In this paper I study how divergent mathematical treatments affect mathematical modelling, with a special focus on utility theory. In particular I examine recent work on the ranking of information states and the discounting of future utilities, in order to show how, by replacing the standard analytical treatment of the models involved with one based on the framework of Nonstandard Analysis, diametrically opposite results are obtained. In both cases, the choice between the standard and nonstandard treatment amounts to a selection (...) of set-theoretical parameters that cannot be made on purely empirical grounds. The analysis of this phenomenon gives rise to a simple logical account of the relativity of impossibility theorems in economic theory, which concludes the paper. (shrink)

Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...) on the nature of models and modeling, the unique issues surrounding this kind of interdisciplinary model building have yet to be independently investigated. In this paper, we utilize evolutionary economics as a case study in the investigation of more general issues concerning interdisciplinary modeling. We begin by critiquing the distinctions currently used within the evolutionary economics literature and propose an alternative carving of the conceptual terrain. We then argue that the three types of evolutionary economics we distinguish capture distinctions that will be important whenever resources of model-based theorizing are borrowed across distinct scientific domains. Our analysis of these model-building strategies identifies several of the unique methodological and philosophical issues that confront interdisciplinary modeling. (shrink)

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...) claim. In particular, we will distinguish between autonomous and embedded toy models, and then argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. _1_ Introduction _2_ Embedded and Autonomous Toy Models _2.1_ Embedded toy models _2.2_ Autonomous toy models _2.3_ Qualification _3_ A Theory of Understanding for Toy Models _3.1_ Preliminaries and requirements _3.2_ The refined simple view _4_ Two Kinds of Understanding with Toy Models _4.1_ Embedded toy models and how-actually understanding _4.2_ Against a how-actually interpretation of all autonomous toy models _4.3_ The how-possibly interpretation of some autonomous toy models _5_ Conclusion. (shrink)

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...) precisely articulate and to defend this claim. In particular, we will distinguish between autonomous and embedded toy models, and, then, argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. (shrink)

This paper aims (a) to provide characterizations of realism and instrumentalism that are philosophically interesting and applicable to economics; and (b) to defend instrumentalism against realism as a methodological stance in economics. Starting point is the observation that ‘all models are false’, which, or so I argue, is difficult to square with the realist's aim of truth, even if the latter is understood as ‘partial’ or ‘approximate’. The three cheers in favour of instrumentalism are: (1) Once we have usefulness, truth (...) is redundant. (2) There is something disturbing about causal structure. (3) It's better to do what one can than to chase rainbows. (shrink)

In a previous article, I argued that some recent philosophical work on the use of fictions in science is, while illuminating about some aspects of current scientific practice, unduly limited to cases of well-established fictions. In other words, this earlier work contended, a philosophical account of scientific fictions can do more than merely describe scientific practices, but can aid in the resolution of disputes about the proper interpretation of scientific theories and the epistemic status of some scientific models. In the (...) present paper, I expand upon the variational account of fictions and idealizations... (shrink)

A natural way to think of models is as abstract entities. If theories employ models to represent the world, theories traffic in abstract entities much more widely than is often assumed. This kind of thought seems to create a problem for a scientific realist approach to theories. Scientific realists claim theories should be understood literally. Do they then imply the reality of abstract entities? Or are theories simply—and incurably—false? Or has the very idea of literal understanding to be abandoned? Is (...) then fictionalism towards scientific theories inevitable? This paper argues that scientific realism can happily co-exist with models qua abstracta. (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)

The process of abstraction and concretisation is a label used for an explicative theory of scientific model-construction. In scientific theorising this process enters at various levels. We could identify two principal levels of abstraction that are useful to our understanding of theory-application. The first level is that of selecting a small number of variables and parameters abstracted from the universe of discourse and used to characterise the general laws of a theory. In classical mechanics, for example, we select position and (...) momentum and establish a relation amongst the two variables, which we call Newton’s 2nd law. The specification of the unspecified elements of scientific laws, e.g. the force function in Newton’s 2nd law, is what would establish the link between the assertions of the theory and physical systems. In order to unravel how and with what conceptual resources scientific models are constructed, how they function and how they relate to theory, we need a view of theory-application that can accommodate our constructions of representation models. For this we need to expand our understanding of the process of abstraction to also explicate the process of specifying force functions etc. This is the second principal level at which abstraction enters in our theorising and in which I focus. In this paper, I attempt to elaborate a general analysis of the process of abstraction and concretisation involved in scientific- model construction, and argue why it provides an explication of the construction of models of the nuclear structure. (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)

