A characterization of the modelling process in science is proposed for science education, based on Mario Bunge’s ideas about the construction of models in science. Galileo’s Dialogues are analysed as a potentially fruitful starting point to implement strategies aimed at modelling in the classroom in the light of that proposal. It is argued that a modelling process for science education can be conceived as the evolution from phenomenological approaches towards more representational ones, emphasizing the role of abstraction and idealization in (...) model construction. The shift of reference of theories—from sensible objects to conceptual objects—and the black-box models construction process, which are both explicitly presented features in Galileo’s Dialogues, are indicated as highly relevant aspects for modelling in science education. (shrink)

In recent decades, philosophers of science have devoted considerable efforts to understand what models represent. One popular position is that models represent fictional situations. Another position states that, though models often involve fictional elements, they represent real objects or scenarios. Though these two positions may seem to be incompatible, I believe it is possible to reconcile them. Using a threefold distinction between different signs proposed by Peirce, I develop an argument based on a proposal recently made by Kralemann and Lattman (...) (in Synthese 190:3397–3420, 2013) that shows that the two aforementioned positions can be reconciled by distinguishing different ways in which a model representation can be used. In particular, on the basis of Peirce’s distinction between icons, indices and symbols, I argue that models can sometimes function as icons, sometimes as indexes and sometimes as symbols, depending on the context in which they are considered and the use that they are developed for because they all have iconic, indexical and symbolic features. In addition, I show that conceiving models as signs enables us to develop an account of scientific representation that meets the main desiderata that Shech (in Synthese 192:3463–3485, 2015) presents. (shrink)

We introduce a novel point of view on the “models as mediators” framework in order to emphasize certain important epistemological questions about models in science which have so far been little investigated. To illustrate how this perspective can help answer these kinds of questions, we explore the use of simplified models in high energy physics research beyond the Standard Model. We show in detail how the construction of simplified models is grounded in the need to mitigate pressing epistemic problems concerning (...) the uncertainty inherent in the present theoretical and experimental contexts. (shrink)

Empirical adequacy matters directly - as it does for antirealists - if we aim to get all or most of the observable facts right, or indirectly - as it does for realists - as a symptom that the claims we make about the theoretical facts are right. But why should getting the facts - either theoretical or empirical - right be required of an acceptable theory? Here we endorse two other jobs that good theories are expected to do: helping us (...) with a) understanding and b) managing the world. Both are of equal, often greater, importance than getting a swathe of facts right, and empirical adequacy fares badly in both. It is not needed for doing these jobs and in many cases it gets in the way of doing them efficiently. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...) models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

Understanding nature of science is widely considered an important educational objective and views of NOS are closely linked to science teaching and learning. Thus there is a lively discussion about what understanding NOS means and how it is reached. As a result of analyses in educational, philosophical, sociological and historical research, a worldwide consensus about the content of NOS teaching is said to be reached. This consensus content is listed as a general statement of science, which students are supposed to (...) understand during their education. Unfortunately, decades of research has demonstrated that teachers and students alike do not possess an appropriate understanding of NOS, at least as far as it is defined at the general level. One reason for such failure might be that formal statements about the NOS and scientific knowledge can really be understood after having been contextualized in the actual cases. Typically NOS is studied as contextualized in the reconstructed historical case stories. When the objective is to educate scientifically and technologically literate citizens, as well as scientists of the near future, studying NOS in the contexts of contemporary science is encouraged. Such contextualizations call for revision of the characterization of NOS and the goals of teaching about NOS. As a consequence, this article gives two examples for studying NOS in the contexts of scientific practices with practicing scientists: an interview study with nanomodellers considering NOS in the context of their actual practices and a course on nature of scientific modelling for science teachers employing the same interview method as a studying method. Such scrutinization opens rarely discussed areas and viewpoints to NOS as well as aspects that practising scientists consider as important. (shrink)

Niels Bohr’s model of the hydrogen atom is widely cited as an example of an inconsistent scientific theory because of its reliance on classical electrodynamics together with assumptions about interactions between matter and electromagnetic radiation that could not be reconciled with CED. This view of Bohr’s model is controversial, but we believe a recently proposed approach to reasoning with inconsistent commitments offers a promising formal reading of how Bohr’s model worked. In this paper we present this new way of reasoning (...) with inconsistent commitments and compare it with other approaches before applying it to Bohr’s model and offering some suggestions for how it might be extended to account for subsequent developments in old quantum theory. (shrink)

