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One interesting twist on the doctor example: we know she is almost always right that it's nothing, that it will all go away and that just two aspirins are ok. The article correctly points out that she will miss one or two cases where it was a terrible disease instead, and that her prescription of aspirin and to go away and rest will be misguided. However... doctors must do more than just cure you. They must also "do…
This story reminded me of a story written by a Czech biologist who studied animals in Papua-New Guinea and went to a hunt with a group of local tribesmen. The dusk was approaching, they were still in the forest and he proposed that they could sleep under a tree. The hunters were adamant in their refusal: no, this is dangerous, a tree might fall on you in your sleep and kill you . He relented, but silently considered…
Is 1:5000 an actually good assessment? I'd suspect it's very much not, especially if you avoid obviously dead trees and maybe move if a storm comes.
Earlier quoted context omitted.
No, Kelly is about what fraction of your bankroll you should bet if you want to maximize your rate of return for a bet with variable odds. It's essential if you want to: * make money by counting cards at Blackjack (the odds are a function of how many 10 cards are left in the deck) * make money at the racetrack with a system like this https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0... * turn a predictive…
That is a much narrower view of the Kelly criterion than the general concept. The general idea is about choosing an action that maximises the expected logarithm of the result. In practise this means, among other things, not choosing an action that gets you close to "ruin", however you choose to measure the result. Another way to phrase it is that the Kelly criterion leads to actions that avoid large losses.
https://en.wikipedia.org/wiki/Kelly_criterion
"The Kelly bet size is found by maximizing the expected value of the logarithm of wealth, which is equivalent to maximizing the expected geometric growth rate"
In real life people often choose to make bets smaller than the Kelley bet. Part of that is that even if you have a good model there are still "unknown unknowns" that will make your model wrong some of the time. Also most people aren't comfortable with the sharp ups and downs and probability of ruin you have with Kelley.
Earlier quoted context omitted.
The "slippery slope" principle applies here though: N+1 enables N+2, which enables N+3 and so on.
but the risk is independent. so once you do the N+1 time safely, you are back to N and your next time is _also_ just an N+1.
This story reminded me of a story written by a Czech biologist who studied animals in Papua-New Guinea and went to a hunt with a group of local tribesmen. The dusk was approaching, they were still in the forest and he proposed that they could sleep under a tree. The hunters were adamant in their refusal: no, this is dangerous, a tree might fall on you in your sleep and kill you . He relented, but silently considered…
Is 1:5000 an actually good assessment? I'd suspect it's very much not, especially if you avoid obviously dead trees and maybe move if a storm comes.
I don't know anything about trees around there, maybe they're really short-lived? For forests around here, it's a gross overestimate.
One interesting twist on the doctor example: we know she is almost always right that it's nothing, that it will all go away and that just two aspirins are ok. The article correctly points out that she will miss one or two cases where it was a terrible disease instead, and that her prescription of aspirin and to go away and rest will be misguided. However... doctors must do more than just cure you. They must also "do…
Earlier quoted context omitted.
> the mere act of doing it more often dramatically changes the risk. Kind of. However, you already know that the first N outings didn't have a disaster. So those should be discarded from your analysis. Doing it N times more has a lot of risk, doing it the N+1th time has barely any.
The "slippery slope" principle applies here though: N+1 enables N+2, which enables N+3 and so on.
It's a convincing fallacy because sometimes you do take N+1 steps. But just like in the article, heuristics aren't always right.
The point is not that bad heuristics are bad, but to think about when heuristics should be used and what value they add.
In the examples, heuristics shouldn't be used to reduce probabilistic occurrence to binary likelihood before deciding to act. Decisions should be informed based on the actual data when available. Application of a heuristic results in a loss of information, which reduces accuracy and applicable scope. Sometimes this can be entirely defeat the purpose.
Perhaps the recommendation is that if you are tempted to use a heuristic, stop and ask if it is necessary, and what you stand to gain from using it instead of other data or new analysis.
Generally agree with the article, though the reverse is obviously, "Heuristic which almost never works", and all the stories are about the 1 out 100000 times the weirdo was right and everyone lauded them as a genius but really they just got lucky the once.
If you have heuristic that works 0.0001% of the time - it's almost as good as one that is correct 99.999% of the time. You will notice and learn to just invert it.
I'm confused. These heuristics don't almost always work. The security guard has a 0% chance of investigating. How is 0% almost always? If you make a confusion matrix its precision and recall is 0. If it almost always worked then its precision and recall would be close to 1.