The discussion of the adaptive landscape in the philosophical literature appears to be divided along the following lines. On the one hand, some claim that the adaptive landscape is either “uninterpretable” or incoherent. On the other hand, some argue that the adaptive landscape has been an important heuristic, or tool in the service of explaining, as well as proposing and testing hypotheses about evolutionary change. This paper attempts to reconcile these two views.

This paper uses a number of examples of diverse types and functions of models in evolutionary biology to argue that the demarcation between theory and practice, or "theory model" and "data model," is often difficult to make. It is shown how both mathematical and laboratory models function as plausibility arguments, existence proofs, and refutations in the investigation of questions about the pattern and process of evolutionary history. I consider the consequences of this for the semantic approach to theories and theory (...) confirmation. The paper attempts to reconcile the insights of both critics and advocates of the semantic approach to theories. (shrink)

Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.

Participation of the Public in Science: Towards a New Kind of Scientific Practice Participation of the public in science has been the object of an increasing number of social and political philosophical studies, but there is still hardly any epistemological study of the topic. While it has been objected that involvement of the public is a threat to the integrity of science, the apparent indifference of philosophers of science seems to testify to its lack of relevance to conceptions of scientific (...) activity. I argue both that it is not a threat to science and that it is relevant to philosophy of science by showing that it constitutes a new kind of epistemic practice. Two main objections to the idea that the involvement of non-scientists, with their situated perspective and contextual values, can form an epistemic practice will be addressed: the first bears on the epistemic potentialities of the cooperation between scientist and non-scientists; the second on the possibility that this cooperation takes the form of a practice. (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)

We examine a case in which non-computable behavior in a model is revealed by computer simulation. This is possible due to differing notions of computability for sets in a continuous space. The argument originally given for the validity of the simulation involves a simpler simulation of the simulation, still further simulations thereof, and a universality conjecture. There are difficulties with that argument, but there are other, heuristic arguments supporting the qualitative results. It is urged, using this example, that absolute validation, (...) while highly desirable, is overvalued. Simulations also provide valuable insights that we cannot yet (if ever) prove. (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)

Combining active externalism in the form of the extended and distributed cognition hypotheses with virtue reliabilism can provide the long sought after link between mainstream epistemology and philosophy of science. Specifically, by reading virtue reliabilism along the lines suggested by the hypothesis of extended cognition, we can account for scientific knowledge produced on the basis of both hardware and software scientific artifacts. Additionally, by bringing the distributed cognition hypothesis within the picture, we can introduce the notion of epistemic group agents, (...) in order to further account for collective knowledge produced on the basis of scientific research teams. (shrink)

Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, I argue that model (...) building is successful even when models are predictively inaccurate for at least three reasons: models allow scientists to explore the possible behaviors of ecological systems; models give scientists simplified means by which they can investigate more complex systems by determining how the more complex system deviates from the simpler model; and models give scientists conceptual frameworks through which they can conduct experiments and fieldwork. Critics often mistake the purposes of model building, and once we recognize this, we can see their complaints are unjustified. Even though models in ecology are not always accurate in their assumptions and predictions, they still contribute to successful science. (shrink)

Much philosophical attention has been devoted to whether economic models explain, and more generally to how scientific models represent. Yet there is an issue more practically important to economics than either of these, which I label the efficiency question: regardless of how exactly models represent, or of whether their role is explanatory or something else, is current modeling practice an efficient way to achieve these goals – or should research efforts be redirected? In addition to showing how the efficiency question (...) has been relatively neglected, I give two examples of the kind of analysis it requires. (shrink)

