6 ms·

Not sure if it’s wrong to put this here, but here is a link to their election forecast. https://projects.economist.com/us-2020-forecast/president https://proje

by cwhiz 6y ago

Not sure if it’s wrong to put this here, but here is a link to their election forecast. https://projects.economist.com/us-2020-forecast/president https://projects.economist.com/us-2020-forecast/president You can compare this to the 538 model and see where these two teams and forecasts disagree.

ta8645 6y ago
Can someone help me understand what odds like this mean in the context of an election? The model says that Trump has a 1 in 10 chance of winning. With a fair 10-sided die it makes sense that you have a 1 in 10 chance of any given side rolling face up. But what is the die that is being rolled in these election statistics? What is the "chance" element that is being predicted?
- weerd 6y ago
  The odds for a face on a d10 would be 1 in 9 (1:9). It's different than probability (1/10 = .1)
  - aecay 6y ago
    It's typical to use "to" rather than "in" when discussing odds. So the odds of getting a 1 on a 10-sided dice are 9 to 1 against (odds are also typically specified with the larger number first, because of the overlap between mathematical odds and betting-shop odds). And the probability of it happening is 1 in 10. (I suspect that counter-pedantry on these lines might be part of why your post is getting downvoted; I wasn't one of the downvoters fwiw.)
    - weerd 6y ago
      TIL, thank you. Looking back, not a great post. Probably deserves the votes.
- aakilfernandes 6y ago
  You're comparing two scenarios, one in which you know all the facts, and one in which you don't. In the dice toss scenario, we know everything relevant. In the election scenario, we don't. A model like this is attempting to say "these are the rules we think exist. Based on the rules, and assuming the data is off by some random distribution, here's what we think could happen". What different forecasters disagree about is what the rules are. For example, the relevance of certain demographic characteristics and the potential variance between polling (conducted prior to the election) and actual election results. There's a huge amount of assumptions, and forecasters disagree on those assumptions. We have very little historical data (polling is very recent) and even with complete historical data, future elections do not always conform to past elections.
  - runawaybottle 6y ago
    I will veer this off into the dreaded political territory even though this is mostly a technical discussion. The Democratic Party proved it was not as progressive as they thought as Sanders lost the primary. The reality is, the country as a whole is also not as liberal either, regardless of what these pollsters are asking people. You think the party is youthful, and ready for progressive ideas, but alas, the party wholly rejected an amazingly progressive candidate in Sanders. You think everyone’s super pissed at Coronavirus handling, and police brutality, healthcare, but alas, you find out people associate BLM protests with crime, and the virus with China, and socialism with unfair wealth redistribution. We can keep learning this the hard way I guess, this is America after all. It’s important the technical discussions are happening this time around, because there was virtually none the last time. The post mortems for these forecasts being wrong again should be a death knell for accumulating bad data. I’m certain the models are good, but I’m not certain the data is. Anyway, if you want my hot take, the conditional forecasting is to save their ass on election night from being embarrassingly wrong again. Imagine writing a giant if-statement that looked something like ‘and if(imWrong) changeMyAnswer’.
    - comex 6y ago
      > The Democratic Party proved it was not as progressive as they thought as Sanders lost the primary. The FiveThirtyEight forecast for the Democratic primary [1] gave Biden the highest chance of winning for most of the process. He did have a steep drop in the month before Super Tuesday (followed by an equally steep rebound), but still, I wouldn't say the forecast was especially bad. That said, polling is always worse for primaries than general elections, since there are more candidates and fewer voters. [1] https://projects.fivethirtyeight.com/2020-primary-forecast/ https://projects.fivethirtyeight.com/2020-primary-forecast/
- aecay 6y ago
  It's less about pure random chance, and more about our uncertainty. Compare it to a weather forecast that says there's a 10% chance of rain tomorrow. In the same way that weather forecasts get better over time (better atmospheric measurements, more sophisticated computer models), we could potentially do more to measure what the outcome of the election will be. And it might be theoretically possible (albeit highly unrealistic) to predict it with complete accuracy, given enough data. But we're not in that situation, hence uncertainty. (There are a couple of caveats about election forecasting as opposed to weather forecasting. The first is the "October surprise," a sudden revelation that changes the election. This cycle, it was arguably Trump's covid diagnosis, although that tended if anything to push the results further in the direction they seemed to be going on their own, rather than upset any trend. The second is that, unlike with weather systems, measuring voter behavior (and widespread reporting on these measures) can change people's behavior. The effect of this is hotly contested, but one of the many explanations of Trump's victory in 2016 which hinged on turnout in a few key states is that those states were predicted wins for Clinton, so Clinton voters didn't bother voting. Despite occasional jokes to the contrary, it doesn't rain just to spite the weatherman.)
