Scott Alexander argues that even seemingly extreme media misinformation usually involves misleading presentation of true facts rather than outright fabrication, examining several reader-provided counterexamples. Longer summary

Scott Alexander responds to criticisms of his previous post about media rarely lying by examining several examples readers provided. He argues that even in extreme cases like Alex Jones' Sandy Hook conspiracy theories or claims about election fraud, media sources are typically highlighting true but misleading facts rather than outright fabricating information. Scott contends this matters because it means efforts to censor 'misinformation' will always require subjective judgment calls rather than being a straightforward process of removing falsehoods. He suggests people want to believe bad actors are doing something fundamentally different than good faith reasoning, but in reality most are just reasoning poorly under uncertainty. Shorter summary

Scott Alexander presents a comprehensive FAQ on prediction markets, arguing for their accuracy, canonicity, and potential to solve the 'crisis of trust' in society. Longer summary

This post is a comprehensive FAQ about prediction markets, explaining what they are, why they are believed to be accurate and canonical, addressing common objections, and describing clever uses for them. Scott Alexander presents prediction markets as a potential solution to the 'crisis of trust' in modern society, arguing that they can provide unbiased, accurate predictions on a wide range of issues. The post also covers the current status of prediction markets and suggests ways people can help promote them. Shorter summary

Scott Alexander explores expert and reader comments on his post about motivated reasoning and reinforcement learning, discussing brain function, threat detection, and the implementation of complex behaviors. Longer summary

Scott Alexander discusses comments on his post about motivated reasoning and reinforcement learning. The post covers expert opinions on brain function and reinforcement learning, arguments about long-term rewards of threat detection, discussions on practical reasons for motivated reasoning, and miscellaneous thoughts on the topic. Key points include debates on how the brain processes information, the role of Bayesian reasoning, and the challenges of implementing complex behaviors through genetic encoding. Scott also reflects on his own experiences and the limitations of reinforcement learning models in explaining human behavior. Shorter summary

Scott Alexander argues against the claim that 'rationality free from ideology doesn't exist', asserting that recognizing irrationality implies the existence of rationality. Longer summary

Scott Alexander critiques the argument that 'there's no such thing as rationality free from ideology'. He argues that if we can identify people who are especially irrational or biased (like Alex Jones), then we must also be able to recognize those who are more rational or objective. Scott contends that while perfect rationality might be impossible, this doesn't mean we can't strive for improvement. He suggests that claiming the impossibility of true rationality is actually arrogant, as it implies one has reached the limits of what's possible. The post emphasizes the importance of recognizing that some approaches can be more rational than others, and that we should learn from those who are better at rationality rather than dismissing the concept entirely. Shorter summary

Scott Alexander evaluates his predictions about the Trump presidency, finding he performed about average overall with some notable successes and failures. Longer summary

Scott Alexander reviews and grades his predictions about Donald Trump's presidency, covering topics from Trump's base diversity to the likelihood of a coup. He analyzes his successes and failures, discussing his performance on prediction markets and his overall accuracy compared to average pundits. Scott concludes that he did about average in his predictions, with some notable successes in race-related predictions and on prediction markets, but also made mistakes in overestimating Trump's competence and underestimating his continued support from Republicans. Shorter summary

Scott Alexander critiques Glen Weyl's anti-technocracy essay, arguing for a more nuanced view of formal mechanisms in decision-making and defending rationalist approaches. Longer summary

Scott Alexander critiques Glen Weyl's essay 'Why I Am Not A Technocrat', arguing that Weyl's definition of technocracy is incoherent and his examples don't fit his own definition. Scott breaks down the concept of technocracy into several axes, including top-down vs. bottom-up, mechanism vs. judgment, and expert vs. popular opinion. He argues that formal mechanisms can be valuable in preventing bias and corruption, using examples like district creation and college admissions. Scott also defends the rationalist and effective altruism communities against Weyl's criticisms, highlighting their successes in areas like pandemic preparedness. He concludes that while critiques of technocracy are important, it's crucial to avoid oversimplifying the issue and to recognize that sometimes technocratic approaches can be beneficial. Shorter summary

Scott Alexander critiques the use of bias arguments in debates, explaining why they're often counterproductive and suggesting more constructive ways to address bias. Longer summary

Scott Alexander discusses the problems with using bias arguments in debates. He argues that these arguments are often unproductive because everyone is biased, people are hypersensitive to biases against their side, it's hard to define bias, and bias arguments don't lead anywhere productive. He suggests that bias arguments can be useful when they provide new information, can be quantified, offer unbiased alternatives, or in private conversations between trusted friends. Scott emphasizes that first-person bias arguments (recognizing one's own biases) are the most valuable, as they allow for honest self-reflection and improvement. Shorter summary

