Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

The field of the philosophy of biology is flourishing in its aim to evaluate and rethink the view inherited from the previous century ---the Modern Synthesis. Different research areas and theories have come to the fore in the last decades in order to account for different biological phenomena that, in the first instance, fall beyond the explanatory scope of the Modern Synthesis. This thesis is anchored and motivated by this revolt in the philosophy of biology. -/- The central target in (...) this context is the possibility of naturalizing teleology, a classical nightmare for the history of biology itself. This requires, principally, understanding the causes of teleological explanations without assuming an unfashioned backward causation of sorts. As the riddles of teleological explanations are about their temporal dimension, I analyze different temporal scales of biological processes: evolutionary, developmental, and physiological. -/- The first one is the one defended in the context of the Modern Synthesis. As expected, one of the aims of this thesis is to evaluate the adequacy of an evolutionary account of teleological explanation. The scrutiny is negative. Evolutionary explanations in the context of the Modern Synthesis lack the necessary causal roots to naturalize teleology. Concerning the physiological scale, a long tradition pushed up by Kant and the organicist movement in the 20th century allows us to better understand how teleological explanations can be naturalized in physiological process. The key notions in this temporal scale are self-organization and the recursive, looped character of physiological process. While the physiological scale may be suitably accounted by contemporaries views, such as Autonomous Systems Theory, different central teleological phenomena remain unexplained from a purely physiological perspective. In particular, different issues concerning the (adaptive) construction of organism ---such as plasticity, robustness, variation, novelty, inheritance--- deserve an ontogenetic analysis. -/- The principal aim of this thesis is to provide a theory of teleological development that falls beyond the Modern Synthesis' framework and is prompted by different insights from the history of biology. I call it Agential Teleosemantics. It rests on two central pillars. First, that developmental processes, beyond any gene-centered stance, can be understood in informational terms; i.e. developmental processes are about the interaction of developmental resources conveying biological information. The second ingredient is agentivity, namely the idea that development is regulated by an agentive system according to the adaptiveness of the phenotypic outcomes produced. The role of agency in Agential Teleosemantic is equivalent to the role of genes in the Modern Synthesis: it is responsible for explaining the order and the adaptive complexity in the living realm. -/- The second target of this thesis regards the possibility of naturalizing intentional explanations in cognitive science. The central project involved in such an aim is known as teleosemantics. Classical teleosemantics however is etiological: it explains the teleofunctions of representational systems in terms of evolutionary processes. The different disputes in the contemporary philosophy of biology provide two insights to analyze teleosemantics in cognitive science. First, the challenges against the Modern Synthesis must be extended to the evolutionary approach of etiological teleosemantics. Second, as Agential Teleosemantics suggests an alternative source for teleofunctions ---ontogeny, I offer an attempt to integrate Agential Teleosemantics into cognitive science in order to provide an alternative teleosemantic project to understand intentional explanations in cognitive science. (shrink)

One claim found in the received historiography of the biometrical school (comprised primarily of Francis Galton, Karl Pearson, and W. F. R. Weldon) is that one of the biometricians' great flaws was their inability to look past their population-focused, statistical, gradualist understanding of evolutionary change – which led, in part, to their ignoring developments in cellular biology around 1900. I will argue, on the contrary, that the work of the biometricians was, from its earliest days, fundamentally concerned with connections between (...) statistical patterns of inheritance and the underlying cellular features that gave rise to them. Such work remained current with contemporary knowledge of chromosomes, cytology, and development; in this article, I explore the first case. The biometricians were thus well positioned to understand the relationship between the patterns of Mendelian inheritance and the statistical distributions with which they primarily occupied themselves. Ignorance of this connection, then, is not the reason why they rejected Mendelism. Further, both Galton and Weldon – though each in their own unique way – decided to turn to biological detail as a way to better justify the generality of their statistical approaches to heredity. Perhaps paradoxically, then, for these biometricians, detail offered an approach to theoretical generality. (shrink)