Philosophers have usually offered a number of ways of describing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and then not analysable. Those descriptions are often so far from Peircian pragmatic prescription and so abstract to result completely unknowable and obscure. The “computational turn” gives us a new way to understand creative processes in a strictly pragmatic sense. In fact, by exploiting artificial intelligence and cognitive science tools, computational philosophy allows (...) us to test concepts and ideas previously conceived only in abstract terms. It is in the perspective of these actual computational models that I find the central role of abduction in the explanation of creative reasoning in science. Creativity and discovery are no more seen as a mysterious irrational process, but, thanks to constructive accounts, as a complex relationship among different inferential steps that can be clearly analysed and identified. I maintain that the computational philosophy analysis of model-based and manipulative abduction and of external and epistemic mediators is important not only to delineate the actual practice of abduction, but also to further enhance the development of programs computationally adequate in rediscovering, or discovering for the first time, for example, scientific hypotheses or mathematical theorems. (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)

In this paper, a modest version of the Semantic View is motivated as both tenable and potentially fruitful for philosophy of science. An analysis is proposed in which the Semantic View is characterized by three main claims. For each of these claims, a distinction is made between stronger and more modest interpretations. It is argued that the criticisms recently leveled against the Semantic View hold only under the stronger interpretations of these claims. However, if one only commits to the modest (...) interpretation for all the claims, then the view obtained, the Modest Semantic View, is tenable and fruitful for the philosophy of science. (shrink)

This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All the (...) discussions point to the position that computational cognitive models can be true theories of cognition. Ó 2008 Elsevier B.V. All rights reserved. (shrink)

: The main contribution of this paper to current philosophical and sociological studies on modeling is to analyze modeling as an object-oriented interdisciplinary activity and thus to bring new insights into the wide, heterogeneous discourse on tools, forms and organization of interdisciplinary research. A detailed analysis of interdisciplinarity in the making of models is presented, focusing on long-standing interdisciplinary collaboration between specialists in infectious diseases, mathematicians and computer scientists. The analysis introduces a novel way of studying the elements of the (...) models as carriers of interdisciplinarity. These elements, being functionally interdependent building blocks, evolve during the modeling work and carry the disciplinary tensions in the process. This shows how the long and challenging process of defining and reformulating the object of research is crucial for understanding the dynamics of interdisciplinarity in the making. (shrink)

This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than (...) as potential theories. I argue that models perform their heuristic function by “constructing” phenomena from data in the sense that they allow the model users who interact with the medium of the models to recognise the phenomena as such. The constructed phenomena assist model users in identifying which conditional hypotheses that are focused on low-level regularities concerning entities such as chemical compounds are more “testworthy,” a concept that links the costs associated with hypothesis testing with the fertility of the hypothesis. (shrink)

What does it take to understand a phenomenon ideally, or to the highest conceivable extent? In this paper, we answer this question by arguing for five necessary conditions for ideal understanding: (i) representational accuracy, (ii) intelligibility, (iii) truth, (iv) reasonable endorsement, and (v) fitting. Even if one disagrees that there is some form of ideal understanding, these five conditions can be regarded as sufficient conditions for a particularly deep level of understanding. We then argue that grasping, novel predictions, and transparency (...) are not reasonable conditions for ideal understanding. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in the context of historical case studies (...) to evidence of current case studies. By taking de Regt’s contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt’s latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can’t be gained by studying historical case studies. (shrink)

Résumé Dans cet article, nous discuterons de l’intégration du numérique à la sémiotique et proposerons qu’une modélisation conceptuelle puisse offrir un pont de dialogue entre ces deux domaines classiquement cloisonnés. Plus précisément, nous avancerons l’hypothèse que tout projet de recherche qui en appellera à l’informatique soit une démarche scientifique que s’il construit une théorie qui contient, en plus des modèles classiques que sont les modèles formel, computationnel et physique, un modèle conceptuel. Ce lieu, où les chercheur-es conceptualisent les multiples dimensions (...) de leur objet de recherche, sera alors défini en tant que socle d’une relation solide entre la sémiotique et l’informatique. Nous verrons que les différentes définitions du modèle conceptuel convergent vers la thèse soutenant que la connaissance scientifique met en œuvre une instance de conceptualisation qui doit identifier les diverses dimensions du problème en plus de devoir s’exprimer de manière à ce qu’elle soit communicable entre les membres des communautés épistémiques. Nous verrons, enfin, qu’un projet de recherché sémiotique exige la construction d’un tel modèle afin de décrire, généraliser et sélectionner des problèmes et que ce même modèle, conceptuel, sera aussi nécessaire si le ou la sémioticien-ne utilise l’informatique dans le cadre de sa recherche.fr. (shrink)

