Scientific Models, Misc

Bohr’s atomic model is one of the better known examples of empirically successful, albeit inconsistent, theoretical schemes in the history of physics. For this reason, many philosophers use this model to illustrate their position for the occurrence and the function of inconsistency in science. In this paper, I proceed to a critical comparison of the structure and the aims of Bohr’s research program – the starting point of which was the formulation of his model – with some of its contemporary (...) philosophical readings. My study comes to conclude that the attempt of certain philosophers to accommodate Bohr’s model to a form of paraconsistent logic obliterates essential aspects of scientists' actual practice and reasoning. (shrink)

Review of True Enough, by Catherine Elgin. Reconstructs three pillars of Elgin's view (focused on truth enough, understanding, and holism); summarizes the book's main arguments against veritism and factivism; presents a general recipe for responding to those arguments; raises several objections to the view.

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, (...) data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

The notion of understanding occupies an increasingly prominent place in contemporary epistemology, philosophy of science, and moral theory. A central and ongoing debate about the nature of understanding is how it relates to the truth. In a series of influential contributions, Catherine Elgin has used a variety of familiar motivations for antirealism in philosophy of science to defend a non- factive theory of understanding. Key to her position are: (i) the fact that false theories can contribute to the upwards trajectory (...) of scientific understanding, and (ii) the essential role of inaccurate idealisations in scientific research. Using Elgin’s arguments as a foil, I show that a strictly factive theory of understanding has resources with which to offer a unified response to both the problem of idealisations and the role of false theories in the upwards trajectory of scientific understanding. Hence, strictly factive theories of understanding are viable notwithstanding these forceful criticisms. (shrink)

This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach (...) focuses on epistemic affordances of different kinds of external representational and other tools employed in model construction. In doing so, the artifactual account gives a unified treatment of different model types as it circumvents the tendency of the fictional and other representational approaches to separate model systems from their “model descriptions”. (shrink)

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackl...

Carrie Figdor argues for literalism, a semantic claim about psychological predicates, on the basis of a scientific claim about the nature of psychological properties. I argue that her scientific claim is based on controversial interpretations of scientific modelling, and that even if it were correct it would not justify her claims that psychological predicates are undergoing radical conceptual change.

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...) evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. (shrink)

From the beginning of time, humans believed they were the center of the universe. Such important beings could be nowhere else than at the very epicenter of existence, with all the other things revolving around them. Was this an arrogant position? Only time will tell. What is certain is that as some people were so certain of their significance, aeons later some other people became too confident in their unimportance. In such a context, the Earth quickly lost its privileged position (...) at the center of the universe and along with this, the ideas of absolute motion and time became unbearable for the modern intellect, which saw nothing but relativeness in everything. After years of accepting the ideas of relativity at face value without doubting them, scientists are now mature enough to start questioning everything as any true scientist would do, including their own basic assumptions. And one would be surprised to see that the basic assumptions of today’s science in physics (and cosmology alike) are based on philosophically dogmatic beliefs that humans are nothing more than insignificant specks of dust. These specks cannot be in any privileged position in the cosmos, nor can their frames of reference. These specks cannot be living on a planet that is not moving while everything else is. There can be no hint of our importance whatsoever. Hence, the Copernican principle that has poisoned scientific thinking for aeons now. When one analyzes the evidence provided by science to support the idea of relativity though, he would see that the same evidence can more easily and simply fit into a model where the Earth stands still. Yet, scientists preferred to revamp all physics by introducing the totally unintuitive ides of relativity – including the absolute limit of the speed of light – than even admitting the possibility of humans having any notion of central position in the cosmos. True scientists though should examine all possible explanations, including those that do not fit their beliefs. To the dismay of so many modern scientists who blindly believe the validity of the theory of relativity at face value, the movement towards a true and honest post-modern science where all assumptions are questioned, necessarily passes through a place where the Earth we live in stands still. Non-relativistic explanations of the Michelson Morley experiment, related to a motionless Earth or to ether, are viable alternatives that deserve their place in modern scientific thought. (shrink)

