Abstract
In a recent paper, Potochnik (Biol Philos 24(2):183–197, 2009) analyses some uses of optimality modelling in light of the anti-adaptationism criticism. She distinguishes two broad classes of such uses (weak and strong) on the basis of assumptions held by biologists about the role and the importance of natural selection. This is an interesting proposal that could help in the epistemological characterisation of some biological practices. However, Potochnik’s distinction also rests on the assumption that all optimality modelling represent the selection dynamic involved in the system of phenomena being considered. Since this assumption does not hold for models belonging to optimal foraging theory (OFT)—one of behavioural ecology’s important modelling traditions—Potochnik’s proposal has to be critically reappraised. In this paper, we briefly discuss what is optimality modelling and what it means for a model to represent a dynamic of selection or of evolution. Then, we demonstrate that OFT modelling is unable to represent either past or contemporary selection dynamics. In order to make this point, we carefully delineate the theory’s rationale. This allows us to identify and analyse the assumptions on which the theory is built, and to circumscribe precisely the role that natural selection plays in it. Next, we show that the distinction of weak and strong uses of optimality modelling is seriously weakened when OFT modelling is taken into account. More precisely, the distinction is either irrelevant (if the assumption that selection dynamics are represented in all optimality modelling is held) or of a modest utility (if the assumption is dropped). However, we suggest that Potochnik’s original proposal could be saved, and that it even constitutes a tool to appraise the marks left in the literature by the evolution of optimality modelling practices in the last four decades, provided that it is made into a tripartite distinction.
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Notes
Through the ‘Adaptationism Project’, Orzack and Sober propose a method for testing the thesis of empirical adaptationism. Summarily, it requires a three part test. Firstly, the optimality model’s predictions must be compared to observations of the natural system of interest and assessed quantitatively. Secondly, whether or not the within-population heterogeneity in phenotype is consistent with the variation predicted by the model must also be assessed. Lastly, it is necessary to conduct an ensemble test on the highest possible number of studies using an optimality model. By quantifying how many independent studies succeed in completing the two first parts of the test (that is, whether 1° there is a quantitative fit between modelling and observations, and 2° the observed inter-individual heterogeneity is accounted for in the model), it could be possible to prove or disprove the thesis of adaptationism (Orzack and Sober 1994b).
An evolutionary stable strategy (ESS) is a strategy that cannot be invaded by a mutant strategy that may arise (Maynard Smith and Price 1973), whereas an unbeatable strategy is one that cannot be outmatched by any alternative strategy whatever its starting frequency (Hamilton 1996). Although some scientist tend to equate the two notions (see, for instance, Nowak and Sigmund 2004; McGill and Brown 2007), Hamilton’s definition is more stringent (Sigmund 2001) and could prove useful in evolutionary games in which migration has to be taken into account.
For instance, the minimisation of time spent foraging is likely to influence fitness because it leaves more time for other activities such as self-grooming and social interactions. It also modifies the exposure to predation if different activities have different risks (Bautista et al. 1998). Nevertheless, demonstrating a correlation between the minimisation of the time spent foraging and the organism’s fitness is not a simple task. As we have underlined in section “Optimality, a multi-use tool”, it requires two things: 1° that a variant of the trait of interest is available for comparison; and 2° that some component of fitness (survival, reproductive success or else) is effectively measured. Imagine the technical difficulties that have to be overcome in order to show that a phenotype spending too much time foraging has a lower fitness resulting from missed copulation opportunities, or from a higher predation rate, in comparison to another phenotype that spends less time foraging.
We nevertheless acknowledge its historically determining role in the development of evolutionary thinking, as well as its importance in the preliminary steps of studies using OFT.
Fitness as adequacy, adaptation or adjustment of a trait to some ecological feature is of course the first sense in which the term was used in Natural History. Gayon’s (1995) study admirably retraces the history of the term in the period in-between Darwin’s Origin of Species and Fisher’s Genetical Theory of Natural Selection. This “qualitative” use of fitness has later become coined adaptedness (for instance in Dobzhansky 1950). Also, some people have attempted to articulate adaptedness and fitness in order to provide a definition of the latter, or to provide a formal account of the principle of natural selection (see for instance Brandon 1980; Burian 1983 or Michod 1986).
Beside the notation, P2 is strictly equivalent to Brandon and Rausher’s (1996) proposition S. For reasons different from theirs, we advocate separating claims about the role and importance of natural selection in bringing about an outcome from claims about the locally optimal character of that outcome that are conjunct in Orzack and Sober’s proposition O. This makes our analysis of Potochnik’s distinction much more intelligible.
Potochnik (2009) provides the reader with repeated definitions of the weak and strong uses. Here is one representative sample of the way both uses are defined. Weak (p. 189) “The weak use of optimality models upon which I have focused involves the claim that an optimality model accurately represents the actual selection dynamics”. Strong (p. 192): “The strong use of an optimality model involves the claim that the model’s representation of selection dynamics provides an accurate picture of the important influences on the evolutionary outcome in question.”
This could be done using P1*, such as: ‘The optimisation model represents a dynamic in which the outcome T is involved’, as we will see in section “Neither strong nor weak, but heuristic”.
The clearest hint about this is the following sentence (pp. 188–189): “There is another common use of optimality models that is still weaker: optimality models are also used to develop and explore adaptive hypotheses at preliminary stages of investigation, when little is known about the evolutionary factors at work. Used in this capacity, the aim of optimality modelling is merely to represent possible selection dynamics, and the standards for success are correspondingly lower.”
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Acknowledgments
This paper has greatly benefitted from the constructive criticism of three anonymous referees for Biology and Philosophy, and from all those precious exchanges with members of the Biogéosciences (Dijon) and Laboratoire de Biométrie et Biologie Evolutive (Lyon 1) labs. Many thanks to P. Monfils for her efficient spellchecking. J.-S. Bolduc was supported financially by the Fonds Québécois de Recherche sur la Société et la Culture (FQRSC).
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Bolduc, JS., Cézilly, F. Optimality modelling in the real world. Biol Philos 27, 851–869 (2012). https://doi.org/10.1007/s10539-012-9333-3
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DOI: https://doi.org/10.1007/s10539-012-9333-3