Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...) rate in an isolated ecosystem. And when studying the exchange of goods, economists consider a situation in which there are only two goods, two perfectly rational agents, no restrictions on available information, no transaction costs, no money, and dealings are done immediately. Their surface structure notwithstanding, no competent scientist would mistake descriptions of such systems as descriptions of an actual system: we know very well that there are no such systems. These descriptions are descriptions of a model-system, and scientists use model-systems to represent parts or aspects of the world they are interested in. Following common practice, I refer to those parts or aspects as target-systems. What are we to make of this? Is discourse about such models merely a picturesque and ultimately dispensable façon de parler? This was the view of some early twentieth century philosophers. Duhem (1906) famously guarded against confusing model building with scientific theorizing and argued that model building has no real place in science, beyond a minor heuristic role. The aim of science was, instead, to construct theories, with theories understood as classificatory or representative structures systematically presented and formulated in precise symbolic.. (shrink)

One important aspect of biological explanation is detailed causal modeling of particular phenomena in limited experimental background conditions. Recognising this allows one to appreciate that a sufficient condition for a reduction in biology is a molecular model of (1) only the demonstrated causal parameters of a biological model and (2) only within a replicable experimental background. These identities—which are ubiquitous in biology and form the basis of ruthless reductions (Bickle, Philosophy and neuroscience: a ruthlessly reductive account, 2003)—are criticised as merely (...) “local” (Sullivan, Synthese 167:511–539, 2009) or “fragmentary” (Schaffner, Synthese, 151(3):377–402, 2006). However, in an instructive case, a biological model is preserved in molecular terms, demonstrating a complex phenomenon that has been successfully reduced. (shrink)

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

Unlike basic sciences, scientific research in advanced technologies aims to explain, predict, and (mathematically) describe not phenomena in nature, but phenomena in technological artefacts, thereby producing knowledge that is utilized in technological design. This article first explains why the covering-law view of applying science is inadequate for characterizing this research practice. Instead, the covering-law approach and causal explanation are integrated in this practice. Ludwig Prandtl's approach to concrete fluid flows is used as an example of scientific research in the engineering (...) sciences. A methodology of distinguishing between regions in space and/or phases in time that show distinct physical behaviours is specific to this research practice. Accordingly, two types of models specific to the engineering sciences are introduced. The diagrammatic model represents the causal explanation of physical behaviour in distinct spatial regions or time phases; the nomo-mathematical model represents the phenomenon in terms of a set of mathematically formulated laws. (shrink)

In this chapter, we discuss a specific kind of progress that occurs in most branches of economics today: progress involving the repeated use of mathematical models. We adopt a functional account of progress to argue that progress in economics occurs through the use of what we call “common recipes” and model templates for defining and solving problems of relevance for economists. We support our argument by discussing the case of 20th century business cycle research. By presenting this case study in (...) detail, we show how model templates are not only reapplied to different phenomena. We also show how scientists first develop them and how, once they are considered less useful, they are replaced with new ones. Finally, our case also illustrates that it is not only the mathematical structure that is reused but that such reuse also requires a shared conceptual vision of the core properties of the phenomenon to be studied. If that vision is no longer shared among economists, a model template can become useless and has to be replaced, sometimes against resistance, with a different one. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.

The 1994 US spectrum auction is now a paradigmatic case of the successful use of microeconomic theory for policy-making. We use a detailed analysis of it to review standard accounts in philosophy of science of how idealized models are connected to messy reality. We show that in order to understand what made the design of the spectrum auction successful, a new such account is required, and we present it here. Of especial interest is the light this sheds on the issue (...) of progress in economics. In particular, it enables us to get clear on exactly what has been progressing, and on exactly what theory has – and has not – contributed to that. This in turn has important implications for just what it is about economic theory that we should value. (shrink)

A conceptual framework for studying the role of global health partnerships (GHPs) in determining policy practices on access to medication is presented. Although GHPs are of a practical nature, they are implicitly theory informed. The narratives used by GHP partners in relating to access to medication have theoretical origins. Building on the theoretical literature on models and the notion of embodied knowledge found in science and technology studies, GHPs are conceptualized as models that mediate between theory and practice. The proposed (...) framework can be used to investigate the role of theory in the creation of GHP models and how the models shape global and national policy practices. A social constructivist approach is suggested as a suitable method for empirical analysis. (shrink)

