It is widely believed that mathematics carries a substantial part of the explanatory burden in science. However, mathematics can also play important heuristic roles of a different kind, being a source of new ideas and approaches, allowing us to build toy models, enhancing expressive power and providing fruitful conceptualizations. In this paper, we focus on the application of dynamical systems theory (DST) within the extended cognition (EC) field of cognitive science, considering this case study to be a good illustration of (...) a general phenomenon. In the paper, we justify both a negative and a positive claim. The negative claim is that dynamical systems theory hardly plays any explanatory role in EC research. We justify our claim by analyzing several accounts of the explanatory role of mathematics and stressing the way mathematical arguments are used in explanations. Our positive claim is that even though, for now, DST has no explanatory power in many of the EC approaches, it still plays an important heuristic role there. In particular, using mathematical notions improves the expressive power of the language and gives a sense of understanding of the phenomena under investigation. The case study of EC allows us to identify and analyze this important role of mathematics, which seems to be neglected in contemporary discussions. (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...) scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

Philosophers have recently paid more attention to the physical aspects of scientific models. The attention is motivated by the prospect that a model’s physical features strongly affect its use and that this suggests re-thinking modelling in terms of extended or distributed cognition. This paper investigates two ways in which physical features of scientific models affect their use and it asks whether modelling is an instance of extended cognition. I approach these topics with a historical case study, in which scientists kept (...) records not only of their findings, but also of some the mental operations that generated the findings. The case study shows how scientists can employ a physical model as an external information store, which allows alternating between mental manipulations, recording the outcome externally, and then feeding the outcome back into subsequent mental manipulations. The case study also demonstrates that a models’ physical nature allows replacing explicit reasoning with visuospatial manipulations. I argue, furthermore, that physical modelling does not need to exemplify a strong kind of extended cognition, the sort for which external features are mereological parts of cognition. It can exemplify a weaker kind, instead. (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)

Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models (...) in the science of morality has been vastly underappreciated. This omission shall be remedied here, offering a much more positive picture on the contributions modelers made to our understanding of morality. (shrink)

Maps and mapping raise questions about models and modeling and in science. This chapter archives map discourse in the founding generation of philosophers of science (e.g., Rudolf Carnap, Nelson Goodman, Thomas Kuhn, and Stephen Toulmin) and in the subsequent generation (e.g., Philip Kitcher, Helen Longino, and Bas van Fraassen). In focusing on these two original framing generations of philosophy of science, I intend to remove us from the heat of contemporary discussions of abstraction, representation, and practice of science and thereby (...) see in a more distant and neutral light the many productive ways in which maps can stand in analytically for scientific theories and models. The chapter concludes by complementing the map analogy – i.e., a scientific theory is a map of the world – with a model analogy, viz., a scientific model is a vehicle for understanding. (shrink)

This article aims to answer what I call the “constitution question of engineering modeling”: in virtue of what does an engineering model model its target system? To do so, I will offer a category-theoretic, structuralist account of design, using the olog framework. Drawing on this account, I will conclude that engineering and scientific models are not only cognitively but also representationally indistinguishable. I will finally propose an axiological criterion for distinguishing scientific from engineering modeling.

Various geoengineering technologies that would deliberately alter the climate system have been proposed as a way to alleviate risks of global warming. Technologies that would shield incoming sunlight to cool the planet, so called solar radiation management, are particularly controversial. Considering insights from social studies of simulation modeling and research on expectations in science and technology, I argue that climate modeling has a central role in producing visions of SRM. I draw upon an empirical analysis of scientific research on SRM (...) to examine how a creative play with technological ideas becomes possible through climate modeling. This enables scientists to project and study environmental impacts of speculative SRM methods in virtual experiments and to develop and refine ideas for adjusting sunlight. Hence, while climate models are used to improve scientific understandings of climate system behavior and to anticipate possible environmental impacts of SRM, they also become inventive tools, allowing scientists to envision novel ways of climate control and optimization. Given the importance of simulation studies to knowledge production on SRM, I critically reflect on the challenges that arise when visions about an engineered climate future are first and foremost produced in climate simulations. (shrink)