This paper studies the “human circulatory statues” which Salomon Reisel designed in the 1670s in order to demonstrate the circulation of the blood and its effect on the brain. It investigates how Reisel intended this project to promote Descartes’ philosophy, and how it relates to contemporary diagrammatic schematizations of the blood circulation system. It further explores Reisel’s claims concerning the epistemological and practical advantages of working with a three-dimensional model and argues that Reisel intended his statua to address the concerns (...) of his fellow physicians and, more specifically, to help in diagnostics. I consider the background, strategy and legacy of the essays in which Reisel presented his devices, as well as their relevance to the general project of the scien­tific journal – one of the earliest – in which they appeared, the Miscellanea Curiosa. Reisel was a leading physician who acted throughout his life as a mediator between the Royal Society and the Academia Naturæ Curiosorum. His articles, the paper argues, have much to tell us much about the role played by the recently established scientific academies and their journals in shaping the transmission of early modern science and medicine, in terms both of theories and of the knowledge embodied in scientific instruments. (shrink)

Quantum mechanics was reformulated as an information theory involving a generalized kind of information, namely quantum information, in the end of the last century. Quantum mechanics is the most fundamental physical theory referring to all claiming to be physical. Any physical entity turns out to be quantum information in the final analysis. A quantum bit is the unit of quantum information, and it is a generalization of the unit of classical information, a bit, as well as the quantum information itself (...) is a generalization of classical information. Classical information refers to finite series or sets while quantum information, to infinite ones. Quantum information as well as classical information is a dimensionless quantity. Quantum information can be considered as a “bridge” between the mathematical and physical. The standard and common scientific epistemology grants the gap between the mathematical models and physical reality. The conception of truth as adequacy is what is able to transfer “over” that gap. One should explain how quantum information being a continuous transition between the physical and mathematical may refer to truth as adequacy and thus to the usual scientific epistemology and methodology. If it is the overall substance of anything claiming to be physical, one can question how different and dimensional physical quantities appear. Quantum information can be discussed as the counterpart of action. Quantum information is what is conserved, action is what is changed in virtue of the fundamental theorems of Emmy Noether (1918). The gap between mathematical models and physical reality, needing truth as adequacy to be overcome, is substituted by the openness of choice. That openness in turn can be interpreted as the openness of the present as a different concept of truth recollecting Heidegger’s one as “unconcealment” (ἀλήθεια). Quantum information as what is conserved can be thought as the conservation of that openness. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...) heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

The Soviet Union had a long and complex relationship with cybernetics, especially in the domain of planning. This article looks at Soviet postwar efforts to draw up plans for the rapidly developing, industrializing, and urbanizing Siberia, where cybernetic models were used to develop a vision of cybernetic socialism. Removed from Moscow bureaucracy and politics, the various planning institutes of the Siberian Academy of Sciences became a key frontier for exploring the potential of cybernetic thinking to offer a necessary corrective to (...) Soviet planning. Researchers there put forth a vision of a dynamic Soviet economy managed through partially automated subsystems, which, while decentralized, would grant the central planning apparatus flexibility, a capacity for emergence, and overall solvency in the face of increasingly complex factors that required consideration. (shrink)

This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, (...) and examines philosophical frameworks for thinking about validation; provides an overview of the various strategies and techniques available for validating simulations, as well as the preparatory steps that have to be taken prior to validation; describes commonly used reference points and mathematical frameworks applicable to simulation validation; reviews the legal prescriptions, and the administrative and procedural activities related to simulation validation; presents examples of best practice that demonstrate how methods of validation are applied in various disciplines and with different types of simulation models; covers important practical challenges faced by simulation scientists when applying validation methods and techniques; offers a selection of general philosophical reflections that explore the significance of validation from a broader perspective. -/- This truly interdisciplinary handbook will appeal to a broad audience, from professional scientists spanning all natural and social sciences, to young scholars new to research with computer simulations. Philosophers of science, and methodologists seeking to increase their understanding of simulation validation, will also find much to benefit from in the text. (shrink)

Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathématique et causale d’un phénomène physique. (...) La différence majeure est qu’une méthode d’AM ne prétend pas représenter une causalité entre les paramètres d’entrées, qui peuvent être de plus de haute dimensionnalité, et ceux de sortie. Nous montrons dans cet article l’intérêt de mettre en œuvre les distinctions épistémologiques entre les différentes fonctions épistémiques d’un modèle, d’une part, et entre la fonction épistémique et l’usage d’un modèle, d’autre part. Enfin, la dernière partie de cet article présente nos travaux en cours sur l’évaluation d’une explication, qui peut être plus persuasive qu’informative, ce qui peut ainsi causer des problèmes d’ordre éthique. (shrink)

According to one influential view, model-building in science is primarily a matter of simplifying theoretical descriptions of real-world target systems using abstraction and idealization. This view, however, does not adequately capture all types of models. Many contemporary models in the natural and social sciences – from physics to biology to economics – stand in a more tenuous relationship with real-world target systems and have a decidedly stipulative element, in that they create, by fiat, ‘model worlds’ that operate according to some (...) set of specified rules. While such models may be motivated by an interest in actual target phenomena, their validity is not – at least not primarily – to be judged by whether they constitute an empirically adequate representation of any particular empirical system. The present paper compares and contrasts three such types of models: minimal models, toy models, and exploratory models. All three share some characteristics and thus overlap in interesting ways, yet they also exhibit significant differences. It is argued that, in all three cases, modal considerations have an important role to play: by exploring the modal structure of theories and phenomena – that is, by probing possibilities in various ways – such models deepen our understanding and help us gain knowledge not only about what there is in the world, but also about what there could be. (shrink)

Epistemology needs to account for the success of science. In True Enough, Catherine Elgin argues that a veritist epistemology is inadequate to this task. She advocates shifting epistemology’s focus away from true belief and toward understanding, and further, jettisoning truth from its privileged place in epistemological theorizing. Pace Elgin, I argue that epistemology’s accommodation of science does not require rejecting truth as the central epistemic value. Instead, it requires understanding veritism in an ecumenical way that acknowledges a rich array of (...) truth-oriented values. ¶ In place of veritism, Elgin offers a holistic epistemology that takes epistemic norms to have their genesis in our collective practice of deliberation. The acceptability of epistemic norms turns on epistemic responsibility, as opposed to reliability, and truth-conduciveness is rejected as the standard of evaluation for arguments and methods of inquiry. I argue, by way of an extended discussion of a high-profile and controversial criminal case, that this leaves epistemic practices and their products inadequately grounded. I offer an alternative, veritistic account of epistemic norms that retains a modified version of truth-conduciveness as a standard of evaluation. However, my alternative account of epistemic norms is congenial to Elgin’s holistic epistemology, and, I suggest, could be incorporated within it. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...) a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

ABSTRACTExperimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. (...) ‘Specimenhood’ involves possessing relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of a class of systems, to which a hypothesis refers. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modelers exert extensive control, they do not exert this control over specimens; although natural experiments utilize specimens, control is diminished. (shrink)

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...) and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (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)

On the basis of a case study in bioengineering, this paper proposes a novel perspective on models in science and engineering. This is done with the help of two notions: representation and design. These two notions are interpreted as referring to modeling relations between vehicles and targets that differ in their respective directions of fit. The representation relation has a vehicle-to-target direction of fit and the design relation has a target-to-vehicle direction of fit. The case study of an organ on (...) chip model illustrates that the technical device can participate in both design and representation relations. The two relations share the same relatum of the organ on chip, but they have different directions of fit. In the design relation, the chip is adjusted to a design plan, in which case we are dealing with a target-to-vehicle direction of fit. In the representation relation, the chip is adjusted to a human organ, in which case we are dealing with a vehicle-to-target direction of fit. The example shows that a conception of modeling as involving only relations with a vehicle-to-target direction of fit is too narrow to account for models in science and engineering. (shrink)

