Optimocracy: Causal Inference on Cross-Jurisdictional Policy Data to Maximize Median Health and Wealth

Abstract: Thousands of jurisdictions (municipal, state, federal, international) have exposed populations to different policies over decades. This cross-jurisdictional variation is a natural experiment. Optimocracy: (1) Apply causal inference to this historical policy data, (2) Identify which policies predict above-average median income and healthy life years, (3) Publish recommendations for every major vote, (4) Track politician alignment with evidence, (5) Algorithmically fund the campaigns of the most aligned policymakers via SuperPAC. Politicians still decide; the algorithm just makes ignoring evidence expensive.

Summary: Thousands of jurisdictions have made different policy and budget choices over decades, creating a natural experiment. Optimocracy applies causal inference to this cross-jurisdictional time-series data to identify which policies predict above-average median income and healthy life years. It then publishes evidence-based recommendations for every major vote, tracks politician alignment, and funds aligned candidates via SuperPAC, making suboptimal policy politically expensive while preserving democratic structures.