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.

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.

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.

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

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.

Scott Alexander shares his experiences and challenges in running a microgrants program, offering insights and advice for others considering similar initiatives.

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

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

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.

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.

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

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

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

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.

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.

Scott explains that while people naturally use emotivism (the 'yay/boo' theory) for moral reasoning, this creates serious problems by lumping everything into 'good' or 'bad' categories, and argues that converting moral debates into specific questions about actions and applying utilitarianism provides a better framework for resolving complicated issues.