Remarks on Constructive Empiricism and on Nora Boyd, “Evidence Enriched,” Philosophy of Science, 85, 201

Counting the Deer in Princeton

Remarks on Constructive Empiricism and on Nora Boyd, “Evidence Enriched,” Philosophy of Science, 85, 2018

Once upon a time, philosophers thought that scientific theories are collections of statements about the world.  The statements have logical connections that could be studied mathematically by the idealization of formal languages, and the statements have semantic relations that could be studied mathematically by the idealization of model theory, supplemented by various accounts of how terms in the language or mathematical objects in the models relate to things one can see, hear or touch.  Then along came constructive empiricism, which kept the idealized models but did away entirely with the formalized language and the logical relations it characterized and said little about how mathematical objects in the models relate to things one can see, hear or touch.   

Rather belatedly, two difficulties with constructive empiricism were noticed. The first was, indeed, how the models relate to things we can see, hear or touch, a matter that is, after all, at the heart of empiricism. The answer given is so odd that one might have thought the author was just kidding. The idea is that the theorist has a mathematical data model, and either that model can be embedded in a model of the theory or it cannot be. Van Fraassen considers a theory T of the growth of the deer population in Princeton, and the theorist’s data model, a graph of the variation of the deer population over time. He writes: “Since this is my representation of the deer population growth, there is for me no difference between the question whether T fits the graph and the question whether T fits the deer population growth” (256). The question of whether the mathematical model describes the actual deer population (not for me, but in fact) does not arise; it is not even sensible.

Suppose we ask a scientist how the curve of deer population growth in Princeton was obtained, and we are told “For each of several years, I counted the number of hoof marks in Princeton and divided by 4.’” We advise the scientist that his curve may be a severe overcount, since the same deer makes many more than 4 hoof marks.  The scientist replies that there is no point to the challenges.  If the critics have a different theory, construct their own data model. Constructive empiricism, after all.

 Suppose a group of physicists launch a mass spectrometer aboard a satellite to record ion concentrations above the atmosphere. They fail to calibrate the instrument before launch, with the result that it returns values in wild disagreement with previous measurements. (This really happened with the Swedish Freya satellite.) Would the scientists use the data anyway to try to publish a new estimate of ion concentrations? Would referees and a journal editor not care?  Of course they would care, and what the scientists actually published was a procedure for calibrating the spectrometer in-flight.

No one who takes science seriously can take seriously this constructive empiricist account of how data and theory meet. Nora Boyd does. Her essay focuses on facts familiar to anyone who has read almost any scientific paper: scientific data typically are accompanied by ancillary information that records the provenance of the measurements: what instruments were used, how they were calibrated and shielded, what resolutions of space or time or other variables were obtained, how were the data censored, or clustered or transformed, what statistical procedures were used, how were the units selected for measurement or treatment, where and when the measurements were made, whether the study was blinded or double-blinded, etc. This sort of information is typically given in the body of scientific reports or in supplementary material or in documents attached to databanks.  

Framing her story as an extension of Van Fraassen’s, she claims the value of such ancillary information is twofold: it helps multiple data sources to be used for related problems or investigations or arguments and it “breaks underdetermination.” I agree it does the first, but not in a way that is accommodated by constructive empiricism. I doubt it does the second in any sense except that of allowing further tests of a theory or theories; if some other theory can account for all of the same possible evidence—Quine’s sense of underdetermination—combining data sets won’t distinguish them.  But the main thing such information does is something she ignores, something to which van Fraassen seems to think there is no point:  it gives assurances that the measurements have not been made by a process that disqualifies them as premises in the assessment of a theory or theories because the measurements are not faithful to the quantities claimed to be measured; and it provides information to investigate whether such assurances are unwarranted.  On constructive empiricist grounds, there is no point to such assurances and no point to arguments that quantities have been mismeasured, or to arguments that data treatments destroyed information, or to objections that in view the provenance of the data the wrong statistical procedures were used, or that the experimental design leaves open alternative explanations of the data whose possibility better designs would have eliminated etc. Boyd misses all of that, perhaps because once science is cast in a constructive empiricist framework, faithfulness to the phenomena, truth, is not the point.

Boyd’s suggestion that ancillary information helps in the proper use of multiple data sets for a question, or the same data set for multiple problems is of course correct, but it is unintelligible in the constructive empiricist framework.  And that is the second belatedly noticed problem with constructive empiricism. On the old-fashioned view, language provides linkages between models. Language makes the connections that a relation in one model is the same relation as in another model. As Hans Halvorson points out, there is no such connection in constructive empiricism, only so many disconnected models, so many monads. A theory that constrains quantities conditionally, Newtonian dynamics for example, has many models under different conditions. One would like to say that the force holding the planets in their orbits is the same as the force acting on pendula, and indeed Newton says just that. On the constructive empiricist reconstruction, these are just different models of the theory, and nothing identifies the property acceleration, in one model with the property, acceleration, in another.  On the old-fashioned philosophy of science that is one of the services of language. Boyd tell me (private communication) that she does not endorse this part of "constructive empiricism," and she does refer to "minimal empiricism." 

Minimal empiricism turns out to be bad wine in new bottles. Citing van Fraassen, she says data are acquired to a theoretical purpose, to support, or not, a particular theory, and data are empirical only with respect to such a purpose.  Being empirical for a purpose is just what has been called, since longtime, being relevant to a theory or hypothesis. So what determines that relevance?  No answer. If I collect data on the spread of California poppies is that relevant to a hypothesis about the acceleration of the universe? Is it if I say that is its purpose?  Of course, there is no theory of relevance in "constructive empiricism" either. If a theory combines dynamics for the universe with dynamics for the spread of poppies, and someone's "data model" for poppies fits into it, is that evidence for the dynamics I postulate for the universe?

Boyd is a new Ph.D from Pitt HPS, and it is not fair to take her to task. Who then?  Pitt HPS. They take smart young people and make them, well, without a sense of what it is personally to discover something worth discovering, even the development of an actually new idea. As Pitt HPS goes, so goes philosophy of science in America, pretty much.