post-election resolution #3: don’t argue on the basis of election counterfactuals

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On Tuesday night, when things looked dimmest for Biden-Harris, I saw plenty of “Bernie would have won” tweets, and also some arguments that the Democrats would gave done better if they had been more moderate on immigration. On the GOP side, too, there will be arguments about whether the party would have performed better if all their candidates were more Trumpian or whether Trump cost them the White House despite strong economic fundamentals.

It is important to argue about: (a) what is valuable and (b) what is politically feasible. The union of those two circles is what we ought to support. People disagree about both questions, and discussion can be helpful.

Our values can influence our estimates of what would win. If you are further left, you may be biased to believe that leftist policies would win–and the same for people all across the spectrum.

However, disciplined political thinkers try to separate the two matters in case what they want may mislead them about what they can win.

It almost never helps to argue with counterfactuals about past elections. The problem with “Bernie would have won” is not that it’s false. It’s unfalsifiable, untestable. It provides no analytic clarity.

I acknowledge that social science often seeks to estimate counterfactuals. For instance, a regression model estimates what would happen to the dependent variable if the independent variable changed from what it actually is. (If we spent more on preventive healthcare, the data may suggest, fewer people would get sick.) This kind of reasoning is essential for thoughtful planning. However, to make counterfactual inferences, we need the right conditions: usually lots of cases, each with many variables, from which we can infer trends. Natural experiments also work nicely. A given election is one ambiguous datapoint that can fit countless theories.

So my resolution is: I will argue about what I value and whether it can win the next election, but not what would have won in 2020. I think that’s a recipe for confusion. In any case, I am not looking forward to that particular form of debate.

post-election resolutions #1 and #2 (less forecasting and less hobbyism)

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Today seems an auspicious occasion to begin posting resolutions for becoming a better person and citizen after the 2020 election.

The first and second resolutions are simple:

  1. Less forecasting, more living in the present. Surveys are valid research tools, but they are particularly hard-pressed to predict future behavior with precision. The polling error that shows up in forecasting sites like FiveThirtyEight reflects the complexity of screening for likely voters–it doesn’t invalidate survey research. But why are we checking FiveThirtyEight in the first place? All the sages teach that we should live in the present or work to change the future. Forecasting violates that advice, but it is almost literally addictive: you get a little dose of pleasure every time a prediction is favorable, and when it isn’t, you can go back for another fix. I hope I can use the methodological limitations of electoral forecasting as a reason to pry myself away from the habit of forecasting everything (COVID-19, the stock market, my own life expectancy).
  2. Less “political hobbyism.” People gave $100 million to Amy McGrath. In many cases, they were doing something to harm Mitch McConnell after he did or said something that made them mad. It didn’t work; he won by 20 points. One hundred million dollars is a lot of money. You could start a new college for that. Part of the problem is a profession–political consultancy–whose interests align poorly with the public interest. Someone made a fortune by fundraising for McGrath. (I wrote an article about this in 1994.) Expressing anger by giving money also has an allure; it’s an easy thing to do after observing something that makes you angry. I actually didn’t donate to Amy McGrath (ironically, I was too aware of the skeptical forecasts for her), but I exhibited other symptoms of political hobbyism in this cycle.

time again for civic courage

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The day after the 2016 election, I posted a blog entitled “Time for Civic Courage” that seemed to strike a chord; it was shared much more than I’m used to. I mainly wrote it for people like myself– those who would not face much personal risk under Trump but who needed to step up. Compared to our fellow human beings, Americans have more leverage over the US government, and we had to protect them by resisting here. I wrote: “no jokes about moving to Canada. No thoughts about giving up on the nation you belong to, even if its majority and its institutions anger you. No opting out.”

On Election Day 2020, I am cautiously optimistic about the outcome, sharing the conventional view that Biden has roughly a 90% chance of winning, and even a 30% chance of a landslide. But there is certainly a risk of another Trump win. And it is now–not after a loss–when we should pledge Civic Courage. Regardless of what happens, we Americans must keep working and struggling for justice and peace. We’ll need plenty of courage and perseverance even if the election turns out well; and if not–that’s the time to show civic character.

youth voting 2020: Tisch College analysis so far

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This is your regular reminder to follow Tisch College’s CIRCLE (@civicyouth) for the best data and analysis on youth voting. A list of their recent releases follows. They will have lots of timely data as the actual election unfolds.

Brian Schaffner is also part of Tisch College. He co-leads the Cooperative Election Study (previously the CCES), which surveyed 71,789 people between Sept. 29th and Oct. 27th. (That is an enormous sample). His analysis of the likely voters in the CES shows why the youth vote is pivotal.

