The philosophy of historical case studies

Cover: The Philosophy of Historical Case StudiesThe Philosophy of Historical Case Studies is now available as a volume of the Boston Studies in the Philosophy and History of Science. I co-edited the book with Tilman Sauer.

The title is bit of grammatical play: one can read the genitive “of” in two different ways. On the one hand, our interest is in the philosophy involved in studying historical cases — how to minimize bias in choosing cases, how to draw robust conclusions from them, how to adjudicate between different interpretations of the same case, and so on. On the other hand, however, we are also talking about the philosophy that emerges from historical case studies: many worthwhile questions in the philosophy of science are best answered (and perhaps only answerable) by looking closely and carefully at the past and present of actual science.

So the volume is part manifesto, part user’s manual, and part affirmation of a research program – we hope that many different kinds of readers will get something out of it.

The table of contents is available on the Springer website. A preprint of my own contribution (with Tim Räz) is available on the PhilSci archive.

Towards a methodology for integrated history and philosophy of science

It has been claimed that the integration of history and philosophy of science is nothing but a marriage of convenience. I think this is wrong — it is really a passionate romance, and I argue why in a recent co-written paper. Beyond a discussion of what is to be gained by integrated HPS in principle, we focus particularly on the methodology of integration in practice: how should we relate philosophical concepts to historical cases, and vice versa? Our penultimate draft is now on the PhilSci Archive.

The paper is forthcoming in a collected volume titled The Philosophy of Historical Case Studies, which was co-edited by Tilman Sauer and myself and will appear in the Boston Studies in the Philosophy and History of Science.

The argument from the good lot

I have uploaded the slides from my second Pittsburgh lunchtime talk. This is an initial presentation of a current paper project. Here is the question: If science proceeds by (1) proposing a number of candidate explanations for a phenomenon, (2) ranking these explanations by explanatory power and (3) accepting the most highly ranked of the candidates, then why should we expect science to arrive at truth? After all, it is always possible that we simply failed to consider the true hypothesis in the first place. This would explain why so many successful — that is, highly ranked — past theories were later abandoned. In recent years this issue has been vigorously pursued by Kyle Stanford, who speaks of the “problem of unconceived alternatives”. In my talk I develop an account of why the problem of unconceived alternatives is not acute in much of the life sciences. More to follow.

How to think new thoughts

Much of science is a kind of puzzle solving activity. You, the scientist, are presented with a phenomenon whose causes and underlying mechanisms we do not yet understand — and your task is to elucidate them. That this succeeds at all inspires awe. That it succeeds fairly regularly and efficiently requires an explanation.

There are two issues to be understood, broadly speaking: (1) how we can tell that a scientific hypothesis is probably true (this is usually called “justification”) and (2) how we come up with hypotheses in the first place (usually called “discovery”). Both stages are crucial. The best tester of hypotheses is helpless if she has nothing to test. And the most creative hypotheses are of limited use if we cannot assess their truth. Importantly, the efficiency of science must depend to a large extent on discovery: on the fact that candidate hypotheses can be produced quickly and reliably.

Not so long ago, philosophers of science believed that discovery is mostly intractable: a matter of happy guesses and creative intuitions. In recent decades, however, it has been argued that systematic insight into scientific hypothesis generation is possible. A particularly nice and approachable example of this type of thinking in the philosophy of biology is given in a recent book by Carl Craver and Lindley Darden (based on their earlier research). They argue that scientists invent new mechanisms by using three main strategies: (1) they transfer mechanisms schemata from related problems (schema instantiation); (2) they transfer mechanism components from related problems (modular subassembly); (3) they investigate how known components or interactions can link up (forward/backward-chaining). A somewhat broader and more historical (but less problem oriented) perspective is given by Jutta Schickore in the Stanford Encyclopedia of Philosophy.

In a new paper, I and my co-author Kärin Nickelsen present our own contribution to the discovery debate. Our work is in the Craver/Darden tradition, but we look in detail at two historical cases — Oxidative Phosphorylation and the Calvin-Benson cycle — to advance the state of the art a bit (by about a paper’s worth). We focus on three areas:

First, we consider “hard cases” of discovery from the history of sciences. By this we mean achievements of acknowledged originality that no one would describe as mere extrapolations of previous knowledge. If a particularly spectacular scientific discovery can be explained in terms of a certain set of discovery strategies, then this speaks to the usefulness and power of these strategies: less complex cases should present no problem to them. So hard cases help our claim that much of scientific creativity is ultimately explicable in terms of the skillful and diligent use of basic heuristics.

Second, we are interested in whether discovery strategies are “substrate neutral” or “domain specific”. Are there general rules for discovering scientific hypotheses, or do strategies only apply to particular fields of inquiry — or even to particular kinds of empirical problems within disciplines? We think that the truth is for once in the middle: discovery strategies seem to be somewhat general, but they need to be applied to highly domain-specific previous knowledge. We discuss instances of this in the paper.

Third, the existing literature does not pay enough attention to the way in which the space of possible hypotheses is explored systematically. In one of our cases, for instance, a particularly interesting scientific hypotheses was arrived at — in part — by simple causal combinatorics. It was known that two types of events, A and B, were correlated. This allowed the following (exhaustive) set of hypotheses to be explored: Does A cause B? Does B cause A? Or do A and B have a common cause? While this procedure may sound simple, its results are anything but.

The paper has just appeared in History and Philosophy of the Life Sciences, and our penultimate draft is available on Pitt’s PhilSci archive.