Constructing dystopian experience: A Neurath-Cartwrightian approach to the philosophy of social technology

Social situations, the object of the social sciences, are complex and unique: they contain so many variable aspects that they cannot be reproduced, and it is even difficult to experience two situations that are alike in many respects. The social scientists' past experiences that serve as their background knowledge to intervene in an existent situation is poor compared to what a traditional epistemologist would consider ideal. A way of dealing with the variable and insufficient background of social scientists is by means of models. But, then, how should we characterize social scientific models? This paper examines Otto Neurath's scientific utopianism as an attempt to deal with this problem. Neurath proposes that social scientists work with utopias: broad imaginative plans that coordinate a multitude of features of a social situation. This notion can be used in current debates in philosophy of science because we notice that utopias, in Neurath's sense, are comparable to models and nomological machines in Nancy Cartwright's conception. A model-based view of science lays emphasis on the fact that scientists learn from the repeated operation of such abstract entities, just as they learn from the repetition of experiments in a laboratory. Hence this approach suggests an approximation between the natural and the social sciences, as well as between science and utopian literature. This is exemplified by analyzing the literary dystopia We, written by Yevgeny Zamyatin, to show that reasoning from and debating about utopian writings, even if fictional and pessimistic, creates phenomena of valuation, which are fundamental for constituting a background of experiences in the social sciences.     

The naturalism of the sciences

The sciences are characterized by what is sometimes called a “methodological naturalism,” which disregards talk of divine agency. In response to those who argue that this reflects a dogmatic materialism, a number of philosophers have offered a pragmatic defense. The naturalism of the sciences, they argue, is provisional and defeasible: it is justified by the fact that unsuccessful theistic explanations have been superseded by successful natural ones. But this defense is inconsistent with the history of the sciences. The sciences have always exhibited what we call a domain naturalism. They have never invoked divine agency, but have always focused on the causal structure of the natural world. It is not the case, therefore, that the sciences once employed theistic explanations and then abandoned them. The naturalism of the sciences is as old as science itself.     

Hobbes on natural philosophy as “True Physics” and mixed mathematics

In this paper, I offer an alternative account of the relationship of Hobbesian geometry to natural philosophy by arguing that mixed mathematics provided Hobbes with a model for thinking about it. In mixed mathematics, one may borrow causal principles from one science and use them in another science without there being a deductive relationship between those two sciences. Natural philosophy for Hobbes is mixed because an explanation may combine observations from experience (the ‘that’) with causal principles from geometry (the ‘why’). My argument shows that Hobbesian natural philosophy relies upon suppositions that bodies plausibly behave according to these borrowed causal principles from geometry, acknowledging that bodies in the world may not actually behave this way. First, I consider Hobbes's relation to Aristotelian mixed mathematics and to Isaac Barrow's broadening of mixed mathematics in Mathematical Lectures (1683). I show that for Hobbes maker's knowledge from geometry provides the ‘why’ in mixed-mathematical explanations. Next, I examine two explanations from De corpore Part IV: (1) the explanation of sense in De corpore 25.1-2; and (2) the explanation of the swelling of parts of the body when they become warm in De corpore 27.3. In both explanations, I show Hobbes borrowing and citing geometrical principles and mixing these principles with appeals to experience.     

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.     

Mechanisms,the interventionist theory,and the ability to use causal relationships

In the area of social science, in particular, although we have developed methods for reliably discovering the existence of causal relationships, we are not very good at using these to design effective social policy. Cartwright argues that in order to improve our ability to use causal relationships, it is essential to develop a theory of causation that makes explicit the connections between the nature of causation, our best methods for discovering causal relationships, and the uses to which these are put. I argue that Woodward's interventionist theory of causation is uniquely suited to meet Cartwright's challenge. More specifically, interventionist mechanisms can provide the bridge from ‘hunting causes’ to ‘using them’, if interventionists (i) tell us more about the nature of these mechanisms, and (ii) endorse the claim that it is these mechanisms—or whatever constitutes them—that make causal claims true. I illustrate how having an understanding of interventionist mechanisms can allow us to put causal knowledge to use via a detailed example from organic chemistry.     

