Abstract
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 stochastic mechanisms in the world. To further understand these stochastic mechanisms, I take the general characterization of mechanism offered by MDC (Philos Sci 76(1):1–25, 2000) and explore how it fits with several of the going philosophical accounts of chance: subjectivism, frequentism, Lewisian best-systems, and propensity. I argue that neither subjectivism, frequentism, nor a best-system-style interpretation of chance will give us what we need from an account of stochastic mechanism, but some version of propensity theory can. I then draw a few important lessons from recent propensity interpretations of fitness in order to present a novel propensity interpretation of stochastic mechanism according to which stochastic mechanisms are thought to have probabilistic propensities to produce certain outcomes over others. This understanding of stochastic mechanism, once fully fleshed-out, provides the benefits of (1) allowing the stochasticity of a particular mechanism to be an objective property in the world, a property investigable by science, (2) a way of quantifying the stochasticity of a particular mechanism, and (3) a way to avoid a problematic commitment to the causal efficacy of propensities (and dispositional properties in general).
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Notes
Following Sober (2010), I don’t take ‘probabilistic’ here to be incompatible with determinism. Rather, I mean it to encapsulate generalizations that are both fundamentally probabilistic (i.e., those that emerge out of genuine indeterministic processes) and those that are statistical (i.e., those that emerge out of deterministic processes).
This term finds its origins with Jeffrey’s use of ‘stochastic process’ (1969)—and later gets briefly mentioned in Salmon (1989). Stochastic mechanisms do not, however, receive any detailed discussion until Glennan (1992) and Glennan (1997)—though he offers no explicit analysis of which philosophical theory of chance to understand them with.
As one of my reviewers notes, a complete account of stochastic mechanism should also specify the locus of a particular mechanism’s stochasticity: where among a mechanism’s entities and activities the stochastic element emerges. I cannot here undertake this project. But recent work by Andersen (2012) helpfully taxonomizes the various ways in which mechanisms fail to behave regularly.
There is a sense in which the argument strategy for this section mirrors the first chapter of Brandon’s (1990) book, Adaptation and Environment. However, rather than natural selection, it is applied to mechanisms.
Following Schaffer (2007), I define Objective Chance as: an understanding of probabilities that meets a set of commonly accepted platitudes regarding its relationship to several related concepts Summarized roughly, they are: Chance-credence: If you have information about the objective chance of an event, you should set your credence level to match that information. Chance-possibility: If you assign a non-zero chance to an event, it must be possible for that event to occur. Chance-future: to say (at some time \(t\)) that some event has a non-extremal objective chance of occurring requires that the event take place in the future. Chance-intrinsicness: If you assign an objective chance to an event occurring after a certain history, then you must assign the same chance to any intrinsic duplicate of such an event with such a history. Chance-causation: If some event appears causally relevant to another event, then the first event must happen before the other. Chance-lawfulness: The laws operating at a given level must be seen to determine the chances at that level. Probability, on the other hand, I understand as merely a measure on a likelihood that an event will occur: where this likelihood may be understood either as a subjective degree of belief or as an objective chance.
A classical example of this is Bruno de Finetti’s (1937) account of subjective probability.
From the standpoint of the mechanisms literature, ‘firing’ can be defined as a general way of saying the mechanism has begun operation.
This is a modified version of an argument defended by Lyon (2011) against a subjectivist understanding of the probabilities in classical statistical mechanics.
Cf, especially Hajek’s famous “Fifteen Arguments Against Finite Frequentism” (1997).
For a particularly forceful articulation of the relevance problem for counterfactual explanation, see Salmon (1989).
Lewis’s own solution to preemption cases (1973b) is to appeal to a notion of ‘causal chain’ which is itself in want of analysis.
Hoefer (1997) and Cohen and Callender (2009) have made considerable efforts to save the BSA account of lawhood which might be extended to apply to the BSA account of chance. That said, I still believe (for reasons outside the purview of this paper) that they have fallen short of offering and articulating a BSA analysis which would comfortably cohere with what we want from an account of stochastic mechanism.
It may be that a BSA analysis of stochastic mechanism would allow for other types of explanation (unificationism perhaps). But what I suggest here is that life scientists seek the sort of explanation where describing the underlying causal structure of an observed fact is what does the explaining.
