The most conspicuous form of agricultural experiment is the field trial, and within the history of such trials, the arrival of the randomised control trial is considered revolutionary. Originating with R.A. Fisher within British agricultural science in the 1920s and 30s, the RCT has since become one of the most prodigiously used experimental techniques throughout the natural and social sciences. Philosophers of science have already scrutinised the epistemological uniqueness of RCTs, undermining their status as the ‘gold standard’ in experimental design. (...) The present paper introduces a historical case study from the origins of the RCT, uncovering the initially cool reception given to this method by agricultural scientists at the University of Cambridge and the National Institute of Agricultural Botany. Rather than giving further attention to the RCT, the paper focuses instead on a competitor method – the half-drill strip – which both predated the RCT and remained in wide use for at least a decade beyond the latter’s arrival. In telling this history, John Pickstone’s Ways of Knowing is adopted, as the most flexible, and productive way to write the history of science, particularly when sciences and scientists have to work across a number of different kinds of place. It is shown that those who resisted the RCT did so in order to preserve epistemic and social goals that randomisation would have otherwise run a tractor through. (shrink)

This volume focuses on various questions concerning the interpretation of probability and probabilistic reasoning in biology and physics. It is inspired by the idea that philosophers of biology and philosophers of physics who work on the foundations of their disciplines encounter similar questions and problems concerning the role and application of probability, and that interaction between the two communities will be both interesting and fruitful. In this introduction we present the background to the main questions that the volume focuses on (...) and summarize the highlights of the individual contributions. (shrink)

This paper contributes to the ongoing reassessment of the controversy between William Bateson and Karl Pearson by characterising what we call “Batesonian Mendelism” and “Pearsonian biometry” as coherent and competing scientific outlooks. Contrary to the thesis that such a controversy stemmed from diverging theoretical commitments on the nature of heredity and evolution, we argue that Pearson’s and Bateson’s alternative views on those processes ultimately relied on different appraisals of the methodological value of the statistical apparatus developed by Francis Galton. Accordingly, (...) we contend that Bateson’s belief in the primacy of cross-breeding experiments over statistical analysis constituted a minimal methodological unifying condition ensuring the internal coherence of Batesonian Mendelism. Moreover, this same belief implied a view of the study of heredity and evolution as an experimental endeavour and a conception of heredity and evolution as fundamentally discontinuous processes. Similarly, we identify a minimal methodological unifying condition for Pearsonian biometry, which we characterise as the view that experimental methods had to be subordinate to statistical analysis, according to methodological standards set by biometrical research. This other methodological commitment entailed conceiving the study of heredity and evolution as subsumable under biometry and primed Pearson to regard discontinuous hereditary and evolutionary processes as exceptions to a statistical norm. Finally, we conclude that Batesonian Mendelism and Pearsonian biometry represented two potential versions of a single genetics-based evolutionary synthesis since the methodological principles and the phenomena that played a central role in the former were also acknowledged by the latter—albeit as fringe cases—and conversely. (shrink)

Shapiro and Sober claim that Walsh, Ariew, Lewens, and Matthen give a mistaken, a priori defense of natural selection and drift as epiphenomenal. Contrary to Shapiro and Sober’s claims, we first argue that WALM’s explanatory doctrine does not require a defense of epiphenomenalism. We then defend WALM’s explanatory doctrine by arguing that the explanations provided by the modern genetical theory of natural selection are ‘autonomous-statistical explanations’ analogous to Galton’s explanation of reversion to mediocrity and an explanation of the diffusion ofgases. (...) We then argue that whereas Sober’s theory of forces is an adequate description of Darwin’s theory, WALM’s explanatory doctrine is required to understand how themodern genetical theory of natural selection explains large-scale statistical regularities. 1 Introduction2 Shapiro and Sober’s ‘Epiphenomenalism Do’s and Don’ts’3 WALM’s Explanatory Doctrine4 Galton’s Autonomous-Statistical Explanation5 A Second Example: The Statistical Explanation of the Diffusion of Gases6 Distinguishing Two Theories of Evolution by Natural Selection7 A Possible Objection: Are Statistical Laws Sufficient for Explanation?8 Conclusion. (shrink)

