8 ms·

Model fits in a tweet and predicts all elections since 1960

52 points by rahimiali 6y ago

garaetjjte 6y ago
Reminds me of 2013 IOCCC entry: https://www.ioccc.org/2013/cable1/hint.html https://www.ioccc.org/2013/cable1/hint.html
shaunxcode 6y ago
For anyone wondering this algorithm indicates a Joe Biden victory.
- TimTheTinker 6y ago
  He tuned it to ensure that outcome.
  - dktoao 6y ago
    More troubling results for 2024 however: win("Joe Biden", "Gary Busey") == "Gary Busey"
    - zxcvbn4038 6y ago
      Only if Vincent Pastore is his running mate.
- zikzak 6y ago
  Unfortunately, Biden is significantly shorter than Trump so it isn't a safe bet he'll win: https://en.wikipedia.org/wiki/Heights_of_presidents_and_presidential_candidates_of_the_United_States?wprov=sfla1 https://en.wikipedia.org/wiki/Heights_of_presidents_and_pres...
  - rootusrootus 6y ago
    Significantly? Isn't Joe Biden 6'0"? Trump is maybe 6'1" (I know he's claimed 6'3", but since he's actually shorter than Justin Trudeau....)
    - mhh__ 6y ago
      https://www.macleans.ca/news/world/the-g7-group-shot-where-donald-trump-cant-hide-from-his-height/ https://www.macleans.ca/news/world/the-g7-group-shot-where-d... I think the source on trump being as tall as he says he is, is from his own doctor (remember the kerfuffle over that, simpler times!)
342346 6y ago
I could make an nth-order polynomial that selects the correct outcome of the past n elections ... and if I shroud it in enough mystery and red arrows I bet at least a few "news" sites would share it
- manuelisimo 6y ago
  Please do! ^_^
- sp332 6y ago
  And with such a high order, the predicted next result would probably be crazy, like -1000 on a scale of 1-10 or something
  - formerly_proven 6y ago
    "With y=0 for Joe Biden, and y=1 for Donald Trump, my model predicts y=154476802108746166441951315019919837485664325669565431700026634898253202035277999"
    - ACow_Adonis 6y ago
      You just need to then overlay a function that takes the remainder of the prediction / 2. Perfect model fit and a seemingly sensible prediction for next time :P All ready for media consumption :) /data scientist here people. Please don't actually do that.
  - phailhaus 6y ago
    Haha that's not even a problem: you can map the reals to (0, 1), so just do that for whatever number that the model spits out and then multiply by 10. Bam, there's your model that perfectly predicts all of the past presidential elections.
- oh_sigh 6y ago
  Just make sure you have a model prepared indicating a win for whichever direction the particular news agency leans. CNN gets the Biden-wins model, Foxnews gets the Trump wins model. Democracy Now! gets the Bernie-wins-outta-nowhere model.
l0b0 6y ago
Funny but disingenuous, since "predicts" implies that the model was created before the 1960 election. This no more predicts a 15-bit value than getting two specific bytes out of a PRNG with a specific seed. It's just like that post a few years ago where newspapers used various dodgy "predictors" like "a [state name] president has never lost a reelection", and someone had collected a bunch of these which turned out wrong.
- messe 6y ago
  You might be thinking of xkcd 1122 "Electoral Precedent": https://xkcd.com/1122/ https://xkcd.com/1122/
- nkozyra 6y ago
  This is in response to a model that similarly "predicated" presidential elections (using the algorithmic power of overfitting)
pronoiac 6y ago
Haha, very funny. It's based on the first three characters of each name.
- saeranv 6y ago
  Hmm... I don't see it. Can you explain further?
  - henryfjordan 6y ago
    def win(A,B): s = ((50*(ord(A[0])-ord(B[0])) + 114*(ord(A[1]) ord(B[1])) + (ord(A[2])-ord(B[2]))) % 199) % 2 return B if s else A ord() basically casts a char into the corresponding integer (ord("A") == 65, for instance). The whole algo is basically just some math based on the first 3 characters in each of A and B
    - jschwartzi 6y ago
      Numerology has been a time-honored way of making predictions for millennia.
      - tibbon 6y ago
        For the longest time didn't they always talk about how the tallest candidate had historically won since some date... And then it stopped working.
        saeranv 6y ago
        Although... if I was building an election model, I would include candidate height as one of my predictors.
      - TimTheTinker 6y ago
        Not using ASCII letter values, though.
    - deleted 6y ago
      [deleted]
saeranv 6y ago
As Nate Silver complains about often, there just aren't that many presidential elections since 1960, it's way to easy to build some overfitted model that seems accurate. As another comment mentions, we could all just build a nth-order polynomial to achieve a 100% accurate prediction on previous data.
