A Model Victory

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  1. Assembly boxes Victory Models by Amati
  2. Knowledge utilization and planned change: An empirical assessment of the A VICTORY model
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H.M.S. VICTORY Model Ship by Bill

Or do the modelers just want to believe it is? I mean the kind of formal academic forecasting that political scientists and economists have been doing since around the late s, which uses factors like economic variables, presidential approval ratings, and horse race polls to predict general elections months in advance. Which factors, exactly, vary considerably.

There are ones that almost exclusively rely on economic data , which provide a tidy explanation for why particular candidates win but don't have a great predictive track record. More reliable are ones like Abramowitz's that use at least some polling — in Abramowitz's case, presidential approval ratings.

Each was attempting to guess what percentage of the two-party vote Obama would get; ultimately, he got 52 percent to Romney's This isn't a perfect gauge of their accuracy; all models have error, and it's possible a model that's generally good flubbed it last time. But it's a decent way to get a shortlist of models to consider this time around:. Few of these forecasters have issued predictions for yet; most of them require more information than is presently available. And those that have come out, or that can be used for preliminary predictions, are all over the place. The re's Abramowitz's model, predicting a Trump win.

Wlezien and Erikson passed along an estimate to me, based on current horse race polling and leading economic indicators , that Clinton would get But there is variance due to the candidates, and both Clinton and Trump can have big effects. In other words, these models are built for generic, typical candidates like John McCain or Mitt Romney, who are well within the Republican mainstream on immigration, not someone who thinks a guy with Mexican ancestry who was born in Indiana is unfit to judge him in federal court and who wants to ban all Muslims from entering the United States.

More generally, some forecasters argue that open-seat races are harder to predict than incumbent reelection years. Campbell's research with collaborators Bryan Dettrey and Hongxing Yin confirmed that both presidential approval ratings and economic factors are less influential in open-seat contests. This makes sense. Presidential approval ratings and the state of the economy seem, intuitively, relevant to whether voters will want to reelect an incumbent.

They're not likely to reelect someone they disapprove of, whom they hold responsible for a poor economy, etc. But it's less clear that a president's popularity would rub off on another person his party nominated, or that that person would get credit for a good economy. Abramowitz says he's agnostic about whether forecasts perform worse in open-seat races; there are just too few data points.

That's fair — the brute fact of the matter is that there have only been 17 presidential elections since World War II upon which political scientists can build predictive models. Even if you were to include every presidential race and you shouldn't, given how little the process that selected, say, John Adams, has in common with modern elections that'd only be That's a small sample size, which makes nailing down model specifications tricky.

For example, the major factor in Abramowitz's model that gives Trump a strong edge is the advantage he gives to incumbent presidents and implicit disadvantage he gives to open-seat contenders of the incumbent's party. This variable is based on comparing 10 races , , , , , , , , , with the other seven. But that's really not a lot to go on.

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If you were to read a medical study, say, where the control group had a sample size of 10 and the treatment group had only seven people, you'd be cautious in interpreting the findings. Or consider what happened in , when Abramowitz adjusted his model to attempt to account for the fact that general elections have been getting closer since He added a new variable that takes away some of the incumbency advantage and that hurts members of the outgoing incumbent's party in open races when the incumbent is popular like, say, Hillary Clinton.

But that new variable resulted in a worse prediction than his regular model made, so he's back to the original. Again, this isn't a criticism of Abramowitz specifically, or indeed of any modeler. This stuff is hard.

Assembly boxes Victory Models by Amati

And to his credit, Abramowitz acknowledges that, and tries to identify cases where inferring from limited past patterns can steer you wrong. Candidates have under and overperformed relative to what his model would expect before, he notes.

He identifies three cases in particular: the underperformance of George McGovern in , Michael Dukakis in , and Al Gore in He interprets the McGovern case as indicating the importance of party unity, and the Dukakis and Gore cases as indicating weak candidates and poorly conducted campaigns. But just because something's not readily quantifiable doesn't mean it's unimportant. Some quantifiable factors also give Abramowitz pause about the model.

Past nominees have followed normal trends in the partisan preferences of different racial groups.

But Donald Trump's vocal anti-immigration rhetoric has led to abnormally low levels of support from Latinos and Asian Americans. Abramowitz has argued that Trump's true level of support from Latinos is in the low 10s, if measured from high-quality surveys with Spanish-language options. In fact, of the major political science models that try to explain presidential elections, three predicted Trump would win and three others predicted only a very narrow Clinton victory.

We ran down their predictions back in August :. Now, it will take some time for the popular vote to be determined, and Clinton may well narrowly win it. But the point is that, in five of these models, these fundamental factors all pointed to a very close race that could conceivably go either way while the other pointed to a Trump landslide. And yet the punditry and elites all assumed — both because of the polls, and simply because the GOP nominee was Donald Trump — that Clinton had nothing to worry about.

This time around, those trends appeared to play out differently along racial lines. And some other voters who disliked Trump felt sufficiently disillusioned by the Democratic Party and Clinton herself to vote for Jill Stein or Gary Johnson instead.

Knowledge utilization and planned change: An empirical assessment of the A VICTORY model

Currently, our political system has fallen prey to depressing gridlock. Clinton herself had been in politics for decades and pretty clearly had no real potential to enact transformative change. Trump, meanwhile, seemed to many white voters to have the potential to shake up the system. They concluded it was time for a change. And now we will see just what kind of change they have brought us. By choosing I Accept , you consent to our use of cookies and other tracking technologies. Few predicted Trump had a good shot of winning.

But political science models did.

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Reddit Pocket Flipboard Email. What the major political science models said In fact, of the major political science models that try to explain presidential elections, three predicted Trump would win and three others predicted only a very narrow Clinton victory. A model by Christopher Wlezien and Robert Erikson was the only one to incorporate current national horse race polling along with economic factors.