[aakilfernandes]

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]

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’.

[Closi]

> Anyway, if you want my hot take, the conditional forecasting is to save their ass on election night from being embarrassingly wrong again.

Well Nate Silver wrote a full critically acclaimed book about why these types of forecast are more useful (and accurate) in reality because they account for uncertainty - he has been doing this for years, ever since he used to write similar algorithms to help bookies pick odds for sporting events, so I think your hot take isn’t based in any world of facts or knowledge on this.

Don’t trust a forecaster that says with certainty that a certain candidate will win, unless they have also bet their life’s earnings on it. Showing your statistical confidence level isn’t a bad thing.

[comex]

> 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/