My argument for scientific realism stands against instrumentalism and its brethren: phenomenalism, claiming the equivalence of meaning of sentences about physical entities to sentences about sensations; fictionalism and the phi-losophy of ‘as-if’, according to which theories or concepts can be reliably used without for the to be true, or for their terms to refer (they can serve as ‘heuristic fictions’ or ‘regulative ideas’, according to Hans Vaihinger); and constructive empiricism, which will be investigated in detail.
Let us proceed by supposing that a given theory T is empirically success-ful, that is to say, it makes accurate observational predictions. Why does everything happen as if T were true? As we have seen, T’s realist supporter typically resorts to the following IBE: if T is a well-established theory, T is empirically successful because the entities it posits exist, and their properties are correctly described byT. Put differently, T’s success is explained by T’s truthlikeness.
In response, the instrumentalist typically advances the following counter-arguments: T’s empirical success is not in need of any explanation. According to van Fraassen (1980) – whose constructive empiricism is an epistemic sort of instrumentalism – there is no wonder that scientific theories are success-ful, because they are the result of natural selection in the jungle of epistemic competition:
...science is a biological phenomenon, an activity by one kind of organism which facilitates its interaction with the environment. I claim that the success of current scientific theories is no miracle. It is not even surprising to the scientific (Darwinist) mind. For any scientific theory is born into a life of fierce competition, a jungle red in tooth and claw. Only the successful theories survive – the ones which in fact latched on to actual regularities in nature. (van Fraassen 1980: 39–40)
Van Fraassen urges us not to ask for the (approximate) truth of theories, but for their empirical adequacy (i.e., for the truth of their observable consequences):
“Do not ask why the mouse runs from its enemy. Species which did not cope with their natural enemies no longer exist. That is why there are only one who
do.” (van Fraassen 1980: 39). However, it is legitimate to ask why precisely this theory and not a different one has survived in the cruel epistemic jungle.
That is, we want to identify some specific feature of the mouse which accounts for what it is that made its behavior fit for survival. We want to know what causes the mouse to run from its enemy. Instead, all that van Fraassen tells us is that the mouse is a survivor because it runs from its enemy. This can barely satisfy our need of an explanation.
A different instrumentalist move is motivated by the pragmatist view that the concept of truth should be defined in terms of pragmatic usefulness. The thought – entertained by Fine (1991), among others – is that in order to ex-plain the empirical success of science, we should not inflate the explanation with any features that go beyond the instrumental reliability of theories. Ac-cordingly, as the instrumentalist proposes, we ought to replace truthlikeness with an epistemically weaker notion, like ‘empirical adequacy’ or ‘pragmatic usefulness’.
Nonetheless, in line with Niiniluoto (1999) and Psillos (1999), I argue that such explanatory strategies have the major inconvenience of not explaining at all the practical success of science. To clarify this, let us first write down the typical realist explanatory schemata:
T is empirically successful, becauseT is truthlike.
Yet, here is what happens if we replace ‘truthlike’ with ‘empirically adequate’:6 T is empirically successful, becauseT is empirically adequate.
But empirical adequacy means just the truth of T’s observational conse-quences, i.e. T’s empirical success. Consequently, the above explanatory schemata is nothing but an idle tautology:
T is empirically adequate, becauseT is empirically adequate.
Therefore, it appears that, following such a strategy, instrumentalism doesn’t actually explain at all.
Now, to be more true to scientific practice, we ought also to explicitly take into account that, as a matter of fact, instrumentalist attitudes are quite often present in science. As Brian Ellis (1985) states, “scientific realists run into trouble when they try to generalize about scientific theories.” They tend to make their cases with rather simple historical examples of causal explanations, which urge a realistic understanding – as is the case study of atomism, which we discuss in chapter 3.
6This is a slight adaptation of Niiniluoto’s (1999: 197) explanatory sentences.