Fisher’s ‘fundamental theorem of natural selection’ is notoriously abstract, and, no less notoriously, many take it to be false. In this paper, I explicate the theorem, examine the role that it played in Fisher’s general project for biology, and analyze why it was so very fundamental for Fisher. I defend Ewens (1989) and Lessard (1997) in the view that the theorem is in fact a true theorem if, as Fisher claimed, ‘the terms employed’ are ‘used strictly as defined’ (1930, p. (...) 38). Finally, I explain the role that projects such as Fisher’s play in the progress of scientific inquiry. (shrink)

Scientists use models to understand the natural world, and it is important not to conflate model and nature. As an illustration, we distinguish three different kinds of populations in studies of ecology and evolution: theoretical, laboratory, and natural populations, exemplified by the work of R.A. Fisher, Thomas Park, and David Lack, respectively. Biologists are rightly concerned with all three types of populations. We examine the interplay between these different kinds of populations, and their pertinent models, in three examples: the notion (...) of “effective” population size, the work of Thomas Park on /Tribolium/ populations, and model-based clustering algorithms such as /Structure/. Finally, we discuss ways to move safely between three distinct population types while avoiding confusing models and reality. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

Over the past fifteen years there has been a considerable amount of debate concerning what theoretical population dynamic models tell us about the nature of natural selection and drift. On the causal interpretation, these models describe the causes of population change. On the statistical interpretation, the models of population dynamics models specify statistical parameters that explain, predict, and quantify changes in population structure, without identifying the causes of those changes. Selection and drift are part of a statistical description of population (...) change; they are not discrete, apportionable causes. Our objective here is to provide a definitive statement of the statistical position, so as to allay some confusions in the current literature. We outline four commitments that are central to statisticalism. They are: 1. Natural Selection is a higher order effect; 2. Trait fitness is primitive; 3. Modern Synthesis (MS)-models are substrate neutral; 4. MS-selection and drift are model-relative. (shrink)

This article discusses the work of George Udny Yule in relation to the evolutionary synthesis and the biometric-Mendelian debate. It has generally been claimed that (i.) in 1902, Yule put forth the first account showing that the competing biometric and Mendelian programs could be synthesized. Furthermore, (ii.) the scientific figures who should have been most interested in this thesis (the biometricians W. F. Raphael Weldon and Karl Pearson, and the Mendelian William Bateson) were too blinded by personal animosity towards each (...) other to appreciate Yule's proposal. This essay provides a detailed account of (i.), maintaining that Yule's 1902 proposal is better understood as a reduction, not a synthesis of the two programs. The results of this analysis are then used to evaluate (ii.), where I will instead argue that Bateson and the biometricians had good reasons to avoid endorsing Yule's account. (shrink)

One of the most remarkable aspects of John Maynard Smith’s work was the fact that he devoted time both to doing science and to reflecting philosophically upon its methods and concepts. In this paper I offer a philosophical analysis of Maynard Smith’s approach to modelling phenotypic evolution in relation to three main themes. The first concerns the type of scientific understanding that ESS and optimality models give us. The second concerns the causal–historical aspect of stability analyses of adaptation. The third (...) concerns the concept of evolutionary stability itself. Taken together, these three themes comprise what I call the natural philosophy of adaptation. (shrink)

The dissemination of models across disciplinary lines has become a phenomenon of interest to philosophers of science. To account for this phenomenon, philosophers have invented two units of analysis. The first identifies to the thing that transfers, model templates. The second identifies the thing to which transferable templates apply, landing zones. There exists a dynamic between the thing that is transferred and the thing to which transferrable templates apply. The use of a transferable template in a new domain requires reconception (...) of domain-specific phenomena. This paper examines two cases of model transfer, the use of the ideal gas law in biology by R.A. Fisher and the use of the virial theorem in chemistry by Richard Bader. These two stories of model transfer in biology and chemistry indicate a dimension to conceptual progress related to this dynamic. Using discourse on model transfer affords philosophers a novel approach for depicting the invention of, for instance, chemical concepts and resulting disputes. (shrink)

