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Comment by rick-jones on Intellectual Dark Matter · 2019-07-17T16:03:55.911Z · score: 7 (5 votes) · LW · GW

Donald Rumsfeld famously identified known knowns, known unknowns, and unknown unknowns. Most people I knew assumed there were no unknown knowns. But I worked as a Knowledge Manager for about five years, and came to believe that unknown knowns — mainly in the form of institutional and tacit knowledge — contained the real gold. I think this is directly relatable to this concept of “intellectual dark matter.

Comment by rick-jones on Ductive Defender: a probability game prototype · 2019-03-31T13:21:39.108Z · score: 1 (1 votes) · LW · GW

Sorry. Just played a couple of more rounds. It makes absolutely no sense to me. To bad, because I had been hoping to like it and enjoy it.

Comment by rick-jones on Ductive Defender: a probability game prototype · 2019-03-30T16:35:00.275Z · score: 0 (2 votes) · LW · GW

Any instructions? I just played a couple of rounds and couldn't see any particular patterns or methodology related to probability or inference or scientific thinking.

Thanks.

Comment by rick-jones on [Question] Tracking accuracy of personal forecasts · 2019-03-21T09:12:26.186Z · score: 2 (2 votes) · LW · GW

Perhaps there's some back story to this post that I missed, so forgive me if what I'm about to say has been discussed.

You might consider reading "Superforecasting: The Art and Science of Prediction," by Philip Tetlock. Or go to the Good Judgment Project web site and watch the 5-part Superforecasting master class.

First, the question has to pass the clairvoyant test. Second, you might want to have some scheme for Bayesian updating your forecast. And then you'll want to use Brier Scores (or something like them) to assess your accuracy.

If you know R, there's actually a Brier score function you can use. But I can't imagine it's very difficult to set up in Excel.

Again, sorry if I'm stating the obvious.

Comment by rick-jones on Monty Hall in the Wild · 2018-06-08T13:35:31.804Z · score: 1 (1 votes) · LW · GW

There are a lot of ways to skin this cat. The most intuitive way I've come across is this:

1. If you choose door A, there is a 1/3 chance of the prize being behind that door. So there is a 2/3 chance that the prize is not behind that door. At this point we distribute the 2/3 chance evenly between doors B and C.

2. When the host reveals the the rotten avocado behind, say, door B, then door C picks up the entire 2/3 chance of having the prize.

When I apply Bayes's Theorem to the problem, I essentially get the same thing. The prior probability of the prize being behind door A is 1/3, and the posterior probability of it being behind door A is also 1/3.