However, I argue here in favor of scientific realism and against a general instrumentalist “reading” of theories, that there is a crucial respect in which the former does systematically better than the latter: it can give causal ex-planations. For the sake of notational simplicity, let us henceforth call T a realistically interpreted, well-established theory, andTI its instrumentalist re-striction to observables. My contention is that, in general, TI cannot offer causal explanations. The argument will be that by systematically rejecting the commitment to the unobservables posited byT,TI frequently blocks our askingwhy-questions.
As Wesley Salmon (1984) puts it, “one obvious fact” about scientific ex-planations is its frequent appeal to unobservable entities.
We explain diseases in terms of microorganisms. ...We explain televi-sion transmistelevi-sion by appealing to electromagnetic waves which propagate through space. We invoke DNA to explain genetic phenomena. We ex-plain human behavior in terms of neurophysiological processes or, some-times, in terms of unconscious motives. (Salmon 1984: 206)
In line with this view, I seek to establish the thesis that unobservables are essential to the causal structure of the world.
Using Salmon’s nomenclature, the constituents of the world’s causal struc-ture are causal interactions, by which “modifications in structure are pro-duced; causal processes, by which “structure and order are propagated from one space-time region of the universe to other times and places (1984: 179);
andcausal laws, which “govern the causal processes and interactions, providing regularities that characterize the evolution of causal processes and the modifi-cations that result from causal interactions.” (1984: 132). What we typically observe are statistical correlations between events. In one of Salmon’s exam-ples, Adams and Baker are students who submitted virtually identical term papers in a course. Undoubtedly, the teacher will be very likely to consider it highly improbable that the papers came out like that by pure chance. Instead he will countenance one of the following reasonable possibilities: “(1) Baker copied from Adams, (2) Adams copied from Baker, or (3) both copied from a common source.” (Salmon 1984: 207). In other words,
There is either (1) a causal process running from Adams’s production of the paper to Baker’s, (2) a causal process running from Baker’s produc-tion of the paper to Adams’s, or (3) a common cause – for example, a paper in a fraternity file to which both Adams and Baker had access. In the case of this third alternative, there are two distinct causal processes running from the paper in the file to each of the two papers submitted by Adams and Baker, respectively. (Salmon 1984: 207)
Suppose it turns out that (3) is the case. We say then that there is anindirect causal relevance between the considered events: The common cause (the re-production of the original paper) is connected through causal processes to each of the separate effects. LetT be the theory positing the relevant causal mech-anisms. T thus explains causally the statistical correlations between eventsA and B. In the above example, A and B are the teacher’s establishing that Adams and Baker have, respectively, submitted virtually identical papers. All the same, in this caseTI (which rejectsT’s unobservable part) will do as well as T. Since both T and TI account for the correlations between the observ-able events in terms of observobserv-able interactions and observobserv-able causal processes, there is no reason not to take TI (instead of T) as the theory providing the right causal explanation. The same point applies when two events aredirectly causally relevant to each other, i.e. when A and B are connected by a causal process through which the causal influence is transmitted. This corresponds either to (1) or to (2) in the term-paper example.
However, there are many familiar circumstances under which TI’s causal explanations clearly fail. Consider the following situation: “Someone threw a stone and broke the window.” As TI’s supporter would have it, it is per-fectly all right to take the stone’s being thrown as the cause, the motion of the stone through the space as the causal process transmitting the causal in-fluence, and the window’s being broken as the effect in an causal connection between observable events. So, after all, T seems to have no monopoly on causal explanations; TI can also explain causally. If this was the case,TI could explain everything thatT can, and so would be a priori preferable on grounds of its ontological parsimony.
Nonetheless, this construal misunderstands the idea of explaining causally.
In the window example, one may legitimately ask, why does a normal window-pane actually break when hit by a stone. T’s advocate can (at least try to) locate an eventC on the spatiotemporal line going from AtoB,C consisting of the absorption of the stone’s kinetic energy into the molecular structure of the glass. C screens off A from B, meaning that knowledge of C renders A and B statistically independent (section 3.2 will detail Salmon’s statistical analysis). Obviously, C’s description must include terminology referring to microphysical entities.
