Graceful degradation in election forecasting

The Washington Post recently published a review of the relative performance of various forecasting methods in the recent US presidential election. Amusingly titled Which election forecast was the most accurate, or rather: The “least wrong”? , the article nicely complements our own post mortem published earlier. Like us it finds that:

  1. Hypermind was one the “least wrong” forecasters of the lot;
  2. Crowd-based methods fared better than statistical poll aggregators.

One take-away is that when all systems failed, human collective foresight failed less than the alternatives. You might call it “graceful degradation”.

Skeptics of crowd wisdom gleefully seize on 2016’s Brexit and Trump forecasting armageddons to argue that our kind can’t predict the future and that it is a hopeless quest at best, a cynical con at worst. That criticism entirely misses the point. Prediction markets have never claimed magical powers to predict everything all the time. That’s just impossible, and the world is better for it. However, the record shows that prediction markets tend to perform better, or fail less, than the alternatives. In that, they help push back the inherent limits of forecasting. That’s all, but it’s remarkable nonetheless.

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