Wuhan Institute of Virology (2), lab leak theory (2), zoonosis (2), Rootclaim (2)

Jan 16, 2024

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Against Learning From Dramatic Events

28 min • 3,906 words • 638 comments • 282 likes • podcast (21 min)

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

Recurring tags: effective altruism (79), rationality (48), COVID-19 (47), cognitive bias (31), critical thinking (23), Bayesian reasoning (18), sexual harassment (7), terrorism (5), probabilistic reasoning (5)

Apr 14, 2020

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A Failure, But Not Of Prediction

31 min • 4,215 words • 863 comments • podcast (26 min)

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

Recurring tags: media criticism (68), risk assessment (13), prediction (13), decision-making (12), probabilistic reasoning (5), pandemic response (5), expert opinion (4)

Oct 08, 2018

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Kavanaugh: A Probability Poll

9 min • 1,179 words • 533 comments • podcast (11 min)

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

Recurring tags: survey analysis (39), political polarization (27), gender differences (21), probabilistic reasoning (5), political bias (5), Supreme Court (2)