Scott Alexander argues that psychiatric diagnoses, while imperfect, are useful tools despite potentially combining multiple conditions with different causes. Longer summary

Scott Alexander discusses the criticism that psychiatric conditions like autism or depression are not unitary categories but rather collections of different conditions with different causes. He argues that this view, while correct, is not as revolutionary as some think and doesn't invalidate the usefulness of these diagnoses. Scott compares psychiatric conditions to medical conditions like pulmonary edema or stroke, which can have multiple causes but are still useful diagnostic categories. He suggests that psychiatric disorders might ultimately be understood as computational conditions, with various biological, psychological, and environmental factors affecting the brain's computational parameters. The post emphasizes that while research into subtypes of conditions like depression hasn't been very productive, current psychiatric diagnoses remain the most useful tool available, despite their limitations. Shorter summary

Scott Alexander defends the validity of intelligence and IQ tests by comparing them to comas and the Glasgow Coma Scale in medicine. Longer summary

Scott Alexander argues that intelligence and IQ tests are valid concepts, analogous to comas and the Glasgow Coma Scale in medicine. He contends that whether there's a single general factor of intelligence is less important than the usefulness of IQ as a predictive measure. Scott draws parallels between how comas and intelligence are measured, showing that both involve multiple factors combined into a single scale used for predictions. He criticizes arguments against the existence of intelligence as often being a motte-and-bailey fallacy, where the easily defensible position (uncertainty about a single general factor) is used to imply that all claims about intelligence are meaningless. Shorter summary

Scott Alexander argues for the value of using quantification and made-up statistics in decision-making, even when imperfect, as they often outperform intuition and reveal biases in our thinking. Longer summary

Scott Alexander discusses the value of using made-up statistics and quantification in decision-making, even when the numbers are imperfect. He argues that this approach can often lead to better outcomes than relying solely on intuition or System 1 thinking. The post begins with an anecdote about teaching Bayes' Theorem, then explores how quantification can improve decision-making in various fields, including utilitarianism and medical diagnosis. Scott emphasizes that while these numbers may be imperfect, they often provide more accurate results than gut feelings, which can be severely biased. He concludes by advocating for applying made-up models to various problems as a way to challenge our intuitions and gain new perspectives. Shorter summary