- albntomat0 6y ago
  This sounds like a frequentist vs Bayesian statistics discussion, which involves (this is a simplification by me, a non-expert in the area) different definitions of probability. The frequentist view is along the lines of rolling a 10 side die hundreds of times, recording the results, and determining that each side comes up equally. The Bayesian view is that the probability measures our certainty about some event. For example, take the hypothetical point in time where all ballots have been cast, but have not been counted. One could use polling data, etc to model the odds that a particular candidate has won. However, the frequentist approach doesn’t really make sense here, as the ground truth already exists (all ballots cast), so rerunning the the event doesn’t make sense. Once again, I’m not an expert, so I recommend looking for additional explanations, if you’re interested.
- bo1024 6y ago
  That's a good question and it's not clear. As someone mentioned, here "chance" includes both uncertainty (facts that we don't know), and randomness of nature (things that will happen in the future that cannot be deterministically deduced from the state of the world today). Depending on your philosophy these may overlap. Next, someone mentioned Bayesian vs frequentist. The frequentist interpretation is roughly that if I go around making my best possible predictions, and we lump together all the things that I predict at 10%, about 1 in 10 of those things happen and the rest don't. But I wouldn't be able to be more specific about which ones in that group are more likely than others. The Bayesian interpretation is that I can really view the world as flipping coins -- I don't care whether it's due to my lack of knowledge or "true" randomness -- and as far as I can tell, the coin flip involved here is 1 in 10. We can also use a gambling interpretation. Here's one based on security of python's random module. Imagine the following three lotteries I offer you. In lottery A, you get $100 if Trump is elected. In lottery B, you get $100 if the following python code returns true on my laptop: random.random() <= 0.09999 In lottery C, you get $100 if this code returns true: random.random() <= 0.10001 If you would rather have lottery A than B, and you'd rather have C than A, then in some sense that you believe Trump has a 1 in 10 chance. Now there's an interesting extra layer to all of this because it's a model predicting, not a person. In a short space, I would basically say that we've trained models to predict in ways that are not inconsistent with any of the interpretations above, when put into situations where that is testable. Then we use them in situations where it might not be, like this.
- StreamBright 6y ago
  It pretty much means nothing. These sort of models produced wrong result again and again. For me the biggest question mark if that if we know that recent (last 5) elections were very close how can you predict somebody winning with 93% chance? Maybe I do not understand something here.
  - dragonwriter 6y ago
    > These sort of models produced wrong result again and again IIRC, 538s election models (house, Senate, and Presidential) have been quite accurate in aggregate.
  - swader999 6y ago
    Agree. Polls are a bad metric to rely on. Only 2% of those asked respond. Impossible to get a statistical sample with that. People are bad at predicting their future behavior. They are dishonest, they don't know or just don't want to tell you. There's a huge class divide right now. The bigger the class divide the worse polls are historically. And we know they are wrong this time around. None of the early voting margins predicted have held close. Better predictors are Google trends, rally participation, and voter registrations.
    - disgruntledphd2 6y ago
      Sure, these are all concerns. However, as long as they are not systematic errors for or against one candidate, they end up not mattering very much. Andrew Gelman (the author of this post) has also done a bunch of work on how different parties supporters become more/less likely to respond to polls based on what the current results are, which has been incorporated into the newer forecasts.
- sjy 6y ago
  > what is the die that is being rolled in these election statistics? A program which randomly generates outcomes for each state, based on probability distributions inferred from the polls, and calculates who wins the election given those outcomes. They run the program repeatedly and report the proportion of simulated wins as the probability of winning. https://en.wikipedia.org/wiki/Monte_Carlo_method https://en.wikipedia.org/wiki/Monte_Carlo_method
- taeric 6y ago
  It means if you saw all of these facts in ten different events, you would not be surprised to see a one to nine split in results. Ish. As you are scaling up, if course. So, think of it as saying these facts basically describe a ten sided die. With no other knowledge, the best you have is that you expect it to behave the same as any other ten sided die.
Balgair 6y ago
Nit: Have they counted for the possibility of a tie? US elections allow for a tie in the Electoral College (which then kicks off a supremely strange and legalistic process).
- cwhiz 6y ago
  I can’t speak for all models but I do know that the 538 model accounts for a tie.
- Jabbles 6y ago
  Before commenting you could at least search for the word "tie": The probability of an electoral-college tie is <1%
  - Balgair 6y ago
    Not everyone can read that little grey font as well as the next person. I guess I apologize for my poor eyesight then.
rsynnott 6y ago
Well, I don’t see how they expect anyone to take this seriously. No cartoon fox at all!

