From positivism to conventionalism: Comte,Renouvier, and Poincaré

Considered in its historical context, conventionalism is quite different from the way in which it has been caricatured in more recent philosophy of science, that is, as a conservative philosophy that allows the preservation of theories through arbitrary ad hoc stratagems. It is instead a liberal outgrowth of Comtean positivism, which broke with the Reidian interpretation of the Newtonian tradition in France and defended a role for hypotheses in the sciences. It also has roots in the social contract political philosophy of Renouvier, who explicitly drew the analogy between conventions in political life and the conventional acceptance of hypotheses in the sciences, and conceived a philosophy that permits scientists to set aside foundational worries and explore new ideas. Although Poincaré and Renouvier may have hesitated to accept certain then recent developments in mathematics and the sciences such as non-Euclidean geometries, this conservatism cannot necessarily be attributed to their conventionalism. It may instead reflect the engineering background they shared with Comte, which emphasizes practical applications. Although Renouvier and Poincaré may have seen no practical use for these new ideas, unlike Comte they did not prohibit others from pursuing them, reflecting conventionalism's more liberal attitude toward recent developments in the sciences.     

Where explanation ends: Understanding as the place the spade turns in the social sciences

Explanations implicitly end with something that makes sense, and begin with something that does not make sense. A statistical relationship, for example, a numerical fact, does not make sense; an explanation of this relationship adds something, such as causal information, which does make sense, and provides an endpoint for the sense-making process. Does social science differ from natural science in this respect? One difference is that in the natural sciences, models are what need “understanding.” In the social sciences, matters are more complex. There are models, such as causal models, which need to be understood, but also depend on background knowledge that goes beyond the model and the correlations that make it up, which produces a regress. The background knowledge is knowledge of in-filling mechanisms, which are normally made up of elements that involve the direct understanding of the acting and believing subjects themselves. These models, and social science explanations generally, are satisfactory only when they end the regress in this kind of understanding or use direct understanding evidence to decide between alternative mechanism explanations.     

An overlooked autograph letter of Galileo on the thermometer

Scientism applies the ideas and methods of the natural sciences to the humanities and social sciences. Herbert Spencer applied the law of the conservation of energy to social questions and arrived at formula answers to the issues of the day. The kind of certitude that Spencer aimed for was possible only by ignoring a system of values. Much as he may have believed that he was above personal beliefs, there are values implicit in Spencer's theories and they are the values of the nineteenth-century British middle class. Reasoning by analogy is as valid in social theory as it is in the natural sciences. Spencer's error was in universally applying the idea of the conservation of energy to social systems by means of identity rather than by analogy. Scientists in Britain, where there was a self-assured scientific community, dismissed Spencer's theories as being unscientific, but he enjoyed a vogue in the United States.     

The Price of Success: Sociologist Harry Alpert,the NSF's First Social Science Policy Architect

Harry Alpert (1912–1977), the US sociologist, is best-known for his directorship of the National Science Foundation's social science programme in the 1950s. This study extends our understanding of Alpert in two main ways: first, by examining the earlier development of his views and career. Beginning with his 1939 biography of Emile Durkheim, we explore the early development of Alpert's views about foundational questions concerning the scientific status of sociology and social science more generally, proper social science methodology, the practical value of social science, the academic institutionalisation of sociology, and the unity-of-science viewpoint. Second, this paper illuminates Alpert's complex involvement with certain tensions in mid-century US social science that were themselves linked to major transformations in national science policy, public patronage, and unequal relations between the social and natural sciences. We show that Alpert's views about the intellectual foundations, practical relevance, and institutional standing of the social sciences were, in some important respects, at odds with his NSF policy work. Although remembered as a quantitative evangelist and advocate for the unity-of-science viewpoint, Alpert was in fact an urbane critic of natural-science envy, social scientific certainty, and what he saw as excessive devotion to quantitative methods.     

