Debates concerning the units and levels of selection have persisted for over fifty years. One major question in this literature is whether units and levels of selection are genuine, in the sense that they are objective features of the world, or merely reflect the interests and goals of an observer. Scientists and philosophers have proposed a range of answers to this question. This Element introduces this literature and proposes a novel contribution. It defends a realist stance and offers a way (...) of delineating genuine levels of selection by invoking the notion of a functional unit. (shrink)

In a recent reply to Takacs and Bourrat’s article (Biol Philos 37:12, 2022), Autzen and Okasha (Biol Philos 37:37, 2022) question our characterization of the relationship between the geometric mean and arithmetic mean measures of fitness. We here take issue with the claim that our characterization falls prey to the mistakes they highlight. Briefly revisiting what Takacs and Bourrat (Biol Philos 37:12, 2022) accomplished reveals that the key issue of difference concerns cases of deterministic but nonconstant growth. Restricting focus to (...) such cases shows that there is in fact no reason for disagreement. (shrink)

Showing that the arithmetic mean number of offspring for a trait type often fails to be a predictive measure of fitness was a welcome correction to the philosophical literature on fitness. While the higher mathematical moments of a probability-weighted offspring distribution can influence fitness measurement in distinct ways, the geometric mean number of offspring is commonly singled out as the most appropriate measure. For it is well-suited to a compounding process and is sensitive to variance in offspring number. The geometric (...) mean thus proves to be a predictively efficacious measure of fitness in examples featuring discrete generations and within- or between-generation variance in offspring output. Unfortunately, this advance has subsequently led some to conclude that the arithmetic mean is never a good measure of fitness and that the geometric mean should accordingly be the default measure of fitness. We show not only that the arithmetic mean is a perfectly reasonable measure of fitness so long as one is clear about what it refers to, but also that it functions as a more general measure when properly interpreted. It must suffice as a measure of fitness in any case where the geometric mean has been effectively deployed as a measure. We conclude with a discussion about why the mathematical equivalence we highlight cannot be dismissed as merely of mathematical interest. (shrink)

The most consistent definition of fitness makes it a static property of organisms. However, this is not how fitness is used in many evolutionary models. In those models, fitness is permitted to vary with an organism’s circumstances. According to this second conception, fitness is dynamic. There is consequently tension between these two conceptions of fitness. One recently proposed solution suggests resorting to conditional properties. We argue, however, that this solution is unsatisfactory. Using a very simple model, we show that it (...) can lead to incompatible fitness values and indecision about whether selection actually occurs. (shrink)