4 ms·
Parents algorithm always works, the one described has a massive failure mode - people slowly learn that they are likely to say 7 and self-censor by picking anot
by rbmktechik 7y ago
Parents algorithm always works, the one described has a massive failure mode - people slowly learn that they are likely to say 7 and self-censor by picking another number. Or maybe in a different culture the distribution changes (say China and the numbers 4, 8).
- FabHK 7y agoExcellent point. The algorithm in the article is predicated on a specific distribution and iid, GP algorithm is predicated only on iid.
- OscarCunningham 7y agoI would guess that in China the probabilities of 4 and 8 goes down. Asking people to pick randomly drives them away from special numbers.
- mrmonkeyman 7y ago7 is our special number.
- Ill_ban_myself 7y agoIn the US 7 is a lucky # and a statistical outlier in that it was more frequently chosen in the article above.
- OscarCunningham 7y agoYou might be right, but my guess is that 7's reputation for luck isn't famous enough to make a difference, and in fact it's common because other numbers seem too "unrandom". We could test by asking people to pick numbers less than 100. I bet people would focus on odd numbers, especially those greater than 50, not ending in 5 or not having both digits the same.
- sokoloff 7y agoI decided to do that. Data and light analysis is here: https://docs.google.com/spreadsheets/d/1Dh0wiTCRkBhckWGXtjZgI_7CX1ZEbqbw4d1KACt8CdU/edit?usp=sharing https://docs.google.com/spreadsheets/d/1Dh0wiTCRkBhckWGXtjZg... I definitely overpaid on Mechanical Turk per response, given how lightning quickly the data came in. (I decided to pay $0.10/HIT for 50 responses and got 68 responses in 8m26s.) I suspect that offering a nickel would have gotten the survey filled in under half an hour still...
- Ill_ban_myself 7y agoMaybe 7 cents?
- bnegreve 7y ago> the one described has a massive failure mode Well, if you're really interested in generating random numbers using a group of people, then yes, this may be a problem. But you should take this as an illustration of the more general problem of generating uniform random numbers from a distribution that is not. If you think about it this way, author's solution can be applied in a number of practical scenarios. For example, if you want to generate a hash values for objects using properties that are not uniformly distributed. Or if you want to generate random numbers from a physical artifact that is not perfectly uniform.