It is worth noting that there has been some dispute as to whether natural selection should count as an MDC mechanism. Skipper and Millstein (2005) argue that it should not. Barros (2008) argues that it should. I need not take a stand on this debate here, however, because my only point is that a PrISM is capable of cohering with the actual practice of scientists searching for and describing mechanisms (whatever the scientists take these mechanisms to be).
See Eagle (2004) for a detailed summary of some of the main objections to propensity accounts. Since my aim is only to defend a local version of propensity theory (apt for achieving a better understanding of stochastic mechansims), I don’t take it as necessary to fend off all of them.
Taken from Rosenberg and Bouchard (2008).
Aka: metaphysical or supervenience base.
Here, it is important to note that I am not committed to any particular view about how these probabilities should be interpreted or where/how we get them. By advocating a PrISM, I do not, thereby, mean to endorse a general, one-size-fits-all propensity view of chance/probability. Indeed I can be a pluralist about metaphysical interpretations of chance/probability because all I’m doing is arguing that propensity is a useful notion in certain explanatory contexts: namely, ones where we seek mechanistic explanations for probabilistic phenomena in the life sciences. It may well be that other interpretations of chance/probability are useful in other contexts.
An objection seems to arise. Namely this: aren’t BSA chances causally relevant in just the same way that propensities are? And if so, wouldn’t this negate the argument I offered (in 2.2.3) against a BSA understanding of stochastic mechanism?
Before I offer my response to this objection, let’s ask why it might seem that BSA chances are causally relevant in the same way that propensities are. Recall that, on the BSA interpretation, the chance of any given outcome occurring is whatever the best systematization of the Humean mosaic of particular facts tells us it is. BSA chances might seem to be causally relevant in the following sense. Just as the word processing program I’m currently using constrains the kinds of causally efficacious interactions I can have when typing these words, so too does BSA chance amount to a constraint on the space of possible causal events that can take place in the world. When the BSA, for example, tells us that there is a 1/6 chance of a six-sided fair die landing on six when I roll it, what it is doing (in effect) is giving us some information regarding what kinds of constraints there are on the ways that I can be causally efficacious in rolling a six with a fair die. E.g., I shouldn’t expect to be able to roll a six ten times in a row. If this is correct, then it seems BSA chances are causally efficacious in just the same way that propensities are. And if this is correct, then it seems we no longer have any theoretical basis for dismissing a BSA interpretation of stochastic mechanism on the grounds that it fails to meet CAUSAL EXPLANATION.
Despite its apparent force, I argue that this objection rests on a mistake. Specifically, I suggest that, on the BSA, the facts constrain the chances; not the other way around. So BSA chances aren’t causally relevant in the way that propensities are.
To see why, consider again the example of my word processing program. On Jackson and Pettit’s view, what makes this program causally relevant is the fact that “[it] ensures that certain things will happen - things satisfying certain descriptions - though all the work of producing those things goes on at a lower, mechanical level” (Ibid, 114). Now ask yourself, do BSA chances ensure that things will happen? Put another way, do BSA chances place constraints on the way that causal events can occur in the world? My intuition is that the answer to both of these questions is no. Rather, it seems to me that (by their very definition), BSA chances are constrained by the causal facts—not the other way around. Indeed, the central point of the BSA account of chance is that the chances supervene on the Humean mosaic of particular matters of fact. Given this central feature of the BSA account, I argue, it must be that those facts constrain the chances; it doesn’t work the other way. And if this is so, BSA chances are not causally relevant in the way that my word processing program is. My word processing program, given that it is realized on my computer, makes it such that certain ways of poking my keys will produce the appearance of certain symbols on my screen (and not others). But BSA chances don’t make anything be the case in the natural world. As such, I take the objection offered in this section not to threaten the arguments I gave in 2.2.3 after all.
For detailed mechanistic analysis of this phenomenon, see the original MDC (2000) paper and Craver’s book Explaining the Brain (2007). For discussion of the science behind successful vs. unsuccessful instances of neurotransmitter release, see Kandel et al. Kandel et al. (2013). For detailed discussion of how this case relates to MDC’s regularity requirement, see Bogen (2005) and Andersen (2012).
References
Abrams, M. (2012). Mechanistic probability. Synthese, 187(2), 343–375.
Andersen, H. (2012). The case for regularity in mechanistic causal explanation. Synthese, 189(3), 415–432.
Anscombe, E. (1971). Causality and determination: An inaugural lecture. In J. Kim, D. Korman, & E. Sosa (Eds.), Metaphysics: An anthology (2nd ed., pp. 386–397). Malden, MA: Wiley-Blackwell Press.