This article presents a challenge that those philosophers who deny the causal interpretation of explanations provided by population genetics might have to address. Indeed, some philosophers, known as statisticalists, claim that the concept of natural selection is statistical in character and cannot be construed in causal terms. On the contrary, other philosophers, known as causalists, argue against the statistical view and support the causal interpretation of natural selection. The problem I am concerned with here arises for the statisticalists because the (...) debate on the nature of natural selection intersects the debate on whether mathematical explanations of empirical facts are genuine scientific explanations. I argue that if the explanations provided by population genetics are regarded by the statisticalists as non-causal explanations of that kind, then statisticalism risks being incompatible with a naturalist stance. The statisticalist faces a dilemma: either she maintains statisticalism but has to renounce naturalism; or she maintains naturalism but has to content herself with an account of the explanations provided by population genetics that she deems unsatisfactory. This challenge is relevant to the statisticalists because many of them see themselves as naturalists. (shrink)

A prevalent narrative locates the discovery of the statistical phenomenon of regression to the mean in the work of Francis Galton. It is claimed that after 1885, Galton came to explain the fact that offspring deviated less from the mean value of the population than their parents did as a population-level statistical phenomenon and not as the result of the processes of inheritance. Arguing against this claim, we show that Galton did not explain regression towards mediocrity statistically, and did not (...) give up on his ideas regarding an inheritance process that caused offspring to revert to the mean. While the common narrative focuses almost exclusively on Galton’s statistics, our arguments emphasize the anthropological and biological questions that Galton addressed. Galton used regression towards mediocrity to support the claim that some biological types were more stable than others and hence were resistant to evolutionary change. This view had implications concerning both natural selection and eugenics. The statistical explanation attributed to Galton appeared later, during the biometrician-mutationist debate in the early 1900s. It was in the context of this debate and specifically by the biometricians, that the development of the statistical explanation was originally attributed to Galton. (shrink)

Most attempts to justify the use of idealized models to explain appeal to the accuracy of the model with respect to difference-making causes. In this article, I argue for an alternative way to just...

I distinguish two roles for a fitness concept in the context of explaining cumulative adaptive evolution: fitness as a predictor of gene frequency change, and fitness as a criterion for phenotypic improvement. Critics of inclusive fitness argue, correctly, that it is not an ideal fitness concept for the purpose of predicting gene-frequency change, since it relies on assumptions about the causal structure of social interaction that are unlikely to be exactly true in real populations, and that hold as approximations only (...) given a specific type of weak selection. However, Hamilton took this type of weak selection, on independent grounds, to be responsible for cumulative assembly of complex adaptations. In this special context, I argue that inclusive fitness is distinctively valuable as a criterion for improvement and a standard for optimality. Yet to call inclusive fitness a criterion for improvement and a standard for optimality is not to make any claim about the frequency with which inclusive fitness optimization actually occurs in nature. This is an empirical question that cannot be settled by theory alone. I close with some reflections on the place of inclusive fitness in the long running clash between ‘causalist’ and ‘statisticalist’ conceptions of fitness. (shrink)

This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a two-step activity, with the first step involving making (...) counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...) are justified by allowing for the application of various modelling techniques. (shrink)

Catherine Elgin has recently argued that a nonfactive conception of understanding is required to accommodate the epistemic successes of science that make essential use of idealizations and models. In this paper, I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call Understanding Realism. According to this view, science aims at (and often achieves) factive scientific understanding of natural phenomena. I (...) contend that this factive scientific understanding is provided by grasping a set of true modal information about the phenomenon of interest. Furthermore, contrary to Elgin’s view, I argue that the facticity of this kind of scientific understanding can be separated from the inaccuracy of the models and theories used to produce it. (shrink)

Over the past decade philosophers of biology have discussed whether evolutionary theory is a causal theory or a phenomenological study of evolution based solely on the statistical features of a population. This article reviews this controversy from three aspects, respectively concerning the assumptions, applications, and explanations of evolutionary theory, with a view to arriving at a definite conclusion in each contention. In so doing I also argue that an implicit methodological assumption shared by both sides of the debate, namely the (...) overconfidence in conceptual analysis as a tool to understand the scientific theory, is the real culprit that has both generated the problem and precluded its solution for such a long time. (shrink)

In this paper, I first argue against various attempts to justify idealizations in scientific models that explain by showing that they are harmless and isolable distortions of irrelevant features. In response, I propose a view in which idealized models are characterized as providing holistically distorted representations of their target system. I then suggest an alternative way that idealized modeling can be justified by appealing to universality.

