Political science models should not be interpreted as predictions because they are often wrong. The models are research tools to help political scientists in their search for rules that govern behavior.

Many of you keep asking about the latest econometric “model” that some political scientists at the University of Colorado have cooked up. Most of what needs to be said is in an NYT op-ed, Political Scientists Are Lousy Forecasters. And this. If you are impressed because they can account for the last eight elections…read this passage from Innumeracy by John Paulos. Speaking as an academic, I’ll say that predicting past events was probably a minimal condition they had to meet to get published. (Illustration: NYT)

My background is in physics and neuroscience, fields that are more mature in what constitutes theory (especially physics). I read the political-science models to find out what what the interesting questions are in their area of research. I view the models as a search for causative principles, as opposed to an application of known laws of cause and effect.

But for information about current conditions during an election season, what they offer pales in comparison with what pure poll aggregators like the Votemaster and I are showing you. In this respect, the researchers are like you — observers. Let me explain, starting with an analogy.

Let us say that we are behavioral scientists, and we are trying to test the idea that rats in a crowded space are more likely to bite one another.  The natural experiment would be to put the rats in an enclosure, vary the enclosure’s size, and count the number of bites per hour. We could fit the data to some curve, which could be used to predict future behavior.

However, that’s only one variable. Now we start wondering. Does it happen more when the lights are on or off? When food is abundant or scarce? Does the sex of the rats matter? And so on. In fact, we could build a predictive equation, in many ways quite similar to the “models” of political scientists.

Two problems become apparent.

The first problem is that eventually, we are going to have to come up with a mechanism to explain what we are observing. We will tire of finding new correlations. Mechanisms are essential for understanding. For example, the link between smoking and lung cancer began as an epidemiological link, but matured over decades with many other discoveries, including the effect of tar on mutations, which mutations make lung cells go out of control, and so on. We will want to turn our findings into laws of behavior…and one hopes, neural circuit mechanisms. (In my view, the same is true of economics and political science.)

The second problem is what I’ve written about before. Every time we add a new part to our “model,”we are adding information…but also maybe noise, in the form of uncertainty. Should our model account for every variation in rat behavior? What if one of our statistical associations arose by chance? We’d better be careful of what we allow into our “model.” We’d want to avoid counting a factor twice. For example, the amount of bedding in the cage is correlated with its area, but we’d probably be wrong to include both bedding and area as parameters.

Let’s add one more condition. We are allowed to make lots of different measurements…but we may only do eight experiments. Now it’s a lot more like the political scientists’ challenge.

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Properly used, a political science “model” is really a tool for discovery. Researchers like those at U. Colorado want to discover quantitative laws of political behavior. Their model is a test of their current thinking. If they’re wrong…they will try again. As for me, I do not find it believable that opinion will swing by 7% between now and November. If Romney wins, it will be by a whisker. In the more probable outcome, Obama will win and there will be ample new material for them to run more regressions. That’s good, though. What, you thought the Coloradans were doing their work for you?

I have been criticized by political scientists for not engaging in what they call “theory.” That is correct, as far as it goes. Instead, I give you, from day to day, an excellent instrument for measuring what is happening. Think of what the Princeton Election Consortium offers as an electoral thermometer, useful to you…and to political scientists too. For instance, the Princeton Election Consortium gave you a highly accurate measurement of two VP bounces.

It is true that this year I did something new: I added a prediction. It is unlike the political science models, in the sense that it contains no insights into economic factors. My assumptions (and here’s more) are clear and simple: (1) opinion can be measured, and (2) its movement in a re-election race is somewhat limited, as shown by past races. These undeniable facts do not require political/economic theory, and there are very few components to argue with.  In political science, this is viewed as a weakness. For the current purpose, making a sound prediction, I see it as a strength.