International Society for History, Philosophy, and Social Studies of Biology

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TUESDAY, JULY 7  /  09:00 - 10:30  /  DS-M540
Organized session / standard talks
Beyond Mechanisms

Stuart Glennan (Butler University, United States); Holly Andersen (Simon Fraser University, Canada)

A cursory review of recent literature might lead one to the following characterization of the mechanistic account of biology and the life sciences: Biological discovery is about searching for the mechanisms responsible for some biological phenomena, and biological explanation involves illustrating just how these mechanisms give rise to those phenomena. But while this characterization captures some examples of biological research, it hardly exhausts them. In this session we will consider several ways in which a mechanistic approach to philosophy of biology is compatible with more varied styles of discovery and explanation. Tudor Baetu considers the challenges that arise from the fact that much of our supposed knowledge of biological processes arises from the extrapolation from and patching together of data from disparate surrogate models. Stuart Glennan, considers the general question of how non-mechanical and non-causal explanations can be squared with mechanistic ontology. Lane DesAutels offers an account of the explanatory role of non-actualized possibles in the life sciences.

The big picture: Contextualization and extrapolation in basic research

Tudor Baetu (Universidade do Vale do Rio dos Sinos, Brazil)

Not only clinical research, but also basic science systematically relies on the epistemic practice of extrapolation from surrogate models, to the point that explanatory accounts presented in review papers and biology textbooks are in fact composite pictures reconstituted from data gathered in a variety of distinct experimental setups. This raises two new challenges to previously proposed mechanistic-similarity solutions to the problem of extrapolation, one pertaining to the absence of mechanistic knowledge in the early stages of research and the second to the large number of extrapolations underpinning explanatory accounts. An analysis of the strategies deployed in experimental research supports the conclusion that, while results from validated surrogate models are treated as a legitimate line of evidence supporting claims about target systems, the overall structure of research projects also demonstrates that extrapolative inferences are not considered ‘definitive’ or ‘sufficient’ evidence, but only partially justified hypotheses subjected to further testing.

Non-mechanistic and non-causal explanation in a causal-mechanical world

Stuart Glennan (Butler University, United States)

Providing one adopts a broad enough conception of mechanisms, there are reasons to believe that we live in a mechanistic world. The world has many parts, with the parts themselves having parts, and the organized (and sometimes disorganized) activities and interactions of these parts collectively are responsible for all the phenomena we find in nature. Mechanisms, in short, are the causal structure of the world. But even if this ontological picture is right, there appear to be many explanatory forms used in biology and across the sciences that do not appeal to mechanisms. Examples include equilibrium explanations, functional explanations, and lineage explanations. In this paper my aim is to give a general account of non-mechanical and non-causal explanation that illustrates how such explanations can be made true by and be informative about a causal-mechanical world.

On the role of unactualized possibilities in biological explanation

Lane DesAutels (University of Notre Dame, United States)

There are many well-known arguments purporting to show that explanation in the sciences should be causal. There are, however, a growing number of philosophers who argue that some of our best scientific explanations are non-causal. In this paper, I draw attention to the role that unactualized possibilities play in our explanations of a few key phenomena in the life sciences: fitness, selection, and (more generally) stochastic mechanisms. Explanations that make indispensible reference to unactualized possibilities, I suggest, don’t seem to fit well within a straight-forward causal explanatory framework. Neither, however, do they seem to be instances of the types of non-causal explanations that have received recent attention in the literature. I propose a modified version of Jackson and Pettit’s (1990) notion of ‘program explanation’ as a way of understanding these explanations.