Abstract.I chart the considerable changes in the status and conception of the Lenz-Ising model from 1920 to 1950 in terms of three phases: In the early 1920s, Lenz and Ising introduced the model in the field of ferromagnetism. Based on an exact derivation, Ising concluded that it is incapable of displaying ferromagnetic behavior, a result he erroneously extended to three dimensions. In the next phase, Lenz and Ising’s contemporaries rejected the model as a representation of ferromagnetic materials because of its (...) conflict with the new quantum mechanics. In the third phase, from the early 1930s to the early 1940s, the model was revived as a model of cooperative phenomena. I provide more detail on this history than the earlier accounts of Brush (1967) and Hoddeson, Schubert, Heims, and Baym (1992) and question some of their conclusions. Moreover, my account differs from these in its focus on the development of the model in its capacity as a model. It examines three aspects of this development: (1) the attitudes on the degree of physical realism of the Lenz-Ising model in its representation of physical phenomena; (2) the various reasons for studying and using it; and (3) the effect of the change in its theoretical basis during the transition from the old to the new quantum mechanics. A major theme of my study is that even though the Lenz-Ising model is not fully realistic, it is more useful than more realistic models because of its mathematical tractability. I argue that this point of view, important for the modern conception of the model, is novel and that its emergence, while perhaps not a consequence of its study, is coincident with the third phase of its development. (shrink)

In this article I develop a model of theoretical understanding in science. This is a philosophical theory that specifies the conditions that are both necessary and sufficient for a scientist to satisfy the construction ‘S understands theory T ’. I first consider how this construction is preferable to others, then build a model of the requisite conditions on the basis of examples from elementary physics. I then show how this model of theoretical understanding can be made philosophically robust and provide (...) a more sophisticated account than we see from models of a similar kind developed by those working in the psychology of physics and artificial intelligence. 1 Introduction2 The Explicandum/Analysandum3 Analysis of ‘S understands T’ 4 The Inferential Model4.1 Which problems are we talking about?4.2 Does the solution have to be true?4.3 Does each cycle have to be correct for S to understand T?4.4 What is problem-solving reliability?4.5 Does every specific inference of a cycle have to be correct?4.6 Does each inference have to use a principle that is part of the theory?5 Theoretical Understanding and Conceptual Expertise: Empirical Considerations6 Conclusion: Inferential Model of Scientific Understanding and Our Traditional Problems. (shrink)

In this paper, I show how idealizations contribute to social activities in science, such as the recruitment of experts to a research project. These contributions have not been explicitly discussed by recent philosophical accounts of scientific idealization. These accounts have focused on how idealizations influence activities like scientific theorization, explanation, and modeling. Other accounts focus on how idealizations influence policy-making and science communication. I expand these accounts by exploring the uses of idealized phylogenetic trees in science. Trees are not only (...) useful for improving our understanding and public communication of evolutionary history, but they also help with the organization of laboratories and collaboration among scientists. Attending to the relation between idealizations and these social practices in science matters. It can help us understand why idealized models become entrenched and why certain social practices change over time. (shrink)

This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These "model systems" are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary cognitive science that construes (...) cognition as occurring in a complex distributed system comprising people and artifacts, I argue that reasoning with model systems is a constraint satisfaction process involving co-construction, manipulation, and revision of mental and physical models. (shrink)

Designing, building, and experimenting with physical simulation models are central problem‐solving practices in the engineering sciences. Model‐based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of “distributed cognition” to interpret data collected in ethnographic and cognitive‐historical studies (...) of two biomedical engineering research laboratories, and articulates the notion of distributed model‐based cognition to answer the question posed in the title. (shrink)

This chapter contains sections titled: Highlights of Past Literature Current Work Future Work.

In this paper, I utilise the growing literature on scientific modelling to investigate the nature of formal semantics from the perspective of the philosophy of science. Specifically, I incorporate the inferential framework proposed by Bueno and Colyvan : 345–374, 2011) in the philosophy of applied mathematics to offer an account of how formal semantics explains and models its data. This view produces a picture of formal semantic models as involving an embedded process of inference and representation applying indirectly to linguistic (...) phenomena. The final aim of the paper is directed at proposing a novel account of the syntax–semantics interface while shedding light on empty categories, semantically null forms, underspecified content and compositionality as a whole. (shrink)

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of the (...) generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling. (shrink)