In this paper I show how certain requirements must be set on any tenable account of scientific representation, such as the requirement allowing for misrepresentation. I then continue to argue that two leading accounts of scientific representation— the inferential account and the interpretational account—are flawed for they do not satisfy such requirements. Through such criticism, and drawing on an analogy from non-scientific representation, I also sketch the outline of a superior account. In particular, I propose to take epistemic representations to (...) be intentional objects that come with reference, semantic contents and a representational code, and I identify faithful representations as representations that act as guides to ontology. (shrink)

This chapter elaborates and develops the thesis originally put forward by Mary Morgan (2005) that some mathematical models may surprise us, but that none of them can completely confound us, i.e. let us unable to produce an ex post theoretical understanding of the outcome of the model calculations. This chapter intends to object and demonstrate that what is certainly true of classical mathematical models is however not true of pluri-formalized simulations with multiple axiomatic bases. This chapter thus proposes to show (...) that - and why - some of these computational simulations that are now booming in the sciences not only surprise us but also confound us. To do so, it shows too that it is needed to elaborate and articulate with some new precision the concept of weak emergence initially due, for its part, to Mark A. Bedau (1997). (shrink)

Although oceanographers such as Roger Revelle are typically associated with key indicators of anthropogenic change, he and other scientists at midcentury had very different scientific priorities and ways of seeing the oceans. How can we join the narrative of the triumph of mathematical, dynamic oceanography with the environmental narrative? Dynamic methods entailed a broad set of values that touched the professional lives of marine scientists in a variety of disciplines all over the world, for better or for worse. The present (...) essay highlights three aspects of “Bergen values” in need of greater exploration by scholars. First, how did the dominance of Scandinavian outlooks influence scientific questions across the broad spectrum of oceanography? Second, did oceanographers’ particular means of making the oceans legible through instrumentation challenge their ability to perceive the oceans differently? Third, given the immense quantity of data, was the historical legacy of the dynamic oceanographers more descriptive than they imagined? (shrink)

One of the most conspicuous features of contemporary modeling practices is the dissemination of mathematical and computational methods across disciplinary boundaries. We study this process through two applications of the Ising model: the Sherrington-Kirkpatrick model of spin glasses and the Hopfield model of associative memory. The Hopfield model successfully transferred some basic ideas and mathematical methods originally developed within the study of magnetic systems to the field of neuroscience. As an analytical resource we use Paul Humphreys's discussion of computational and (...) theoretical templates. We argue that model templates are crucial for the intra- and interdisciplinary theoretical transfer. A model template is an abstract conceptual idea associated with particular mathematical forms and computational methods. (shrink)

The behavior/structure methodological dichotomy as locus of scientific inquiry is closely related to the issue of modeling and theory change in scientific explanation. Given that the traditional tension between structure and behavior in scientific modeling is likely here to stay, considering the relevant precedents in the history of ideas could help us better understand this theoretical struggle. This better understanding might open up unforeseen possibilities and new instantiations, particularly in what concerns the proposed technological modification of the human condition. The (...) sequential structure of this paper is twofold. The contribution of three philosophers better known in the humanities than in the study of science proper are laid out. The key theoretical notions interweaving the whole narrative are those of mechanization, constructability and simulation. They shall provide the conceptual bridge between these classical thinkers and the following section. Here, a panoramic view of three significant experimental approaches in contemporary scientific research is displayed, suggesting that their undisclosed ontological premises have deep roots in the Western tradition of the humanities. This ontological lock between core humanist ideals and late research in biology and nanoscience is ultimately suggested as responsible for pervasively altering what is canonically understood as “human”. (shrink)

With the interest in studying science as practice came an interest in the material artefacts and things that form part of scientific activities in the laboratory, the field, the classroom, or the political arena. This shift in interest in connection with new modes of knowledge production raises new questions regarding the “archive” of science: what should be preserved and where to make it possible to reconstruct scientific practices in the desired detail? While digital media may be able to bridge some (...) of the traditional divisions between the collection of scientific artefacts in museums and the written archival depositories, the move to performing science in silico produces new challenges in respect to establishing the material archives of current science. The paper will discuss these and related questions with special reference to the archives of the contemporary life sciences. (shrink)

