Scott analyzes whether AI forecasters will plateau near human levels or keep improving, arguing that even non-miraculous progress could yield 4-12 percentage point improvements in prediction market accuracy.

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 examines the Metaculus Threat To Democracy Index, which uses crowdsourced forecasting to measure democratic health, discussing its advantages over traditional expert-based indices and its vulnerabilities to manipulation and selection bias.

Scott Alexander reviews recent developments in prediction markets and forecasting, including regulatory changes, platform pivots, and debates about the field's future.

Scott Alexander analyzes the surprisingly low existential risk estimates from a recent forecasting tournament, particularly for AI risk, and explains why he only partially updates his own higher estimates.

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

A study on expert prediction of behavioral economics experiments finds that experts have only a slight advantage over non-experts, suggesting that a separate 'rationality' skill may be more important than specific expertise.

Scott Alexander shares and comments on highlights from Philip Tetlock's 'Superforecasting', discussing forecasting, cognitive biases, and organizational effectiveness.