Engineers must deal with risks and uncertainties as a part of their professional work and, in particular, uncertainties are inherent to engineering models. Models play a central role in engineering. Models often represent an abstract and idealized version of the mathematical properties of a target. Using models, engineers can investigate and acquire understanding of how an object or phenomenon will perform under specified conditions. This paper defines the different stages of the modeling process in engineering, classifies the various sources of (...) uncertainty that arise in each stage, and discusses the categories into which these uncertainties fall. The paper then considers the way uncertainty and modeling are approached in science and the criteria for evaluating scientific hypotheses, in order to highlight the very different criteria appropriate for the development of models and the treatment of the inherent uncertainties in engineering. Finally, the paper puts forward nine guidelines for the treatment of uncertainty in engineering modeling. (shrink)

We inquire into the question whether the Aristotelean or classical \emph{ideal} of science has been realised by the Model Revolution, initiated at Stanford University during the 1950ies and spread all around the world of philosophy of science --- \emph{salute} P.\ Suppes. The guiding principle of the Model Revolution is: \emph{a scientific theory is a set of structures in the domain of discourse of axiomatic set-theory}, characterised by a set-theoretical predicate. We expound some critical reflections on the Model Revolution; the conclusions (...) will be that the philosophical problem of what a \emph{scientific theory} is has \emph{not} been solved yet --- \emph{pace} P.\ Suppes. While reflecting critically on the Model Revolution, we also explore a proposal of how to complete the Revolution and briefly address the intertwined subject of \emph{scientific representation}, which has come to occupy center stage in philosophy of science over the past decade. (shrink)

Modelling became one of the primary tools of mathematical economic research in the twentieth century, but when we look at examples of how nonanalogical models were first built in economics, both the process of making representations and aspects of the representing relation remain opaque. Like early astronomers, economists have to imagine how the hidden parts of their world are arranged and to make images, that is, create models, to represent how they work. The case of the Edgeworth Box, a model (...) widely used for theoretical work in twentieth-century economics, provides a good example to explore the process of making mathematical representations of the economy. It shows how, in making these new representations, conceptual elements were developed which could not have been represented in the older verbal forms of economics. (shrink)

Prandtl's work on the boundary layer theory is an interesting example for illustrating several important issues in philosophy of science such as the relation between theories and models and whether it is possible to distinguish, in a principled way, between pure and applied science. In what follows I discuss several proposals by the symposium participants regarding the interpretation of Prandtl's work and whether it should be characterized as an instance of applied science. My own interpretation of this example (1999) emphasised (...) the degree of autonomy embedded in Prandtl's boundary layer model and the way it became integrated in the larger theoretical context of hydrodynamics. In addition to extending that discussion here I also claim that the characterization of applied science which formed the basis for the symposium does not enable us to successfully distinguish applied science from the general practice of 'applying' basic scientific knowledge in a variety of contexts. (shrink)

It has long been held that the structure of a protein is determined solely by the interactions of the atoms in the sequence of amino acids of which it is composed, and thus the stable, biologically functional conformation should be predictable by ab initio or de novo methods. However, except for small proteins, ab initio predictions have not been successful. We explain why this is the case and argue that the relationship among the different methods, models, and representations of protein (...) structure is one of integrative pluralism. Our defence appeals to specific features of the complexity of the functional protein structure and to the partial character of representation in general. We present examples of integrative strategies in protein science. 1. Introduction2. Partiality of Representation3. Protein Functional Complexity4. Modelling Protein Structure4.1 Integrating ab initio and experimental models4.2 Integrating multiple experimental models5. Conclusion. (shrink)

Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among (...) the members of the consensus community. I address the question of under what conditions it is likely that a consensus is in fact knowledge based. I argue that a consensus is likely to be knowledge based when knowledge is the best explanation of the consensus, and I identify three conditions—social calibration, apparent consilience of evidence, and social diversity, for knowledge being the best explanation of a consensus. (shrink)

Meynell’s contention is that feminists should attend to pictures in science as distinctive bearers of epistemic content that cannot be reduced to propositions. Remarks on the practice and function of medical illustration—specifically, images Nancy Tuana used in her discussion of the construction of ignorance of women’s sexual function (2004)—show pictures to be complex and powerful epistemic devices. Their affinity with perennial feminist concerns, the relation between epistemic subject and object, and the nature of social knowledge, are of particular interest.

