DesAutels, Lane ThomasIn 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.enDissertationPhilosophy of scienceBiologyChanceMechanismProbabilityStochastic