To express it differently, although causal explanations can in some cases be given merely by reference to observable events, the former ought to be com-patible with underlying causal mechanisms by which some conserved physical quantity is transmitted from the cause to the effect. From this perspective, causal explanations relying only on correlations between observable events, though often satisfactory for common purposes, are in fact mere fragments of more detailed descriptions, given in terms of unobservable causal
interac-tions and of transmission of causal influence through continuous spatiotempo-ral processes. Knowledge of these hidden mechanisms is inherent to scientific investigation. As Philip Dawid (2001) puts it,
such deeper understanding of [the hidden workings of our units] ... is vital for any study of inference about ‘causes of effects’, which has to take into account what has been learned, from experiments, about the inner workings of the black box. (Dawid 2001: 60).7
By definition,TI’s defender cannot present a causal process to parallel the one posited byT, since TI only talks of observables. Thus, TI’s explanatory capability will not answer many of our legitimate why-questions. T’s explana-tory superiority overTI is thus reinstated.
Certainly, antirealists of the Humean tradition will reject causal talk alto-gether. By assuming the existence of causes, my argument seems actually to assume realism. My answer is straightforward: by taking scientific practice at face value, I also assume the legitimacy of causal talk. It is not among the purposes to answer here skepticism about causation.8
A different objection is that describing causal relations in terms of under-lying unobservable mechanisms is question-begging within the debate about scientific realism. Although antirealists like van Fraassen turn themselves occa-sionally to unobservables for explanatory purposes, they explicitly deny belief in such entities (cf. van Fraassen 1980: 151–2). That is, although van Fraassen turns toT’s theoretical posits for purposes of pragmatic explanation, he does not acceptT as true, but only as empirically adequate. He is agnostic about T’s unobservables.
Again, I shall not delve too much into the details of refuting this objection.
I refer to Kukla’s (1998) own argument, drawing on Friedman (1982) – which
7In the same fragment, Dawid admits that probing into the hidden parts of the causal mechanisms is not necessary for assessing ‘effects of causes’, “which can proceed by an es-sentially ‘black box’ approach, simply modelling dependence on the response on whatever covariate information happens to be observed for the test unit.” (Dawid 2001: 60)
8Note, however, that in the complex task of identifying causal structures from probabilistic relationships among events, supporters of causality are in good company. Judea Pearl (2000), for example, argues in great detail for the advantages of encoding knowledge in causal rather than probabilistic structures. I share with him the intuition that “probabilistic relationships, such as marginal and conditional independencies, may be helpful in hypothesizing initial causal structures from uncontrolled observations. However, once knowledge is cast in causal structure, those probabilistic relationships tend to be forgotten; whatever judgments people express about conditional independencies in a given domain are derived from the causal structure acquired. This explains why people feel confident asserting certain conditional independencies (e.g., that the price of beans in China is independent of the traffic in Los Angeles) having no idea whatsoever about the numerical probabilities involved (e.g., whether the price of beans will exceed $10 per bushel) (Pearl 2000: 25)”.
will be discussed in detail in 4.8 – showing that it is inconsistent to use the language of T while bracketing (or plainly rejecting) T’s unobservable part.
The argument-line is that if one believes T’s observable consequences, and if the existence of an entity X is among the observable consequences of T, then one must believe in the existence ofX. Consequently,TI’s defender who accepts T’s unobservables for pragmatic purposes comes eventually to believe in (at least some) unobservables, thus contradicting TI itself.
Let us summarize the steps of the argument:
(1) Well-established theories committed to unobservables, such as T, gener-ally allow the formulation of frameworks for causal, as well as for other – abstract model, functional, and systematic forms of explanation.
(2) TI precludes the search for causal explanations appealing to unobserv-ables.
(3) Causal explanations appealing to unobservables are essential to scientific investigation.
(4) Therefore, we should always prefer T to TI. T in fact accommodates all above enumerated sorts of explanatory frameworks, while TI bars, by definition, at least the possibility of explaining causally in terms of unobservables.
Let us now proceed by rejecting a few general criticisms against IBE, which is the pillar of the explanatory defence of realism.