Abstract
The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism.
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Notes
Understood in this way, the optimality approach also includes game-theoretic models, which are used when trait fitnesses are frequency-dependent. The fitness functions used in game-theoretic models take into account not only the environment, but also the trait values (strategies) of the other members of the population. For this reason, game-theoretic models often do not predict a single optimal trait value, but an optimal distribution of trait values in the population. Some game-theoretic models even predict that there will not be a stable distribution of trait values, but a continual cycle of change (Hofbauer and Sigmund 1998). My points in this paper apply to this broad class of models, for they pertain to all equilibrium models that do not represent genetic transmission, regardless of whether a single optimal trait value is predicted.
If there is a large amount of deviation between predicted and observed trait values, then an optimality model may be taken to fail, even in the weak use outlined here. This point is discussed in the section “Lessons from the adaptationist critique”.
The members of Joan Roughgarden’s lab helped me grasp this point. Also, note that the exploration of possible evolutionary dynamics is consistent with the eventual aim of understanding the actual evolutionary dynamics.
Lloyd (1988) distinguishes between three types of confirmation: fit between predicted and observed outcome; independent tests of assumptions; and variety of evidence. Testing the components of an optimality model is an instance of the second type of confirmation. These components should be carefully tested for the weak use of an optimality model as well, for this assures that the optimality model accurately represents the selection dynamics. Yet the strong use of an optimality model creates a further reason to test the model’s components, for undetected inaccuracies can conceal the failure of optimality, which undermines the success of the strong use.
See, for example, Maynard Smith et al.’s (1985) discussion of developmental constraints.
An anonymous referee has pointed out that an appreciation for the weaker uses of optimality models might be used to argue for methodological adaptationism—that the best approach to studying biological systems is to look for adaptations (Godfrey-Smith 2001). I agree that this is possible, but it is not a road I would take. As this discussion indicates, I think a myopic focus on adaptations is misguided; non-selective influences on evolution warrant investigation in their own right.
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Acknowledgments
This paper has benefitted from the insights of Elliott Sober, Joan Roughgarden and the members of her lab, Peter Godfrey-Smith, Patrick Forber, and two anonymous referees for Biology and Philosophy.
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Potochnik, A. Optimality modeling in a suboptimal world. Biol Philos 24, 183–197 (2009). https://doi.org/10.1007/s10539-008-9143-9
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DOI: https://doi.org/10.1007/s10539-008-9143-9