The dissemination of models across disciplinary lines has become a phenomenon of interest to philosophers of science. To account for this phenomenon, philosophers have invented two units of analysis. The first identifies to the thing that transfers, model templates. The second identifies the thing to which transferable templates apply, landing zones. There exists a dynamic between the thing that is transferred and the thing to which transferrable templates apply. The use of a transferable template in a new domain requires reconception (...) of domain-specific phenomena. This paper examines two cases of model transfer, the use of the ideal gas law in biology by R.A. Fisher and the use of the virial theorem in chemistry by Richard Bader. These two stories of model transfer in biology and chemistry indicate a dimension to conceptual progress related to this dynamic. Using discourse on model transfer affords philosophers a novel approach for depicting the invention of, for instance, chemical concepts and resulting disputes. (shrink)

The dissemination of models across disciplinary lines has become a phenomenon of interest to philosophers of science. To account for this phenomenon, philosophers have invented two units of analysis. The first identifies to the thing that transfers, model templates. The second identifies the thing to which transferable templates apply, landing zones. There exists a dynamic between the thing that is transferred and the thing to which transferrable templates apply. The use of a transferable template in a new domain requires reconception (...) of domain-specific phenomena. This paper examines two cases of model transfer, the use of the ideal gas law in biology by R.A. Fisher and the use of the virial theorem in chemistry by Richard Bader. These two stories of model transfer in biology and chemistry indicate a dimension to conceptual progress related to this dynamic. Using discourse on model transfer affords philosophers a novel approach for depicting the invention of, for instance, chemical concepts and resulting disputes. (shrink)

Fisher’s ‘fundamental theorem of natural selection’ is notoriously abstract, and, no less notoriously, many take it to be false. In this paper, I explicate the theorem, examine the role that it played in Fisher’s general project for biology, and analyze why it was so very fundamental for Fisher. I defend Ewens and Lessard in the view that the theorem is in fact a true theorem if, as Fisher claimed, ‘the terms employed’ are ‘used strictly as defined’. Finally, I explain the (...) role that projects such as Fisher’s play in the progress of scientific inquiry. (shrink)

Fisher’s ‘fundamental theorem of natural selection’ is notoriously abstract, and, no less notoriously, many take it to be false. In this paper, I explicate the theorem, examine the role that it played in Fisher’s general project for biology, and analyze why it was so very fundamental for Fisher. I defend Ewens and Lessard in the view that the theorem is in fact a true theorem if, as Fisher claimed, ‘the terms employed’ are ‘used strictly as defined’. Finally, I explain the (...) role that projects such as Fisher’s play in the progress of scientific inquiry. (shrink)

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)

The idea of ‘reversion’ or ‘atavism’ has a peculiar history. For many authors in the latenineteenth and early-twentieth centuries – including Darwin, Galton, Pearson, Weismann, and Spencer, among others – reversion was one of the central phenomena which a theory of heredity ought to explain. By only a few decades later, however, Fisher and others could look back upon reversion as a historical curiosity, a non-problem, or even an impediment to clear theorizing. I explore various reasons that reversion might have (...) appeared to be a central problem for this first group of figures, focusing on their commitment to a variety of conceptual features of evolutionary theory; discuss why reversion might have then ceased to be an interesting phenomenon; and, finally, close with some more general thoughts about the death of scientific problems. (shrink)

Work throughout the history and philosophy of biology frequently employs ‘chance’, ‘unpredictability’, ‘probability’, and many similar terms. One common way of understanding how these concepts were introduced in evolution focuses on two central issues: the first use of statistical methods in evolution (Galton), and the first use of the concept of “objective chance” in evolution (Wright). I argue that while this approach has merit, it fails to fully capture interesting philosophical reflections on the role of chance expounded by two of (...) Galton's students, Karl Pearson and W.F.R. Weldon. Considering a question more familiar from contemporary philosophy of biology—the relationship between our statistical theories of evolution and the processes in the world those theories describe—is, I claim, a more fruitful way to approach both these two historical actors and the broader development of chance in evolution. (shrink)