by cwhiz 6y ago

ta8645 6y ago

Can someone help me understand what odds like this mean in the context of an election? The model says that Trump has a 1 in 10 chance of winning. With a fair 10-sided die it makes sense that you have a 1 in 10 chance of any given side rolling face up. But what is the die that is being rolled in these election statistics? What is the "chance" element that is being predicted?

weerd 6y ago
The odds for a face on a d10 would be 1 in 9 (1:9). It's different than probability (1/10 = .1)
- aecay 6y ago
  It's typical to use "to" rather than "in" when discussing odds. So the odds of getting a 1 on a 10-sided dice are 9 to 1 against (odds are also typically specified with the larger number first, because of the overlap between mathematical odds and betting-shop odds). And the probability of it happening is 1 in 10. (I suspect that counter-pedantry on these lines might be part of why your post is getting downvoted; I wasn't one of the downvoters fwiw.)
  - weerd 6y ago
    TIL, thank you. Looking back, not a great post. Probably deserves the votes.
aakilfernandes 6y ago
You're comparing two scenarios, one in which you know all the facts, and one in which you don't. In the dice toss scenario, we know everything relevant. In the election scenario, we don't. A model like this is attempting to say "these are the rules we think exist. Based on the rules, and assuming the data is off by some random distribution, here's what we think could happen". What different forecasters disagree about is what the rules are. For example, the relevance of certain demographic characteristics and the potential variance between polling (conducted prior to the election) and actual election results. There's a huge amount of assumptions, and forecasters disagree on those assumptions. We have very little historical data (polling is very recent) and even with complete historical data, future elections do not always conform to past elections.
- runawaybottle 6y ago
  I will veer this off into the dreaded political territory even though this is mostly a technical discussion. The Democratic Party proved it was not as progressive as they thought as Sanders lost the primary. The reality is, the country as a whole is also not as liberal either, regardless of what these pollsters are asking people. You think the party is youthful, and ready for progressive ideas, but alas, the party wholly rejected an amazingly progressive candidate in Sanders. You think everyone’s super pissed at Coronavirus handling, and police brutality, healthcare, but alas, you find out people associate BLM protests with crime, and the virus with China, and socialism with unfair wealth redistribution. We can keep learning this the hard way I guess, this is America after all. It’s important the technical discussions are happening this time around, because there was virtually none the last time. The post mortems for these forecasts being wrong again should be a death knell for accumulating bad data. I’m certain the models are good, but I’m not certain the data is. Anyway, if you want my hot take, the conditional forecasting is to save their ass on election night from being embarrassingly wrong again. Imagine writing a giant if-statement that looked something like ‘and if(imWrong) changeMyAnswer’.
  - comex 6y ago
    > The Democratic Party proved it was not as progressive as they thought as Sanders lost the primary. The FiveThirtyEight forecast for the Democratic primary [1] gave Biden the highest chance of winning for most of the process. He did have a steep drop in the month before Super Tuesday (followed by an equally steep rebound), but still, I wouldn't say the forecast was especially bad. That said, polling is always worse for primaries than general elections, since there are more candidates and fewer voters. [1] https://projects.fivethirtyeight.com/2020-primary-forecast/ https://projects.fivethirtyeight.com/2020-primary-forecast/
aecay 6y ago
It's less about pure random chance, and more about our uncertainty. Compare it to a weather forecast that says there's a 10% chance of rain tomorrow. In the same way that weather forecasts get better over time (better atmospheric measurements, more sophisticated computer models), we could potentially do more to measure what the outcome of the election will be. And it might be theoretically possible (albeit highly unrealistic) to predict it with complete accuracy, given enough data. But we're not in that situation, hence uncertainty. (There are a couple of caveats about election forecasting as opposed to weather forecasting. The first is the "October surprise," a sudden revelation that changes the election. This cycle, it was arguably Trump's covid diagnosis, although that tended if anything to push the results further in the direction they seemed to be going on their own, rather than upset any trend. The second is that, unlike with weather systems, measuring voter behavior (and widespread reporting on these measures) can change people's behavior. The effect of this is hotly contested, but one of the many explanations of Trump's victory in 2016 which hinged on turnout in a few key states is that those states were predicted wins for Clinton, so Clinton voters didn't bother voting. Despite occasional jokes to the contrary, it doesn't rain just to spite the weatherman.)
albntomat0 6y ago
