Scott Alexander praises Polymarket's election success but argues their Trump odds were mispriced, explaining why Trump's win doesn't significantly validate their numbers over other forecasters. Longer summary

Scott Alexander congratulates Polymarket for their success during the recent election, but argues that their Trump shares were mispriced by about ten cents. He uses Bayes' Theorem to explain why Trump's victory doesn't significantly vindicate Polymarket's numbers. Scott compares the situation to non-money forecasters like Metaculus versus real-money markets like Polymarket, explaining why he initially trusted the former more. He discusses the impact of a large bettor named Theo on Polymarket's odds and addresses several objections to his argument. Scott concludes that while prediction markets are valuable, they can sometimes fail and require critical thinking. 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