Uncertainty Quantification
Uncertainty quantification focuses on the range and strength of belief, not just the headline number. A market might show a 60% chance, but that alone doesn’t say how stable or fragile that estimate is. Uncertainty quantification fills that gap.
In prediction markets, uncertainty is revealed through volatility, liquidity, spread behavior, and how probabilities respond to small trades. As traders react to information at different speeds and with different confidence levels, these signals accumulate. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this behavior is visible in prediction markets data as periods of calm confidence or restless adjustment.
Over time, uncertainty often narrows as information becomes clearer. It can also widen suddenly when new risks appear or assumptions break.
Why It Matters
Uncertainty quantification prevents false confidence. It helps analysts and decision-makers interpret prediction markets data with appropriate caution.
How is uncertainty quantified in prediction markets?
It is inferred from multiple signals rather than a single metric. Analysts look at probability dispersion, volatility, bid–ask spreads, liquidity depth, and reaction speed to new information. Together, these elements in prediction markets data indicate how confident the market really is.
Why isn’t probability alone enough to measure uncertainty?
Probability shows which outcome is favored, not how stable that belief is. Two markets can share the same probability while having very different uncertainty levels. Uncertainty quantification adds context that probability alone cannot capture in prediction markets data.
How does uncertainty change over an event’s lifecycle?
Uncertainty is usually highest early, when information is sparse. It declines as milestones pass and assumptions are confirmed. Tracking this change helps analysts understand when prediction markets data becomes more reliable.
Real-World Example
A Kalshi market shows a steady probability for weeks, but wide spreads and frequent small reversals suggest high uncertainty. After an official confirmation, spreads tighten and volatility drops, indicating uncertainty has been resolved even before final resolution.
Uncertainty Quantification and FinFeedAPI
Quantifying uncertainty requires rich, time-stamped market context. FinFeed's Prediction Markets API provides structured prediction markets data so developers and analysts can measure uncertainty, track confidence changes, and build more robust forecasting systems.