ArgumentThis article studies the roles three science-based models play in Dutch policy and decision making processes. Key is the interaction between model construction and environment. Their political and scientific environments form contexts that shape the roles of models in policy decision making. Attention is paid to three aspects of the wider context of the models: a) the history of the construction process; b) the political and scientific environments; and c) the use in policy processes over longer periods of time. Models (...) are more successfully used when they are constructed in astablepolitical and scientific environment. Stability and certainty within a scientific field seems to be a key predictor for the usefulness of models for policy making. The economic model is more disputed than the ecology-based model and the model that has its theoretical foundation in physics and chemistry. The roles models play in policy processes are too complex to be considered as straightforward technocratic powers. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (shrink)

The book provides a historical and systematic exposition of the semantic theory of truth formulated by Alfred Tarski in the 1930s. This theory became famous very soon and inspired logicians and philosophers. It has two different, but interconnected aspects: formal-logical and philosophical. The book deals with both, but it is intended mostly as a philosophical monograph. It explains Tarski’s motivation and presents discussions about his ideas as well as points out various applications of the semantic theory of truth to philosophical (...) problems. (shrink)

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a "number crunching" technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes (...) that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models. (shrink)

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only (...) from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth. (shrink)

In this paper, we explore the link between understanding and transformative decisions. Paul (2014) suggests that one important aspect of making some decisions is that we make them not just on the basis of what data from other people tell us, but based on our own acquaintance with how the decision affects us. In this paper, we draw out a parallel between the sort of reasoning that Paul argues is required for authentic decision making and the sort of epistemic grasp (...) of a subject matter that is required for understanding. The central claim of this paper is that one’s ability to make a decision authentically is proportional to one’s ability to model what it is like to have the possible resultant experience. We endorse a broad notion according to which one can model something by thinking about relevantly similar things. (shrink)

In this paper, I argue that explanations just ARE those sorts of things that, under the right circumstances and in the right sort of way, bring about understanding. This raises the question of why such a seemingly simple account of explanation, if correct, would not have been identified and agreed upon decades ago. The answer is that only recently has it been made possible to analyze explanation in terms of understanding without the risk of collapsing both to merely phenomenological states. (...) For the most part, theories of explanation were for 50 years held hostage to the historical accident that they far outstripped in sophistication corresponding accounts of understanding. (shrink)

Traditional nomological accounts of scientific explanation have assumed that a good scientific explanation consists in the derivation of the explanandum’s description from theory (plus antecedent conditions). But in more recent philosophy of science the adequacy of this approach has been challenged, because the relation between theory and phenomena in actual scientific practice turns out to be more intricate. This critique is here examined for an explanatory paradigm that was groundbreaking for 20th century physics and chemistry (and their interrelation): Bohr’s first (...) model of the atom and its explanatory relevance for the spectrum of hydrogen. First, the model itself is analysed with respect to the principles and assumptions that enter into its premises. Thereafter, the origin of the model’s explanandum is investigated. It can be shown that the explained “phenomenon” is itself the product of a host of modelling accomplishments that stem from an experimental tradition related to 19th century chemistry, viz. spectroscopy. The relation between theory and phenomenon is thus mediated in a twofold way: by (Bohr’s) theoretical model and a phenomenological model from spectroscopy. In the final section of the paper an account is outlined that nevertheless permits us to acknowledge this important physico-chemical achievement as a case of (nomological) explanation. (shrink)

Over the course of the last three decades, computer simulations have become a major tool of doing science and engaging with the world, not least in an effort to predict and intervene in a future to come. Born in the context of the Second World War and the discipline of physics, simulations have long spread into most diverse fields of enquiry and technological application. This paper introduces a topical collection focussing on simulations in the life sciences. Echoing the current state (...) of tinkering, fast developments, segmentation of knowledge and interdisciplinary collaboration, and in an effort to bridge the science-humanities divide, the contributors to this collection come from multiple disciplinary backgrounds, including information studies, cognitive sciences, philosophy and biology. The ambiguous character of simulations, their cutting across scientific disciplines, analysis and prediction, understanding and doing, gave rise to their success in contemporary life sciences and has been the object of much scientific debate. One of the main aims of this topical collection, by contrast, is to call into question the assumption of an obvious use and easy transfer of methods between fields of knowledge as diverse as, e.g. physics and biology. The collection presents historical case studies from various biological sub-fields. The articles study how simulations are used and the ways they contribute specifically to our understanding of life. Taking up Sergio Sismondo’s description of simulations as “compromises” and “glue”, they also critically engage with the question of what exactly the life sciences have been gluing together over the last two decades. (shrink)

