Challenges the smart-versus-dumb money dichotomy in prediction markets by synthesizing research from Snowberg/Wolfers/Zitzewitz, INSEAD's BIN model, and Wharton's cognitive search framework. Argues that noisy traders fund the probability space rather than serve as exit liquidity, and compares how binary CLOBs versus continuous probability markets decompose and harness noise differently.

Fits a Bayesian hierarchical model to 292 million trades across 327,000 contracts on Kalshi and Polymarket to decompose calibration errors into structured components: universal horizon effects, domain-specific biases, and trade-size scale effects, which together explain 87.3% of variance on Kalshi. Finds persistent underconfidence in political markets where prices compress toward 50%, and shows that large trades amplify this pattern on Kalshi but not on Polymarket, pointing to platform-specific microstructure differences.

Proposes a two-stage screener that uses Granger causality to find lead-lag pairs across Kalshi Economics markets, then passes candidates through an LLM that checks whether the proposed direction has a plausible economic transmission mechanism based on event descriptions. The LLM re-ranker barely moves the win rate (51.4% to 54.5%), but it dramatically shrinks the downside — average losing trade drops from $649 to $347 by filtering out statistically fragile links that look good in backtests but break in practice.