Prediction markets respond rapidly to events such as the capture of Osama bin Laden and the performance of political candidates in debates, incorporating information that traditional models cannot. Market traders quickly factored in the potential impact of these events on the election outcome, unlike traditional models that rely on slower-changing variables. This demonstrates the ability of prediction markets to capture the influence of variables not included in conventional models, like debate performances. This characteristic extends to various forecasting problems, including COVID and climate forecasting, making prediction markets valuable for incorporating unique and critical information.
Experts often build models to help predict how systems will behave. But what happens if, instead of asking the experts to build models, we ask laypeople to simply predict outcomes?
This is what happens in 'prediction markets'. And it turns out that in some situations, the 'wisdom of the crowd' often outperforms experts' models.
To break down what prediction markets are and how they work, we're joined by Rajiv Sethi, Professor of Economics at Barnard College at Columbia University and External Professor at the Santa Fe Institute.
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