This paper offers an analytic perspective on epistemic dependence that is grounded in theoretical discussion and field observation at the same time. When in the course of knowledge creation epistemic labor is divided, collaborating scientists come to depend upon one another epistemically. Since instances of epistemic dependence are multifarious in scientific practice, I propose to distinguish between two different forms of epistemic dependence, opaque and translucent epistemic dependence. A scientist is opaquely dependent upon a colleague if she does not possess (...) the expertise necessary to independently carry out, and to profoundly assess, the piece of scientific labor which her colleague is contributing. If the scientist does possess the necessary expertise, I argue, her dependence is translucent. However, the distinction between opaque and translucent epistemic dependence does not exhaust dependence relations in scientific practice, because many dependence relations are neither entirely opaque nor translucent. I will discuss why this is the case, and show how we can make sense of the gray zone between opaque and translucent epistemic dependence. (shrink)

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...) models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (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)

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

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

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

A general view in philosophy of science says that the appropriateness of an object to act as a surrogate depends on the user’s decision to utilize it as such. This paper challenges this claim by examining the role of surrogative reasoning in high-throughput sequencing technologies as they are used in contemporary microbiome science. Drawing on this, we argue that, in technology-driven surrogates, knowledge about the type of inference practically permitted and epistemically justified by the surrogate constrains their use and thus (...) puts a limit to the user’s intentions to use any object as a surrogate for what they please. Ignoring this leads to a serious epistemic misalignment, which ultimately prevents surrogative reasoning. Thus, we conclude that knowledge about the type of surrogate reasoning that the technologies being used allow is fundamental to avoid misinterpreting the consequences of the data obtained with them, the hypothesis this data supports, and what these technologies are surrogates of. (shrink)

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

Thought experiments, models, diagrams, computer simulations, and metaphors can all be understood as tools of the imagination. While these devices are usually treated separately in philosophy of science, this paper provides a unified account according to which tools of the imagination are epistemically good insofar as they improve scientific imaginings. Improving scientific imagining is characterized in terms of epistemological consequences: more improvement means better consequences. A distinction is then drawn between tools being good in retrospect, at the time, and in (...) general. In retrospect, tools are evaluated straightforwardly in terms of the quality of their consequences. At the cutting edge, tools are evaluated positively insofar as there is reason to believe that using them will have good consequences. Lastly, tools can be generally good, insofar as their use encourages the development of epistemic virtues, which are good because they have good epistemic consequences. (shrink)

Making sense of why something succeeded or failed is central to scientific practice: it provides an interpretation of what happened, i.e. an hypothesized explanation for the results, that informs scientists’ deliberations over their next steps. In philosophy, the realism debate has dominated the project of making sense of scientists’ success and failure claims, restricting its focus to whether truth or reliability best explain science’s most secure successes. Our aim, in contrast, will be to expand and advance the practice-oriented project sketched (...) by Arthur Fine in his work on the Natural Ontological Attitude. An important obstacle to articulating a positive program, we suggest, has been overlooking how scientists adopt standardized rules and procedures in order to define and operationalize meanings for success and failure relative to their situated goals. To help fill this gap, we introduce two new ideas, design specifications and track records, and show how they advance our ability to make sense of scientific modeling practices while maintaining a deflationary stance toward the realism debate. (shrink)

. Through examining a case study of a major fluids modelling code, this paper charts two key properties of software as a material for building models. Scientific software development is characterized by piecemeal growth, and as a code expands, it begins to manifest frustrating properties that provide an important axis of motivation in the laboratory. The first such feature is a tendency towards brittleness. The second is an accumulation of supporting technologies that sometimes cause scientists to express a frustration with (...) the bureaucracy of highly regulated working practices. Both these features are important conditions for the pursuit of research through simulation. (shrink)

How do models represent reality? There are two conditions that scientific models must satisfy to be representations of real systems, the aboutness condition and the epistemic condition. In this article, I critically assess the two main fictionalist theories of models as representations, the indirect fiction view and the direct fiction view, with respect to these conditions. And I develop a novel proposal, what I call ‘the new fiction view of models’. On this view, models are akin to fictional stories; they (...) represent real-world phenomena if they stand in a denotation relation with reality; and they enable knowledge of reality via the generation of theoretical hypotheses, model–world comparisons and direct attributions. (shrink)