Ariew, A., & Lewontin, R. C. (2004). The confusions of fitness. British Journal for the Philosophy of Science, 55(2), 347–363.
Barros, B. (2008). Natural selection as a mechanism. Philosophy of Science, 75(3), 306–322.
Beatty, J. (1995). The evolutionary contingency thesis. In G. Wolters & J. Lennox (Eds.), Concepts, theories, and rationality in the biological sciences, second pittsburgh-konstanz colloquium in the philosophy of science (pp. 45–81). Pittsburgh, PA: University of Pittsburgh Press.
Beatty, J. H., & Finsen, S. (1989). Rethinking the propensity interpretation of fitness: A peek inside pandora’s box. In M. Ruse (Ed.), What the philosophy of biology is: Essays for David Hull (pp. 17–30). Ann Arbor, MI: Kluwer Academic.
Bechtel, W. & Abrahamsen, A. (2005). Explanation: a mechanist alternative. In: C. F. Craver & L. Darden (Eds.), Special Issue: “Mechanisms in Biology,” Studies in History and Philosophy of Biological and Biomedical Sciences 36(2), 421–441.
Bogen, J. (2005). Regularities and causality; generalizations and causal explanations. In Special Issue: “Mechanisms in Biology,” In: C. F. Craver & L. Darden (Eds.), Special Issue: “Mechanisms in Biology,” Studies in History and Philosophy of Biological and Biomedical Sciences 36(2), 397–420.
Brandon, R. N. (1978). Adaptation and evolutionary theory. Studies in History and Philosophy of Science, 9(3), 181–206.
Brandon, R. N. (1990). Adaptation and environment. Princeton, N.J.: Princeton University Press.
Brandon, R. N., & Carson, S. (1996). The indeterministic character of evolutionary theory: No ‘no hidden variables proof’ but no room for determinism either. Philosophy of Science, 63(3), 315–337.
Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.
Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. New York: Oxford University Press.
Cohen, J., & Callender, C. (2009). A better best system account of lawhood. Philosophical Studies., 145(1), 1–34.
de Finetti, B. (1937). La prévision: ses lois logiques, ses sources subjectives. Annales de l’Institut Henri Poincaré, 7: 1–68; translated as “Foresight. Its Logical Laws, Its Subjective Sources,” In: H. E. Kyburg, Jr. and H. E. Smokler (Eds.), Studies in Subjective Probability. Robert E. Krieger Publishing Company, 1980.
Eagle, A. (2004). Twenty-one arguments against propensity analyses of probability. Erkenntnis, 60(3), 371–416.
Earman, J., & Roberts, J. (2002). Ceteris paribus, lost. Erkenntnis, 57(3), 281–301.
Fodor, J. (1991). You can fool some of the people all of the time, everything else being equal; hedged laws and psychological explanations. Mind, 100(397), 19–34.
Glennan, S. (1992). Mechanisms, Models, and Causation. Ph.D. Dissertation. Chicago, IL: University of Chicago.
Glennan, S. (1996). Mechanisms and the nature of causation. Erkenntnis, 44(1), 49–71.
Glennan, S. (1997). Probable causes and the distinction between subjective and objective chance. Noûs, 31(4), 496–519.
Hajek, A. (1997). “Mises redux”–redux: Fifteen arguments against finite frequentism. Erkenntnis, 45(2/3), 209–227.
Harvey, W. (1628). Exercitatio Anatomica de Motu Cordis et Sanguinis in Animalibus. Frankfurt. (Translated 1889) as On the Motion of the Heart and Blood in Animals. London: George Bell and Sons.
Hoefer, C. (1997). On Lewis’s objective chance: Humean supervenience debugged. Mind, 106(422), 322–334.
Jackson, F., & Pettit, P. (1990). Program explanation: A general perspective. Analysis, 50(2), 107–117.
Jacob, F., & Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology, 3, 318–356.
Jeffery, R. C. (1969). Statistical explanation vs. statistical inference. In N. Rescher (Ed.), Essays in Honor of Carl G. Hempel (pp. 104–113). Dordrecht: D. Reidel Publishing Co.
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (2013). Principles of neural science (5th ed.). New York: McGraw-Hill Inc.
Lewis, D. (1973a). Counterfactuals. Boston: Harvard University Press.