This paper argues that in some explanations mathematics are playing an explanatory rather than a representational role, and that this feature unifies many types of non-causal or non-mechanistic explanations that some philosophers of science have been recently exploring under various names. After showing how mathematics can play either a representational or an explanatory role by considering two alternative explanations of a same biological pattern—“Bergmann’s rule”—I offer an example of an explanation where the bulk of the explanatory job is done by (...) a mathematical theorem, and where mechanisms involved in the target systems are not explanatorily relevant. Then I account for the way mathematical properties may function in an explanatory way within an explanation by arguing that some mathematical propositions involving variables non directly referring to the target system features constitute constraints to which a whole class of systems should comply, provided they are describable by a mathematical object concerned by those propositions. According to such “constraint account”, those mathematical facts are directly entailing the explanandum, as a consequence of such constraints. I call those explanations “structural”, because here properties of mathematical structures are accounting for the explanandum; various kinds of mathematical structures thereby define various types of structural explanations. (shrink)

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

A recent debate over the causal foundations of evolutionary theory pits those who believe that natural selection causally explains long-term, adaptive population change against those who do not. In this paper, I argue that this debate – far from being an invention of several articles in 2002 – dates from our very first engagements with evolution as a quantified, statistical science. Further, when we analyze that history, we see that a pivotal figure in the early use of statistical methodology in (...) evolutionary theory, W.F.R. Weldon, changes his mind about precisely the central claim at issue. I close by drawing some morals which I think the case can offer for the contemporary debate going forward. (shrink)

A subarea of the debate over the nature of evolutionary theory addresses what the nature of the explanations yielded by evolutionary theory are. The statisticalist line is that the general principles of evolutionary theory are not only amenable to a mathematical interpretation but that they need not invoke causes to furnish explanations. Causalists object that construction of these general principles involves crucial causal assumptions. A recent view claims that some biological explanations are statistically autonomous explanations (SAEs) whereby phenomena are accounted (...) for statistically and which prescind from micro-causal details. I raise three major problems for this account and then advance a view which unifies SAEs as mathematical explanations: the MSAE view. The MSAE view not only resolves the issues bedeviling the original SAE account but serves to importantly broaden the class of non-causal explanations in population biology. (shrink)

The concept of explanation is central to scientific practice. However, scientists explain phenomena in very different ways. That is, there are many different kinds of explanation; e.g. causal, mechanistic, statistical, or equilibrium explanations. In light of the myriad kinds of explanation identified in the literature, most philosophers of science have adopted some kind of explanatory pluralism. While pluralism about explanation seems plausible, it faces a dilemma Explanation beyond causation, Oxford University Press, Oxford, pp 39–56, 2018). Either there is nothing that (...) unifies all instances of scientific explanation that makes them count as explanations, or there is some set of unifying features, which seems incompatible with explanatory pluralism. Different philosophers have adopted different horns of this dilemma. Some argue that no unified account of explanation is possible. Others suggest that there is a set of necessary features that can unify all explanations under a single account Explanation beyond causation, Oxford University Press, Oxford, pp 74–95, 2018; Strevens in Depth: an account of scientific explanation, Harvard University Press, Cambridge, 2008). In this paper, we argue that none of the features identified by existing accounts of explanation are necessary for all explanations. However, we argue that a unified account can still be provided that accommodates pluralism. This can be accomplished, we argue, by reconceiving of scientific explanation as a cluster concept: there are multiple subsets of features that are sufficient for providing an explanation, but no single feature is necessary for all explanations. Reconceiving of explanation as a cluster concept not only accounts for the diversity of kinds of explanations, but also accounts for the widespread disagreement in the explanation literature and enables explanatory pluralism to avoid Pincock’s dilemma. (shrink)

Statistical reasoning is an integral part of modern scientific practice. In The Seven Pillars of Statistical Wisdom Stephen Stigler presents seven core ideas, or pillars, of statistical thinking and the historical developments of each of these pillars, many of which were concurrent with developments in biology. Here we focus on Stigler’s fifth pillar, regression, and his discussion of how regression to the mean came to be thought of as a solution to a challenge for the theory of natural selection. Stigler (...) argues that the purely mathematical phenomenon of regression to the mean provides a resolution to a problem for Darwin’s evolutionary theory. Thus, he argues that the resolution to the problem for Darwin’s theory is purely mathematical, rather than causal. We show why this argument is problematic. (shrink)