Scott Alexander argues against broadening the definitions of words like 'lie' and 'abuse', as it dilutes their meaning and reduces their usefulness in identifying problematic behavior. Longer summary

Scott Alexander argues against broadening the definition of words like 'lie' and 'abuse' to include less severe actions. He contends that this dilutes the meaning of these terms, making them less useful for identifying genuinely problematic behavior. The post discusses how overly broad definitions can lead to everyone being labeled as liars or abusers, which removes the stigma and informational value of these terms. Scott also explains how this can be exploited by bad actors to unfairly stigmatize others. He extends this argument to other terms like 'disabled', 'queer', and 'autistic', suggesting that while some broadening of definitions can be useful, it's never right to define a term so broadly that it applies to everyone or no one. Shorter summary

Scott Alexander explains postmodernism to rationalists, using the Dark Age debate as an example, and discusses its applications, risks, and critiques. Longer summary

Scott Alexander attempts to explain postmodernism to rationalists, using the debate about the existence of a European Dark Age as an example. He describes postmodernism as focusing on how politically-motivated people weave facts to tell specific stories, rather than on the facts themselves. The post discusses how this applies to various fields and how everyone uses postmodernist thinking sometimes. Scott also explores the potential risks of postmodernism collapsing into ignoring disagreeable facts and addresses critiques of the philosophy. He concludes by comparing rationalist and postmodernist approaches to dealing with subjectivity and bias. Shorter summary

Scott Alexander examines his evolving view on scientific consensus, realizing it's more reliable and self-correcting than he previously thought. Longer summary

Scott Alexander reflects on his changing perspective towards scientific consensus, sharing personal experiences where he initially believed he was defying consensus but later discovered that the scientific community was often ahead of or aligned with his views. He discusses examples from various fields including the replication crisis, nutrition science, social justice issues, and AI risk. Alexander concludes that scientific consensus, while not perfect, is remarkably effective and trustworthy, often self-correcting within a decade of new evidence emerging. Shorter summary

Scott Alexander cautions against basing opinions on limited research, using examples from medicine and economics to show how cherry-picking studies can lead to opposing conclusions. Longer summary

Scott Alexander warns against relying on a single study or a limited selection of studies to form opinions on complex issues. He illustrates this with examples from medical research and the minimum wage debate, showing how cherry-picking studies can lead to opposing conclusions. The post emphasizes the importance of considering the full body of evidence, including meta-analyses and expert opinions, while also being aware of potential biases in research and reporting. Scott concludes by advocating for skepticism and thorough investigation when evaluating claims backed by scientific studies. Shorter summary

Scott Alexander discusses how people tend to seek advice that reinforces their existing tendencies and proposes considering the opposite of appealing advice. Longer summary

Scott Alexander explores the idea that advice is often useful for some people but harmful for others, depending on their natural tendencies. He suggests that people often gravitate towards advice that aligns with their existing inclinations, potentially exacerbating their biases. The post discusses various examples of opposing advice pairs and how different groups promote different sides. Scott proposes the idea of 'advice reversal', where individuals consider doing the opposite of advice they find appealing, as it might be more beneficial for them personally. He concludes with a checklist for when to consider reversing advice. Shorter summary

Scott Alexander discusses the concept of one-sided tradeoffs, using examples from college admissions to life hacks, and suggests ways to find opportunities for 'free' gains in various decisions. Longer summary

Scott Alexander explores the concept of one-sided tradeoffs using college admissions as a starting point. He explains how most decisions involve tradeoffs between different qualities, but suggests ways to find opportunities for 'free' gains. These include insider trading (having unique knowledge), bias compensation (exploiting others' biases), and comparative advantage (specializing in a specific area). He applies this framework to policy debates, life hacks, and personal decisions, arguing that understanding these concepts can help identify opportunities where one can gain benefits without significant downsides. The post concludes with examples like considering nootropics if one isn't afraid of taking drugs, or buying houses on streets with rude names for a discount. Shorter summary

Scott Alexander explores how selection bias might create the stereotype of angry, vocal atheists, and speculates on how this concept might apply to other groups. Longer summary

This post discusses how selection bias may contribute to the stereotype of atheists as loud and angry. Scott argues that while religious people are visible in many contexts, atheists are typically only noticed when criticizing religion or advocating for atheist causes. This creates a false impression that atheists are obsessed with attacking religion. The post suggests that most atheists rarely discuss their lack of belief, but these individuals don't get attention as atheists. Scott then extends this concept to other groups, speculating that similar selection biases might contribute to stereotypes about Muslims, Christians in secular areas, and even ethnic groups. 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