Playing with molecules

Recent philosophy of science has seen a number of attempts to understand scientific models by looking to theories of fiction. In previous work, I have offered an account of models that draws on Kendall Walton’s ‘make-believe’ theory of art. According to this account, models function as ‘props’ in games of make-believe, like children’s dolls or toy trucks. In this paper, I assess the make-believe view through an empirical study of molecular models. I suggest that the view gains support when we look at the way that these models are used and the attitude that users take towards them. Users’ interaction with molecular models suggests that they do imagine the models to be molecules, in much the same way that children imagine a doll to be a baby. Furthermore, I argue, users of molecular models imagine themselves viewing and manipulating molecules, just as children playing with a doll might imagine themselves looking at a baby or feeding it. Recognising this ‘participation’ in modelling, I suggest, points towards a new account of how models are used to learn about the world, and helps us to understand the value that scientists sometimes place on three-dimensional, physical models over other forms of representation.     

The Munsell Color System: A scientific compromise from the world of art

Color systems make accurate color specification and matching possible in science, art, and industry by defining a coordinate system for all possible color perceptions. The Munsell Color System, developed by the artist Albert Henry Munsell in the early twentieth century, has influenced color science to this day. I trace the development of the Munsell Color System from its origins in the art world to its acceptance in the scientific community.Munsell's system was the first to accurately and quantitatively describe the psychological experience of color. By considering the problems that color posed for Munsell's art community and examining his diaries and published material, I conclude that Munsell arrived at his results by remaining agnostic as to the scientific definition of color, while retaining faith that color perceptions could be objectively quantified. I argue that Munsell was able to interest the scientific community in his work because color had become a controversial topic between physicists and psychologists. Parts of Munsell's system appealed to each field, making it a workable compromise. For contrast, I suggest that three contemporary scientists with whom Munsell had contact – Wilhelm Ostwald, Ogden Rood, and Edward Titchener – did not reach the same conclusions in their color systems because they started from scientific assumptions about the nature of color.     

Analogical reflection as a source for the science of life: Kant and the possibility of the biological sciences

In contrast to the previously widespread view that Kant's work was largely in dialogue with the physical sciences, recent scholarship has highlighted Kant's interest in and contributions to the life sciences. Scholars are now investigating the extent to which Kant appealed to and incorporated insights from the life sciences and considering the ways he may have contributed to a new conception of living beings. The scholarship remains, however, divided in its interest: historians of science are concerned with the content of Kant's claims, and the ways in which they may or may not have contributed to the emerging science of life, while historians of philosophy focus on the systematic justifications for Kant's claims, e.g., the methodological and theoretical underpinnings of Kant's statement that living beings are mechanically inexplicable. My aim in this paper is to bring together these two strands of scholarship into dialogue by showing how Kant's methodological concerns (specifically, his notion of reflective judgment) contributed to his conception of living beings and to the ontological concern with life as a distinctive object of study. I argue that although Kant's explicit statement was that biology could not be a science, his implicit and more fundamental claim was that the study of living beings necessitates a distinctive mode of thought, a mode that is essentially analogical. I consider the implications of this view, and argue that it is by developing a new methodology for grasping organized beings that Kant makes his most important contribution to the new science of life.     

Modelling and representing: An artefactual approach to model-based representation

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation.     

Maxwell's color statistics: From reduction of visible errors to reduction to invisible molecules

This paper presents a cross-disciplinary and multi-disciplinary account of Maxwell's introduction of statistical models of molecules for the composition of gases. The account focuses on Maxwell's deployment of statistical models of data in his contemporaneous color researches as established in Cambridge mathematical physics, especially by Maxwell's seniors and mentors. The paper also argues that the cross-disciplinary, or cross-domain, transfer of resources from the natural and social sciences took place in both directions and relied on the complex intra-disciplinary, or intra-domain, dynamics of Maxwell's researches in natural sciences, in color theory, physical astronomy, electromagnetism and dynamical theory of gases, as well as involving a variety of types of communicating and mediating media, from material objects to concepts, techniques and institutions.     