Lewis, D. (1973b). Causation. Journal of Philosophy, 70(17), 556–567.
Lewis, D. (1980). A subjectivist’s guide to objective chance. In R. C. Jeffrey (Ed.), Studies in inductive logic and probability (pp. 83–132). Berkeley: University of California Press.
Lyon, A. (2011). Determinisitc probability: Neither chance nor credence. Synthese, 182(3), 413–432.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25.
Matthen, M., & Ariew, A. (2002). Two ways of thinking about fitness and natural selection. The Journal of Philosophy, 99(2), 55–83.
Mendel, G. (1865). Experiments on plant hybrids. In: C. Stern & E. Sherwood (Eds.), (1966), The origin of genetics, a mendel source book. (pp. 1–48). San Francisco: W. H. Freeman.
Mills, S. K., & Beatty, J. H. (1979). The propensity interpretation of fitness. Philosophy of Science., 46(2), 263–286.
Millstein, R. L. (2003a). How not to argue for the indeterminism of evolution: A look at two recent attempts to settle the issue. In A. Huttemann (Ed.), Determinism in physics and biology (pp. 91–107). Mentis: Mentis.
Millstein, R. L. (2003b). Interpretations of probability in evolutionary theory. Philosophy of Science, 70(5), 1317–1328.
Pence, C., & Ramsey, G. (2013). A new foundation for the propensity interpretation of Fitness. British Journal for the Philosophy of Science, 64(4), 851–881.
Peirce, C. S. (1910). Note on the doctrine of chances. In C. Harteshorne & P. Weiss (Eds.), Collected papers of Charles S. Peirce (Vol. II, p. 408). Cambridge, MA: Harvard University Press.
Popper, K. R. (1957). The propensity interpretation of the calculus of probability and the quantum theory. In S. Körner (Ed.), The Colston Papers (Vol. 9, pp. 65–70).
Ramsey, G. (2006). Block fitness. Studies in History and Philosophy of Biological and Biomedical Sciences, 37(3), 484–498.
Ramsey, G. (2012). Driftability. Synthese, 190(17), 3909–3928.
Reichenbach, H. (1949). The theory of probability. Berkeley: University of California Press.
Rosenberg, A., & Bouchard, F. (2008). Fitness. The Stanford Encyclopedia of Philosophy (Winter 2012 ed,), Edward N. Zalta (Ed.), URL = http://plato.stanford.edu/archives/win2012/entries/davidson/
Salmon, W. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.
Salmon, W. (1989). Four decades of scientific explanation. Minneapolis: University of Minnesota Press.
Schaffer, J. (2007). Deterministic chance? British Journal for the Philosophy of Science, 58(2), 113–140.
Skipper, R. A. Jr. & Millstein, R. L. (2005). Thinking about evolutionary mechanisms: natural selection. In: Special Issue: “Mechanisms in Biology,” In: C. F. Craver & L. Darden (Eds.) Studies in history and philosophy of biological and biomedical sciences, 36(2), 327–347.
Sober, E. (2001). The two faces of fitness. In E. Sober (Ed.), Conceptual issues in evolutionary biology (2nd ed., pp. 25–38). London: MIT Press.
Sober, E. (2010). Evolutionary theory and the reality of macro-probabilities. In E. Ellery & J. H. Fetzered (Eds.), The place of probability in science (pp. 133–161). New York: Springer.
von Mises, R. (1957). Probability, statistics and truth, revised english ed. New York: Macmillan.
Venn, J., (1876). The Logic of Chance, (\(2{\rm nd}\) ed.), London: Macmillan; reprinted, New York: Chelsea Publishing Co., 1962.
Walsh, D., Lewens, T., & Ariew, A. (2002). The trials of natural selection and drift. Philosophy of Science, 69(3), 452–473.
Woodward, J. (2003). Making things happen: A theory of causal explanation. New York: Oxford University Press.
Acknowledgments
My sincere thanks to Lindley Darden, Aidan Lyon, Peter Carruthers, Jessica Pfeifer, Grant Ramsey, Eric Saidel, Nancy Hall, Mark Englebert, Robert Richardson, and my anonymous reviewers for their helpful comments on earlier drafts of this paper.
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DesAutels, L. Toward a propensity interpretation of stochastic mechanism for the life sciences. Synthese 192, 2921–2953 (2015). https://doi.org/10.1007/s11229-015-0694-4
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DOI: https://doi.org/10.1007/s11229-015-0694-4