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 strong case has been made for the role and value of mechanistic reasoning in process-oriented sciences, such as molecular biology and neuroscience. This paper shifts focus to assess the role of mechanistic reasoning in an area where it is neither obvious nor expected: population genetics. Population geneticists abstract away from the causal-mechanical details of individual organisms and, instead, use mathematics to describe population-level, statistical phenomena. This paper, first, develops a framework for the identification of mechanistic reasoning where it is (...) not obvious: mathematical and mechanistic styles of scientific reasoning. Second, it applies this framework to demonstrate that both styles are integrated in modern investigations of evolutionary biology. Characteristic of the former, applied population genetic techniques provide statistical evidence for associations between genotype, phenotype, and fitness. Characteristic of the latter, experimental interventions provide causal-mechanical evidence for associations between the very same relationships, often in the same model organisms. The upshot is a richer perspective of how evolutionary biologists build evidence for hypotheses regarding adaptive evolution and a general framework for assessing the scope of mechanistic reasoning across the sciences. (shrink)

The relationship between statistical mechanics and population genetics has a long history. Both take advantage of statistics to address the behavior of large groups of entities. The main objective of this article is to assess the obstacles population genetics is meeting in its claim to explain biological phenomena from the conceptual apparatus of statistical mechanics according to two recent articles. Several tools available to the latter are missing in the former. Thus, in the absence of an adequate justification of the (...) relevance of the analogy and without the satisfaction of certain basic assumptions, I argue that the explanatory strategy of the analogously based model is seriously compromised. La relation entre mécanique statistique et génétique des populations a une longue histoire. À l’instar de la première, la seconde tire profit des statistiques afin de traiter le comportement des grands ensembles d’entités. L’objectif principal du présent article est d’identifier les obstacles que rencontre la génétique des populations dans sa prétention d’explication des phénomènes biologiques à partir de l’appareillage conceptuel de la mécanique statistique selon deux articles récents. Or, plusieurs outils dont dispose cette dernière manque à la première. Ainsi, en l’absence d’une justification adéquate de la pertinence de l’analogie et sans la satisfaction de certaines hypothèses fondamentales, je soutiens que la stratégie explicative du modèle analogue est sérieusement compromise. (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 philosophy of science community mourns the loss of Margaret Catherine Morrison, who passed away on January 9, 2021, after a long battle with cancer. Margie, as she was known to all who knew her, was highly regarded for her influential contributions to the philosophy of science, particularly her studies of the role of models and simulations in the natural and social sciences. These contributions made her a world-leading philosopher of science, instrumental in shifting philosophers' attention from the structure of (...) scientific theories to the practice of science. Her sophisticated studies of the function of models in scientific practice drew on detailed knowledge of the theories and experiments of physics as well as the history of physics. In emphasizing the autonomy of scientific models and their interventional character, her insights had some affinity with Cartwright's and Hacking’s views on phenomenological laws, entity realism, the instrumentalist interpretation of scientific theories, and the disunity of science. But Morrison’s approach was distinguished by the conviction that the existence of unobservable entities cannot be defended independently of the theories that support their evidence, and that scientific practice cannot be adequately understood without examining the reasons for theory unification. (shrink)

Cultural evolutionary theory has been alternatively compared to a theory of forces, such as Newtonian mechanics, or the kinetic theory of gases. In this article, I clarify the scope and significance of these metatheoretical characterisations. First, I discuss the kinetic analogy, which has been recently put forward by Tim Lewens. According to it, cultural evolutionary theory is grounded on a bottom-up methodology, which highlights the additive effects of social learning biases on the emergence of large-scale cultural phenomena. Lewens supports this (...) claim by arguing that it is a consequence of cultural evolutionists’ widespread commitment to population thinking. While I concur with Lewens that cultural evolutionists often actually conceive cultural change in aggregative terms, I think that the kinetic framework does not properly account for the explanatory import of population-level descriptions in cultural evolutionary theory. Starting from a criticism of Lewens’ interpretation of population thinking, I argue that the explanatory role of such descriptions is best understood within a dynamical framework—that is, a framework according to which cultural evolutionary theory is a theory of forces. After having spelled out the main features of this alternative interpretation, I elucidate in which respects it helps to outline a more accurate characterisation of the overarching structure of cultural evolutionary theory. (shrink)