2020 CES Presidential vote preferences (likely voters)

Meanwhile, follow Tisch’s Institute for Democracy and Higher Education for detailed information on college students, our Metric Geometry and Gerrymandering Group for research on districting, and our Center for State Policy and Analysis for Massachusetts-related information, including work on the ranked-choice voting ballot initiative here.

What kind of a claim is “Biden has an 87% chance of winning”? (on the metaphysics of probability)

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If you’re spending all your time nervously checking the election forecast on FiveThirtyEight.com, your mental health may suffer. You can stop checking and do something productive to improve the world. Or you can become intrigued about what a forecast means, read Alan Hájek’s “Interpretations of Probability” in The Stanford Encyclopedia of Philosophy (Fall 2019 Edition), and write some rambling reflections. That is the Path I have chosen.

Four years ago, Nate Silver’s FiveThirtyEight gave Hilary Clinton about a 75% chance of winning the 2016 election. The fact that she lost did not invalidate this prediction–outcomes with probabilities of 25% happen often. Looking retrospectively, it seems right that Trump’s chances were small. He had to win narrowly in just the right combination of states.

Of course, we now know that he did win. In October 2016, an omniscient deity would have known that already. The deity could have known it in either of two ways: by looking into the future, or by understanding the complete present situation with one week still left to go in the election. Presumably, if you could know exactly what every American was thinking about politics, the precise distance to their nearest polling place, whose contact lists everyone was on, what Putin was up to, how heat and humidity were distributed on the face of the globe, and everything else about the situation with one week to go in the election, you would know what would happen with the vote. (I leave aside the possibility that that the universe incorporates physical randomness at the quantum level that affects things like the outcome of an election a week away.)

Applying that theory to our present circumstances, we would say that either Biden has a 100% chance of winning the 2020 election or Trump has a 100% chance. These are falsifiable claims, and a maximum of one of them will turn out to be true. Every other probability estimate will turn out to be false, because either Biden or Trump will actually win.

Yet is seems rational to say that Biden has almost a 90% chance of winning right now, and wrong to say that he has a 100% chance–and even more wrong to say that Trump has a 90% chance. A lot of data and experience go into a plausible prediction. Even if Trump will win in 2020, he doesn’t have a 90% chance right now. Another Trump victory would be a second improbable event. But again, the actual vote won’t invalidate either a 10% or a 90% estimate of Trump’s chances, because either one is compatible with him winning or losing.

A different way to make sense of this is to say: If the election were held 100 times, Biden would win almost 90% of them. But that is weird in several ways. The election cannot be held 100 times in a row, and if we repeated it at all, the repetition would affect the outcomes. If we imagine 100 identical universes that all unfold separately from now until next week, perhaps Biden would win in 90 of them. Or perhaps the future is determined by the current situation, which must the same in all of the 100 identical universes. Then they must all turn out the same way. We just don’t know which way.

Forecasters like Nate Silver use simulations with random (or pseudo-random) numbers built in. Those are meant to model the actual world. But they are not replicas of the real world, which–leaving aside quantum physics–seems to have just one future that is determined by the state of things now.

Another interpretation is that giving Biden a 90% chance today is simply an assessment of our knowledge level. It’s as much about us as it’s about the world. Biden actually has a 100% or a 0% chance, but we (unlike an omniscient deity) don’t know which of those is right. However, the tools of forecasting allow us to estimate how much knowledge we have–with precision. In fact, Nate Silver’s estimate rises and falls by the hour.

According to this subjective interpretation of probability, when Silver’s estimate moves from 85%-86%, he has not invalidated his previous prediction but has updated his assessment of the best possible level of knowledge at the present time. Once the election is over, our knowledge will become complete, and we will rightly say that the odds are 100% in favor of what actually happened.

Two problems occur to me about this interpretation. First, a prediction is not falsifiable in the usual way (and falsifiability is a hallmark of science).

Second, how much knowledge is “possible” is relative to circumstances. Anyone who could see all the current, private, survey data at the congressional-district level would have more knowledge than Nate Silver has. But he knows a whole lot more than I do. His estimates seem to be measures of how much certainty he is entitled to, based on the work he has done and money he has spent. If I say that Biden has an 87% chance because that’s what I read on FiveThirtyEight, I am really saying that I believe Nate Silver’s claim that he has an 87% level of confidence. But how could I test whether that estimate is correct? How can we know that he is right to raise or lower the estimate by a point? Certainly not by waiting to find out what happens next week.