First, let me say that I enjoy your blog a lot. It’s one of the very few I take seriously. So I’m not trying to be annoying or argumentative; I’m trying to understand your objections more carefully from first principles, precisely because I take your views seriously.

I say “from first principles,” because I’m a layperson in political science. In other branches of science (with which I’m more familiar), data collection and data analysis precedes data interpretation. So if we perform an experiment, we try not to have our expectations for the phenomenon we’re studying come to bear on our measurements. Nor do we want to build our prejudices into the mathematical analysis of that data. Then we’ll just be confirming our prejudices, which is dull. We go to great lengths to avoid this.

For example, Kepler developed his laws of planetary motion by studying the data from Brahe’s remarkably accurate observations. If Brahe had decided early on that all planets must be moving in perfect circles and fudged his numbers accordingly, Kepler would have developed bogus principles.

Similarly, I want to argue that if a goal of political science is to develop “laws of voter motion,” then the starting point should be remarkably accurate data.

Now when I speak about “voter motion” or “what voters are doing,” I just mean which way they are voting. I think you don’t need any theory to find that out; you just need an election! Theory would then hopefully explain subtler information: how voters made the choices they did, what values weighed on their minds, how they consumed news, etc., etc. This is all information that cannot — and should not — be packed into a single vote tally; rather, it should explain something nontrivial about it. But before you can explain that vote tally, you have to have that vote tally.

Now the problem is, elections are uncommon, so election data don’t come quickly enough. So we might try to cheat and get hold of some imperfect election data now. We might start with polls, but since the data they provide are very noisy, we might try to eliminate that noise by averaging polls using Bayesian statistics, and we might add whatever other information we might be able to scoop up to estimate probabilities. This gives you some bit of data against which to test theories that might purport to explain how people vote.

Now, how should the information used in this estimate be modeled? I’d argue that we want the best possible estimates of the real probability, given everything we know. How do we work out which model is best? I suggest only two criteria: (1) it’s rooted in sound mathematics, and (2) it gives excellent estimates for data from earlier elections.

I’d argue that theory specifically must not enter in analyzing the accuracy of the model, because you’re not yet trying to explain your data; you’re still just trying to collect and analyze it! (I mean “analyze” just in the sense of statistical analysis, not explanation.) Inserting the theory whose hypotheses you wanted to test against this data at this point would ultimately mean begging the question.

My view of Nate Silver’s work is that it is sophisticated data collection and analysis. If, as you say, he hasn’t got a theory in mind when he does this work, then so much the better. This provides us all with data — tied to no particular worldview — that can serve as testing ground for different theories that hope to explain why voters vote as they do.

Thanks for the advice.

In theory, there is nothing wrong with using new information to update one’s forecast, assuming the new information is meaningful in the context of what we are trying to measure. It’s hard to judge whether that is the case with Nate’s model, since we don’t have access to the forecast model’s parameters. As for sorting out exactly what voters are doing – well, that is precisely my point. You need a theory for that. If, in the end, all you are doing is basing your forecast on what people tell you they are going to do, there’s no theory to explain why they acted as they did. At some point you have to have a theory – I don’t know what Nate’s theory is, and – based on his last comment – my sense is he’s content not to have one, in the end. There’s nothing wrong with that. But it’s not what drives most political scientists – we are interested in explanation more than prediction.

As a mathematician, I would certainly love to know more details about Nate Silver’s computational methods, but I don’t regard the fact that there are small daily movements alone as evidence that these methods are flawed. Rather, it seems to me quite natural to expect such tiny variations.

I take your point that polling data do not explain what causes voters to behave as they do. But in order to develop a good theory about voters’ behavior, don’t we first have to sort out just what it is they are doing? As I understand it, Nate Silver’s computations provide nothing more than an estimate of the probability that voters will behave in a particular way, based on all the data we can collect now.

Am I still missing the point?

You make a number of interesting points that deserve a more complete response than is possible in a comments section. I’ll try to respond more fully in a separate post.