The Compleat FalsifierAuthor(s): Philip KitcherSource: Psychological Inquiry, Vol. 1, No. 2 (1990), pp. 158-159Published by: Taylor & Francis, Ltd.Stable URL: http://www.jstor.org/stable/1448775 .

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158 COMMENTARIES

The Compleat Falsifier

Philip Kitcher Department of Philosophy

University of California, San Diego

I agree with almost everything in Paul Meehl's charac- teristically well-informed and wide-ranging article. His dis- cussion shows very clearly how to salvage what is important in Popper's influential (and much-misunderstood) emphasis on falsification, while accommodating the familiar points about the involvement of auxiliary assumptions in situations of testing. In large measure, my comments explore alter- native ways of presenting and developing Meehl's meth- odological points, sometimes attempting to free them from what I take to be unnecessarily cumbersome machinery, sometimes offering rival elaborations.

Lakatos (1970) refined Popper's (1959) already sophisti- cated analysis of refutations by trying to understand how to respond to a Duhemian predicament-a situation in which, from the conjunction of a hypothesis H and a set of auxiliary assumptions A one can derive an experimental/observational prediction 0 that is inconsistent with the observed finding -0. The heart of Meehl's methodological proposal is that preservation of H by amending A-as, for example, when Galileo "saved" Copernicanism from the failure to observe stellar parallax by rejecting auxiliary assumptions about the size of the universe-should depend on the previous track record of H. The track record of H is assessed by looking at its prior ability to predict (typically in conjunction with other auxiliaries) findings that would have been antecedently im- probable ("damned strange coincidences"). Meehl ultimate- ly offers a formal measure for evaluating track records. I comment on this later.

Consider various strategies for responding to a Duhemian predicament: We might (a) refuse to accept - 0, (b) abandon some part of A, or (c) scrap H. Meehl 's consideration of these strategies seems to me a bit too atomistic. What is at stake is not the absolute worthiness of H, as measured by its past track record, but the overall epistemic goodness of the bodies of belief that would result from various modifications. Sup- pose, for example, that we were to refuse to incorporate the problematic finding -0. That is not, as sociologists of sci- ence sometimes seem to suggest, a free move. Background considerations guide us in judging when an observation has been poorly or well made, an experiment well or ill done. Of course, we could abandon our prior beliefs about observa- tional standards, but the consequences of such modifications would themselves have to be reckoned with. Similar remarks apply to suggested modifications of the auxiliary hypotheses (along any of the dimensions that Meehl distinguishes). The consequences of those modifications for other parts of our belief systems have to be investigated: Do we have to give up well-established and apparently correct problem-solutions or abandon hypotheses with good track records? It is not hard to see that the exploration of the consequences might quickly ramify, bringing in a host of background hypotheses that are not involved in the initial predicament. My difference with Meehl here lies in the cast of characters whose track records will ultimately deserve scrutiny.

I want to make three points about this variant on Meehl's

methodological analysis. First, there is an apparently simple way to amend an auxiliary hypothesis without making major modifications in the system. One maintains that previously successful applications of the auxiliary are correct but that it breaks down in the particular case at hand. "If the theory is quantitative, altering an auxiliary to take care of a falsifier in one domain will, if that auxiliary appears in other domains as well, generate falsifications in them, because the data that fitted the original auxiliary mathematical function will now, curve-fitting problems aside, no longer fit them." Unless, of course, we modify the auxiliary by letting the function take a new argument: fnew(x, old domains) = fOld(x), fnew(X, prob- lematic domain) = whatever we like. This of course is ad hoc in the literal sense, tailoring the auxiliary to suit trouble. Do we need a big methodological principle to debar such moves? I don't think so. Sometimes there may be good reasons to treat the problematic domain differently, on other occasions (most occasions) considerations of uniformity of treatment will follow from our background beliefs. Thus, to introduce the cooked auxiliary will require revision of our views about uniformities and similarities. If those views have good track records, scrapping them will entail costs, and those costs militate against the proposed modification.

Second, Meehl rightly stresses the difference in centrality of various beliefs, and he suggests that a "core postulate [is] one that appears in every derivation chain." This is a useful idea which begins to break down the static conception of a scientific theory as a set of beliefs, in favor of focusing on the ways in which statements are actually used. I would go fur- ther. Pace Meehl, the conception of scientific theories as axiomatic deductive systems whose axioms are principles of high generality is no longer widely accepted. In some quar- ters, it has given way to the so-called "semantic conception" which takes a theory to be a class of models (Giere, 1989; van Fraassen, 1980). My own preferred account of theories is closer to Meehl's: Think of a theory as a set of problem- solving patterns which are instantiated in derivations that yield explanations and/or predictions. To learn the theory is to acquire the ability to use the patterns. Thus, to learn clas- sical mechanics is to have the skill to analyze dynamical situations in terms of Newton's, Lagrange's, or Hamilton's equations; to learn genetics is to be able to propose hypoth- eses about underlying distributions of alleles and to deploy them to generate expected distributions of traits in crosses (see Kitcher, 1984, 1989, for further discussion). The prob- lem-solving patterns we accept embody our ideas about what depends on what, what is akin to what. Successful patterns are not to be lightly abandoned, nor are their views of the similarities in nature to be capriciously subverted.

