Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a (...) model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation. (shrink)

The central question of this thesis is how one can learn about particular targets by using models of those targets. A widespread assumption is that models have to be representative models in order to foster knowledge about targets. Thus the thesis begins by examining the concept of representation from an epistemic point of view and supports an account of representation that does not distinguish between representation simpliciter and adequate representation. Representation, understood in the sense of a representative model, is regarded (...) as a success term. That is, a representative model is one relatum in a relation of adequate representation (Chapter 2). When a representative model represents a target, it allows users of this model to learn something about the target. It is argued that a representative model has this epistemic function because it shares relevant features with the target. This presupposes a similarity view of representation. Similarity views of representation face serious objections, which will be rebutted (Chapter 3). One way of spelling out a similarity view of representation is to defend an indirect view of representation. In this thesis, which does not argue for an indirect view, it is assumed that the indirect view is a good option, if not the best, for articulating the similarity view. It is demonstrated how such an indirect view can be expanded to account for cases of technological modeling. A case study in bioengineering is used to show that the indirect view of representation must acknowledge a distinction between two directions of fit in relations between vehicles and targets. In this context the notion of design is interpreted as a relation between a vehicle and a target, thereby connecting ideas from philosophy of science with ideas from philosophy of technology (Chapter 4). Fictionalist accounts of models are intended to tackle the issue of the ontology of models. In this thesis, however, two prominent fictionalist accounts are discussed from an epistemological point of view in light of the central question regarding how one can learn about targets by using models. This question is addressed from the standpoint of Waltonian fictionalism. The result of the discussion is that the two discussed Waltonian fictionalist accounts cannot sufficiently answer the question. These accounts are criticized for their inability to deliver a satisfactory epistemology of representation (Chapter 5). Although Waltonian fictionalism is criticized, the present thesis also shows that the foundational theory of Waltonian fictionalism, the theory of make-believe can nevertheless be used to account for the distinction between projections and predictions that is made by the Intergovernmental Panel on Climate Change (Chapter 6). (shrink)

This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related (...) to actual representational tools, and coordinated among different scientists, and with real-world phenomena. The artifactual account focuses, in contrast, on the culturally established external representational tools that enable, embody, and extend scientific imagination and reasoning. While there are commonalities between models and fictions, it is argued that the focus should be on the fictional uses of models rather than considering models as fictions. (shrink)

In this paper, I appeal to a distinction made by David Lewis between identifying and determining semantic content in order to defend a complementarity thesis expressed by Anjan Chakravartty. The thesis states that there is no conflict between informational and functional views of scientific modeling and representation. I then apply the complementarity thesis to well-received theories of pictorial representation, thereby stressing the fruitfulness of drawing an analogy between the nature of fictions in art and in science. I end by attending (...) to the problem of depicting impossible fictions. It is suggested that progress can be made by understanding the role of impossible fictions in science, namely, allowing researchers to probe into the possible structure and representational capacities of scientific theory. (shrink)

Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...) are justified by allowing for the application of various modelling techniques. (shrink)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...) models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (shrink)

A survey of models in immunology is conducted and distinct kinds of models are characterized based on whether models are material or conceptual, the distinctiveness of their epistemic purpose, and the criteria for evaluating the goodness of a model relative to its intended purpose. I argue that the diversity of models in interdisciplinary fields such as immunology reflects the fact that information about the phenomena of interest is gathered from different sources using multiple methods of investigation. To each model is (...) attached a description specifying how information about a phenomenon of interest has been acquired, highlighting points of commonality and difference between the methodological and epistemic histories of the information encapsulated in different models. These points of commonality and difference allow investigators to integrate findings from different models into more comprehensive explanatory accounts, as well as to troubleshoot anomalies and faulty accounts by going back to the original building blocks. (shrink)

This article develops an approach to modelling and models in science—the hybrid view—that is against model fictionalism of a recent stripe. It further argues that there is a version of fictionalism about models to which my approach is neutral and which makes sense only if one adopts a special sort of antirealism. Otherwise, my approach strongly suggests that one stay away from fictionalism and embrace realism directly.

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

In this entry I will offer a survey of the contemporary debate on fic- tionalism, which is a distinctive anti-realist view about certain regions of discourse that are valued for their usefulness rather than their truth.

Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two (...) other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

In this paper, we argue that symbols are conventional vehicles whose chief function is denotation, while models are epistemic vehicles, and their chief function is to show what their targets are like in the relevant aspects. And we explain why this is incompatible with the deflationary view on scientific modeling. Although the same object may serve both functions, the two vehicles are conceptually distinct and most models employ both elements. With the clarification of this point we offer an alternative account (...) to the deflationary view – the Hybrid Account; and we defend our account in contrast with deflationism. (shrink)

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

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

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...) I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice. (shrink)

In this essay I shall focus on the adoption of the Semantic Approach by structural realists, including myself, who have done so on the grounds that it wears its structuralist sympathies on its sleeve. Despite this, the SA has been identified as standing in tension with the ontological commitments of the so-called ’ontic’ form of this view and so I shall explore that tension before discussing the usefulness of the SA in framing scientific representation and concluding with a discussion of (...) the implications of the ontological status of theories and models themselves. (shrink)

The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically idle. (...) Based on these observations, an alternative is developed. (shrink)

This paper motivates, explains, and defends a new account of the content of thought experiments. I begin by briefly surveying and critiquing three influential accounts of thought experiments: James Robert Brown’s Platonist account, John Norton’s deflationist account that treats them as picturesque arguments, and a cluster of views that I group together as mental model accounts. I use this analysis to motivate a set of six desiderata for a new approach. I propose that we treat thought experiments primarily as aesthetic (...) objects, specifically fictions, and then use this analysis to characterize their content and ultimately assess their epistemic success. Taking my starting point from Kendall Walton’s account of representation (Mimesis as make-believe, Harvard University Press, Cambridge, 1990), I argue that the best way to understand the content of thought experiments is to treat them as props for imagining fictional worlds. Ultimately, I maintain that, in terms of their form and content, thought experiments share more with literary fictions and pictorial representations than with either argumentation or observations of the Platonic realm. Moreover, while they inspire imaginings, thought experiments themselves are not mental kinds. My approach redirects attention towards what fixes the content of any given thought experiment and scrutinizes the assumptions, cognitive capacities and conventions that generate them. This view helps to explain what seems plausible about Brown’s, Norton’s, and the mental modelers’ views. (shrink)

This paper examines issues of ontology and methodology in behavioral economics: the attempt to increase the explanatory and predictive power of economic theory by providing it with more psychologically plausible foundations. Of special interest is the epistemological status of neoclassical economic theory within behavioral economics, the runaway success story of contemporary economics. Behavioral economists aspire to replace the fundamental assumptions of orthodox, neoclassical economic theory. Yet, behavioral economists have gone out of their way to praise those very assumptions. Matthew Rabin, (...) for example, writes that behavioral economics “is not only built on the premise that [orthodox] economic methods are great, but also that most mainstream economic assumptions are great.” These apparently contradictory attitudes toward neoclassical theory raises the question of what, exactly, its epistemological status within behavioral economics is. This paper argues that the paradox can be resolved, and the question answered, by thinking of the epistemological status of neoclassical theory within behavioral economics in terms of Max Weber’s ideal types: analytical constructs that are not intended to be descriptively true of anything but which nevertheless can be used for a variety of theoretical purposes. The analysis is consistent with many of the insights from the philosophical literature on models in science and has important implications for the practice of economics—behavioral and neoclassical—as well as for the very nature of rationality. (shrink)

In this essay, I critically evaluate components of Michael Weisberg’s approach to models and modeling in his book Simulation and Similarity. First, I criticize his account of the ontology of models and mathematics. Second, I respond to his objections to fictionalism regarding models arguing that they fail. Third, I sketch a deflationary approach to models that retains many elements of his account but avoids the inflationary commitments.

Dimensional analysis can offer us explanations by allowing us to answer What-if–things-had-been-different? questions rather than in virtue of, say, unifying diverse phenomena, important as that is. Additionally, it is argued that dimensional analysis is a form of modelling as it involves several of the aspects crucial in modelling, such as misrepresenting aspects of a target system. By highlighting the continuities dimensional analysis has with forms of modelling we are able to describe more precisely what makes dimensional analysis explanatory and understand (...) otherwise puzzling aspects of dimensional reasoning, such as introducing fictitious dimensions and excluding dimensionally relevant information to characterise some systems. Finally, thinking of dimensional arguments as a form of modelling allows an explication of the role abstraction and multiple realisability; not as compatibility with other possible worlds but as compatibility with different fictional descriptions of our own world. (shrink)

Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...) a way that prevents those hypotheses from being false by virtue of their constituent idealizations. (shrink)

The problem of cooperation for rational actors comprises two sub problems: the problem of the intentional object (under what description does each actor perceive the situation?) and the problem of common knowledge for finite minds (how much belief iteration is required?). I will argue that subdoxastic signalling can solve the problem of the intentional object as long as this is confined to a simple coordination problem. In a more complex environment like an assurance game signals may become unreliable. Mutual beliefs (...) can then bolster the earlier attained equilibrium. I will first address these two problems by means of an example, in order to draw some more general lessons about combining evolutionary theory and rationality later on. (shrink)

