Expected Information Gain
In prediction markets, prices reflect current beliefs based on available information. Expected information gain looks ahead and estimates how much those beliefs might shift as new data arrives.
This concept focuses on uncertainty reduction. Markets with high expected information gain are those where upcoming events, disclosures, or trading activity are likely to meaningfully change probabilities.
Analysts use expected information gain to understand which markets are most informative to watch. It highlights where prediction markets data is likely to become more valuable as conditions evolve.
Why It Matters
Not all markets provide the same insight. Expected information gain helps users focus on prediction markets that are likely to deliver meaningful updates and reduce uncertainty.
What does expected information gain mean in prediction markets?
In prediction markets, expected information gain reflects how much future trading or events could change current probabilities. It is linked to uncertainty, disagreement, and upcoming information releases. Markets with stable beliefs usually have low expected gain. Markets with unresolved questions tend to have higher expected gain.
How is expected information gain estimated using prediction markets data?
Expected information gain is estimated by analyzing current uncertainty in prediction markets data. Analysts may look at probability dispersion, volatility, and time remaining until resolution. These factors help estimate how much beliefs could still shift. The estimate updates as new data enters the market.
Why is expected information gain useful in prediction markets APIs?
Prediction markets APIs provide continuous data needed to track uncertainty and belief changes. This allows models to estimate expected information gain programmatically across many markets. Analysts can prioritize monitoring, allocate resources, or trigger alerts based on expected insight value. APIs make this analysis scalable and automated.
Real-World Example
On Polymarket, a market early in an election cycle may show high expected information gain. As debates, polls, and news unfold, probabilities are likely to shift more than in a late-stage market.
Expected Information Gain and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data needed to estimate expected information gain. Analysts can combine probability distributions, volatility signals, and time-based metrics. This supports uncertainty tracking, market prioritization, and research workflows. The API enables consistent analysis across prediction markets.