Modern science is, to a large extent, a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play here? These questions have kept philosophers of science busy for many years, and much work has been done to identify modeling as the central activity of theoretical science. At the same time, these questions have been addressed by methodologically-minded scientists, albeit from a different (...) point of view. While philosophers typically have an eye on general aspects of scientific modeling, scientists typically take their own science as the starting point and are often more concerned with specific methodological problems. There is, however, also much common ground in middle, where philosophers and scientists can engage in a productive dialogue, as the present volume demonstrates. To do so, the editors of this volume have invited eight leading scientists from cosmology, climate science, social science, chemical engeneering and neuroscience to reflect upon their modeling work, and eight philosophers of science to provide a commentary. (shrink)

Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition (...) is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge. (shrink)

This is the introduction to the special issue of the Spanish journal Ágora-Papeles de Filosofía (31/2, 2012) devoted to new Ibero-American contributions to metatheoretical structuralism.

Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to (...) choose among them. (shrink)

IN FISICA LA COMPLESSITÀ FA IL SUO INGRESSO NELLA FISICA STATISTICA PER POI APPARIRE NELLO STUDIO DEI COMPORTAMENTI COLLETTIVI NELLA MATERIA CONDENSATA E DELLA SOFT MATTER, E DA ULTIMO NELLA NUOVA TEORIA DEL CAMBIAMENTO. RIDUZIONISMO ED EMERGENZA NON SONO APPROCCI OPPOSTI BENSÌ COMPLEMENTARI.

Cet article revient sur la pratique scientifique et les thèses épistémologiques de Jean-Marie Legay concernant les modèles, les simulations et les systèmes complexes. Il montre qu'il y a une cohérence entre sa thèse anti-représentationnaliste concernant les modèles et les simulations et sa caractérisation même des systèmes complexes : une simulation informatique, seule, n'est pas une expérience au sens fort car, en l'isolant, on perd la dimension complexe de toute entreprise d'expérimentation scientifique dès lors qu'il y manque le modélisateur, le terrain (...) d'inspiration originelle ainsi que les objectifs de simulation, qui en interagissant à eux trois forment un système complexe. Vouloir prendre une simulation séparée de son modélisateur et de son ancrage empirique originel pour une expérience au sens fort, vouloir la considérer à elle seule comme une captation intégrale, dans la machine, de la complexité des systèmes étudiés par ailleurs en sciences de la vie et de l'environnement, ce serait croire que l'aventure scientifique elle-même n'aurait plus désormais, avec l'essor contemporain de l'informatique de modélisation, à être considérée elle-même comme complexe. C'est bien cette seule idée qui lui paraissait déraisonnable. (shrink)

I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. (...) This whole approach is further supported by a brief exposition of some recent work in cognitive, or usage-based, linguistics. Finally, with all the above as background, I criticize the recently much discussed idea that claims involving scientific models are really fictions. (shrink)

Why do policies fail? How can we objectively choose the best policy from two (or more) competing alternatives? How can we create better policies? To answer these critical questions this book presents an innovative yet workable approach. Avoiding Policy Failure uses emerging metapolicy methodologies in case studies that compare successful policies with ones that have failed. Those studies investigate the systemic nature of each policy text to gain new insights into why policies fail. -/- In addition to providing intriguing directions (...) for research, this book also suggests a bold new standard for evaluating policies. While this method is broadly generalizable, specific examples are provided showing how to develop better Economic Policy, Military Policy, and Constitutional Organizations. This book shows scholars, researchers, and policy analysts how to develop more effective policies so that we may achieve our highest aspirations and avoid the horrendous failures of the past. (shrink)

Several prominent philosophers of science, most notably Ron Giere, propose that scientific theories are collections of models and that models represent the objects of scientific study. Some, including Giere, argue that models represent in the same way that pictures represent. Aestheticians have brought the picturing relation under intense scrutiny and presented important arguments against the tenability of particular accounts of picturing. Many of these arguments from aesthetics can be used against accounts of representation in philosophy of science. I rely on (...) Dominic Lopes' recent summary of arguments against various views of picturing and reformulate some of them to fit the philosophy of science context. My specific targets here are Giere and Steven French. I go on to argue that assuming all scientific models and images represent in the same way is not the best guide to understanding scientific practice. (shrink)