This paper is an interpretation and defense of Richard Levins’ “The Strategy of Model Building in Population Biology,” which has been extremely influential among biologists since its publication 40 years ago. In this article, Levins confronted some of the deepest philosophical issues surrounding modeling and theory construction. By way of interpretation, I discuss each of Levins’ major philosophical themes: the problem of complexity, the brute-force approach, the existence and consequence of tradeoffs, and robustness analysis. I argue that Levins’ article is (...) concerned, at its core, with justifying the use of multiple, idealized models in population biology. (shrink)

Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in for a physically (...) different kind of process. I discuss the advantages of this approach in the context of evolutionary biology. (shrink)

The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...) ‘make-believe’ theory of representation in art. I argue that this account allows us to understand theoretical modelling without positing any object of which scientists’ modelling assumptions are true. (shrink)

We discuss in this paper the scope of abduction in Economics. The literature on this type of inference shows that it can be interpreted in different ways, according to the role and nature of its outcome. We present a formal model that allows to capture these various meanings in different economic contexts.

Call a bit of scientific discourse a description of a missing system when (i) it has the surface appearance of an accurate description of an actual, concrete system (or kind of system) from the domain of inquiry, but (ii) there are no actual, concrete systems in the world around us fitting the description it contains, and (iii) that fact is recognised from the outset by competent practitioners of the scientific discipline in question. Scientific textbooks, classroom lectures, and journal articles abound (...) with such passages; and there is a widespread practice of talking and thinking as though there are systems which fit the descriptions they contain perfectly, despite the recognition that no actual, concrete systems do so—call this the face value practice. There are, furthermore, many instances in which philosophers engage in the face value practice whilst offering answers to epistemological and methodological questions about the sciences. Three questions, then: (1) How should we interpret descriptions of missing systems? (2) How should we make sense of the face value practice? (3) Is there a set of plausible answers to (1) and (2) which legitimates reliance on the face value practice in our philosophical work, and can support the weight of the accounts which are entangled with that practice? In this paper I address these questions by considering three answers to the first: that descriptions of missing systems are straightforward descriptions of abstract objects, that they are indirect descriptions of “property-containing” abstracta, and that they are (in a different way) indirect descriptions of mathematical structures. All three proposals are present in the literature, but I find them wanting. The result is to highlight the importance of developing a satisfactory understanding of descriptions of missing systems and the face value practice, to put pressure on philosophical accounts which rely on the practice, and to help us assess the viability of certain approaches to thinking about models, theory structure, and scientific representation. (shrink)

The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure–activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. (...) It does so by exploring the tension and problems raised by the possibility of causal explanation afforded by quantitative structure–activity relationship models. It argues that the specificity of predictive modelling promotes rethinking of the regulation of chemicals. (shrink)

Conceptual constructive models are a type of scientific model that can be used to construct or reshape the target phenomenon conceptually. Though it has received scant attention from the philosophers, it raises an intriguing issue of how a conceptual constructive model can construct the target phenomenon in a conceptual way. Proponents of the conception of conceptual constructive models are not being explicit about the application of the constructive force of a model in the target construction. It is far from clear (...) that how a conceptual constructive model exerts its constructive force on the constructed phenomenon of interest. Consequently, the function and the epistemic status of a conceptual constructive model are dubious at best. Making use of the conception of abstraction-as-aggregation, I argue that a conceptual constructive model can be used to construct the phenomenon of interest conceptually via a two-step process of abstraction: (1) abstracting away the lower-level details; and (2) aggregating the relevant information into a higher-level composite element. I contend that this process of abstraction, which is not playing the representational role as in a typical representational model, confers the constructive force on a model. (shrink)

The philosophy of measurement studies the conceptual, ontological, epistemic, and technological conditions that make measurement possible and reliable. A new wave of philosophical scholarship has emerged in the last decade that emphasizes the material and historical dimensions of measurement and the relationships between measurement and theoretical modeling. This essay surveys these developments and contrasts them with earlier work on the semantics of quantity terms and the representational character of measurement. The conclusions highlight four characteristics of the emerging research program in (...) philosophy of measurement: it is epistemological, coherentist, practice oriented, and model based. (shrink)

In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a (...) unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question. (shrink)

