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You could make the argument that he always has been. Just that now he has FU-money, so he’ll go for ever-increasing levels of “outrage” to keep those plates spi
by slater 1y ago
You could make the argument that he always has been. Just that now he has FU-money, so he’ll go for ever-increasing levels of “outrage” to keep those plates spinning
- jrm4 1y agoHe absolutely always has been. Nassim Nicholas Taleb hates him for a very good and simple reason. Lack of skin-in-the-game. It's just arguably immoral to make predictions for a living in such a way that "being wrong" doesn't harm you.
- softwaredoug 1y agoI don’t get this criticism. The models he - and others like him - make are probabilistic. 70% probability Hillary would win was actually accurate. 30% probability events happen all them time. The ones with “skin in the game” in 2016 said there was a 99% chance that Hillary would win. And that they’d eat their shoe, etc, if Trump won. They were in so much disbelief that Trump could win they built models just for confirmation bias.
- jstanley 1y ago> 70% probability Hillary would win was actually accurate. Based on what?
- wahnfrieden 1y agoProbably hadn’t expected https://en.m.wikipedia.org/wiki/Russian_interference_in_the_2016_United_States_elections https://en.m.wikipedia.org/wiki/Russian_interference_in_the_...
- softwaredoug 1y agoBased on historically common polling errors and how state polling errors correlate. 70% is far more likely than 99%.
- AftHurrahWinch 1y agoThis: https://vincentarelbundock.github.io/Rdatasets/doc/dslabs/polls_us_election_2016.html https://vincentarelbundock.github.io/Rdatasets/doc/dslabs/po...
- jstanley 1y agoThat's circular reasoning. My model says there's a 70% chance of Foo, and that is actually accurate. How do you know it's accurate? Because my model said so. It might have been accurate! 30% probability events happen 3 times out of 10. We just have no way to know if it truly was accurate.
- softwaredoug 1y agoYou can “know” because we have decades of polling and election outcomes. It’s not black and white “know”, 70% is the mean of a probability distribution. It’s more accurate to say, this model predicts “Foo” because historically polls like this favor Foo 70% of the time. But these are probabilities and have wide errors. It’s on the reader to have a level of statistical knowledge. These are more handicaps than “predictions”. The same way we predict whether it might rain tomorrow, who might wins tomorrows game, without a Time Machine.
- jstanley 1y agoTo say that polls are accurate in general is fine! To say that a specific probability given by a poll was accurate is meaningless, there is no way to know.
- softwaredoug 1y agoYou can test your model on past elections IE X% of the time my model predicted the right result in this particular election. You can test weather forecasts on the weather in the past. You can test your model on a sports game on past games. We do this all the time in many fields. With different degrees of certainty (error bars are small or large). The entire basis of machine learning and predictions people use in everyday life is based on this assumption
- amalcon 1y agoThis is a weird criticism. Are meteorologists immoral? They are wrong a lot, with (occasionally) deadly consequences. It doesn't really hurt them - because their predictions are still pretty useful.