Scott examines the rise of AI superforecasters that now match or exceed top human forecasters, explores how they work and their current performance, and analyzes implications for decision-making, prediction markets, and the future role of AI opinions. Longer summary

Scott discusses the emergence of AI superforecasters that are now matching or slightly exceeding top human forecasters in prediction accuracy. He describes how these AI systems work (using scaffolding around frontier models like GPT/Claude), demonstrates their use with examples from FutureSearch and Preseen, and analyzes their performance on platforms like Metaculus where they're competing in tournaments against humans. The post explores both near-term implications (AI forecasters being easier to access than human superforecasters, potentially influencing policy and business decisions) and longer-term possibilities (AI forecasters serving as an 'opinion layer' for AI systems, transformation of prediction markets into AI-vs-AI competitions). Scott argues these developments could be genuinely beneficial, giving people access to superhuman forecasting ability just as AI threatens other aspects of society. Shorter summary

Scott uses Claude AI to help research California primary races and finds its tailored candidate analyses and recommendations align well with his eventual voting choices, suggesting AI advisors could improve democratic participation. Longer summary

Scott Alexander demonstrates how he used Claude AI to help research and make decisions for local California primary elections. He shares detailed examples of Claude's analysis of candidates and ballot measures, showing how the AI provided comprehensive summaries of candidates' positions, backgrounds, and endorsements tailored to his stated political preferences (centrist liberal, YIMBY, abundance-oriented). He tested Claude's recommendations against his own eventual choices across 10 races, finding strong agreement (5 perfect matches, 3 second-choices). Scott concludes that AI voting advisors could be valuable both for people who don't have time for deep research and for enhancing the research of those who do, and suggests this could be important for democratic decision-making in a post-AGI future. Shorter summary

Scott argues that taking the Giving What We Can Pledge to donate a fixed percentage of income is the single most impactful decision most people can make, eliminating donation stress while maximizing charitable impact. Longer summary

Scott advocates for taking the Giving What We Can Pledge, arguing that committing to donate a fixed percentage of income (typically 10%) to effective charities is one of the most impactful decisions someone can make. He describes how he used to feel stressed and irrational about charitable giving before discovering the pledge, and explains that having a predetermined commitment eliminates the guilt and decision fatigue of responding to individual fundraising appeals. The post emphasizes that for most people, financial donations are their most powerful tool for changing the world, and that making a binding pledge - rather than relying on willpower for each donation - is the key to actually following through on altruistic values. Shorter summary

Scott Alexander explores different types of prediction markets, their uses, limitations, and ethical considerations in decision-making and information gathering. Longer summary

Scott Alexander discusses various types of prediction markets, including information markets, decision markets, attention markets, and action markets. He explores their potential uses, limitations, and ethical considerations. The post covers how these markets can be used to predict past and present events, guide decision-making, allocate attention to important issues, and incentivize actions. Scott also discusses the challenges and potential pitfalls of each type of market, such as trust issues, the need for resolution, and potential for abuse. He concludes by drawing parallels between prediction markets and concepts in AI safety and neuroscience. 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 shares his experiences and challenges in running a microgrants program, offering insights and advice for others considering similar initiatives. Longer summary

Scott Alexander recounts his experience running a microgrants program, detailing the challenges and complexities involved. He discusses the difficulty of evaluating grant proposals, relying on expert advisors, dealing with applicants' poor grant-writing skills, and navigating the moral dilemmas of effective altruism. Scott concludes by questioning whether running such a program is worthwhile for most people, suggesting alternatives like donating to established charities, and proposing a new impact certificate-based system for future grant-making. Shorter summary

Scott Alexander discusses the dangers of relying on 'Heuristics That Almost Always Work' through various examples, highlighting their limitations and potential consequences. Longer summary

Scott Alexander explores the concept of 'Heuristics That Almost Always Work' through various examples, such as a security guard, doctor, futurist, skeptic, interviewer, queen, and weatherman. He argues that while these heuristics are correct 99.9% of the time, they provide no real value and could be replaced by a rock with a simple message. The post highlights the dangers of relying too heavily on such heuristics, including wasted resources on experts, false confidence, and the potential for catastrophic failures when the rare exceptions occur. Scott concludes by noting that those who dismiss rationality often rely on these heuristics themselves, and emphasizes the importance of being aware of the 0.1% of cases where the heuristics fail. Shorter summary

Scott analyzes motivated reasoning as misapplied reinforcement learning, explaining how it might arise from the brain's mixture of reinforceable and non-reinforceable architectures. Longer summary