jeffbarr 6y ago
This looks and smells like it was evolved by a genetic algorithm to produce those (and perhaps only those) results.
- jrumbut 6y ago
  It's based on the characters in the candidate's name, so yeah (though I doubt a genetic algorithm was involved)
sxp 6y ago
For those missing the context, this model was a reductio ad absurdum response to https://twitter.com/MartinDaubney/status/1314590150245183488 https://twitter.com/MartinDaubney/status/1314590150245183488 You can overfit any small dataset using a relatively simple error reduction method.
- nkozyra 6y ago
  Exactly. The troubling part is that even with overfitting his model still missed two elections.
  - jawr 6y ago
    That’s probably intentional, no one gets 100% on the exam.
    - NoOneNew 6y ago
      So... the bug is a feature?
- rmrfstar 6y ago
  My model is a hashmap that contains my training data. Missing keys are forecast uniformly randomly. It perfectly matches 100% of my training data.
deleted 6y ago
[deleted]
Mountain_Skies 6y ago
Given how unique casting votes will be this election due to the pandemic, I don't know how anyone can be comfortable making predictions about the outcome based on past data. Polling depends on having a good screen for Likely Voters, which is difficult in a normal year but this year is going to take a miracle to get correct. Given that pretty much every state is winging it a slightly different way, coming up with models that work for even just the battleground states is going to involve as much luck as skill. Pollsters that get it wrong will end up with a credibility drop more than deserved while those who get it right will get more of an increase than deserved.
- Slikey 6y ago
  Did you look at the tweet and it's content?
- 0xy 6y ago
  Nate Silver and pretty much every pundit got it spectacularly wrong in 2016 and had zero hit to credibility, he and his colleagues are still somehow trusted. Same story across the pond for Brexit, which polls botched spectacularly. 2020 is too uncertain to be predicted in my opinion, there's way too many things we haven't seen before.
  - jlawson 6y ago
    Nate actually gave Trump a reasonable chance of winning (I want to say 20%?) which if you understand how probability works doesn't really hurt his credibility. 20% chances happen all the time. The people who really took the hit were orgs like NYT who gave Hillary like a 99% win probability on the day before the election.
    - sciurus 6y ago
      29% chance, actually. Washington Post had a piece that mentioned this today. "On Election Day that year, FiveThirtyEight’s forecast gave Trump a 29 percent chance of winning. This was a better likelihood than was reflected for Trump in a number of other forecasts, though it still amounted to just under a 1-in-3 chance he would win. The site’s Nate Silver wrote that there was a 1-in-10 chance that Trump would lose the popular vote and win an electoral college majority, which is of course what happened." https://www.washingtonpost.com/politics/2020/10/12/how-think-about-trumps-chances-winning-reelection/ https://www.washingtonpost.com/politics/2020/10/12/how-think...
amelius 6y ago
The training set equals the validation set?
EvanAnderson 6y ago
I am reminded of the Atari 2600 game Pitfall and its use of a polynomial counter to procedurally generate the game world[1]. [1] https://www.youtube.com/watch?v=MBT1OK6VAIU&feature=youtu.be&t=22m44s https://www.youtube.com/watch?v=MBT1OK6VAIU&feature=youtu.be...
- abiogenesis 6y ago
  Also see Elite [1] for a similar procedurally generated game universe. [1] https://en.wikipedia.org/wiki/Elite_(video_game)#Development https://en.wikipedia.org/wiki/Elite_(video_game)#Development
kibwen 6y ago
Analogous to this relevant XKCD from 2012: https://xkcd.com/1122/ https://xkcd.com/1122/
Jedd 6y ago
All USA elections. (There's a certain predictably about gratuitously grandiose claims actually being constrained to 5% of the world.)
zvr 6y ago
Serious question: how does one go about finding such functions? I mean, given N inputs and known outputs, how does one design a function that is less than a N-order polynomial? Are there techniques or tools available, besides the elementary examples of simplifying a Karnaugh map?
mikewarot 6y ago
That has got to be the best compression of a deep neural network that I've ever seen. ;-)

52 points by rahimiali 6y ago

garaetjjte 6y ago

Reminds me of 2013 IOCCC entry: https://www.ioccc.org/2013/cable1/hint.html https://www.ioccc.org/2013/cable1/hint.html

shaunxcode 6y ago

For anyone wondering this algorithm indicates a Joe Biden victory.

