Abstract
The propensity interpretation of fitness (PIF) holds that evolutionary fitness is an objectively probabilistic causal disposition (i.e., a propensity) toward reproductive success. I characterize this as the conceptual foundation of the PIF. Reproductive propensities are meant to explain trends in actual reproductive outcomes. In this paper, I analyze the minimal theoretical and ontological commitments that must accompany the explanatory power afforded by the PIF’s foundation. I discuss three senses in which these commitments are less burdensome than has typically been recognized: the PIF’s foundation is (i) compatible with a principled pluralism regarding the mathematical relationship between measures of individual and trait reproductive success; (ii) independent of the propensity interpretation of probability; and (iii) independent of microphysical indeterminism. The most substantive ontological commitment of the PIF’s foundation is to objective modal structures wherein macrophysical probabilities and causation can be found, but I hedge against metaphysically inflationary readings of this modality.
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Notes
Historically, fitness was often identified with actual reproductive outcomes. That is, the organisms or types who actually outcompete their rivals were identified, by definition, as being fitter than those rivals. This is clearly incompatible with fitness’s traditional explanatory role, since it reduces to circularity: those organisms or types who achieve the greatest reproductive success do so because they are the ones who achieve the greatest reproductive success (Brandon, 1978).
By “modal structure,” I mean that there are facts of the matter as to what would and would not occur in counterfactual scenarios, and that these facts can be given certain structural descriptions. Section 5 provides further elaboration of these ideas.
See Rosenberg (1983, p. 459) for an opposing perspective that takes the explanatory value of fitness (and probabilities in general) to be of a purely heuristic sort. I resist Rosenberg’s characterization of mere heuristics as being genuinely explanatory.
Larger populations are equivalent to “longer trials” in the sense that, for a type at a given initial frequency, it will take comparatively more sampling instances to push that type to fixation or elimination as we increase the initial population size.
In interventionist terms: let the relevant physical properties be represented by the variable P and let reproductive outcomes be represented by the variable R. A counterfactual change in P would change the probability distribution over the possible values R could take. In a causal graph, we would represent the relationship as \(P\rightarrow R\) to indicate that P is a causal parent of R.
The inclusion of the parenthetical here is a nod to Sober (2013), who argues that trait-fitness differences, but not trait fitnesses themselves, are population-level propensities. In the next section, I defend the view that trait fitnesses are population-level propensities.
Drouet and Merlin (2015) reach a superficially similar conclusion, in that they also claim the PIF does not depend on the PIP. However, they dispense with the explanatory power of causal dispositions and resort to a purely statistical analysis of fitness explanations, which I take to be a rejection of the conceptual foundation of the PIF. The view they defend is not, in this important sense, a variant of the PIF.
Note that, in the literature, both Pr() and P() are commonly used interchangeably to represent probabilities and/or propensities. For clarity in the following discussion of the distinction between probabilities and propensities, I will use the notation P() to represent all conditional probabilities, any of which may or may not represent propensities, and the notation Pr() only for those probabilities that are explicitly claimed to represent propensities.
See also Niiniluoto (1988, pp. 103–104), who makes basically the same point in a footnote. Thanks to an anonymous reviewer for bringing this to my attention.
See Humphreys (2004, pp. 668–669) and Suárez (2013, pp. 80–83) for the full formal paradox. Briefly: photons are fired from a laser toward a half-silver mirror at some time \(t_{1}\); at a later time \(t_{2}\), the photons hit or miss the mirror with some probability; and at a still later time \(t_{3}\), the photons are transmitted through or absorbed by the mirror with some probability. The formal contradiction arises within the probability calculus when we combine a number of seemingly reasonable assumptions about the propensity of the experimental setup at \(t_{1}\) to produce outcomes at later times.
Sect. 5 details how objective probability and causation can emerge together from properties of a modal structure.
