Hong-Page theorem: diversity beats ability in collective estimation
Hong-Page Theorem: A diverse group of problem-solvers can outperform a group of high-ability solvers.
Key insight: individual error = bias² + variance; crowd mean cancels variance when estimates are independent.
Left: individual estimates (dots) vs aggregates. Right: accuracy vs diversity scatter — adding a worse-but-different estimator often improves the ensemble.
Correlation ρ reduces diversity benefit — crowds work best when estimators are independent.