I’m also always reminded of Rainman trying to predict the Wonder Wheel in Vegas and not realizing it plays by very different rules than blackjack, where you can count the cards and make predictions.
Not sure it’s a Dunning-Kruger Effect. Investment bubbles drag people in. He thought it was overvalued when he doubled his money and got out. He was probably right but the market stayed irrational. Looks like he was buying the dips on the way down too.
“Tales abound of how he invested early, and cashed out with 100% profits as market valuations went to what seemed to him unjustified levels. However, as prices continued to advance, he supposedly invested again at the peak and lost most of his fortune in the crash that followed”
This is a lesson to not assume you're good at investing if otherwise intelligent.
The Intelligent Investor, by Graham, brings up the archetypical example of a doctor/dentist and his financial misadventures. His (or her) disadvantage is that they're obviously intelligent which pushes them to be overconfident in a field in which they have zero experience. Overconfidence breeds poor risk management and eventual trouble.
The first rule of the Dunning–Kruger club is that you don't know you're in the Dunning–Kruger club. Depending on the topic, even the most intelligent of us can get tangled up
The second rule is that if you think you're in the club, you probably aren't. The third row is that if you only think you're in the club to lessen your chances of being in the club, you're more likely than someone who doesn't even know about the club to be in the club.
What people think of Dunning-Kruger is almost never the same as what it is.
The Dunning-Kruger hypothesis is that non-experts are (as a whole) not good at estimating their performance. This does not mean that they think that they are good, just that they think they are better than they are. They still tend to rate themselves as worse than an expert would rate themselves.
My favourite explanation for this effect is not that people are bad at estimating their own performance, but that they are bad at estimating the range of performance of the population. They scale and shift their performance into a range they think is reasonable.
Imagine a hypothetical test that uniformly scores a population from 0-100; everyone is spread out evenly across the range of scores. The average and median are both 50, and the same number of people get 32 as get 95, and every other score as well.
Now imagine everybody is absolutely accurate at picking their own ranking on this test out of the population.
Take two people, one who knows they are exactly average at this test, better than 50% of people; and one who is an expert, better than 90% of people. Based on the set up we know that these people will score 50 and 90.
Now we 'scale and shift' the score axis.
Say for example, that both people assume that the range of test scores will be between 40 and 90; the worst performer will get 40, and the best will get 90. Under this assumption the two people will predict their scores as 65 (vs 40, +25 over actual!!) and 85 (vs 90, -5 under actual!!) respectively.
The worse performer grossly overestimated their performance, and the expert mildly underestimated their performance, even though both knew perfectly what their relative performance was.
This shows that your estimation of your performance on a test can be wildly skewed by incorrectly estimating what the range of scores will be.
Financial/economic history is incredibly important if one wants to invest successfully. Much of the things we see in the markets had happened before, but not necessarily in our lifetimes. For example there were entire decades in the US history when bonds easily outperformed stocks, yet because of the last decade your typical retail investor thinks bonds are for people that don't want good long term gains.
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[ 3.1 ms ] story [ 48.3 ms ] threadNewton got hit hard by this I think.
I’m also always reminded of Rainman trying to predict the Wonder Wheel in Vegas and not realizing it plays by very different rules than blackjack, where you can count the cards and make predictions.
“Tales abound of how he invested early, and cashed out with 100% profits as market valuations went to what seemed to him unjustified levels. However, as prices continued to advance, he supposedly invested again at the peak and lost most of his fortune in the crash that followed”
The Intelligent Investor, by Graham, brings up the archetypical example of a doctor/dentist and his financial misadventures. His (or her) disadvantage is that they're obviously intelligent which pushes them to be overconfident in a field in which they have zero experience. Overconfidence breeds poor risk management and eventual trouble.
The Dunning-Kruger hypothesis is that non-experts are (as a whole) not good at estimating their performance. This does not mean that they think that they are good, just that they think they are better than they are. They still tend to rate themselves as worse than an expert would rate themselves.
My favourite explanation for this effect is not that people are bad at estimating their own performance, but that they are bad at estimating the range of performance of the population. They scale and shift their performance into a range they think is reasonable.
Imagine a hypothetical test that uniformly scores a population from 0-100; everyone is spread out evenly across the range of scores. The average and median are both 50, and the same number of people get 32 as get 95, and every other score as well.
Now imagine everybody is absolutely accurate at picking their own ranking on this test out of the population.
Take two people, one who knows they are exactly average at this test, better than 50% of people; and one who is an expert, better than 90% of people. Based on the set up we know that these people will score 50 and 90.
Now we 'scale and shift' the score axis.
Say for example, that both people assume that the range of test scores will be between 40 and 90; the worst performer will get 40, and the best will get 90. Under this assumption the two people will predict their scores as 65 (vs 40, +25 over actual!!) and 85 (vs 90, -5 under actual!!) respectively.
The worse performer grossly overestimated their performance, and the expert mildly underestimated their performance, even though both knew perfectly what their relative performance was.
This shows that your estimation of your performance on a test can be wildly skewed by incorrectly estimating what the range of scores will be.
"How The Economic Machine Works" https://youtu.be/PHe0bXAIuk0
https://rose.ai/dashboard/daily.observations.20190918.the.mi...
https://en.wikipedia.org/wiki/Tulip_mania