This article examines Cohen’s “transcendental method”, Windelband’s “critical method”, the neo-Kantian distinctions between natural science and the humanities (i. e., human or cultural sciences), and Weber’s account of ideal-typical explanations. The Marburg and the Southwest Schools of neo-Kantianism have in common that their respective philosophies of science focused on method, but they substantially differ in their approaches. Cohen advanced the “transcendental method”, which was taken up and transformed by Natorp and Cassirer; later, it became influential in neo-Kantian approaches to 20th (...) century physics. Windelband distinguished between facts and values, linking the former to the “genetic” method of history and the latter to the “critical” method of philosophy; and between the “nomothetic” and “idiographic” methods of the empirical sciences, a distinction further elaborated by Rickert. The distinction does not give rise to a sharp discrimination but is rather what Weber would later call an ideal type. All these approaches contribute in different ways to understanding the structure of scientific knowledge, focusing on different aspects of the general path of the empirical sciences between rationalism and empiricism. (shrink)

Lakatos’s methodology of scientific research programmes centres around series of theories, with little regard to the role of models in theory construction. Modifying it to incorporate model-groups, clusters of developmental models that are intended to become new theories, provides a description of the model dynamics within the search for physics beyond the standard model. At the moment, there is no evidence for BSM physics, despite a concerted search effort especially focused around the standard model account of electroweak symmetry breaking. Using (...) the framework provided by Lakatosian research programmes, we can capture the way the periphery of a model-group changes as the available parameter space shrinks, while its central tenets remain untouched by unfavourable experimental findings. By way of motivation, I provide two case studies of model-groups that offer alternative mechanisms for electroweak symmetry breaking: supersymmetry and composite-Higgs models. Both of these model-groups are under pressure from the discovery of the Higgs boson, yet they have both been active research projects in the years after the Higgs discovery. However, a proper assessment of the progress of an ongoing research programme is impossible through a purely Lakatosian lens, so I propose replacing it with Laudan’s problem-solving account, which provides ongoing assessment, while offering normative guidance concerning the pursuit-worthiness of research programmes. My incorporation of model-groups into Lakatosian research programmes captures the developments of two attempts to expand our physical description of the world, and Laudan’s problem-solving rationality allows us to assess their pursuit-worthiness. (shrink)

In recent years, the use of historical cases in philosophy of science has become a proper topic of reflection. In this article I will contribute to this research by means of a discussion of one very famous example of case-based philosophy of science, namely the debate on the London & London model of superconductivity between Cartwright, Suárez and Shomar on the one hand, and French, Ladyman, Bueno and Da Costa on the other. This debate has been going on for years, (...) without any satisfactory resolution. I will argue here that this is because both sides impose on the historical case a particular philosophical conception of scientific representation that does not do justice to the historical facts. Both sides assume, more specifically, that the case concerns the discovery of a representational connection between a given experimental insight – the Meissner effect – and the diamagnetic meaning of London and London’s new equations of superconductivity. I will show, however, that at the time of the Londons’ publication, neither the experimental insight nor the meaning of the new equations was established: both were open for discussion and they were stabilized only later. On the basis of this historical discussion, I will then propose an alternative approach to the case study: the case should not be seen as a site of confrontation between pre-existing philosophical accounts, but rather as a way to historically elaborate and develop philosophical concepts. I will then show how approaching the historical episode in this way suggests an alternative approach to the philosophical study of representation, according to which it involves the establishment, over time, of a stable connection between constellations of different elements that, through discussion and engagement with alternative views and approaches, come to constitute phenomenon and meaning. (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)

According to the received view Feynman diagrams are a bookkeeping device in complex perturbative calculations. Thus, they do not provide a representation or model of the underlying physical process. This view is in apparent tension with scientific practice in high energy physics, which analyses its data in terms of “channels”. For example the Higgs discovery was based on the observation of the decay H → γγ – a process which can be easily represented by the corresponding Feynman diagrams. I take (...) issue with this tension and show that on closer analysis the story of the Higgs discovery should be told differently. (shrink)

Cet article associe trois approches issues de l’institutionnalisme historique : l’école historique allemande, l’institutionnalisme américain originel et la théorie de la régulation. Il en dégage les principes méthodologiques partagés et analyse leur application au processus d’élaboration théorique. Codes JEL : B15, B25, B40, B52, E02, Z13.