TimTheTinker 6y ago
He tuned it to ensure that outcome.
- dktoao 6y ago
  More troubling results for 2024 however: win("Joe Biden", "Gary Busey") == "Gary Busey"
  - zxcvbn4038 6y ago
    Only if Vincent Pastore is his running mate.
zikzak 6y ago
Unfortunately, Biden is significantly shorter than Trump so it isn't a safe bet he'll win: https://en.wikipedia.org/wiki/Heights_of_presidents_and_presidential_candidates_of_the_United_States?wprov=sfla1 https://en.wikipedia.org/wiki/Heights_of_presidents_and_pres...
- rootusrootus 6y ago
  Significantly? Isn't Joe Biden 6'0"? Trump is maybe 6'1" (I know he's claimed 6'3", but since he's actually shorter than Justin Trudeau....)
  - mhh__ 6y ago
    https://www.macleans.ca/news/world/the-g7-group-shot-where-donald-trump-cant-hide-from-his-height/ https://www.macleans.ca/news/world/the-g7-group-shot-where-d... I think the source on trump being as tall as he says he is, is from his own doctor (remember the kerfuffle over that, simpler times!)

342346 6y ago

I could make an nth-order polynomial that selects the correct outcome of the past n elections ... and if I shroud it in enough mystery and red arrows I bet at least a few "news" sites would share it

manuelisimo 6y ago
Please do! ^_^
sp332 6y ago
And with such a high order, the predicted next result would probably be crazy, like -1000 on a scale of 1-10 or something
- formerly_proven 6y ago
  "With y=0 for Joe Biden, and y=1 for Donald Trump, my model predicts y=154476802108746166441951315019919837485664325669565431700026634898253202035277999"
  - ACow_Adonis 6y ago
    You just need to then overlay a function that takes the remainder of the prediction / 2. Perfect model fit and a seemingly sensible prediction for next time :P All ready for media consumption :) /data scientist here people. Please don't actually do that.
- phailhaus 6y ago
  Haha that's not even a problem: you can map the reals to (0, 1), so just do that for whatever number that the model spits out and then multiply by 10. Bam, there's your model that perfectly predicts all of the past presidential elections.
oh_sigh 6y ago
Just make sure you have a model prepared indicating a win for whichever direction the particular news agency leans. CNN gets the Biden-wins model, Foxnews gets the Trump wins model. Democracy Now! gets the Bernie-wins-outta-nowhere model.

l0b0 6y ago

Funny but disingenuous, since "predicts" implies that the model was created before the 1960 election. This no more predicts a 15-bit value than getting two specific bytes out of a PRNG with a specific seed. It's just like that post a few years ago where newspapers used various dodgy "predictors" like "a [state name] president has never lost a reelection", and someone had collected a bunch of these which turned out wrong.

messe 6y ago
You might be thinking of xkcd 1122 "Electoral Precedent": https://xkcd.com/1122/ https://xkcd.com/1122/
nkozyra 6y ago
This is in response to a model that similarly "predicated" presidential elections (using the algorithmic power of overfitting)

pronoiac 6y ago

Haha, very funny. It's based on the first three characters of each name.

saeranv 6y ago
Hmm... I don't see it. Can you explain further?
- henryfjordan 6y ago
  def win(A,B): s = ((50*(ord(A[0])-ord(B[0])) + 114*(ord(A[1]) ord(B[1])) + (ord(A[2])-ord(B[2]))) % 199) % 2 return B if s else A ord() basically casts a char into the corresponding integer (ord("A") == 65, for instance). The whole algo is basically just some math based on the first 3 characters in each of A and B
  - jschwartzi 6y ago
    Numerology has been a time-honored way of making predictions for millennia.
    - tibbon 6y ago
      For the longest time didn't they always talk about how the tallest candidate had historically won since some date... And then it stopped working.
      - saeranv 6y ago
        Although... if I was building an election model, I would include candidate height as one of my predictors.
    - TimTheTinker 6y ago
      Not using ASCII letter values, though.
  - deleted 6y ago
    [deleted]

saeranv 6y ago

As Nate Silver complains about often, there just aren't that many presidential elections since 1960, it's way to easy to build some overfitted model that seems accurate. As another comment mentions, we could all just build a nth-order polynomial to achieve a 100% accurate prediction on previous data.