The similarity view of scientific representation has recently been subjected to strong criticism. Much of this criticism has been directed against a ?naive? similarity account, which tries to explain representation solely in terms of similarity between scientific models and the world. This article examines the more sophisticated account offered by the similarity view's leading proponent, Ronald Giere. In contrast to the naive account, Giere's account appeals to the role played by the scientists using a scientific model. A similar move is (...) often made by defenders of resemblance theories of depiction, who invoke the role played by the artist, or by the viewers of a painting. In this article I look to debates over depiction to assess the difficulties facing those who wish to defend the similarity view of scientific representation. I then turn to examine Giere's account. Ultimately, I argue, this account is unsuccessful: while appealing to the role of scientists offers a promising way to defend the similarity view, Giere's own account does not capture what it is that scientists do when they use a model to represent the world. (shrink)

Recently, a number of authors have suggested that we understand scientific models in the same way as fictional characters, like Sherlock Holmes. The biggest challenge for this approach concerns the ontology of fictional characters. I consider two responses to this challenge, given by Roman Frigg, Ronald Giere and Peter Godfrey-Smith, and argue that neither is successful. I then suggest an alternative approach. While parallels with fiction are useful, I argue that models of real systems are more aptly compared to works (...) that portray real people, like the Emperor Claudius. This approach will allow us to avoid problems with fictional characters. (shrink)

In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by analyzing from a (...) semiotic point of view the representational relation which is characteristic of models. We then corroborate our hypothesis (2) by discussing the conceptual differences between icons, i.e. models, and indexical and symbolic signs and (3) by putting forward a general classification of all icons into three functional subclasses (images, diagrams, and metaphors). Subsequently, we (4) integratively refine our results by resorting to two influential and, as can be shown, complementary philosophy of science approaches to models. This yields the following result: models are determined by a semiotic structure in which a subject intentionally uses an object, i.e. the model, as a sign for another object, i.e. the original, in the context of a chosen theory or language in order to attain a specific end by instituting a representational relation in which the syntactic structure of the model, i.e. its attributes and relations, represents by way of a mapping the properties of the original, which hence are regarded as similar in a relevant manner. (shrink)

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)

Recent philosophy of science has seen a number of attempts to understand scientific models by looking to theories of fiction. In previous work, I have offered an account of models that draws on Kendall Walton’s ‘make-believe’ theory of art. According to this account, models function as ‘props’ in games of make-believe, like children’s dolls or toy trucks. In this paper, I assess the make-believe view through an empirical study of molecular models. I suggest that the view gains support when we (...) look at the way that these models are used and the attitude that users take towards them. Users’ interaction with molecular models suggests that they do imagine the models to be molecules, in much the same way that children imagine a doll to be a baby. Furthermore, I argue, users of molecular models imagine themselves viewing and manipulating molecules, just as children playing with a doll might imagine themselves looking at a baby or feeding it. Recognising this ‘participation’ in modelling, I suggest, points towards a new account of how models are used to learn about the world, and helps us to understand the value that scientists sometimes place on three-dimensional, physical models over other forms of representation. (shrink)

What is a model? Surprisingly, in philosophical texts, this question is asked (sometimes), but almost never – answered. Instead of a general answer, usually, some classification of models is considered. The broadest possible definition of modeling could sound as follows: a model is anything that is (or could be) used, for some purpose, in place of something else. If the purpose is “answering questions”, then one has a cognitive model. Could such a broad definition be useful? Isn't it empty? Can (...) one derive useful consequences from it? I'm trying to show that there is a lot of them. (shrink)

Starting from the sixties of the past century theory change has become a main concern of philosophy of science. Two of the best known formal accounts of theory change are the post-Popperian theories of verisimilitude (PPV for short) and the AGM theory of belief change (AGM for short). In this paper, we will investigate the conceptual relations between PPV and AGM and, in particular, we will ask whether the AGM rules for theory change are effective means for approaching the truth, (...) i.e., for achieving the cognitive aim of science pointed out by PPV. First, the key ideas of PPV and AGM and their application to a particular kind of propositional theories - the so called "conjunctive propositions" - will be illustrated. Afterwards, we will prove that, as far as conjunctive propositions are concerned, AGM belief change is an effective tool for approaching the truth. (shrink)

Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical models, models now come (...) in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...) an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)

Unentwegt werden neue technische Produkte gestaltet. Doch was macht die technische Gestaltung aus? Wie lässt sich ihr Gegenstand - (noch) nicht existierende Artefakte - adäquat auf den Begriff bringen? Michael Kuhn begreift technische Ideen vor ihrer Realisierung als Fiktionen. Er bietet eine fiktionstheoretische Rekonstruktion der Gestaltungstätigkeit und entwickelt hieraus eine Ethik der Gestaltung. Der stark interdisziplinäre Zugang zwischen Technikphilosophie und Ingenieurwissenschaften liefert neue Erkenntnisse für beide Fachrichtungen und stellt wertvolle Grundlagen bereit.

