Prediction markets are most useful when forecasting short to medium term observable events (under 24 months), especially in the following cases:
When the past becomes irrelevant:
Traditional forecasting methods such as time series forecasting rely on the past to predict the future, the assumption being that the past can inform the future.
But sometimes you’ll have little relevant or reliable data at your disposal to make useful projections, and hanging on to historical data may skew your forecasts. Predictions markets aggregate all the available relevant data using the wisdom of crowds.
When knowledge is decentralized:
Nobody knows everything, but everybody knows something. Usually, we solve hard problems by asking an expert: an engineer, a doctor, a lawyer. But sometimes when a problem is too complex, when too many variables are involved for a single expert to handle, or when there is too little data to train an artificial intelligence individual expertise falls short.
Prediction markets (or prediction polls like our Prescience platform) help consolidate the informed guesses of the many based on all the available data.
When the situation is fluid:
Forecasts need to integrate new information continuously, you need real time forecasts to be able to react accordingly.
Human forecasters excel at integrating new information because they spot things that AI would miss, information available on the ground but not yet in data bases.