h a l f b a k e r yFunny peculiar.
add, search, annotate, link, view, overview, recent, by name, random
news, help, about, links, report a problem
browse anonymously,
or get an account
and write.
register,
|
|
|
Run trillions of pseudo-random simulations that predict: stock market prices, temperature, match outcomes, election outcomes etc against historical data - or whatever it is you're trying to predict.
Rank each seed by success rate. Keep trying more random simulations until you find one that has
a 99% historical success rate. Make a big announcement in large letters:
Our new automated analyst is right 99% of the time*. Get yours today while quantities last.
And in the smallest letters you can get your hands on:
*Past Performance Is Not Indicative Of Future Results
Survival Bias
https://en.wikipedi...i/Survivorship_bias
[ixnaum, Apr 26 2017]
Gambler's Fallacy
https://en.wikipedi...i/Gambler's_fallacy
[ixnaum, Apr 26 2017]
Overfitting
https://en.wikipedia.org/wiki/Overfitting
[ixnaum, Apr 26 2017]
Become Astoundingly Rich
Become Astoundingly Rich
An outline for a simplified physical implementation. [zen_tom, Apr 26 2017]
[link]
|
| |
.. so I crunched some numbers in a simulation to see how many tries I needed to find a survivor that matched at least 99% of the flips |
|
| |
for 5 random coin flips, it takes ~50 tries |
|
| |
for 10 random coin flips, it takes ~ 1000 tries |
|
| |
for 20 random coin flips, it takes ~1 million tries |
|
| |
I guess I could have saved my self the simulation and used a little math instead ... oh well. |
|
| |
This is just a very inefficient way of designing a very good model of a phenomenon. |
|
| |
You could do the same thing with any enginerring challenge. Bolt together random parts and see how fast it goes. Keep trying different random parts and eventually you will have a really fast car. |
|
| |
Not only is it inefficient - it's plain wrong. |
|
| |
I was hoping that it would be apparent, but this is mostly one of those sarcastic ideas. It's poking fun at models that promise to predict future just because they succeeded in the past. I'm hoping it highlights how powerful survival bias can be (even though it's wrong). There is a bit of Gambler's fallacy thrown in for good measure too. See links |
|
| |
I recognise the sarcasm. However, consider this: suppose
you have a pseudorandom number generator algorithm
which after various combinations of functions produces an
accurate set of results up until now. Does that still
completely rule out the possibility that you have in fact
discovered an accurate algorithm which will continue to
work? |
|
| |
Yes, it is completely ruled out by the same mechanism that drives gambler's fallacy (link). |
|
| |
Basically it boils down to this: the subsequent outcomes are completely independent from the ones preceding them (even though it really doesn't seem so - thus the fallacy) |
|
| |
Here is another way to explain it. Let's say that we were not talking about random number generators but instead we had a neural network that was trained to do the predictions. There is a well known problem called "overfitting" (link). What it means is that instead of making generalizations about how something works, the machine essentially learns to parrot back what it saw verbatim. This is useless, because unless it sees the exactly same thing in the future (which is unlikely) it will be wrong. |
|
| |
What I've done with this bad invention is invented the most extreme example of over-fitting. |
|
| |
So why bother inventing this? I find it useful as an illustration of the pitfalls of predictive models and how even bad models seem trustworthy. I believe the world is full of this snake oil where fake models are passed as the real deal. I'm hoping that the croissants given for this idea are for the sarcasm of it, not the practicality. |
|
| |
With an idea like this, you could become astoundingly rich! |
|
| |
I absolutely get where you're coming from, [ixnaum]. But
I don't agree that it completely rules out the discovery of
a successful algorithm. What you describe is pretty
similar to the way evolution works. Arbitrary genetic
mutation usually leads to reduced fitness but occasionally
increases it. What this does is to throw out the
mutations, as it were, which don't work. If there is some
kind of deterministic process behind something, this
usually won't, but occasionally will, successfully predict
the future of the process long term. |
|
| |
How does it differ from natural selection? Incidentally,
my croissant is indeed sarcastic, don't worry. |
|
| |
There's other ways to achieve the same outcome. I heard
about people writing a high-cost subscription investment
advice newsletter. They divided the US into East/West
and sent out free samples saying "Gold will go UP in value
next week" to the East and "Gold will go DOWN in value
next week" to the West. After gold did whatever it did,
they ignored the 1/2 of the country which got the wrong
prediction and sent another pair of newsletters to the
'successful' half of the country and said "Now Gold is going
to go DOWN or UP" accordingly to the North and South
regions. Once again, they were
right half the time and told the NorthWest of the country,
that their prescience was proven and if the prospective
customers wanted to avail themselves of the newsletter's
predictive powers in the future they needed to
subscribe.... |
|
| |
Yeah, well, this gambler's fallacy thing is only believed by
none gamblers |
|
| |
//I don't agree that it completely rules out the discovery
of a successful algorithm. What you describe is pretty
similar to the way evolution works. |
|
| |
Yes, this is the key difference. There are good algorithms
and bad algorithms that exhibit identical results when
predicting the past. Genetic algorithm with some
radomness could be very useful. |
|
| |
I should have made it clear that in this idea the random
simulations are performed by a pseudorandom number
generator. The word "simulation" is poor word choice
because simulation connotes some kind of modeling that's
happening under the hood. This is not the case with
pseudo random number generator. |
|
| |
To be more specific, when I did my proof of concept I just
used rand(seed) function. I find it fascinating that even
though
the past results of rand(seed) function may be identical
or even
superior to useTheBestModelsOutThere() function, the
results don't
give the whole story. In fact the results confuse us about
what algorithm is superior. |
|
| |