In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided (...) by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry. (shrink)

This article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such (...) that a unified account based on the widely accepted idea of modelling as indirect representation does not similarly apply to both. I defend a similar conclusion, but argue that the distinction between animal models and theoretical models does not always track a difference in the justification of model-to-target inferences. Case studies of the use of animal models in biomedicine are presented to illustrate this. However, Levy and Currie’s point can be argued for in a different way. I argue for the following distinction. Model organisms function as surrogate sources of evidence, from which results are transferred to their targets by empirical extrapolation. By contrast, theoretical modelling does not involve such an inductive step. Rather, theoretical models are used for drawing conclusions from what is already known or assumed about the target system. Codifying assumptions about the causal structure of the target in external representational media allows one to apply explicit inferential rules to reach conclusions that could not be reached with unaided cognition alone. (shrink)

Research methods and their developers play a crucial role in bringing together scientific fields. Scientists, who wish to have their methods acknowledged and used in another discipline have to bridge the gaps between different practices and worldviews, and they often experience hostility, incredulity, and in general feelings of ‘otherness’, while changing the established practice in a scientific field. Researchers representing newly emerging fields such as materials science have to overcome obstacles that are caused more by feelings of threat and fear (...) than by arguments rooted in epistemic or social considerations. In using the habitus concept of Pierre Bourdieu, I analyze in this article statements and observations of the scientists who in the 1950s and 1960s have introduced physical methods into chemistry and in so doing merged characteristic features of research communities into a new unit. The habitus of physicists, chemists and method-oriented researchers are shaped by hierarchical relationships inside the disciplines, and between them. Studying emotional expressions in interviews and self-descriptions gives access to hitherto neglected parts of the historical processes leading to new disciplines and fields. The brief case studies presented are seen as exemplars, and the analysis offered is intended more to provide a possible model for further research than as giving conclusive answers. (shrink)

There is nowadays consensus in the community of didactics of science regarding the need to include the philosophy of science in didactical research, science teacher education, curriculum design, and the practice of science education in all educational levels. Some authors have identified an ever-increasing use of the concept of ‘theoretical model’, stemming from the so-called semantic view of scientific theories. However, it can be recognised that, in didactics of science, there are over-simplified transpositions of the idea of model. In this (...) sense, contemporary philosophy of science is often blurred or distorted in the science education literature. In this paper, we address the discussion around some meta-theoretical concepts that are introduced into didactics of science due to their perceived educational value. We argue for the existence of a ‘semantic family’, and we characterise four different versions of semantic views existing within the family. In particular, we seek to contribute to establishing a model-based didactics of science mainly supported in this semantic family. (shrink)

This paper explores the case of using robots to simulate evolution, in particular the case of Hamilton’s Law. The uses of robots raises several questions that this paper seeks to address. The first concerns the role of the robots in biological research: do they simulate something or do they participate in something? The second question concerns the physicality of the robots: what difference does embodiment make to the role of the robot in these experiments. Thirdly, how do life, embodiment and (...) social behavior relate in contemporary biology and why is it possible for robots to illuminate this relation? These questions are provoked by a strange similarity that has not been noted before: between the problem of simulation in philosophy of science, and Deleuze’s reading of Plato on the relationship of ideas, copies and simulacra. (shrink)

The last decade and a half has seen an ardent development of self-organised criticality, a new approach to complex systems, which has become important in many domains of natural as well as social science, such as geology, biology, astronomy, and economics, to mention just a few. This has led many to adopt a generalist stance towards SOC, which is now repeatedly claimed to be a universal theory of complex behaviour. The aim of this paper is twofold. First, I provide a (...) brief and non-technical introduction to SOC. Second, I critically discuss the various bold claims that have been made in connection with it. Throughout, I will adopt a rather sober attitude and argue that some people have been too readily carried away by fancy contentions. My overall conclusion will be that none of these bold claims can be maintained. Nevertheless, stripped of exaggerated expectations and daring assertions, many SOC models are interesting vehicles for promising scientific research.Author Keywords: Self-organised criticality; Scaling law; Models; Formal analogy. (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)