This sounds like a frequentist vs Bayesian statistics discussion, which involves (this is a simplification by me, a non-expert in the area) different definitions of probability. The frequentist view is along the lines of rolling a 10 side die hundreds of times, recording the results, and determining that each side comes up equally. The Bayesian view is that the probability measures our certainty about some event. For example, take the hypothetical point in time where all ballots have been cast, but have not been counted. One could use polling data, etc to model the odds that a particular candidate has won. However, the frequentist approach doesn’t really make sense here, as the ground truth already exists (all ballots cast), so rerunning the the event doesn’t make sense. Once again, I’m not an expert, so I recommend looking for additional explanations, if you’re interested.
bo1024 6y ago
That's a good question and it's not clear. As someone mentioned, here "chance" includes both uncertainty (facts that we don't know), and randomness of nature (things that will happen in the future that cannot be deterministically deduced from the state of the world today). Depending on your philosophy these may overlap. Next, someone mentioned Bayesian vs frequentist. The frequentist interpretation is roughly that if I go around making my best possible predictions, and we lump together all the things that I predict at 10%, about 1 in 10 of those things happen and the rest don't. But I wouldn't be able to be more specific about which ones in that group are more likely than others. The Bayesian interpretation is that I can really view the world as flipping coins -- I don't care whether it's due to my lack of knowledge or "true" randomness -- and as far as I can tell, the coin flip involved here is 1 in 10. We can also use a gambling interpretation. Here's one based on security of python's random module. Imagine the following three lotteries I offer you. In lottery A, you get $100 if Trump is elected. In lottery B, you get $100 if the following python code returns true on my laptop: random.random() <= 0.09999 In lottery C, you get $100 if this code returns true: random.random() <= 0.10001 If you would rather have lottery A than B, and you'd rather have C than A, then in some sense that you believe Trump has a 1 in 10 chance. Now there's an interesting extra layer to all of this because it's a model predicting, not a person. In a short space, I would basically say that we've trained models to predict in ways that are not inconsistent with any of the interpretations above, when put into situations where that is testable. Then we use them in situations where it might not be, like this.
StreamBright 6y ago
It pretty much means nothing. These sort of models produced wrong result again and again. For me the biggest question mark if that if we know that recent (last 5) elections were very close how can you predict somebody winning with 93% chance? Maybe I do not understand something here.
- dragonwriter 6y ago
  > These sort of models produced wrong result again and again IIRC, 538s election models (house, Senate, and Presidential) have been quite accurate in aggregate.
- swader999 6y ago
  Agree. Polls are a bad metric to rely on. Only 2% of those asked respond. Impossible to get a statistical sample with that. People are bad at predicting their future behavior. They are dishonest, they don't know or just don't want to tell you. There's a huge class divide right now. The bigger the class divide the worse polls are historically. And we know they are wrong this time around. None of the early voting margins predicted have held close. Better predictors are Google trends, rally participation, and voter registrations.
  - disgruntledphd2 6y ago
    Sure, these are all concerns. However, as long as they are not systematic errors for or against one candidate, they end up not mattering very much. Andrew Gelman (the author of this post) has also done a bunch of work on how different parties supporters become more/less likely to respond to polls based on what the current results are, which has been incorporated into the newer forecasts.
sjy 6y ago
> what is the die that is being rolled in these election statistics? A program which randomly generates outcomes for each state, based on probability distributions inferred from the polls, and calculates who wins the election given those outcomes. They run the program repeatedly and report the proportion of simulated wins as the probability of winning. https://en.wikipedia.org/wiki/Monte_Carlo_method https://en.wikipedia.org/wiki/Monte_Carlo_method
taeric 6y ago
It means if you saw all of these facts in ten different events, you would not be surprised to see a one to nine split in results. Ish. As you are scaling up, if course. So, think of it as saying these facts basically describe a ten sided die. With no other knowledge, the best you have is that you expect it to behave the same as any other ten sided die.

Balgair 6y ago

Nit: Have they counted for the possibility of a tie? US elections allow for a tie in the Electoral College (which then kicks off a supremely strange and legalistic process).

cwhiz 6y ago
I can’t speak for all models but I do know that the 538 model accounts for a tie.
Jabbles 6y ago
Before commenting you could at least search for the word "tie": The probability of an electoral-college tie is <1%
- Balgair 6y ago
  Not everyone can read that little grey font as well as the next person. I guess I apologize for my poor eyesight then.

rsynnott 6y ago

Well, I don’t see how they expect anyone to take this seriously. No cartoon fox at all!