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Prediction, History and Political Science Prediction, history and political science Abstract Political science usually requires either prediction or contextual historical work to succeed. Because of the difficulty of prediction, the main focus should often be contextual historical work. Both of these methods favor narrow-scope explanations. I illustrate, via an example, the role that this still leaves for theory. I conclude by assessing the scope for political science to offer policy advice. All of this tells against several practices that are widespread in the discipline. 1. Introduction and main claims What methods are appropriate for political science?1 I argue for prediction and contextual historical work, and that these two methods each favor narrow-scope explanations. The arguments apply to field sciences generally, but I concentrate on political science.2 The structure of the paper is as follows: in this section, I state some basic distinctions and then my main theses and targets. In section 2, I make the case for what has been called ‘scientific prediction’. In sections 3 to 7, I examine the implications for scientific prediction of a series of methodological challenges, namely underdetermination, causal fragility, and noise. In section 8, I discuss what role is left for theory, and in section 9, I illustrate this role via an example. Finally, in section 10, I consider the scope for political science to offer policy advice. 1.1 Different types of prediction The word ‘prediction’ is ambiguous. Two distinctions are important: 1) Forward-looking prediction: predictions about future data; versus Retrospective prediction: predictions about past data. 2) Simple prediction: the attempt simply to predict future or past outcomes; versus Scientific prediction: the attempt to empirically test particular theories or models. Often, scientific prediction concerns the impact of varying just one focal variable, holding other variables constant or in some other way controlling for confounders. Scientific prediction may also concern conditional predictions, such as what will happen if a policy-maker intervenes in a certain way. I borrow the term ‘scientific prediction’ from Dowding and Miller (2019).3 Dowding and Miller discuss only the second of the above distinctions explicitly, although they 1 My focus in this paper is on epistemological rather than ideological or political considerations. 2 By ‘field sciences’ I mean non-laboratory investigations of systems that are not engineered artefacts. 3 Dowding and Miller’s distinction between scientific and non-scientific prediction is fruitful. (They label non-scientific prediction ‘pragmatic’.) As they note, within philosophy of science this distinction was noted by Popper (1989), among others. See also Salmon (1981) and Watkins (1968). 1 implicitly nod to the first distinction as well by defining non-scientific prediction to encompass only forward-looking prediction. I keep the two distinctions distinct. To clarify the relations between the different kinds of prediction: first, scientific prediction is a subset of simple prediction. A simple prediction is in addition scientific only if it concerns data gathered in certain epistemologically propitious conditions, namely those conditions suitable for testing a theory or model. Second, scientific prediction, and simple prediction generally, can be either forward-looking or retrospective. Retrospective simple prediction is just description of the past. 1.2 Different types of explanation There is a difference of degree between: Wide-scope explanations, which apply to many cases; versus Narrow-scope explanations, which are very local or contextual and that, in the limit, may apply only to a single case.4 An explanation might or might not be derived from a theory or model (section 8). Theories and models themselves vary in scope, but what matters for our purposes is the scope of explanations (section 8). Throughout, I have in mind causal explanations. This is not because I rule out other kinds of explanation but rather is because causal explanations are the kind of explanation usually offered in political science.5 For the most part, the relevant models are causal too. 1.3 Main theses I argue that, in political science: Thesis 1) While any kind of prediction is desirable, scientific prediction is especially so. Thesis 2) Usually, the purpose of scientific prediction is fulfilled only by forward-looking prediction or by contextual historical work. Thesis 3) Usually, narrow-scope explanation is favored. 1.4 Targets Some widespread practices in political science fall foul of the above theses: 1) A lack of emphasis on forward-looking prediction. 2) Forward-looking predictions, when they are made, being wide-scope. 3) Wide-scope retrospective testing, such as much large-n statistical work. 4) Resources being devoted to ‘pure theory’, in other words devoted to building up a repertoire of wide-scope models divorced from frequent empirical application. 2. Scientific prediction is desirable Causal knowledge is central to political science, as it is to most sciences, because it is the key to explanation, intervention and extrapolation. The standard template for causal 4 Exactly how we should individuate ‘cases’ here is no doubt itself contextual. 5 There is a literature on the relation between causal and structural or functional explanations. I do not discuss that here. 2 inference is to change one variable while keeping all else equal. Scientific prediction typically concerns the results of just such changes, and therefore delivers causal knowledge. Simple prediction, by contrast, is concerned purely with actual outcomes, which in field environments are typically the result of many different variables varying all at once. Therefore, simple predictions that are non-scientific do not deliver causal knowledge even when accurate. Thus, Thesis 1: Scientific prediction is especially desirable. Confirmation of theory is useful for purposes other than causal inference too (Dowding and Miller 2019). Therefore, because it is the means to get confirmation of theory, scientific prediction is useful for these other purposes too. 3. Underdetermination Suppose that candidate X wins an election. There are likely many plausible explanations of why X won. This creates a problem of underdetermination: the mere occurrence of the same headline fact, namely that X won, cannot by itself discriminate between the many different explanations of that fact. This problem is likely to be especially acute if an explanandum is qualitative, as when we seek to explain merely who won an election rather than by exactly how much. But even in quantitative cases, as when we seek to explain X’s margin of victory, the bar is often lowered, because only a partial or approximate fit with the data is demanded: a model may be endorsed even though it ‘explains’ only some but not all of the relevant variation.6 Such a lowered bar might be cleared by many models, and so the underdetermination problem remains. One solution to underdetermination is forward-looking prediction. If you have to stick your neck out in advance, that removes the possibility of fudging awkward outcomes after the fact.7 Many models or pundits might be able to explain the outcome of one election, even of five elections, relatively plausibly. But few are able to predict five correctly in advance. Lucky guesses, although possible of one election, are much less likely of five, especially if the target of prediction is quantitative. Insisting that accurate prediction be forward-looking discriminates between competing models more effectively than does allowing accurate prediction to be retrospective.8 Logically speaking, indefinitely many models fit any given body of evidence. But methodologically speaking, the key issue is whether those different models, in addition to being logically possible, are also plausible or to be taken seriously. It is at this 6 Not all explanations are derived from models (section 8), but the points in the text carry through mutatis mutandis. 7 Howson and Urbach (1993) and Worrall (2014), among others, give formal Bayesian demonstrations of this point. A similar conclusion can be demonstrated in non-Bayesian ways too. 8 At least for the most part. The advantage is epistemic and contingent rather than logically necessary. But, while there are cases in which forward-looking prediction is not favored, in political science it usually is. 3 methodological level that the confirmatory asymmetry between forward-looking and retrospective prediction carries bite.9 4. Contextual historical work Forward-looking prediction is not the only solution to the underdetermination problem. A second solution is if there are no plausible alternative explanations, in which case the mere fact of retrospective fit is decisive: if no other plausible theory or model can accommodate the past evidence then the fact that your model can tells strongly in its favor. In political science though, this situation applies only rarely. It is hard to prove this claim in a non-anecdotal way but, as with the election example, in political science there are usually many plausible explanations after the fact (Dowding 2016). A third solution to the underdetermination problem is more promising: we may gather additional evidence that favors one explanation over others (Northcott 2019a).10 If so, there is no need to rely on forward-looking prediction to break an epistemic tie. Additional evidence may favor, say, one explanation of an election result over others. This evidence might take
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