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 essay, I attempt to assess Henk de Regt and Dennis Dieks recent pragmatic and contextual account of scientific understanding on the basis of an important historical case-study: understanding in Newton’s theory of universal gravitation and Huygens’ reception of universal gravitation. It will be shown that de Regt and Dieks’ Criterion for the Intelligibility of a Theory (CIT), which stipulates that the appropriate combination of scientists’ skills and intelligibility-enhancing theoretical virtues is a condition for scientific understanding, is too strong. (...) On the basis of this case-study, it will be shown that scientists can understand each others’ positions qualitatively and quantitatively, despite their endorsement of different worldviews and despite their convictions as what counts as a proper explanation. (shrink)

Economists reason causally. Like many other scientists, they aim at formulating justified causal claims about their object of study. This thesis contributes to our understanding of how causal reasoning proceeds in economics. By using the research on the causes of unemployment as a case study, three questions are adressed. What are the meanings of causal claims? How can a causal claim be adequately supported by evidence? How are causal beliefs affected by incoming facts? In the process of answering these semantic, (...) epistemic and dynamical questions, the richness of causal reasoning in economics is brought to the foreground. In the part on semantics, it is argued that the meaning of causal generalizations in policy-oriented economics is best captured by using the concept of ‘inferential relation’ rather than the one of ‘referential relation’. This part contributes to develop an inferentialist semantics by distinguishing among types of inferential relations. In the part on epistemology, it is argued that justification in sciences like economics often relies, and ought to rely, on evidential variety―i.e. the combination of evidence from multiple sources. An interpretation of independence among evidential sources is offered: two evidential sources are fully independent if and only if the reasons why they would be unreliable do not overlap. It is then shown that the variety-of-evidence thesis does not hold under extreme circumstances. The part on dynamics focuses on deviant-case study. A storyline is provided for what deviant-case study aims at and how it proceeds in what is characterized as an ‘eclectic science’. (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)

A question at the intersection of scientific modeling and public choice is how to deal with uncertainty about model predictions. This "high-level" uncertainty is necessarily value-laden, and thus must be treated as irreducibly subjective. Nevertheless, formal methods of uncertainty analysis should still be employed for the purpose of clarifying policy debates. I argue that such debates are best informed by models which integrate objective features with subjective ones. This integrated subjectivism is illustrated with a case study from the literature on (...) monetary policy. The paper concludes with some morals for the use of models in determining climate policy. (shrink)

Quite rightly, philosophers of physics examine the theories of physics, theories like Quantum Mechanics, Quantum Field Theory, the Special and General Theories of Relativity, and Statistical Mechanics. Far fewer, however, examine how these theories are put to use; that is to say, little attention is paid to the practices of theoretical physicists. In the early 1950s David Bohm and David Pines published a sequence of four papers, collectively entitled, ‘A Collective Description of Electron Interaction.’ This essay uses that quartet as (...) a case study in theoretical practice. In Part One of the essay, each of the Bohm-Pines papers is summarized, and within each summary an overview is given, framing a more detailed account. In Part Two theoretical practice is broken into six elements: (a) the use of models, (b) the use of theory, (c) modes of description and narrative, (d) the use of approximations, (e) experiment and theory, (f) the varied steps employed in a deduction. The last element is the largest, drawing as it does from the earlier ones. Part Three enlarges on the concept of ‘theoretical practice,’ and briefly outlines the subsequent theoretical advances which rendered the practices of Bohm and Pines obsolete, if still respected. (shrink)

ArgumentThis paper is the first part of a two-part examination of computer modeling practice and philosophy. It discusses electrical engineer Jay Forrester's work on Industrial Dynamics, later called System Dynamics. Forrester developed Industrial Dynamics after being recruited to the newly-established School of Industrial Management at the Massachusetts Institute of Technology (MIT), which had been seeking a novel pedagogical program for management for five years before Forrester's arrival. We argue that Industrial Dynamics should be regarded in light of this institutional context. (...) Unlike economics, as well as operations research and management science (twin fields which were also gaining traction at that time), Industrial Dynamics was not meant to be applied by mathematicians and technical specialists in consultation with managers, but by managers themselves. This concern shifted the emphasis in modeling from the specialist act of analysis of a crucial problem to the managerial act of choosing which problems were crucial and developing effective policies around them. (shrink)

Scientific models occupy centre stage in scientific practice. Correspondingly, in recent literature in the philosophy of science, scientific models have been a focus of research. However, little attention has been paid so far to the ontology of scientific models. In this essay, I attempt to clarify the issues involved in formulating an informatively rich ontology of scientific models. Although no full-blown theory—containing all ontological issues involved—is provided, I make several distinctions and point to several characteristic properties exhibited by scientific models (...) that are relevant for individuating scientific models. (shrink)