Brian Epstein has recently argued that a thoroughly microfoundationalist approach towards economics is unconvincing for metaphysical reasons. Generally, Epstein argues that for an improvement in the methodology of social science we must adopt social ontology as the foundation of social sciences; that is, the standing microfoundationalist debate could be solved by fixing economics’ ontology. However, as I show in this paper, fixing the social ontology prior to the process of model construction is optional instead of necessary and that metaphysical-ontological commitments (...) are often the outcome of model construction, not its starting point. By focusing on the practice of modeling in economics the paper provides a useful inroad into the debate about the role of metaphysics in the natural and social sciences more generally. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

In this paper, we argue that, under a specific set of circumstances, designing and employing certain kinds of virtual reality (VR) experiences can be unethical. After a general discussion of simulations and their ethical context, we begin our argu-ment by distinguishing between the experiences generated by different media (text, film, computer game simulation, and VR simulation), and argue that VR experiences offer an unprecedented degree of what we call “perspectival fidelity” that prior modes of simulation lack. Additionally, we argue that (...) when VR experiences couple this perspectival fidelity with what we call “context realism,” VR experiences have the ability to produce “virtually real experiences.” We claim that virtually real experiences generate ethical issues for VR technologies that are unique to the medium. Because subjects of these experiences treat them as if they were real, a higher degree of ethical scrutiny should be applied to any VR scenario with the potential to generate virtually real experiences. To mitigate this unique moral hazard, we propose and defend what we call “The Equivalence Principle.” This principle states that “if it would be wrong to allow subjects to have a certain experience in reality, then it would be wrong to allow subjects to have that experience in a virtually real setting.” We argue that such a principle, although limited in scope, should be part of the risk analysis conducted by any Institutional Review Boards, psychologists, empirically oriented philosophers, or game designers who are using VR technology in their work. (shrink)

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make (...) a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked. (shrink)

This paper proposes a novel distinction between accounts of scientific representation: it distinguishes thin accounts from thick accounts. Thin accounts focus on the descriptive aspect of representation whereas thick accounts acknowledge the evaluative aspect of representation. Thin accounts focus on the question of what a representation as such is. Thick accounts start from the question of what an adequate representation is. In this paper, I give two arguments in favor of a thick account, the Argument of the Epistemic Aims of (...) Modeling and the Argument of the Normativity of the Practice of Modeling. I also discuss possible objections to a thick account: the Argument from Misrepresentation and the Objections from Model Testing. The conclusion will be that the arguments on balance support a thick account of representation. (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)

This paper proposes to recover the topic of the philosophy of scientific method from its late nineteenth-century roots. The subject matter of scientific method sprouted from key inferential ingredients identified by Charles Peirce. In this paper, the historical path is traversed from the viewpoint of contemporary Cognitive Structural Realism (CSR). Peirce’s semiotic theory of methods and practices of scientific inquiry prefigured CSR’s reliance on embodied informational structures and experimentation upon forms of relations that model generic scientific domains. Three results are (...) shown to follow from this convocation: (i) a naturalization of Peirce’s interconnected abductive, deductive and inductive stages of the logic of science, here characterized de novo in terms of CSR. (ii) a perspective to scientific modeling that incorporates processes of abstraction and generalization as originated from Peirce’s logic of science, and (iii) diagrammatic reasoning as a pivotal method in analyzing scientific reasoning in experimental practices. (shrink)

Theoreticians that defend a form of realism regarding natural kinds minimally entertain the belief that the world features divisions into kinds and that the natural kind concept is a useful tool for philosophy of science. The objective of this paper is to challenge these assumptions. First, we challenge realism toward natural kinds by showing that the main arguments for their existence, which rely on the epistemic success of natural kinds, are unsatisfactory. Second, we show that, whether they exist or not, (...) natural kinds are expendable when it comes to describing and analyzing scientific explanations accurately. (shrink)

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...) equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry. (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)

