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Cakoluchiam comments on 2012 Survey Results - Less Wrong
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Note also that in the last two surveys the mean and median answers were approximately correct, whereas this time even the first quartile answer was too late by almost a decade. So it's not just a matter of overconfidence -- there also was a systematic error. Note that Essay Towards Solving a Problem in the Doctrine of Chances was published posthumously when Bayes would have been 62; if people estimated the year it was published and assumed that he had been approximately in his thirties (as I did), that would explain half of the systematic bias.
This question was biased against people who don't believe in history.
For my answer, which was wildly wrong, I guesstimated by interpolating backward using the rate of technological and cultural advance in various cultures throughout my lifetime, the dependency of such advances on Bayesian and related logics, with an adjustment for known wars and persecution of scientists and an assumption that Bayes lived in the western world. I should have realized that my confidence on estimates of each of these (except the last) was not very good and that I really shouldn't have had any more than marginal confidence in my answer, but I was hoping that the sheer number of assumptions I made would approach statistical mean of my confidences and that the overestimates would counterbalance the underestimates.
The real lesson I learned from this exercise is that I shouldn't have such high confidence in my ability to produce and compound a statistically significant number of assumptions with associated confidence levels.
Have you read Malcolm Gladwell - Blink? It's a fun book that doesn't take too long, which hella makes up for the occasional failure of rigor. Anyhow, the conclusion is that even on hard problems, expert-trusted models will still have very few parameters. And those parameters don't have to be the same things you'd use if you were a perfect reasoner - what's important is that you can use it as an indicator.