Information Aggregation

The theoretical foundation comes from economist Friedrich Hayek, who argued in 1945 that no central planner can possess all the knowledge dispersed across millions of individuals, but the price system aggregates it spontaneously. Prediction markets operationalize this insight: each trader brings unique private information -- inside knowledge, specialized expertise, local observation -- and converts it into a trade. When a trader with superior knowledge buys Yes/No Shares on an underpriced outcome, the Contract Price rises toward the true probability. Competing traders then update their own beliefs from the price signal, creating a self-reinforcing convergence. This is why the Price as Probability principle works: prices reflect not one analyst's view but the weighted consensus of all active market participants, scaled by the capital they are willing to stake on their belief.

Decades of research confirm that Prediction Market prices outperform alternative forecasting methods in many domains. The Iowa Electronic Markets beat Gallup poll forecasts in 74% of U.S. presidential elections studied. Corporate decision markets at firms like Google and HP produced more accurate internal forecasts than official management projections. Wisdom of the Crowd theory provides additional support: aggregating independent probability estimates reduces individual errors even without financial incentives. However, information aggregation is not perfect. Longshot Bias causes systematic overpricing of rare events. Thin Liquidity slows convergence. Calibration studies on Polymarket and Kalshi show excellent aggregate accuracy but notable errors in low-volume markets. Manipulation attempts can temporarily distort prices, though arbitrageurs typically restore accuracy quickly.