Types of experiments and causal process tracing: What happened on the Kaibab Plateau in the 1920s

In a well-cited book chapter, ecologist Jared Diamond characterizes three main types of experiment performed in community ecology: laboratory experiment, field experiment, and natural experiment. Diamond argues that each form of experiment has strengths and weaknesses, with respect to, for example, realism or the ability to follow a causal trajectory. But does Diamond's typology exhaust the available kinds of cause-finding practices? Some social scientists have characterized something they call “causal process tracing.” Is this a fourth type of experiment or something else? I examine Diamond's typology and causal process tracing in the context of a case study concerning the dynamics of wolf and deer populations on the Kaibab Plateau in the 1920s, a case that has been used as a canonical example of a trophic cascade by ecologists but which has also been subject to controversy. I argue that ecologists have profitably deployed causal process tracing together with other types of experiment to help settle questions of causality in this case. It remains to be seen how widespread the use of causal process tracing outside of the social sciences is (or could be), but there are some potentially promising applications, particularly with respect to questions about specific causal sequences.     

‘The Great Fiasco’ of the 1948 presidential election polls: status recognition and norms conflict in social science

All three ‘scientific’ pollsters (Crossley, Gallup and Roper) wrongly predicted incumbent President Harry Truman’s defeat in the 1948 presidential election, and thus faced a potentially serious legitimacy crisis. This ‘fiasco’ occurred at a most inopportune time. Social science was embroiled in a policy debate taking place in the halls of Congress. It was fighting a losing battle to be included, along with the natural sciences, in the National Science Foundation, for which legislation was being drafted. Faced with the failure of the polls, the Social Science Research Council (SSRC) intervened quickly to prevent social science’s adversaries from using this event to degrade further its status. After all, many social scientists considered the sample survey as the paramount tool of social research, and sampling as one of social science’s greatest innovation. Concurrently, there was an ongoing conflict among polling practitioners themselves—between advocates of probability sampling and users of quotas, like the pollsters. The SSRC committee appointed to evaluate the polling debacle managed to keep this contentious issue of sampling from becoming the centre of attention. Given the inauspicious environment in which this event happened, the SSRC did not wish to advertise the fact that the house of social science was in turmoil.     

Incommensurabilities in the work of Thomas Kuhn

I distinguish between two ways in which Kuhn employs the concept of incommensurability based on for whom it presents a problem. First, I argue that Kuhn’s early work focuses on the comparison and underdetermination problems scientists encounter during revolutionary periods (actors’ incommensurability) whilst his later work focuses on the translation and interpretation problems analysts face when they engage in the representation of science from earlier periods (analysts’ incommensurability). Secondly, I offer a new interpretation of actors’ incommensurability. I challenge Kuhn’s account of incommensurability which is based on the compartmentalisation of the problems of both underdetermination and non-additivity to revolutionary periods. Through employing a finitist perspective, I demonstrate that in principle these are also problems scientists face during normal science. I argue that the reason why in certain circumstances scientists have little difficulty in concurring over their judgements of scientific findings and claims while in others they disagree needs to be explained sociologically rather than by reference to underdetermination or non-additivity. Thirdly, I claim that disagreements between scientists should not be couched in terms of translation or linguistic problems (aspects of analysts’ incommensurability), but should be understood as arising out of scientists’ differing judgments about how to take scientific inquiry further.     

Biologist Edwin Grant Conklin and the idea of the religious direction of human evolution in the early 1920s

Edwin Grant Conklin, renowned US embryologist and evolutionary popularizer, publicly advocated a social vision of evolution that intertwined science and modernist Protestant theology in the early 1920s. The moral prestige of professional science in American culture — along with Conklin’s own elite scientific status — diverted attention from the frequency with which his work crossed boundaries between natural science, religion and philosophy. Writing for broad audiences, Conklin was one of the most significant of the religious and modernist biological scientists whose rhetoric went well beyond simply claiming that certain kinds of religion were amenable to evolutionary science; he instead incorporated religion itself into evolution's broadest workings. A sampling of Conklin's widely-resonant discourse suggests that there was substantially more to the religion-evolution story in the 1920s US than many creationist-centred narratives of the era imply.     

