Explains the SKC (Srinivasan, Karger, Chen) mechanism for prediction markets on unverifiable outcomes. Markets resolve using crowd consensus as the outcome, with delta-based scoring rewarding participants for moving markets toward final consensus. Enables markets for subjective questions lacking ground truth.

Educational thread on the game-theoretic foundations of prediction markets. Explains why truth-telling is the dominant strategy through incentive compatibility, details how LMSR works as a proper scoring rule, and argues prediction market builders need economists and game theory experts on their teams.

Academic survey of prediction mechanism design from a mechanism design perspective. Covers scoring rules, market scoring rules (LMSR), cost-function-based market makers, dynamic parimutuel markets, incentive compatibility, combinatorial markets, and peer prediction systems for subjective events where ground truth doesn't exist.

Proposes a mechanism for prediction markets where outcomes cannot be objectively verified. Uses the last reporter's prediction as a reference point, creating incentives for truthful reporting through negative cross-entropy payments. Proves truthful reporting is a perfect Bayesian equilibrium.

Investigates combinatorial prediction markets, which extend the standard model to support forecasts on conditional events (e.g., A given B) and Boolean combinations of events rather than only base events. Reports experimental results comparing combinatorial versus flat market structures on forecasting accuracy and calibration. Co-authored by Robin Hanson, whose LMSR underpins most automated prediction markets.