Debate on the nature of representation in cognitive systems tends to oscillate between robustly realist views and various anti‐realist options. I defend an alternative view, deflationary realism, which sees cognitive representation as an offshoot of the extended application to cognitive systems of an explanatory model whose primary domain is public representation use. This extended application, justified by a common explanatory target, embodies idealisations, partial mismatches between model and reality. By seeing representation as part of an idealised model, deflationary realism avoids (...) the problems with robust realist views, while keeping allegiance to realism. (shrink)

The paper addresses the problem of imaginative resistance in science, that is, why and under what circumstances imagination sometimes resists certain scenarios. In the first part, the paper presents and discusses two accounts concerning the problem and relevant for the main thesis of this study. The first position is that of Gendler (Journal of Philosophy 97:55–81, 2000), (Gendler, in: Nichols (ed) The Architecture of the Imagination: New essays on pretence, possibility and fiction, Oxford University Press, New York, 2006a), (Gendler & (...) Liao, in: Gibson, Carroll (eds) The routledge companion to philosophy of literature, Routledge, New York, 2016), according to which imaginative resistance mainly concerns evaluative scenarios, presenting deviant moral attitudes. The second account examined is that of Kim et al. (in: Cova, Réhault (eds) Advances in experimental philosophy of aesthetics, Bloomsbury, London, 2018), who insisted on the link between imaginative resistance on the one hand and counterfactual and counterdescriptive scenarios on the other. In the light of both theories, this paper discusses the importance of addressing the problem of imaginative resistance in the scientific enterprise in the light of some mechanisms of embodied simulation, based on the activity of mirror neurons and investigated within the framework of the Embodied Simulation Theory. (shrink)

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question ‘What does a particular neuronal system do?’ is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends (...) some support to the idea that a single neuronal structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation. (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)

Most attempts to justify the use of idealized models to explain appeal to the accuracy of the model with respect to difference-making causes. In this article, I argue for an alternative way to just...

Many phenomena in the natural world are complex, so scientists study them through simplified and idealised models. Philosophers of science have sought to explain how these models relate to the world. On most accounts, models do not represent the world directly, but through target systems. However, our knowledge of target systems is incomplete. First, what is the process by which target systems come about? Second, what types of entity are they? I argue that the basic conception of target systems, on (...) which other conceptions depend, is as parts of the world. I outline the process of target system specification and show that it is a crucial step in modelling. I also develop an account of target system evaluation, based on aptness. Paying close attention to target system specification and evaluation can help scientists minimise the frequency and extent of mistakes, when they are using models to investigate phenomena in complex real-world systems. (shrink)

The recent discussion of fictional models has focused on imagination, implicitly considering fictions as something nonconcrete. We present two cases from synthetic biology that can be viewed as concrete fictions. Both minimal cells and alternative genetic systems are modal in nature: they, as well as their abstract cousins, can be used to study unactualized possibilia. We approach these synthetic constructs through Vaihinger’s notion of a semi-fiction and Goodman’s notion of semifactuality. Our study highlights the relative existence of such concrete fictions. (...) Before their realizations neither minimal cells nor alternative genetic systems were any well-defined objects, and the subsequent experimental work has given more content to these originally schematic imaginings. But it is as yet unclear whether individual members of these heterogeneous groups of somewhat functional synthetic constructs will eventually turn out to be fully realizable, remain only partially realizable, or prove outright impossible. (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)

The aim of this paper is to explore the possibility that, at least, some metaphysical debates are ‘metalinguistic negotiations’. I will take the dispute between the dominant approaches of realism and the anti-realism ones about the ontological status of scientific models as a case-study. I will argue that such a debate may be better understood as a disagreement, at bottom normatively, motivated, insofar as a normative and non-factual question may be involved in it: how the relevant piece of language ought (...) to be used. Even though I will generally assess the prospects for a broadly deflationist approach, I shall outline a sense in which the dispute can be recast as ‘minimally substantive’. (shrink)

Drawing on ‘interpretational’ accounts of scientific representation, I argue that the use of so-called ‘toy models’ provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models (...) should nevertheless be considered accurate representations. A corollary of my discussion is a novel way of thinking about idealization more generally: idealized models may distort features of their targets, but they needn’t misrepresent them. (shrink)