Question

One can distinguish economics from other scientific inquiries either by specifying some set of causal factors or by specifying some range of phenomena. The phenomena with which economists are concerned are production, consumption, distribution and exchangeparticu- larly via markets. However, to the extent that many different causal factors are relevant to economic phenomena, including the laws of thermodynamics, metallurgy, geography and social norms, even the laws governing digestion, economics cannot be distinguished from other inquiries only by the phenomena it studies. Discuss how, if at all, Mercantilist and Physiocratic economic thought distinguish economic inquiry from other scientific inquiries? (3 paragraphs)

Answer 1

In assessing these claims, it will be useful to take a step back and ask just what it is that these competing models of statistical explanation (Hempel's IS model and Salmon's SR model) are intended to be reconstructions of. In the literature on this topic two classes of examples or applications figure prominently. First, there are examples drawn from quantum- mechanics (QM). Suppose, for example, a particle has a probability \(p\) that is strictly between 0 and 1 of penetrating a potential barrier. Models of statistical explanation assume that if the particle does penetrate the barrier, QM explains this outcome—the IS and SR models are intended to capture the structure of such explanations. Second, there are examples drawn from biomedical (or epidemiological) and social scientific applications—recovery from strep or, to cite one of Salmon's extended illustrations (Salmon, 1971), the factors relevant to juvenile delinquency in teen-age boys.

This is, to say the least, a heterogeneous class of examples. In the case of QM, the usual understanding is that the various no-hidden variable results establish that any empirically adequate theory of quantum mechanical phenomena must be irreducibly indeterministic. It is thus plausible that when we use the Schrödinger equation to derive the probability that a particle with a certain kinetic energy will tunnel through a potential barrier of a certain shape, this representation satisfies the SR model's “objective homogeneity” condition—there are no additional omitted variables that would affect the probability of barrier penetration. By contrast, it seems quite unlikely that this homogeneity condition will be satisfied in most (indeed, in any) of the biomedical and sociological illustrations that have figured in the literature on statistical explanation. In the case of recovery from strep, for example, it is very plausible that there are many other factors besides the two mentioned above that affect the probability of recovery—these additional factors will include the state of the subject's immune system, various features of the subject's general level of health, the precise character of the strain of disease to which the subject is exposed (resistant versus non-resistant is almost certainly too coarse-grained a dichotomy) and so on. Similarly for episodes of juvenile delinquency. In these cases, in contrast to the cases from quantum mechanics, we lack a theory or body of results that delimits the factors that are potentially relevant to the probability of the outcome that interests us. Thus, in realistic examples of assemblages of statistically relevant factors from biomedicine and social science, the objective homogeneity condition is unlikely to be satisfied, or in any practical sense, satisfiable.

A related difference concerns the way in which statistical evidence figures in these two sorts of applications. Some quantum mechanical phenomena such as radioactive decay are irreducibly indeterministic. By contrast, in the biomedical and social scientific applications, while the relevant evidence is “statistical”, there is typically no corresponding assumption that the phenomena of interest are irreducibly indeterministic. This particularly clear in connection with the social scientific examples (such as risk factors for juvenile delinquency) that Salmon discusses. Here the relevant methodology involves so-called causal modeling or structural equation techniques. At least on the most straightforward way of applying such procedures, the equations that govern whether a particular individual becomes a juvenile delinquent are (if interpreted literally) deterministic. According to such approaches, the phenomena being modeled look as though they are indeterministic because some of the variables which are relevant to their behavior, the influence of which is summarized by a so-called error term, are unknown or unmeasured. Statistical information about the incidence of juvenile delinquency among individuals in various conditions plays the role of evidence that is used to estimate parameters (the coefficients) in the deterministic equations that are taken to describe the processes governing the onset of delinquency. A similar point holds for at least many biomedical examples.