In this topical section, we highlight the next step of research on modeling aiming to contribute to the emerging literature that radically refrains from approaching modeling as a scientific endeavor. Modeling surpasses “doing science” because it is frequently incorporated into decision-making processes in politics and management, i.e., areas which are not solely epistemically oriented. We do not refer to the production of models in academia for abstract or imaginary applications in practical fields, but instead highlight the real entwinement of science (...) and policy and the real erosion of their boundaries. Models in decision making – due to their strong entwinement with policy and management – are utilized differently than models in science; they are employed for different purposes and with different constraints. We claim that “being a part of decision-making” implies that models are elements of a very particular situation, in which knowledge about the present and the future is limited but dependence of decisions on the future is distinct. Emphasis on the future indicates that decisions are made about actions that have severe and lasting consequences. In these specific situations, models enable not only the acquisition of knowledge (the primary goal of science) but also enable deciding upon actions that change the course of events. As a result, there are specific ways to construct effective models and justify their results. Although some studies have explored this topic, our understanding of how models contribute to decision making outside of science remains fragmentary. This topical section aims to fill this gap in research and formulate an agenda for additional and more systematic investigations in the field. (shrink)

In recent years, philosophers of science have begun to realize that the clear separation of the creation of models in academia and the application of models outside science is not possible. When these philosophers address hybrid contexts in which science is entwined with policy, business, and other realms of society, these often practically oriented realms no longer represent ‘the surroundings’ of science but rather are considered an essential part of it. I argue—and demonstrate empirically—that the judgement of a theory or (...) model conducted by scientists in such hybrid contexts may contain two parts: one is truth supportive and the other is utility oriented. In relation to the debate on science and values, the article seeks to reinforce the argument in modern philosophy of science that the boundaries between epistemic and non-epistemic values are blurred. The article stresses that non-epistemic values may be also understood as values that contribute to the instrumental success of a theory or model and—in this way—influence scientific practice in the hybrid contexts. (shrink)

Is there a unique way to de-idealize models? If not, how might the possible ways of reducing the distortion between models and reality differ from each other? Based on an empirical case study conducted in financial markets, this paper discusses how a popular valuation model (the Discounted Cash Flow model) idealizes reality and how the market participants de-idealize it in concrete market situations. In contrast to Cartwright's view that economic models are generally over-constrained, this paper suggests that valuation models are (...) under-constrained. This serves as the reason why the relaxation of simplifying assumptions and concretization do not work as methods of de-idealization. The paper finds that financial market participants de-idealize models using commentary that takes the form of judgment. As a conclusion, a hypothesis is formulated that proposes that the more underdetermined the model is the bigger role narrative and other pragmatic elements play in the process of model application. (shrink)

In “The Toolbox of Science” (1995) together with Towfic Shomar we advocated a form of instrumentalism about scientific theories. We separately developed this view further in a number of subsequent works. Steven French, James Ladyman, Otavio Bueno and Newton Da Costa (FLBD) have since written at least eight papers and a book criticising our work. Here we defend ourselves. First we explain what we mean in denying that models derive from theory – and why their failure to do so should (...) be lamented. Second we defend our use of the London model of superconductivity as an example. Third we point out both advantages and weaknesses of FLBD’s techniques in comparison to traditional Anglophone versions of the semantic conception. Fourth we show that FLBD’s version of the semantic conception has not been applied to our case study. We conclude by raising doubts about FLBD’s overall project. (shrink)

Scientific representation is a currently booming topic, both in analytical philosophy and in history and philosophy of science. The analytical inquiry attempts to come to terms with the relation between theory and world; while historians and philosophers of science aim to develop an account of the practice of model building in the sciences. This article provides a review of recent work within both traditions, and ultimately argues for a practice-based account of the means employed by scientists to effectively achieve representation (...) in the modelling sciences. (shrink)

This paper asks how, in science in general and in economics in particular, theoretical models aid the understanding of real-world phenomena. Using specific models in economics and biology as test cases, it considers three alternative answers: that models are tools for isolating the ‘capacities’ of causal factors in the real world; that modelling is ‘conceptual exploration’ which ultimately contributes to the development of genuinely explanatory theories; and that models are credible counterfactual worlds from which inductive inferences can be made. The (...) paper argues that the ‘credible worlds’ account captures significant aspects of scientific practice, even if many modellers see their work as conceptual exploration. (shrink)