Historicism and the failure of HPS

This paper suggests that the failure to integrate history and philosophy of science properly may be explained by incompatible metaphysics implied by these fields. Historians and sociologists tend to be historicists, who assume that all objects of research are variable in principle, while philosophers look for permanent and essential qualities. I analyse, how the historicists and essentialist approaches differ with regard to the research objects of general history, history of science and science itself. The implied historicism makes some radical pronouncements by Latour on ontological variance understandable. I will also consider, whether there could be something like a historicist philosophy of science. The historicisation of the natural world proves most challenging, but both certain traditional disciplines and some recent advances in physical and life sciences indicate compatibility with historicism. One should note that historicism does not alter how ‘truth’ is understood. Historicism does not question the reality of objects either; only their eternality.     

Modelling and knowledge transfer in complexity science

I analyse the construction and transfer of models in complexity science. Thereby, I introduce a distinction between (i) vertical model construction, which is based on knowledge about a specific target system, (ii) horizontal model construction, which is based on the alteration of an existing model and therefore does not require any references to a specific target system; and (iii) the transfer of models, which consists of the assignment of an existing model to a new target system. I argue that, in complexity science, all three of those modelling activities take place. Furthermore, I show that these activities can be divided into two general categories: (i) the creation of a repository of models without specific target systems, which have been created by large-scale horizontal construction; and (ii) the transfer of these models to particular target systems in the natural sciences, which can also be followed by an extension of the transferred model through vertical construction of adaptions and additions to its dynamics. I then argue that this interplay of different modelling activities in complexity science provides a mechanism for the transfer of knowledge between different scientific fields. It is also crucial to the interdisciplinary nature of complexity science.     

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.     

Rehabilitating the regulative use of reason: Kant on empirical and chemical laws

In his Kritik der reinen Vernunft, Kant asserts that laws of nature “carry with them an expression of necessity” (A159/B198). There is, however, widespread interpretive disagreement regarding the nature and source of the necessity of empirical laws of natural sciences in Kant's system. It is especially unclear how chemistry—a science without a clear, straightforward connection to the a priori principles of the understanding—could contain such genuine, empirical laws. Existing accounts of the necessity of causal laws unfortunately fail to illuminate the possibility of non-physical laws. In this paper, I develop an alternative, ‘ideational’ account of natural laws, according to which ideas of reason necessitate the laws of some non-physical sciences. Chemical laws, for instance, are grounded on ideas of the elements, and the chemist aims to reduce her phenomena to these elements via experimentation. Although such ideas are beyond the possibility of experience, their postulation is necessary for the achievement of reason's theoretical ends: the unification and explanation of the cognitions of science.     

Understanding climate phenomena with data-driven models

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.     

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.     

Scrutinizing thing knowledge

In his book Thing Knowledge Davis Baird argues that our accustomed understanding of knowledge as justified true beliefs is not enough to understand progress in science and technology. To be more accurate he argues that scientific instruments are to be seen as a form of “objective knowledge” in the sense of Karl Popper.I want to examine if this idea is plausible. In a first step I want to show that this proposal implies that nearly all man-made artifacts are materialized objective knowledge. I argue that this radical change in our concept of knowledge demands strong reasons and that Baird does not give them. I take a look at the strongest strand of arguments of Baird's book—the arguments from cognitive autonomy—and conclude that they do not suffice to make Baird's view of scientific instruments tenable.     

State of the field: Are the results of science contingent or inevitable?