What is natural selection? I address this question by exploring the relation between two debates: Is natural selection a mechanism? Is natural selection a causal or a statistical theory? I argue that the first can be assessed only relative to a model and that, following the second, there are two fundamentally different and independent kinds of models, Modern-Synthesis and Darwinian models. MS-models, I argue, are not mechanistic even if they are causal. D-models, in contrast, are mechanistic. A causal-mechanistic interpretation of (...) D-models is thus compatible with a statistical interpretation of MS-models. Natural selection, I conclude, lacks a single, unifying nature. (shrink)

There is a widespread philosophical interpretation of natural selection in evolutionary theory: natural selection, like mutation, migration, and drift are seen as forces that propel the evolution of populations. Natural selection is thus a population level causal process. This account has been challenged by the Statistics, claiming that natural selection is not a population level cause but rather a statistical feature of a population. This paper examines the nature of the aforementioned ontological debate and the nature of statistical explanations given (...) by population genetics. I claim that the Modern Synthesis provides good explanations of the changes in trait structure of populations without appealing to detailed causal information about the individual trajectories of the members of a population. (shrink)

The phenomenon of regression toward the mean is notoriously liable to be overlooked or misunderstood; regression fallacies are easy to commit. But even when regression phenomena are duly recognized, it remains perplexing how they can feature in explanations. This article develops a philosophical account of regression explanations as “statistically autonomous” explanations that cannot be deepened by adducing details about causal histories, even if the explananda as such are embedded in the causal structure of the world. That regression explanations have statistical (...) autonomy was first suggested by Ian Hacking and has recently been defended and elaborated by André Ariew, Yasha Rohwer, and Collin Rice. However, I will argue that these analyses fail to capture what regression’s statistical autonomy consists in and how it sets regression explanations apart from other kinds of explanation. The alternative account I develop also shows what is amiss with a recent denial of regression’s statistical autonomy. Marc Lange has argued that facts that can be explained as regression phenomena can in principle also be explained by citing a conjunction of causal histories. The account of regression explanation developed here shows that his argument is based on a misunderstanding of the nature of statistical autonomy. (shrink)

In this paper I critically evaluate Reisman and Forber’s :1113–1123, 2005) arguments that drift and natural selection are population-level causes of evolution based on what they call the manipulation condition. Although I agree that this condition is an important step for identifying causes for evolutionary change, it is insufficient. Following Woodward, I argue that the invariance of a relationship is another crucial parameter to take into consideration for causal explanations. Starting from Reisman and Forber’s example on drift and after having (...) briefly presented the criterion of invariance, I show that once both the manipulation condition and the criterion of invariance are taken into account, drift, in this example, should better be understood as an individual-level rather than a population-level cause. Later, I concede that it is legitimate to interpret natural selection and drift as population-level causes when they rely on genuinely indeterministic events and some cases of frequency-dependent selection. (shrink)

In this paper, I will conduct three interrelated analyses. First, I will develop an analysis of various concepts in the history of biology that used to refer to individual-level phenomena but were then reinterpreted by the Modern Synthesis in terms of populations. Second, a similar situation can be found in contemporary evolutionary theory. While different approaches reflect on the causal role of developing organisms in evolution, proponents of the Modern Synthesis refrain from any substantial change by reinterpreting and explaining individual-level (...) phenomena from a population perspective. Finally, I will approach these historical and contemporary debates by arguing for the statistical reading of natural selection, which holds that explanations by natural selection are statistical. My main conclusion is that the historical conceptual reinterpretations belong to a new explanatory strategy developed by the Modern Synthesis based on population thinking. Adopting the statistical point of view has three advantages for the issues discussed in this paper. First, understanding historical conceptual change as part of an explanatory shift fits with the emergence of population biology as a discipline that employs statistical methods. Second, concerning current debates in evolutionary biology, the statisticalist reading can validate the goal of both sides of the dispute. It ascribes an invaluable role to the population statistical explanation of the MS and also commends the study of developmental and organismal causes of adaptive evolution. Finally, the division of explanatory roles in evolutionary biology, embarrassed by statisticalism, can be related to the different interpretations that important biological concepts have undergone throughout history and contemporary biology, i.e., that the division of explanatory roles allows for a division of conceptual interpretations. (shrink)