Scott explores the concept of motivated reasoning as misapplied reinforcement learning in the brain. He contrasts behavioral brain regions that benefit from hedonic reinforcement learning with epistemic regions where such learning would be detrimental. The post discusses how this distinction might explain phenomena like 'ugh fields' and motivated reasoning, especially in novel situations like taxes or politics where brain networks might be placed on a mix of reinforceable and non-reinforceable architectures. Scott suggests this model could explain why people often confuse what is true with what they want to be true. Shorter summary

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

Scott Alexander critiques Bryan Caplan's constraints vs preferences model of mental illness, proposing instead a goals vs urges framework that better explains both mental and physical health issues. Longer summary

Scott Alexander responds to Bryan Caplan's critique of psychiatry, focusing on Caplan's distinction between constraints and preferences in mental illness. Scott argues that this model is flawed and doesn't accurately represent mental or even many physical illnesses. He proposes a more nuanced model based on goals (endorsed preferences) and urges (unendorsed preferences), using examples to show how this better explains behavior in both mental and physical health contexts. Scott concludes that this model allows for a more libertarian approach, supporting individuals in achieving their goals, whether through addressing constraints or managing urges. Shorter summary

A fictional tale about choosing between cosmic principles when given ultimate power, ultimately satirizing decision paralysis and the concept of balance. Longer summary

This post is a fictional story about a person who finds an Artifact that grants mastery of the universe. The protagonist encounters a series of demon-like entities, each representing different philosophical concepts such as Order, Chaos, Balance, Excess, and various meta-levels of these ideas. Each entity tries to convince the protagonist to use the Artifact for their domain. The story becomes increasingly complex and absurd as more entities appear, presenting increasingly meta arguments about decision-making and balance. In the end, the protagonist, overwhelmed by the complexity, hastily chooses 'normal Balance' and destroys the Artifact. The story concludes with a reflection on the questionable wisdom of this choice and the protagonist's reluctance to spend more time on such an important decision. Shorter summary

Scott Alexander reviews 'Inadequate Equilibria' by Eliezer Yudkowsky, a book exploring systemic failures, decision-making, and the limits of social consensus. Longer summary

Scott Alexander reviews Eliezer Yudkowsky's book 'Inadequate Equilibria', which explores why bad situations persist despite pressure to improve them, when to trust social consensus vs. personal reasoning, and the pitfalls of overusing the Outside View in decision-making. The book offers a framework for analyzing systemic failures and inefficiencies, but Scott finds its treatment of the Outside View somewhat disappointing. He recommends reading the book despite its flaws, citing its thought-provoking nature and useful conceptual toolbox. Shorter summary

Scott Alexander examines the underutilization of prediction aggregation platforms like Metaculus, exploring potential reasons and expressing surprise at their lack of widespread adoption. Longer summary

Scott Alexander discusses the potential of prediction aggregation platforms like Metaculus, questioning why they haven't gained more traction despite their proven accuracy and utility. He explores various explanations, including government regulation, public perception, and signaling issues. The post includes insights from Prof. Aguirre of Metaculus, who highlights challenges such as limited resources and the difficulty many people have in understanding probabilistic predictions. Scott expresses surprise at the lack of wider adoption and suggests that the bottleneck in scaling these platforms seems unnecessary given the abundance of interested, capable predictors. Shorter summary

Scott Alexander examines the ethics of sympathy for workers in difficult professions, exploring the tension between economic incentives and personal experiences in shaping our views on labor issues. Longer summary

Scott Alexander explores the ethics of sympathy for workers in difficult professions, comparing his support for striking junior doctors with his lesser sympathy for struggling adjunct professors. He examines the role of personal experience, economic incentives, and societal obligations in shaping our views on these issues. The post delves into the complexities of 'skin in the game' arguments, discussing whether those directly affected by a situation have unique insights or are too biased to offer objective assessments. Scott uses personal anecdotes and hypothetical scenarios to illustrate the tension between rational economic arguments and emotional realities, ultimately questioning whether personal experience provides knowledge that can't be fully reduced to factual propositions. Shorter summary

Scott Alexander uses a king-and-viziers analogy to argue that people can be inherently good even when their actions seem selfish, and explores the nature of evil and human goodness. Longer summary

Scott Alexander explores the concept of human goodness using an analogy of a wise king misled by evil viziers. He argues that people can be inherently good even when their actions seem selfish, much like the king who makes bad decisions based on biased information. Scott suggests that we should identify people with the 'king' of their minds rather than the 'viziers', seeing them as fundamentally good despite their actions. He discusses the nature of evil, defining it as certain habits of mind that make it easy for one's 'viziers' to mislead them. The post ends by relating this concept to Trivers' theory of consciousness. Shorter summary