by From Adam Fforde Whilst it may superficially appear clear, an alleged ability of a theory to predict is easily shown to depend upon a host of tangled factors, so things are not clear at all. At an extreme, to start with, a theory that is right 51% of the time could feasibly be described as predictive, but is not likely to be. Yet if the point is to win bets placed very many times, then it could be thought of as predictive. Theories from physics, such a Newton’s laws of motion, are widely felt to be predictive, but this is within certain bounds, about which quite a lot is known. On the one hand, for example, as velocities approach the speed of light, so mass, assumed constant, is thought to vary. Again, just as Newtonian space is conceptually made up of lines, with no presence outside
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from Adam Fforde
Whilst it may superficially appear clear, an alleged ability of a theory to predict is easily shown to depend upon a host of tangled factors, so things are not clear at all.
At an extreme, to start with, a theory that is right 51% of the time could feasibly be described as predictive, but is not likely to be. Yet if the point is to win bets placed very many times, then it could be thought of as predictive. Theories from physics, such a Newton’s laws of motion, are widely felt to be predictive, but this is within certain bounds, about which quite a lot is known. On the one hand, for example, as velocities approach the speed of light, so mass, assumed constant, is thought to vary. Again, just as Newtonian space is conceptually made up of lines, with no presence outside one dimension, and points, with no presence at all, so mass is assumed to be something that can be situated at a single point, a centre of gravity. All of this can be understood to mean that the apparent clarity of Newtonian physics is not what makes it acceptable under some circumstance as a guide to action. The extent to which it matters that observables necessarily seen to flout the scientific metaphor involved – lines as measured have width, points in time have duration, forces cannot be directly observed – and are therefore associated with an ability to insure the resulting object (say, an aeroplane) depends on the local and social context. To develop this argument, if gun-laying was being done for “extremely inaccurate” riflemen in a war of accepted extreme levels of attrition (consider if the guns were aimed by cloned animals), then prediction that entailed a 51% accuracy rate could be, one can imagine, accepted, as it would arguably “win the war”. There is no escape from the social context in which beautiful theory like Newton’s might – or might not – be used.[1]
Further, as Lakatos 1970 pointed out, to make sense of data requires observation theories, and the accuracy of observation – whatever that means – likely has some bearing on the way in which terms within theory map to observables. Thus, whilst predictive power may seem clear, it is not. One is tempted to conclude that predictive power exists when it is said to exist; this is done by some community, with reference to all the complex tangles human communities generally seem to be able to manage. They will therefore likely often argue about it. If this conclusion is reasonable then what can be said about predictive power?
What comes from my discussion of the contrast between the different criteria defining the acceptability of theory that we find in Crombie and Nisbet is that prediction is most important in that it requires two things, and neither are to do with prediction per se, as it is generally understood (e.g. “getting a rocket to the moon”).
First is the requirement for comparison between theories as a matter of procedure. If, however, this is not part of scientific procedure and a single truth is required, then this choice is logically done outside of scientific procedure.
Second is explicit management of the shift between suspension of scepticism in theorisation (Crombie’s inductive phase, when theory is empirically-founded) and its resumption when theory can be, if the empirics suggest, abandoned. Following such norms, theory has to be protected, but not for ever, and it has also to be killable.
This view of the nature of predictability seems to me to be novel, and also to allow us to get away from somewhat fruitless debates. Economics as a science is about providing insights and improved understandings, and this is shown by its method.
[1] As McCloskey 1985 puts it: “The numbers are necessary material. But they are not sufficient to bring the matter to a scientific conclusion. Only the scientists can do that, because “conclusion” is a human idea, not Nature’s. It is a property of human minds, not of the statistics.” (p. 112). And: “It is not true, as most economists think, that . . . statistical significance is a preliminary screen, a necessary condition, through which empirical estimates should be put. Economists will say, “Well, I want to know if the coefficient exists, don’t I?” Yes, but statistical significance can’t tell you. Only the magnitude of the coefficient, on the scale of what counts in practical, engineering terms as nonzero, tells you. It is not the case that statistically insignificant coefficients are in effect zero” (stress added p. 118). Quoted in Fforde, 2013.
from
Adam Fforde, “Economics as a science: understanding its procedures and the irrelevance of prediction”, real-world economics review, issue no. 81, 30 September 2017, pp. 91-109, http://www.paecon.net/PAEReview/issue81/Fforde81.pdf