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)

We suggest that human culture exhibits key Darwinian evolutionary properties, and argue that the structure of a science of cultural evolution should share fundamental features with the structure of the science of biological evolution. This latter claim is tested by outlining the methods and approaches employed by the principal subdisciplines of evolutionary biology and assessing whether there is an existing or potential corresponding approach to the study of cultural evolution. Existing approaches within anthropology and archaeology demonstrate a good match with (...) the macroevolutionary methods of systematics, paleobiology, and biogeography, whereas mathematical models derived from population genetics have been successfully developed to study cultural microevolution. Much potential exists for experimental simulations and field studies of cultural microevolution, where there are opportunities to borrow further methods and hypotheses from biology. Potential also exists for the cultural equivalent of molecular genetics in “social cognitive neuroscience,” although many fundamental issues have yet to be resolved. It is argued that studying culture within a unifying evolutionary framework has the potential to integrate a number of separate disciplines within the social sciences. (shrink)

Though the concept of ‘stylized fact’ plays an important role in the economic literature, there is little analysis of the definition and evaluative use of the term. A permissive account of stylized facts is developed which focuses on their mediating role between models and empirical evidence. The mediation relationship restricts stylized facts by requiring concrete empirical targets. On the other hand, there is much legitimate diversity within the permissive account; key dimensions of diversity are argued to be the part of (...) the model targeted (explanans vs. explanandum) and degree of partiality of correspondence. The use of stylized fact in model evaluation points to the downside of stylization: decreased ability to support strong claims about particular targets. In the final analysis, the role of stylized facts in model-based explanation depends on the view taken of the relative importance of generality and empirical adequacy. (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)

: 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)

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)

In the seventeenth century, Newton published his famous experimentum crucis, in which he claimed that light is heterogeneous and is composed of rays with different refrangibilities. Experiments, especially the crucial experiment, were important for justifying Newton’s theory of light, and eventually his theory of color. A century later, Goethe conducted a series of experiments on the nature of color, especially in contradistinction to Newton, and he defended his research with a methodological principle formulated in “Der Versuch als Vermittler.” Goethe’s principle (...) included two elements: a series of experiments and resultant higher empirical evidence, which functioned as mediator between the objective and the subjective . Although the notion of experimentum crucis became popular among scientists for reconstructing experimental research and for justifying theories, especially for rhetorical purposes, Newton’s justification of his theory of light and color is best reconstructed in terms of Goethe’s methodological principle. Finally, Goethe’s principle has important consequences for the contemporary philosophical underdetermination thesis. (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)

Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...) comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)

Is knowledge in the natural sciences discovered or constructed? For objectivists, scientific knowledge is discovered through investigations into a mind-independent, natural world. For constructivists, such knowledge is produced through negotiations among members of a professional guild. I examine the clash between the two positions and propose that scientific knowledge is the concurrent outcome from investigations into a natural world and from consensus reached through negotiations of a professional guild. Specifically, I introduce the general methodological notion, instituting science, which incorporates both (...) the discovery and the construction processes in the generation of scientific knowledge. To that end, I use a case study from the biomedical sciences to illustrate the notion. I conclude with a discussion of how this methodological notion helps to address the debate between objectivists and constructivists over the generation of scientific knowledge, and of how it compares with others who have also attempted to address the debate. (shrink)

What I call theoretical abduction (sentential and model-based)certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides (...) otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notions of ``perceptual and inceptual rehearsal'' and of ``external representation''. (shrink)

More than a hundred years ago, the American philosopher C. S. Peirce suggested the idea of pragmatism as a logical criterion to analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, philosophers have usually offered a number of ways of construing hypotheses generation, but all aim at demonstrating that the activity of generating hypotheses is paradoxical, illusory or obscure, and thus (...) not analyzable. The “computational turn” gave us a new way to understand creative processes in a strictly pragmatic sense. Artificial Intelligence and Cognitive Science tools allow us to test concepts and ideas previously conceived in abstract terms. It is in the perspective of these actual models that we find the central role of abduction in the explanation of creative reasoning in science. What I call theoretical abduction certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper. (shrink)