Toward a propensity interpretation of stochastic mechanism for the life sciences

The life sciences are rife with probabilistic generalizations. Mendel (1865) discovered that the chance of a hybrid green and yellow pea plant to produce yellow peas in the F2 generation is .75. In neuroscience, the release of neurotransmitters can fail to result in the successful initiation of electrical activity in a particular postsynaptic neuron up to 90% of the time (Kandel et al. 2013, 271). Evolution by natural selection is subject to the whims of genetic mutation—where the evolutionary consequences of genetic mutation are conceptualized in terms of the chance (per unit of time) a gene has of changing from one state to another (Sober 2010). A question of significant import to philosophers of science is: what makes these statements true? What in the world, if anything, grounds these probabilistic facts? In what follows, I suggest that it makes good sense to think of the truth of (at least some of) the probabilistic generalizations made in the life sciences as metaphysically grounded in biological mechanisms in the world. These biological mechanisms underlie and produce the observable phenomena, and these biological mechanisms are themselves—in some sense— chancy. I call them stochastic mechanisms. But how should we understand such stochastic mechanisms? To begin to answer this question, I formulate two desiderata that any adequate account of stochastic mechanism should meet. I then take the general characterization of mechanism offered by Machamer, Darden, and Craver (2000) and explore how it fits 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 a bestsystem-style account of chance meets the proposed desiderata, but some version of propensity theory can. Because I will not be able to consider every possible interpretation of chance, the ensuing arguments should not be taken to be strict, deductive, arguments for a propensity-backed