We argue that the economy of nature constitutes an invocation of structure in the biological sciences, one largely missed by philosophers of biology despite the turn in recent years toward structural explanations throughout the philosophy of science. We trace a portion of the history of this concept, beginning with the theologically and economically grounded work of Linnaeus, moving through Darwin’s adaptation of the economy of nature and its reconstitution in genetic terms during the first decades of the Modern Synthesis. What (...) this historical case study reveals, we argue, is a window into the shifting landscape of the explanatory and ontic uses of structural concepts. In Linnaeus, the economy of nature has both ontic and explanatory import; in Darwin the ontic and explanatory aspects start to come apart ; and finally, in the Modern Synthesis, the economy of nature is replaced by the conceptual toolkit of population genetics, the structural elements of which are nearly entirely explanatory. Having traced a historical trajectory of structural concepts that moves from an ontic formulation to an increasingly explanatory one, we conclude by outlining some insights for structural realism. (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)

There are two motivations commonly ascribed to historical actors for taking up statistics: to reduce complicated data to a mean value (e.g., Quetelet), and to take account of diversity (e.g., Galton). Different motivations will, it is assumed, lead to different methodological decisions in the practice of the statistical sciences. Karl Pearson and W. F. R. Weldon are generally seen as following directly in Galton’s footsteps. I argue for two related theses in light of this standard interpretation, based on a reading (...) of several sources in which Weldon, independently of Pearson, reflects on his own motivations. First, while Pearson does approach statistics from this "Galtonian" perspective, he is, consistent with his positivist philosophy of science, utilizing statistics to simplify the highly variable data of biology. Weldon, on the other hand, is brought to statistics by a rich empiricism and a desire to preserve the diversity of biological data. Secondly, we have here a counterexample to the claim that divergence in motivation will lead to a corresponding separation in methodology. Pearson and Weldon, despite embracing biometry for different reasons, settled on precisely the same set of statistical tools for the investigation of evolution. (shrink)

This paper argues for two related theses. The first is that mathematical abstraction can play an important role in shaping the way we think about and hence understand certain phenomena, an enterprise that extends well beyond simply representing those phenomena for the purpose of calculating/predicting their behaviour. The second is that much of our contemporary understanding and interpretation of natural selection has resulted from the way it has been described in the context of statistics and mathematics. I argue for these (...) claims by tracing attempts to understand the basis of natural selection from its early formulation as a statistical theory to its later development by R.A. Fisher, one of the founders of modern population genetics. Not only did these developments put natural selection of a firm theoretical foundation but its mathematization changed the way it was understood as a biological process. Instead of simply clarifying its status, mathematical techniques were responsible for redefining or reconceptualising selection. As a corollary I show how a highly idealised mathematical law that seemingly fails to describe any concrete system can nevertheless contain a great deal of accurate information that can enhance our understanding far beyond simply predictive capabilities. (shrink)

The paper discusses the scope and influence of eugenics in defining the scientific programme of statistics and the impact of the evolution of biology on social scientists. It argues that eugenics was instrumental in providing a bridge between sciences, and therefore created both the impulse and the institutions necessary for the birth of modern statistics in its applications first to biology and then to the social sciences. Looking at the question from the point of view of the history of statistics (...) and the social sciences, and mostly concentrating on evidence from the British debates, the paper discusses how these disciplines became emancipated from eugenics precisely because of the inspiration of biology. It also relates how social scientists were fascinated and perplexed by the innovations taking place in statistical theory and practice. (shrink)

The paper discusses the scope and influence of eugenics in defining the scientific programme of statistics and the impact of the evolution of biology on social scientists. It argues that eugenics was instrumental in providing a bridge between sciences, and therefore created both the impulse and the institutions necessary for the birth of modern statistics in its applications first to biology and then to the social sciences. Looking at the question from the point of view of the history of statistics (...) and the social sciences, and mostly concentrating on evidence from the British debates, the paper discusses how these disciplines became emancipated from eugenics precisely because of the inspiration of biology. It also relates how social scientists were fascinated and perplexed by the innovations taking place in statistical theory and practice. (shrink)

The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. It is (...) argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models. Introduction Models in statistics—‘of what population is this a random sample?’ The fundamental lemma Controversy about models Exploratory data analysis as a model-critical approach. (shrink)

The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. It is (...) argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models. 1. Introduction2. Models in statistics—‘of what population is this a random sample?’3. The fundamental lemma4. Controversy about models5. Exploratory data analysis as a model-critical approach. (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)