This paper presents a survey of the literature on the problem of contingency in science. The survey is structured around three challenges faced by current attempts at understanding the conflict between “contingentist” and “inevitabilist” interpretations of scientific knowledge and practice. First, the challenge of definition: it proves hard to define the positions that are at stake in a way that is both conceptually rigorous and does justice to the plethora of views on the issue. Second, the challenge of distinction: some features of the debate suggest that the contingency issue may not be sufficiently distinct from other philosophical debates to constitute a genuine, independent philosophical problem. And third, the challenge of decidability: it remains unclear whether and how the conflict could be settled on the basis of empirical evidence from the actual history of science. The paper argues that in order to make progress in the present debate, we need to distinguish more systematically between different expressions that claims about contingency and inevitability in science can take. To this end, it introduces a taxonomy of different contingency and inevitability claims. The taxonomy has the structure of an ordered quadruple. Each contingency and each inevitability claim contains an answer to the following four questions: (how) are alternatives to current science possible, what types of alternatives are we talking about, how should the alternatives be assessed, and how different are they from actual science?     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

Existential risk,creativity & well-adapted science

Existential risks, particularly those arising from emerging technologies, are a complex, obstinate challenge for scientific study. This should motivate studying how the relevant scientific communities might be made more amenable to studying such risks. I offer an account of scientific creativity suitable for thinking about scientific communities, and provide reasons for thinking contemporary science doesn't incentivise creativity in this specified sense. I'll argue that a successful science of existential risk will be creative in my sense. So, if we want to make progress on those questions we should consider how to shift scientific incentives to encourage creativity. The analysis also has lessons for philosophical approaches to understanding the social structure of science. I introduce the notion of a ‘well-adapted’ science: one in which the incentive structure is tailored to the epistemic situation at hand.     

Understanding,explanation, and unification

In this article I argue that there are two different types of understanding: the understanding we get from explanations, and the understanding we get from unification. This claim is defended by first showing that explanation and unification are not as closely related as has sometimes been thought. A critical appraisal of recent proposals for understanding without explanation leads us to discuss the example of a purely classificatory biology: it turns out that such a science can give us understanding of the world through unification of the phenomena, even though it does not give us any explanations. The two types of understanding identified in this paper, while strictly separate, do have in common that both consist in seeing how the individual phenomena of the universe hang together. Explanations give us connections between the phenomena through the asymmetric, ‘vertical’ relation of determination; unifications give us connections through the symmetric, ‘horizontal’ relation of kinship. We then arrive at a general definition of understanding as knowledge of connections between the phenomena, and indicate that there might be more than two types of understanding.     

The cognition-knowledge distinction in Kant and Dilthey and the implications for psychology and self-understanding

Both Kant and Dilthey distinguish between cognition and knowledge, but they do so differently in accordance with their respective theoretical interests. Kant’s primary cognitive interest is in the natural sciences, and from this perspective the status of psychology is questioned because its phenomena are not mathematically measurable. Dilthey, by contrast, reconceives psychology as a human science.For Kant, knowledge is conceptual cognition that has attained certainty by being part of a rational system. Dilthey also links knowledge with certainty; however, he derives the latter from life-experience rather than from reason. Dilthey’s psychology begins with the self-certainty of lived experience and life-knowledge, but this turns out to fall short of cognitive understanding. In the final analysis, both Kant and Dilthey move beyond psychology to arrive at self-understanding. Because of his doubts about introspection, Kant replaces psychology with a pragmatic anthropology to provide a communal framework for self-understanding. Dilthey supplements psychology with other human sciences as part of a project of anthropological reflection.     

Kant and the scope of analogy in the life sciences

In the present paper I investigate the role that analogy plays in eighteenth-century biology and in Kant's philosophy of biology. I will argue that according to Kant, biology, as it was practiced in the eighteenth century, is fundamentally based on analogical reflection. However, precisely because biology is based on analogical reflection, biology cannot be a proper science. I provide two arguments for this interpretation. First, I argue that although analogical reflection is, according to Kant, necessary to comprehend the nature of organisms, it is also necessarily insufficient to fully comprehend the nature of organisms. The upshot of this argument is that for Kant our understanding of organisms is necessarily limited. Second, I argue that Kant did not take biology to be a proper science because biology was based on analogical arguments. I show that Kant stemmed from a philosophical tradition that did not assign analogical arguments an important justificatory role in natural science. Analogy, according to this conception, does not provide us with apodictically certain cognition. Hence, sciences based on analogical arguments cannot constitute proper sciences.     

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.