In this paper I challenge Paolo Palmieri’s reading of the Mach-Vailati debate on Archimedes’s proof of the law of the lever. I argue that the actual import of the debate concerns the possible epistemic (as opposed to merely pragmatic) role of mathematical arguments in empirical physics, and that construed in this light Vailati carries the upper hand. This claim is defended by showing that Archimedes’s proof of the law of the lever is not a way of appealing to a non-empirical (...) source of information, but a way of explicating the mathematical structure that can represent the empirical information at our disposal in the most general way. (shrink)

In 1969, the influent French philosopher Alain Badiou published a book called "The concept of Model: An Introduction to the Materialist Epistemology of Mathematics". A recent reprint gives the opportunity to trace back and analyze its main arguments. This paper essentially aims to present and explain Badiou's arguments against the representationalist vision of models in empirical sciences and for a materialist interpretation of formal systems coupled with semantic models in mathematics. Now that the practices of scientific modeling and simulation have (...) extensively spread across all scientific disciplines and their mutual frontiers, it is stimulating to revisit such a critic of the misleading representational power of models and simulations. However, the question is asked whether such a critique is still relevant against current complex modeling and simulation systems. Finally, it is suggested that what is rejected in such an epistemology is some kind of pluralism. This strict rejection can be one of the explaining factors that have led most current epistemologists of models to regenerate pragmatist analyses instead of the Marxist “praxist” ones. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section 2 gives (...) conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

Arabidopsis is currently the most popular and well-researched model organism in plant biology. This paper documents this plant's rise to scientific fame by focusing on two interrelated aspects of Arabidopsis research. One is the extent to which the material features of the plant have constrained research directions and enabled scientific achievements. The other is the crucial role played by the international community of Arabidopsis researchers in making it possible to grow, distribute and use plant specimen that embody these material features. (...) I argue that at least part of the explosive development of this research community is due to its successful standardisation and to the subsequent use of Arabidopsis specimen as material models of plants. I conclude that model organisms have a double identity as both samples of nature and artifacts representing nature. It is the resulting ambivalence in their representational value that makes them attractive research tools for biologists. (shrink)

As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue (...) that a proper understanding and application of robustness analysis is able to resolve them. ‡The author would like to thank Cristina Bicchieri, Michelle Foa, Paul Humphreys and Michael Weisberg for their helpful comments and suggestions. †To contact the author, please write to: Department of Philosophy, University of Pennsylvania, 433 Logan Hall, 249 S. 36th Street, Philadelphia, PA, 19104-6304; e-mail: [email protected] (shrink)

Dans un nombre croissant de domaines scientifiques - sciences de la nature, sciences humaines aussi bien que sciences des artefacts -, la simulation ne joue plus le rôle de succédané temporaire d'une théorie encore en gésine parce que non encore élaborée ; c'est-à-dire qu'elle ne joue plus systématiquement le rôle d'un modèle provisoire ou d'un schéma servant à condenser les mesures. C'est qu'elle n'a pas la nature d'un signe graphique, linguistique ou mathématique. Elle joue au contraire de plus en plus (...) le rôle d'une réplication détaillée du réel : elle est une intuition reconstruite avec justesse mais sans abstraction rationnelle et généralisante, donc sans transfiguration iconique. Une des limites de l'épistémologie dialectique de Bachelard tient à ce qu'elle ne donne pas les concepts pour penser assez distinctement ces pratiques contemporaines de simulation. C'est pourquoi l'A. propose de concevoir l'idée que la science contemporaine se développe non plus selon la seule dialectique matérialisme / rationalisme, mais selon la triade rationalisme/computationalisme/matérialisme. (shrink)

We propose that scientific representation is a special case of a more general notion of representation, and that the relatively well worked-out and plausible theories of the latter are directly applicable to thc scientific special case. Construing scientific representation in this way makes the so-called “problem of scientific representation” look much less interesting than it has seerned to many, and suggests that some of the debates in the literature are concerned with non-issues.

Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.