It is a radical thesis because irreducible chances are usually understood to rationally constrain one’s credence in the occurrence of an event in a principled way. If the irreducible chance of event P occurring is 0.2, one is rationally compelled to set their credence in the occurrence of that event to 0.2, no matter what other information one might learn about the physical world up to and including time t (this is the ‘Principal Principle’ introduced by Lewis (1980)). The usual argument for incompatibilism regarding determinism and irreducible chance is that, in a deterministic world, the rational credence for an event can always in principle be reduced to 0 or 1 (or made to approach these values as a limit, perhaps) by gathering ever more precise information about the microphysical state of the world. The most vivid illustration of this issue invokes a hypothetical ‘Laplacian intelligence,’ which can know the microstate of the entire universe with arbitrary precision and calculate the future and past trajectories with perfect computational ability according to the universe’s dynamical laws. At face, this seems to entail that irreducible chances must be ‘baked in’ to the fundamental dynamics of the universe, if any such things as irreducible chances exist. Clever arguments are needed if the in-principle credence-constraining aspects of irreducible chances are to be compatible with determinism. See Ismael (2009, 2011) for a few such clever arguments. See also List and Pivato (2015) for an argument that, if successful, would entail that the counterfactual macro-probabilities described in this section are, in a meaningful sense, irreducible chances.
A related point has been raised by von Plato (1983, p. 45) with respect to the method of arbitrary functions in which the production of stable frequencies is explained by the following property exhibited by some spaces of initial microstates: For any continuous distribution on the initial space, and given the dynamics of the system, the same resulting proportion of outcomes will be obtained. As for coin flips, in this respect, so too for evolving populations. For much the same reasons, we have evidence that the structures of the spaces of initial microstates in evolutionary systems, together with the system dynamics, uniquely determine evolutionary outcome probabilities. However, see de Canson (2022) for a discussion of some complications regarding how exactly the method of arbitrary functions relates to system dynamics and objective probabilities.
Bourrat (2017) defends objective deterministic probability by drawing primarily on the ‘natural range’ conception of probability developed by Rosenthal (2010). See also Abrams (2012) and Strevens (2011) for closely related views. Exploring the differences between Rosenthal’s, Abrams’, and Strevens’ frameworks goes beyond the scope of this paper, but it is quite plausible that all three are classifiable as specific subtypes of the counterfactual probability framework. See also Batterman and Rice (2014) for an approach that is amenable to a “modal robustness” analysis of macrophysical dynamics.
This distinction is inspired by Sober (1984). Macro-probabilities are predictive of particular outcomes to some degree, just not optimally so.
For simplicity, I assume in this example that the volume of the possibility space of a macrostate splits equally among its possible microstates, but I do not assume that this is always the case in reality. Further discussion of this point is given in Sect. 5.3.
An example of a view with which these intermediate values of \(S^{*}\) may be compatible can be found in the ‘real patterns’ view of Dennett (1991), which he describes as a kind of ‘mild realism’ with respect to macroscopic entities. This would also represent one way in which microstates could unequally share a macrostate’s possibility space.
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Acknowledgements
For countless hours of revision and discussion on earlier versions of this work, I owe a special thanks to Prasanta Bandyopadhyay. I am also grateful to many others for insightful comments, including Gordon Brittan Jr., Sahotra Sarkar, Caitlin Mace, John Norton, Alan Love, Joel Velasco, Scott Gilbert, Stuart Newman, Kate MacCord, Kristen Intemann, Bonnie Sheehey, Daniel Flory, and two anonymous referees. Their contributions have markedly improved this work, and any flaws that remain do so only in spite of their efforts.
Funding
This work was presented as a poster at the Philosophy of Science Association (PSA) 2022 Meeting, the travel for which was funded by the PSA’s associated NSF travel grant, as well as by the History and Philosophy Department of Montana State University.
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Mayne, Z.J. The conceptual foundation of the propensity interpretation of fitness. Synthese 203, 10 (2024). https://doi.org/10.1007/s11229-023-04437-3
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DOI: https://doi.org/10.1007/s11229-023-04437-3