So to my third point. In domains that are theory-rich-like mechanics, electromagnetic theory, genetics, or neo-Darwi- nian evolutionary biology-there are stringent constraints on the moves available in Duhemian predicaments. Recognition of the involvement of auxiliaries in testing can easily spook the aspiring methodologist, making it seem as though there

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COMMENTARIES 159

are numerous options for revision, numerous ramifying ave- nues of modification to explore. But, in the sciences I have mentioned, most of these avenues are short blind alleys, terminated by some problem-solving practice with a dazzling track record. Meehl's hope is to make areas of soft psycholo- gy as rigorous as the parts of physics, chemistry, and biology that he rightly admires. The trouble is that, when a domain is theory-poor-when there are few successful general patterns to fix ideas about uniformities and similarities-the pos- sibilities for alternative responses to the same empirical find- ings multiply. Areas of science, like individual scientists, seem to be subject to Robert Merton's (1968) "Matthew effect": To those that already have much theory, oppor- tunities for further refinements shall be given. Perhaps this explains the tendencies of some social scientists to honor a grand vision of their subject, even when they see that that vision is highly problematic. (I recall the poignant response of an anthropologist to a lecture I gave on the pitfalls of human sociobiology: "This may be bad, but you should have seen what we were doing before").

I close with two small queries. Meehl likes the idea that hypotheses gain credit by explaining or predicting coinci- dences, an idea descending from Reichenbach (1971) that has been worked out in some detail by Salmon (1984). So do I. But there are lurking troubles, generated in part by the celebrated Bell inequalities in quantum mechanics (see Van Fraassen, 1985, for suggestions that these call into question the view that hypotheses that explain correlations are always to be commended). I also worry that Meehl's devotion to the "crud factor" is in tension with his advocacy of Salmon's principle. Isn't the point of the "crud factor" that there are genuinely chance coincidences in the world, so that it would be wrong to praise hypotheses for explaining them?

My second question concerns the discussion of the Spielraum and the overall measure of epistemic goodness that Meehl offers. Meehl recognizes that there are two di- mensions of theory appraisal, informativeness and cor- rectness, and the definition of Ci is supposed to weight them. The definition itself is perfectly natural, but I have troubles with the scale Meehl constructs. Specifically, his discussion of the "worst case" seems mistaken: God might be able to able to draw conclusions from hypotheses with "inverse

verisimilitude," but, if we employed them, we would go dramatically astray. Moreover, Meehl leaves out of consid- eration a different type of worst case, the completely tolerant hypothesis. If In(H) = 0, then Ci(H) = 0. So there's at least one (and, I believe, two) ways to achieve a minimum for Ci of 0. I conclude that Meehl's normalization is faulty and that Ci runs from 0 to 1.

This is carping. Meehl offers a wealth of valuable insights for practitioners. He modestly takes himself to be summing up what "every philosopher of science knows." That is quite wrong. Meehl's original distillation of what is best in con- temporary thinking about testing ought to be widely read by (full-time) philosophers. There are few who could write with such authority and good sense, many who could learn more than a little from his essay.

Note

Philip Kitcher, Department of Philosophy, University of California, San Diego, La Jolla, CA 92093.

References

Giere, R. (1989). Explaining science. Chicago: University of Chicago Press.

Kitcher, P. (1984). 1953 and all that. A tale of two sciences. Philosophical Review, 93, 335-373.

Kitcher, P. (1989). Explanatory unification and the causal structure of the world. In P. Kitcher & W. Salmon (Eds.), Scientific explanation (Min- nesota Studies in the Philosophy of Science XIII, pp. 410-505). Min- neapolis: University of Minnesota Press.

Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 91-195). Cambridge, England: Cambridge University Press.

Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56- 63.

Popper, K. (1959). The logic of scientific discovery. London: Hutchinson. Reichenbach, H. (1971). The direction of time. Berkeley: University of

California Press. Salmon, W. (1984). Scientific explanation and the causal structure of the

world. Princeton, NJ: Princeton University Press. Van Fraassen, B. (1980). The scientific image. Oxford, England: Oxford

University Press. Van Fraassen, B. (1985). Salmon on explanation. Journal of Philosophy,

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