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 paper proposes an account of accurate scientific representation in terms of techniques that produce data from a target phenomenon. I consider an approach to accurate representation that abstracts from such epistemic factors, justified by a thesis I call Ontic Priority. This holds that criteria for representational accuracy depend on a pre-established account of the nature of the relation between a model and its target phenomenon. I challenge Ontic Priority, drawing on the observation that many working scientists do not have (...) access to information allowing them to describe this relation between model and target or compare them accordingly. I critique the ability of an ontic-first approach to provide accuracy criteria in such cases and present historical support for an alternative according to which a model is accurate if and only if integrating the model into a theory of the data acquisition process yields well-fitting predictions of patterns in the data. (shrink)

There is considerable correspondence between theories and models used in biology and the social sciences. One type of model that is in use in both biology and the social sciences is the fitness landscape model. The properties of the fitness landscape model have been applied rather freely in the social domain. This is partly due to the versatility of the model, but it is also due to the difficulties of transferring a model to another domain. We will demonstrate that in (...) order to transfer the biological fitness landscape model to the social science it needs to be substantially modified. We argue that the syntactic structure of the model can remain unaltered, whilst the semantic dimension requires considerable modification in order to fit the specific phenomena in the social sciences. We will first discuss the origin as well as the basic properties of the model. Subsequently, we will demonstrate the considerations and modifications pertaining to such a transfer by showing how and why we altered the model to analyse collective decision-making processes. We will demonstrate that the properties of the target domain allow for a transfer of the syntactic structure but don’t tolerate the semantic transfer. (shrink)

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to (...) achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control. (shrink)

In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison of models (...) that differ only for the assumption under test; iii) the replacement of specific assumptions with different ones can encounter specific difficulties in scientific practice. These claims will be supported by a case study in population ecology and a case study in geographical economics. (shrink)

We develop an account of how mutually inconsistent models of the same target system can provide coherent information about the system. Our account makes use of ideas from the debate surrounding rob...

It remains at best controversial to claim, non-figuratively, that plants are cognitive agents. At the same time, it is taken as trivially true that many animals are cognitive agents, arguably through an implicit or explicit appeal to natural science. Yet, any given definition of cognition implicates at least some further processes, such as perception, action, memory, and learning, which must be observed either behaviorally, psychologically, neuronally, or otherwise physiologically. Crucially, however, for such observations to be intelligible, they must be counted (...) as evidence for some model. These models in turn point to homologies of physiology and behavior that facilitate the attribution of cognition to some non-human animals. But, if one is dealing with a model of animal cognition, it is tautological that only animals can provide evidence, and absurd to claim that plants can. The more substantive claim that, given a general model of cognition, only animals but not plants can provide evidence, must be evaluated on its merits. As evidence mounts that plants meet established criteria of cognition, from physiology to behavior, they continue to be denied entry into the cognitive club. We trace this exclusionary tendency back to Aristotle, and attempt to counter it by drawing on the philosophy of modelling and a range of findings from plant science. Our argument illustrates how a difference in degree between plant and animals is typically mistaken for a difference in kind. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...) argue that the falsehoods do not figure into the understanding’s content by being elements of its periphery or core. (iii) Drawing lessons from case studies, I argue that the falsehoods merely enable understanding. When working with such falsehoods, only the truths we extract from them are elements of the content of our understanding. (iv) I argue that the extraction view is compatible with the thesis that falsehoods can have an epistemic value by virtue of being the very falsehoods they are. (shrink)

ArgumentThis paper challenges three assumptions common in the literature on expertise: that expertise is linearly derived from scientific knowledge; that experts always align with the established institutional order; and that expertise is a property acquired by individuals. We criticize these ideas by juxtaposing three distinct expert practices involved with flood risk management in England. Virtual engineering is associated with commercial consultancy and relies on standardized software packages to assess local flood inundation. Mathematical experimentation refers to academic scientists creating new digital (...) renderings of the physical dynamics of flooding. Participatory modeling denotes research projects that aim to transform the relationships between experts and local communities. Focusing on different modes of modeling we contribute an analysis of how particular models articulate with specific politics of knowledge as experts form relationships with flood risk management actors. Our empirical study also shows how models can contribute to re-distribution of expertise in local flood risk management. (shrink)

This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, (...) the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models. (shrink)

Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...) I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students’ mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students’ mental representations of models in terms of evolving relational structures in greater detail than previously done. (shrink)