Dispersed Information (Prediction Market)

Knowledge scattered across individuals that prediction markets aggregate into a cohesive forecast.

What is Dispersed Information (Prediction Market)?

Dispersed information refers to the fragmented, often tacit knowledge held by individuals across a population, which prediction markets aggregate into accurate forecasts through the trading of digital assets. As Alex Tabarrok cites Hayek’s 1945 paper in the transcript, this information—such as a Pennsylvania resident’s insight into local voting trends or an employee’s knowledge of a project’s delays—is difficult to centralize but can be surfaced via market prices. In the 2024 election, Polymarket’s prices reflected such dispersed inputs, outperforming polls with biased samples (5% response rates).

The power of prediction markets lies in incentivizing participants to reveal this information through financial stakes, as Scott Kominers notes, with prices converging to a “convex combination” of individual beliefs. For example, Hewlett-Packard’s internal market aggregated employees’ dispersed knowledge about printer sales, improving forecasts. Thin markets, however, struggle to capture enough dispersed information, as seen in the 2016 election’s inaccuracies, while thick markets maximize diversity and accuracy.

Blockchain enhances this process by providing a transparent, auditable platform for trading, ensuring dispersed information is reliably aggregated. This makes prediction markets valuable for applications like election forecasting, scientific replicability, or corporate planning, where capturing widespread knowledge is critical for accurate predictions.