Kant on anatomy and the status of the life sciences

This paper contributes to recent interest in Kant's engagement with the life sciences by focusing on one corner of those sciences that has received comparatively little attention: physical and comparative anatomy. By attending to remarks spread across Kant's writings, we gain some insight into Kant's understanding of the disciplinary limitations but also the methodological sophistication of the study of anatomy and physiology. Insofar as Kant highlights anatomy as a paradigmatic science guided by the principle of teleology in the Critique of the Power of Judgment, a more careful study of Kant's discussions of anatomy promises to illuminate some of the obscurities of that text and of his understanding of the life sciences more generally. In the end, it is argued, Kant's ambivalence with regard to anatomy gives way to a pessimistic conclusion about the possibility that anatomy, natural history, and, by extension, the life sciences more generally might one day become true natural sciences.     

Does environmental science crowd out non-epistemic values?

While no one denies that science depends on epistemic values, many philosophers of science have wrestled with the appropriate role of non-epistemic values, such as social, ethical, and political values. Recently, philosophers of science have overwhelmingly accepted that non-epistemic values should play a legitimate role in science. The recent philosophical debate has shifted from the value-free ideal in science to questions about how science should incorporate non-epistemic values. This article engages with such questions through an exploration of the environmental sciences. These sciences are a mosaic of diverse fields characterized by interdisciplinarity, problem-orientation, policy-directedness, and ubiquitous non-epistemic values. This article addresses a frequently voiced concern about many environmental science practices: that they ‘crowd out’ or displace significant non-epistemic values by either (1) entailing some non-epistemic values, rather than others, or by (2) obscuring discussion of non-epistemic values altogether. With three detailed case studies – monetizing nature, nature-society dualism, and ecosystem health – we show that the alleged problem of crowding out emerges from active debates within the environmental sciences. In each case, critics charge that the scientific practice in question displaces non-epistemic values in at least one of the two senses distinguished above. We show that crowding out is neither necessary nor always harmful when it occurs. However, we do see these putative objections to the application of environmental science as teaching valuable lessons about what matters for successful environmental science, all things considered. Given the significant role that many environmental scientists see for non-epistemic values in their fields, we argue that these cases motivate lessons about the importance of value-flexibility (that practices can accommodate a plurality of non-epistemic values), transparency about value-based decisions that inform practice, and environmental pragmatism.     

Model robustness as a confirmatory virtue: The case of climate science

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques.     

Renouvier and the method of hypothesis

Renouvier was among the first philosophers in France to break with the nineteenth-century inductivist tradition and defend the use of hypotheses in science. Earlier in the century, the humanistically-educated eclectic spiritualist philosophers who dominated French academic life had followed Reid in proscribing the use of hypotheses. Renouvier, who was educated in the sciences, took up the Comtean positivist alternative and developed it further. He began by defending hypotheses that anticipate laws governing the phenomena, but then eventually adopted a more liberal attitude towards hypotheses that postulate unobservable entities and processes as well. He also came to the realization that, from an epistemological point of view, all of empirical science is hypothetical. Renouvier used the tentative character of scientific knowledge as a premise from which to critique those who would claim scientific status for their social philosophies, and maintained a distinction between normative philosophical and empirical scientific inquiries.     

The diverse aims of science

There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. I suggest that continuing, widespread idealization calls into question the idea that science aims for truth. If instead science aims to produce understanding, this would enable idealizations to directly contribute to science's epistemic success. I also use the fact of widespread idealization to motivate the idea that science's wide variety aims, epistemic and non-epistemic, are best served by different kinds of scientific products. Finally, I show how these diverse aims—most rather distant from truth—result in the expanded influence of social values on science.     

Value management and model pluralism in climate science

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections.