Ready-to-hand Dean Eckles blogs on people and technology

Simplicity has its draws. A simple model of some phenomena can be quick to understand and test. But with the resources we have today for theory building and prediction, it is worth recognizing that many phenomena of interest (e.g., in social sciences, epidemiology) are very, very complex. Using a more complex model can help. It’s great to try many simple models along the way — as scaffolding — but if you have a large enough N in an observational study, a larger model will likely be an improvement.

One obvious way a model gets more complex is by adding predictors. There has recently been a good deal of attention on using the frequency of search terms to predict important goings-on — like flu trends. Sharad Goel et al. (blog post, paper) temper the excitement a bit by demonstrating that simple models using other, existing public data sets outperform the search data. In some cases (music popularity, in particular), adding the search data to the model improves predictions: the more complex combined model can “explain” some of the variance not handled by the more basic non-search-data models.

This echos one big takeaway from the Netflix Prize competition: committees win. The top competitors were all large teams formed from smaller teams and their models were tuned combinations of several models. That is, the strategy is, take a bunch of complex models and combine them.

One way of doing this is just taking a weighted average of the predictions of several simpler models. This works quite well when your measure of the value of your model is root mean squared error (RMSE), since RMSE is convex.

While often the larger model “explains” more of the variance, what “explains” means here is just that the R-squared is larger: less of the variance is error. More complex models can be difficult to understand, just like the phenomena they model. We will continue to need better tools to understand, visualize, and evaluate our models as their complexity increases. I think the committee metaphor will be an interesting and practical one to apply in the many cases where the best we can do is use a weighted average of several simpler, pretty good models.

In finding relevant research, once one has found something interesting, it can be really useful to do “reverse citation” searches.

Google Scholar is often my first stop when finding research literature (and for general search), and it has this feature — just click “Cited by 394″. But it is not very useful when your starting point is highly cited. What I often want to do is to do a keyword search of the papers that cite my highly-cited starting point.

While there is no GUI for this search within these resultsin Google Scholar, you can actually do it by hacking the URL. Just add the keyword query to the URL.

This is the URL one gets for all resources Google has as citing Allport’s “Attitudes” (1935):

http://scholar.google.com/scholar?cites=9150707851480450787&hl=en

And this URL searches within those for “indispensable concept”:

http://scholar.google.com/scholar?hl=en&cites=9150707851480450787&q=indispensable+concept

In this particular case, this gives us many examples of authors citing Allport’s comment that the attitude is the most distinctive and indispensable concept in social psychology. This example highlights that this can even just help get more useful “snippets” in the search results, even if it doesn’t narrow down the results much.

I find this useful in many cases. Maybe you will also.