Metaphors are found all throughout science: in published papers, working hypotheses, policy documents, lecture slides, grant proposals, and press releases. They serve different functions, but perhaps most striking is the way they enable understanding, of a theory, phenomenon, or idea. In this paper, we leverage recent advances on the nature of metaphor and the nature of understanding to explore how they accomplish this feat. We attempt to shift the focus away from the epistemic value of the content of metaphors, to (...) the epistemic value of the metaphor’s consequences. Many famous scientific metaphors are epistemically good, not primarily because of what they say about the world, but because of how they cause us to think. Specifically, metaphors increase understanding either by improving our sets of representations, or by making it easier for us to encode and process data about complex subjects by changing how we are disposed to conceptualize those subjects. This view hints towards new positions concerning testimonial understanding, factivity, abilities, discovery via metaphor, and the relation between metaphors and models. (shrink)

This paper argues that there is no single universal conception of scientific explanation that is consistently employed throughout the whole domain of Higgs physics—ranging from the successful experimental search for a standard model Higgs particle and the hitherto unsuccessful searches for any particles beyond the standard model, to phenomenological model builders in the Higgs sector and theoretical physicists interested in how the core principles of quantum field theory apply to spontaneous symmetry breaking and the Higgs mechanism. Yet the coexistence of (...) deductive-statistical, unificationist, model-based, and statistical-relevance explanations does not amount to a fragmentation of the discipline, but allows elementary particle physicists to simultaneously pursue a plurality of research strategies and keep the field together by joint convictions about the SM and shared explanatory ideals. These convictions include that the SM both represents a successful explanation of the available particle data and contains aspects in need of further explanation. Especially in the domain of BSM physics, explanatory ideals typically appear as stories motivating the different models and linking them to the whole of the discipline. (shrink)

The paper studies the topography of the model landscape of the physics in the Higgs sector both within the Standard Model of Elementary Particle Physics and beyond in the months before the discovery of a SM Higgs boson. At first glance, this landscape appears fragmented into a large number of different models and research communities. But it also clusters around certain guiding ideas, among them supersymmetry or dynamical symmetry breaking, in which representative and narrative features of the models are combined. (...) These models do not stand for themselves, waiting to be experimentally confirmed and elevated to the status of theory. Rather do they, quite in the sense advocated by Morgan and Morrison, enjoy a far-reaching autonomy. Typically models in the Higgs sector entertain three types of mediating relationships. First, they mediate between the SM and the data in those instances where the SM contains some uncertainty in the values of its basic parameters. Second, they mediate between BSM physics and the data by instantiating the core ideas behind these often speculative generalizations of the SM as stories—in Hartmann’s sense—that motivate or justify the respective model. Third, the fact that Higgs models within BSM physics reproduce the SM predictions in the low-energy limit functions as a consistency constraint that does not involve any additional autonomy. Due to the second type of mediating relationship, the representative features of Higgs models BSM are complex. (shrink)

Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent (...) modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place. (shrink)

This essay examines the issue of false assumptions in models via a case study of a prominent economic model of sustainable development, wherein the assumption of an infinite future plays a central role. Two proposals are found to be helpful for this case, one based on the concept of derivational robustness and the other on understanding. Both suggest that the assumption of an infinite future, while arguably legitimate in some applications of the model, is problematic with respect to what I (...) call “Parfitian” welfare functions. This result is relevant to debates about discounting the future in economics and environmental ethics. (shrink)

Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...) more parsimonious assumptions and often replace theoretical understanding and knowledge about mechanisms by careful experimental design. Thus, it is worthwhile to study in detail how nonparametric models serve as inferential engines in science. (shrink)

Empiricist modal epistemologies can be attractive, but are often limited in the range of modal knowledge they manage to secure. In this paper, I argue that one such account – similarity-based modal empiricism – can be extended to also cover justification of many scientifically interesting possibility claims. Drawing on recent work on modelling in the philosophy of science, I suggest that scientific modelling is usefully seen as the creation and investigation of relevantly similar epistemic counterparts of real target systems. On (...) the basis of experiential knowledge of what is actually the case with the models, one can draw justified conclusions about what is de re possible for the target systems. (shrink)

When did the concept of model begin to be used in mathematics? This question appears at first somewhat surprising since “model” is such a standard term now in the discourse on mathematics and “modelling” such a standard activity that it seems to be well established since long. The paper shows that the term— in the intended epistemological meaning—emerged rather recently and tries to reveal in which mathematical contexts it became established. The paper discusses various layers of argumentations and reflections in (...) order to unravel and reach the pertinent kernel of the issue. The specific points of this paper are the difference in the epistemological concept to the usually discussed notions of model and the difference between conceptions implied in mathematical practices and their becoming conscious in proper reflections of mathematicians. (shrink)