jeffbarr 6y ago

This looks and smells like it was evolved by a genetic algorithm to produce those (and perhaps only those) results.

jrumbut 6y ago
It's based on the characters in the candidate's name, so yeah (though I doubt a genetic algorithm was involved)

sxp 6y ago

For those missing the context, this model was a reductio ad absurdum response to https://twitter.com/MartinDaubney/status/1314590150245183488 https://twitter.com/MartinDaubney/status/1314590150245183488 You can overfit any small dataset using a relatively simple error reduction method.

nkozyra 6y ago
Exactly. The troubling part is that even with overfitting his model still missed two elections.
- jawr 6y ago
  That’s probably intentional, no one gets 100% on the exam.
  - NoOneNew 6y ago
    So... the bug is a feature?
rmrfstar 6y ago
My model is a hashmap that contains my training data. Missing keys are forecast uniformly randomly. It perfectly matches 100% of my training data.

deleted 6y ago

[deleted]

Mountain_Skies 6y ago

Given how unique casting votes will be this election due to the pandemic, I don't know how anyone can be comfortable making predictions about the outcome based on past data. Polling depends on having a good screen for Likely Voters, which is difficult in a normal year but this year is going to take a miracle to get correct. Given that pretty much every state is winging it a slightly different way, coming up with models that work for even just the battleground states is going to involve as much luck as skill. Pollsters that get it wrong will end up with a credibility drop more than deserved while those who get it right will get more of an increase than deserved.

Slikey 6y ago
Did you look at the tweet and it's content?
0xy 6y ago
Nate Silver and pretty much every pundit got it spectacularly wrong in 2016 and had zero hit to credibility, he and his colleagues are still somehow trusted. Same story across the pond for Brexit, which polls botched spectacularly. 2020 is too uncertain to be predicted in my opinion, there's way too many things we haven't seen before.
- jlawson 6y ago
  Nate actually gave Trump a reasonable chance of winning (I want to say 20%?) which if you understand how probability works doesn't really hurt his credibility. 20% chances happen all the time. The people who really took the hit were orgs like NYT who gave Hillary like a 99% win probability on the day before the election.
  - sciurus 6y ago
    29% chance, actually. Washington Post had a piece that mentioned this today. "On Election Day that year, FiveThirtyEight’s forecast gave Trump a 29 percent chance of winning. This was a better likelihood than was reflected for Trump in a number of other forecasts, though it still amounted to just under a 1-in-3 chance he would win. The site’s Nate Silver wrote that there was a 1-in-10 chance that Trump would lose the popular vote and win an electoral college majority, which is of course what happened." https://www.washingtonpost.com/politics/2020/10/12/how-think-about-trumps-chances-winning-reelection/ https://www.washingtonpost.com/politics/2020/10/12/how-think...

amelius 6y ago

The training set equals the validation set?

EvanAnderson 6y ago

I am reminded of the Atari 2600 game Pitfall and its use of a polynomial counter to procedurally generate the game world[1]. [1] https://www.youtube.com/watch?v=MBT1OK6VAIU&feature=youtu.be&t=22m44s https://www.youtube.com/watch?v=MBT1OK6VAIU&feature=youtu.be...

abiogenesis 6y ago
Also see Elite [1] for a similar procedurally generated game universe. [1] https://en.wikipedia.org/wiki/Elite_(video_game)#Development https://en.wikipedia.org/wiki/Elite_(video_game)#Development

kibwen 6y ago

Analogous to this relevant XKCD from 2012: https://xkcd.com/1122/ https://xkcd.com/1122/

Jedd 6y ago

All USA elections. (There's a certain predictably about gratuitously grandiose claims actually being constrained to 5% of the world.)

zvr 6y ago

Serious question: how does one go about finding such functions? I mean, given N inputs and known outputs, how does one design a function that is less than a N-order polynomial? Are there techniques or tools available, besides the elementary examples of simplifying a Karnaugh map?

mikewarot 6y ago

That has got to be the best compression of a deep neural network that I've ever seen. ;-)