1001 PredictionBook Nights

gwern

I explain what I've learned from creating and judging thousands of predictions on personal and real-world matters: the challenges of maintenance, the limitations of prediction markets, the interesting applications to my other essays, skepticism about pundits and unreflective persons' opinions, my own biases like optimism & planning fallacy, 3 very useful heuristics/approaches, and the costs of these activities in general.

Plus an extremely geeky parody of Fate/Stay Night.

This essay exists as a large section of my page on predictions markets on gwern.net: http://www.gwern.net/Prediction%20markets#1001-predictionbook-nights

I explain what I've learned from creating and judging thousands of predictions on personal and real-world matters: the challenges of maintenance, the limitations of prediction markets, the interesting applications to my other essays, skepticism about pundits and unreflective persons' opinions, my own biases like optimism & planning fallacy, 3 very useful heuristics/approaches, and the costs of these activities in general.

Plus an extremely geeky parody of Fate/Stay Night.

This essay exists as a large section of my page on predictions markets on gwern.net: http://www.gwern.net/Prediction%20markets#1001-predictionbook-nights

Kinda surprising to me that you can beat dumb luck in inaccuracy.

It shouldn't be. Assume that your pundits in general do no better than chance. In a large sample, some of them are going to have to have to do really badly. Even if your pool on average is better than chance one should still expect a few much worse.

That said, even given that, -8.7 by their metric looks really badly.

According to that study, being a lawyer by training was one of the things that caused predictors to do badly. Note that Cal Thomas doesn't fall into that category.

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