In this paper, I discuss the discovery of the DNA structure by Francis Crick and James Watson, which has provoked a large historical literature but has yet not found entry into philosophical debates. I want to redress this imbalance. In contrast to the available historical literature, a strong emphasis will be placed upon analysing the roles played by theory, model, and evidence and the relationship between them. In particular, I am going to discuss not only Crick and Watson's well-known model (...) and Franklin's x-ray diffraction pictures (the evidence) but also the less well known theory of helical diffraction, which was absolutely crucial to Crick and Watson's discovery. The insights into this groundbreaking historical episode will have consequences for the ‘new’ received view of scientific models and their function and relationship to theory and world. The received view, dominated by works by Cartwright and Morgan and Morrison ([1999]), rather than trying to put forth a ‘theory of models’, is interested in questions to do with (i) the function of models in scientific practice and (ii) the construction of models. In regard to (i), the received view locates the model (as an idealized, simplified version of the real system under investigation) between theory and the world and sees the model as allowing the application of the former to the latter. As to (ii) Cartwright has argued for a phenomenologically driven view and Morgan and Morrison ([1999]) for the ‘autonomy’ of models in the construction process: models are determined neither by theory nor by the world. The present case study of the discovery of the DNA structure strongly challenges both (i) and (ii). In contrast to claim (i) of the received view, it was not Crick and Watson's model but rather the helical diffraction theory which served a mediating purpose between the model and the x-ray diffraction pictures. In particular, Cartwright's take on (ii) is refuted by a comparison of Franklin's bottom-up approach with Crick and Watson's top-down approach in constructing the model. The former led to difficulties, which only a strong confidence in the structure incorporated in the model could circumvent. How to Get to the Structure 1.1 X-ray diffraction and its synthesis 1.2 Model building and Pauling's panache 1.3 The structure of proteins 1.3.1 A failed inference to the best explanation 1.3.2 The misleading 5.1 Å spot in proteins and how to get rid of it 1.3.3 Derived predictions from Pauling's alpha-helix of protein molecules The CCV Theory of Helical X-Ray Diffraction 2.1 The role of the CCV theory in the discovery of the DNA structure Killing the Helix 3.1 Appreciating all evidence—in vain Conclusion Epilogue: Chargaff's Ratios CiteULike Connotea Del.icio.us What's this? (shrink)

Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models as (...) those phases in which learning from models takes place. (shrink)

The paper studies the nature of understanding in condensed matter physics (CMP), mediated by the successful employment of its models. I first consider two obvious candidates for the criteria of model-based understanding, Van Fraassen's sense of empirical adequacy and Hacking's instrumental utility , and conclude that both are unsatisfactory. Inspired by Hasok Chang's recent proposal to reformulate realism as the pursuit of ontological plausibility in our system of knowledge, we may require the model under consideration to be understood (or intelligible) (...) before claiming model-based understanding. Here the understanding of a model typically consists of the following: figuring out at least one plausible (preferably realistic) physical mechanism for the model, determining the theoretical consequences of the model by mathematically probing it and developing our physical intuitions about the model. I consider the q-state Potts model to illustrate. After having understood a model, we may employ the model to understand its target phenomena in the world. This is done by matching one of the interpretative models of the model with the central features of the phenomena. The matching should be motivated (ideally both theoretically and empirically) in the sense that we have good reason to believe that the central features of the phenomena can be thought of as having more or less the same structure as postulated by the interpretative model. In conclusion, I propose a two-stage account of model-based understanding in CMP: (1) understanding of a model and (2) matching a target phenomenon with a well-motivated interpretative model of the model. Empirical success and instrumental utility both play their roles in the evaluation of how successful the model is, but are not the essential part of model-based understanding. (shrink)

Review essay of How to do science with models. A philosophical primer. Springer briefs in philosophy, Axel Gelfert., 129, Price € 49,99 softcover, ISBN: 978-3-319-27954-1.

Semantic realism can be characterised as the idea that scientific theories are truth-bearers, and that they are true or false in virtue of the world. This notion is often assumed, but rarely discussed in the literature. I examine how it fares in the context of the semantic view of theories and in connection with the literature on scientific representation. Making sense of semantic realism requires specifying the conditions of application of theoretical models, even for models that are not actually used, (...) which leads to several difficulties. My conclusion is that semantic realism is far more demanding than one would expect. Finally, I briefly examine some pragmatist alternatives. (shrink)