Mechanism and Chance: Toward an Account of Stochastic Mechanism for the Life Sciences

Abstract

In this dissertation, my aim is to develop some important new resources for explaining probabilistic phenomena in the life sciences. In short, I undertake to articulate and defend a novel account of stochastic mechanism for grounding probabilistic generalizations in the life sciences. To do this, I first offer some brief remarks on the concept of mechanism in the history of philosophical thought. I then lay out some examples of probabilistic phenomena in biology for which an account of stochastic mechanism seems explanatorily necessary and useful: synaptic transmission in the brain, protein synthesis, DNA replication, evolution by natural selection, and Mendelian inheritance. Next, I carefully examine the concept of regularity as it applies to mechanisms--building on a recent taxonomy of the ways mechanisms may (or may not) be thought to behave regularly. I then employ this taxonomy to sort out a recent debate in the philosophy of biology: is natural selection regular enough to count as a mechanism? I argue that, by paying attention to the forgoing taxonomy, natural selection can be seen to meet the regularity requirement just fine. I then turn my attention to the question of how we should understand the chance we ascribe to stochastic mechanisms. To do this, I form a list of desiderata that any account of stochastic mechanism must meet. I then explore how mechanisms fit with several of the going philosophical accounts of chance: subjectivism, frequentism (both actual and hypothetical), Lewisian best-systems, and propensity. I argue that neither subjectivism, frequentism, nor best-system-style accounts of chance will meet all of the proposed desiderata, but some version of propensity theory can. Borrowing from recent propensity accounts of biological fitness and drift, I then go on to explore the prospects for developing a propensity interpretation of stochastic mechanism (PrISM) according to which propensities are (i) metaphysically analyzable and operationally quantifiable via a function of probability-weighted ways a mechanism might fire and (ii) not causally efficacious but nonetheless explanatorily useful. By appealing to recent analyses of deterministic and emergent chance, I argue further that this analysis need not be vulnerable to the threat of metaphysical determinism.