A Model Discipline: Political Science and the Logic of Representations

I wish I had read this book while I was receiving my graduate training in formal methods. Clarke and Primo tackle head-on a number of thorny philosophy-of-science questions that are usually ignored by political scientists and persuasively argue that the community of political scientists has committed itself to an untenable understanding of models and their purposes. Rather than merely criticizing, they offer a viable alternative to the hypothesis-testing that now dominates the field: namely, we should think of models as objects like maps, and evaluate them not as to their truth (since objects are neither true nor false) but as to their usefulness.On their discussion of usefulness, Primo and Clarke earn two cheers. In Chapter Four, they explain how theoretical models could play a variety of constructive roles: as foundations for more models, as a way of organizing existing empirical generalizations, as a means of investigating causal mechanisms, or as devices for forecasting (which they note are relatively rare in political science outside of election studies). All of this is helpful--but I was somewhat disappointed that in their exploration of the purposes these models can play, the authors almost entirely eschew the question: "useful to whom?" For my taste, Primo and Clarke are a bit too comfortable assuming that the answer to this question is generally, "useful to the community of political scientists." If political science is to matter beyond the walls of academia, practitioners must ask which models can be useful to others, including politicians and policymakers. One way to do this is to frankly evaluate models for their pedagogical usefulness--models can play a vital role in preparing undergraduates to be better citizens, or to prepare students of policymaking to be more sophisticated and capable in their roles. Certainly, the best models to come out of political science (Olson's Logic of Collective Action, Arrow's impossibility theorem) have excelled in this dimension, but many far more complicated contemporary models are unlikely to do so. Cutting-edge modelers would do well to try to imagine end-users of their work other than their peers.Still, in spite of this small reservation, I would enthusiastically recommend this book for anyone enduring the rigors of graduate education in the social sciences. (I suspect economists and sociologists could benefit from this book nearly as much as political scientists.) Asking ourselves why, how, and to whom our models may be useful should be at the heart of the enterprise of modern political science, rather than being viewed as an unneeded distraction, and Clarke and Primo have done great work by so insightfully bringing these issues to the fore.

- Note that this is a modified version of the review that appears on my blog, Biased Transmission ([...]).This book tackles the role of theoretical and statistical modeling in political science. Phil Arena has a great review covering the book's main points at his blog ([...]). My goal is to avoid duplicating his efforts - so I suggest reading his review there.Since I try to keep a foot in both evolutionary ecology and political science, I have been exposed to the methods of both. I am, to my knowledge, the only non-political-science grad student to attend an EITM summer school, so may have more insight into the context of their argument. But since most of my methods training has been in on the ecology side, this will be somewhat of an outsider's view.First, I really liked this book and agreed with most of the authors' philosophy of modeling. One of their main points is that the goal of theoretical models is rarely prediction. Most often they are tools for reasoning about the world. They might help evaluate the logical consistency of ideas, define terms and clarify arguments, or identify important new areas of research - but these many uses are not always clear to the non-modeler. For example, in evolutionary biology, Hamilton's rule is the result of a simple model intended to demonstrate the mechanisms of kin selection and that the big misunderstanding of E.O. Wilson's  recent crusade against kin selection  is mistaking it for a predictive model.The standard story I keep hearing in political science is that the field has taken the "theoretical models as predictive models" to the extreme. I have been warned, repeatedly, that the field's top journal, APSR, will not publish purely theoretical models without some nod at an empirical "test" of the model. Because of my experience at EITM, I agree with Phil that, the relationship between theory and statistics are hardly monolithic.Primo and Clarke's view of theoretical models would be very uncontroversial in ecology, population biology, population genetics, and related fields. These, like political science, are fields of complex dynamics systems with important interactions at different levels and a heavy reliance on observational data for the large scale processes. (International relations and ecosystem ecology are both hindered by a sample size of one planet and the difficulty of logistically and ethically conducting large-scale experiments.)Clarke and Primo argue that theoretical models are like maps. Maps are designed for a specific purpose and what determines a good map is whether it is useful for the purpose for which it is designed. In the same way that it is weird to argue about whether a map is "true" or "false" (since all maps are false in most respects), it is weird to argue about whether a model is true or false.*I was surprised by their discussion of empirical models since Clarke and Primo did not take their argument to its seemingly logical conclusion by arguing for a model selection approach to empirical analysis (as opposed to the standard null hypothesis testing - NHT). The NHT view of the world is that models are true and false and the job of science is to reject the false ones. In a null hypothesis test, the first step is to pick a "null" model (which, in practice, is almost always a very terrible model) and assume it is true.The model selection view of the world assumes that all models are false - or incomplete views of the world - but that some models are better than others for specific purposes. They try to distinguish between models based on some criteria of usefulness. One criteria might be out-of-sample prediction. Failing that, they can use criteria based on information theory. Another (important) criteria could be theoretical relevance based on a priori reasoning.I found this omission surprising, not because I've seen a lot of model selection statistics in political science** (I haven't), but because, in ecology, as soon as you start reading about statistical models not being "true" or "false," this seems to be next argument. The standard reference for this approach is Burnham and Anderson's  Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach .Overall, I recommend this book anyone interested in modeling in political science. I especially found their taxonomy of model types helpful (see Phil's review for details). As someone who aspires to both a theoretical and empirical modeling program in political science, I hope that the authors manage to shift the field's view of modeling more towards their own.* - They are not the first to make this analogy. I couldn't remember whether I first saw it in  McElreath and Boyd  or  Miller and Page  - so I looked it up and it was in both.** - Phil Arena pointed out to me that Kevin Clarke has written about model selection elsewhere. But that makes it even more surprising to me that it was not in the book.

Every political science grad student should have to read this book --- my students do. It's a nice introduction to what political science/political scientists should be striving for.

it was a one of the required books for my graduate class, the political inquiry. It was helpful and useful in this regard.