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.

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)

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)

It is plausible to think that, in order to actively employ models in their inquiries, scientists should be aware of their existence. The question is especially puzzling for realists in the case of abstract models, since it is not obvious how this is possible. Interestingly, though, this question has drawn little attention in the relevant literature. Perhaps the most obvious choice for a realist is appealing to intuition. In this paper, I argue that if scientific models were abstract entities, one (...) could not be aware of them intuitively. I deploy my argumentation by building on Chudnoff’s elaboration on intuitive awareness. Furthermore, I shortly discuss some other options to which realists could turn in order to address the question of awareness. (shrink)

Many philosophers have drawn parallels between scientific models and fictions. In this paper I will be concerned with a recent version of the analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an ineliminable role in (...) the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model-systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model-systems is misguided. If model-systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model-systems. (shrink)

It is common to distinguish between conscious mental episodes and standing mental states — those mental features like beliefs, desires or intentions, which a subject can have even if she is not conscious, or when her consciousness is occupied with something else. This paper presents a view of standing mental states according to which these states are less real than episodes of consciousness. It starts from the usual view that states like beliefs and desires are not directly present to the (...) mind, but are posited to explain thought and behaviour. Ascriptions of these states are attempts to model a subject’s “Worldview”: the totality of their cognitive, conative and affective dispositions. When ascribing standing states, we often significantly simplify the complexity of the Worldview. This simplification is a feature of models in general. The Worldview is also distinguished from the “Habitus” — the totality of dispositions that are associated with character traits. As with the Worldview, modelling the Habitus should be treated as a useful simplification of a very complex reality. It is argued that although these models have some similarities with fictions, these similarities are outweighed by the dissimilarities. (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)

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)

In this essay, I first consider a popular view of models and modeling, the similarity view. Second, I contend that arguments for it fail and it suffers from what I call “Hughes’ worry.” Third, I offer a deflationary approach to models and modeling that avoids Hughes’ worry and shows how scientific representations are of apiece with other types of representations. Finally, I consider an objection that the similarity view can deal with approximations better than the deflationary view and show that (...) this is not so. (shrink)

Science provides us with representations of atoms, elementary particles, polymers, populations, genetic trees, economies, rational decisions, aeroplanes, earthquakes, forest fires, irrigation systems, and the world’s climate. It's through these representations that we learn about the world. This entry explores various different accounts of scientific representation, with a particular focus on how scientific models represent their target systems. As philosophers of science are increasingly acknowledging the importance, if not the primacy, of scientific models as representational units of science, it's important to (...) stress that how they represent plays a fundamental role in how we are to answer other questions in the philosophy of science. This entry begins by disentangling ‘the’ problem of scientific representation, before critically evaluating the current options available in the literature. (shrink)

Veritism, the position that truth is necessary for epistemic acceptability, seems to be in tension with the observation that much of our best science is not, strictly speaking, true when interpreted literally. This generates a paradox: truth is necessary for epistemic acceptability; the claims of science have to be taken literally; much of what science produces is not literally true and yet it is acceptable. We frame Elgin’s project in True Enough as being motivated by, and offering a particular resolution (...) to, this paradox. We discuss the paradox with a focus on scientific models and argue that there is another resolution available which is compatible with retaining veritism: rejecting the idea that scientific models should be interpreted literally. (shrink)

The ontological status of theories themselves has recently re-emerged as a live topic in the philosophy of science. We consider whether a recent approach within the philosophy of art can shed some light on this issue. For many years philosophers of aesthetics have debated a paradox in the (meta)ontology of musical works (e.g. Levinson [1980]). Taken individually, there are good reasons to accept each of the following three propositions: (i) musical works are created; (ii) musical works are abstract objects; (iii) (...) abstract objects cannot be created. However it seems clear that, if one wants to avoid inconsistency, one cannot commit to all three. Following up recent developments courtesy of Cameron ([2008a]), we consider how one might respond to the corresponding set of propositions in the (meta)ontology of scientific theories. 1 Introduction2 Setting up the Problem3 What to Reject? 3.1 Scientific theories are not created? 3.2 Scientific theories are not abstract objects? 3.3 Abstract objects can be created?4 A Fourth Way: Truth-maker Theory5 Objections and Replies 5.1 What are the truth-makers? 5.2 Objections from World 3 5.3 Fictionalism 5.4 Concrete/abstract6 Conclusion. (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, I propose that the debate in epistemology concerning the nature and value of understanding can shed light on the role of scientific idealizations in producing scientific understanding. In philosophy of science, the received view seems to be that understanding is a species of knowledge. On this view, understanding is factive just as knowledge is, i.e., if S knows that p, then p is true. Epistemologists, however, distinguish between different kinds of understanding. Among epistemologists, there are those who (...) think that a certain kind of understanding—objectual understanding—is not factive, and those who think that objectual understanding is quasi-factive. Those who think that understanding is not factive argue that scientific idealizations constitute cognitive success, which we then consider as instances of understanding, and yet they are not true. This paper is an attempt to draw lessons from this debate as they pertain to the role of idealizations in producing scientific understanding. I argue that scientific understanding is quasi-factive. (shrink)

