The notion of fitness is usually equated to reproductive success. However, this actualist approach presents some difficulties, mainly the explanatory circularity problem, which have lead philosophers of biology to offer alternative definitions in which fitness and reproductive success are distinguished. In this paper, we argue that none of these alternatives is satisfactory and, inspired by Mumford and Anjum’s dispositional theory of causation, we offer a definition of fitness as a causal dispositional property. We argue that, under this framework, the distinctiveness (...) that biologists usually attribute to fitness—namely, the fact that fitness is something different from both the physical traits of an organism and the number of offspring it leaves—can be explained, and the main problems associated with the concept of fitness can be solved. Firstly, we introduce Mumford and Anjum's dispositional theory of causation and present our definition of fitness as a causal disposition. We explain in detail each of the elements involved in our definition, namely: the relationship between fitness and the functional dispositions that compose it, the emergent character of fitness, and the context-sensitivity of fitness. Finally, we explain how fitness and realized fitness, as well as expected and realized fitness are distinguished in our approach to fitness as a causal disposition. (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)

The propensity nature of evolutionary fitness has long been appreciated and is nowadays amply discussed. The discussion has, however, on occasion followed long standing conflations in the philosophy of probability literature between propensities, probabilities, and frequencies. In this paper, I apply a more recent conception of propensities in modelling practice to some of the key issues, regarding the mathematical representation of fitness and how it may be regarded as explanatory. The ensuing complex nexus of fitness emphasises the distinction between biological (...) propensities and the probability distributions over offspring numbers that they give rise to; and how critical it is to distinguish the possession conditions of the underlying dispositional properties from those of their probabilistic manifestations. (shrink)

The propensity account of fitness intends to solve the classical tautologicity issue by identifying fitness with a disposition, the ability to survive and reproduce. As proponents recognized early on, this account requires operational independence from actual reproductive success to avoid circularity and vacuousness charges. They suggested that operational independence is achieved by measuring fitness values through optimality models. Our goal in this article is to develop this suggestion. We show that one plausible procedure by which these independent operationalizations could be (...) thought to take place, and which is in accordance with what is said in the optimality literature, is unsound. We provide two independent lines of reasoning to show this. We then provide a sketch of a more adequate account of the role of optimality models in evolutionary contexts and draw some consequences. (shrink)

Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources of uncertainty (...) are combined to produce an estimate for a complex, unobservable quantity. These models have been carefully analyzed in recent biological work, which has determined the relationship between these sources of uncertainty, both quantitatively and qualitatively. I suggest here that this case shows us the limitations of univocal analyses of probability in evolution, as well as the simple dichotomy between “subjective” and “objective” probabilities, and I conclude by gesturing toward ways in which we might introduce more sophisticated interpretive taxonomies of probability as a path toward advancing debates on probability in the life sciences. (shrink)

Fitness is a central but notoriously vexing concept in evolutionary biology. The propensity interpretation of fitness is often regarded as the least problematic account for fitness. It ties an individual's fitness to a probabilistic capacity to produce offspring. Fitness has a clear causal role in evolutionary dynamics under this account. Nevertheless, the propensity interpretation faces its share of problems. We discuss three of these. We first show that a single scalar value is an incomplete summary of a propensity. Second, we (...) argue that the widespread method of “abstracting away” environmental idiosyncrasies by averaging over reproductive output in different environments is not a valid approach when environmental changes are irreversible. Third, we point out that expanding the range of applicability for fitness measures by averaging over more environments or longer time scales (so as to ensure environmental reversibility) reduces one's ability to distinguish selectively relevant differences among individuals because of mutation and eco‐evolutionary feedbacks. This series of problems leads us to conclude that a general value of fitness that is both explanatory and predictive cannot be attained. We advocate for the use of propensity‐compatible methods, such as adaptive dynamics, which can accommodate these difficulties. (shrink)

Fitness plays many roles throughout evolutionary theory, from a measure of populations in the wild to a central element in abstract theoretical presentations of natural selection. It has thus been the subject of an extensive philosophical literature, which has primarily centered on the way to understand the relationship between fitness values and reproductive outcomes. If fitness is a probabilistic or statistical quantity, how is it to be defined in general theoretical contexts? How can it be measured? Can a single conceptual (...) model for fitness be offered that applies in all biological cases, or must fitness measures be case-specific? Philosophers have explored these questions over the last several decades, largely in the context of an influential definition of fitness proposed in the late 1970s: the propensity interpretation. This interpretation as first described undeniably suffers from significant difficulties, and debate regarding the tenability of amendments and alternatives to it remains unsettled. (shrink)

Resumen -/- El objetivo de este trabajo consiste en analizar las relaciones entre los modelos de optimalidad y la selección natural. Defenderemos que esas relaciones pueden dividirse en dos tipos, en tanto hay dos tipos de explicaciones seleccionistas, que llamaremos “históricas” y “ahistóricas”. Las explicaciones históricas revelan como una población dada adquiere un rasgo que es adaptativo en ese ambiente e involucran muchas generaciones, variación, etc. Las explicaciones ahistóricas, explican por qué, en determinado momento, ciertos tipos de organismos tienen un (...) mayor éxito reproductivo que otros. Mostraremos que los modelos de optimalidad pueden jugar un rol en la determinación del explanandum de las explicaciones histórica seleccionistas, esto es, que ayudan a reconocer qué rasgos son adaptativos. Por otra parte, mostramos que los modelos de optimalidad nos permiten determinar a veces la parte del explanans de las explicaciones ahistóricas (particularmente, el concepto de fitness). -/- Abstract -/- The goal of this paper is to analyze the relations between optimality models and natural selection. We contend that these relationships can be divided into two kinds, as there are two kinds of natural selection explanations, which we call “historical” and “ahistorical”. Historical explanations reveal how a given population acquires a trait which is adaptive in its environment, and involves many generations, variations, etc. Ahistorical ones, explain why, at a given moment, certain kinds of organisms have a greater reproductive success than others. We show that optimality models can play a role in determining the explanandum of historical selectionist explanations, that is, they help us to recognize which traits are adaptive. And, on the other hand, that optimality models sometimes allow us to determine part of the explanans of ahistorical explanations (particularly, the concept of fitness). (shrink)

In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)