Thinking of models in science as fictions is said to be helpful, not merely because models are known or assumed to be false, but because work on the nature of fiction helps us understand what models are and how they work. I am unpersuaded. For example, instead of trying to assimilate truth-in-a model to truth-in-fiction we do better to see both as special and separate cases of the more general notion truth-according-to-a-corpus. Does enlightenment go the other way? Do we better (...) understand fiction's capacity to generate knowledge by thinking of it as a kind of modelling? If we see, as we should, fictions and models are parts of larger patterns of cognitive activity that include institutional frameworks, the best answer is no. (shrink)

This article argues that the credibility of both theoretical and empirical models in economics is best understood through their connection with the empirical aspects of the real world. The discussion herein demonstrates that the similarity between the model and the real world is not enough to justify a theoretical model’s explanatory power. The best way to secure the model’s credibility is to prove the existence of representation theorems.

Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the (...) ideas that scientific models are ‘similar’, or structurally morphic, to their target systems. I argue that these approaches are misguided, and that at best these relationships concern the accuracy of a pre-existing representational relationship. I also pay particular attention to the sense in which target systems can be appropriately taken to exhibit a ‘structure’, and van Fraassen’s recent argument concerning the pragmatic equivalence between representing phenomena and data. My next target is the idea that models should not be seen as objects in their own right, but rather what look like descriptions of them are actually direct descriptions of target systems, albeit not ones that should be understood literally. I argue that these approaches fail to do justice to the practice of scientific modelling. Finally I turn to the idea that how models represent is grounded, in some sense, in their inferential capacity. I compare this approach to anti-representationalism in the philosophy of language and argue that analogous issues arise in the context of scientific representation. Part iii contains my positive proposal. I provide an account of scientific representation based on Goodman and Elgin’s notion of representation-as. The result is a highly conventional account which is the appropriate level of generality to capture all of its instances, whilst remaining informative about the notion. I illustrate it with reference to the Phillips-Newlyn machine, models of proteins, and the Lotka-Volterra model of predator-prey systems. These examples demonstrate how the account must be understood, and how it sheds light on our understanding of how models are used. I finally demonstrate how the account meets the desiderata laid out at the beginning of the thesis, and outline its implications for further questions from the philosophy of science; not limited to issues surrounding the applicability of mathematics, idealisation, and what it takes for a model to be ‘true’. (shrink)

The paper aims to provide a detailed assessment of Tim Crane’s recent invocation of the notion of scientific models in the way of dealing with the issue of the brain’s representational states. In this paper, I assess Crane’s proposal under a charitable and a less charitable reading. I argue that Crane’s use of scientific models is at best compatible with his expression of psychological realism. However, Crane’s use of model-based strategy by no means underlay, support, or strengthen his psychological realism. (...) Rather, traditional reasons from the metaphysics of mind, acting quite separately from empirical scientific reasons, must be used to support this realism, and Crane has merely removed the threat posed to these metaphysical arguments by the existence of the so-called mereological fallacy in empirical neuroscience. Therefore, while he has saved the neuroscientific language from philosophical attack, he has perhaps even strengthened the divide between neuroscience and philosophy. I conclude the paper by outlining the sketch of a broadbrush scientifically informed strategy for defending psychological realism. (shrink)

Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also drawn parallels (...) between models and fiction. This chapter examines these approaches to scientific modeling and considers the challenges they face. (shrink)

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: It is often assumed without argument that fictionalism in the philosophy of science contradicts scientific realism. This paper is a critical analysis of this assumption. The kind of fictionalism that is at present discussed in philosophy of science is characterised, and distinguished from fictionalism in other areas. A distinction is then drawn between forms of fictional representation, and two competing accounts of fiction in science are discussed. I then outline explicitly what I take to be the argument for the (...) incompatibility of scientific realism with fictionalism. I argue that some of its premises are unwarranted, and are moreover questionable from a fictionalist perspective. The conclusion is that fictionalism is neutral in the realism-antirealism debate, pulling neither in favour nor against scientific realism. (shrink)

Climatologists have recently introduced a distinction between projections as scenario-based model results on the one hand and predictions on the other hand. The interpretation and usage of both terms is, however, not univocal. It is stated that the ambiguities of the interpretations may cause problems in the communication of climate science within the scientific community and to the public realm. This paper suggests an account of scenarios as props in games of make-belive. With this account, we explain the difference between (...) projections that should be make-believed and other model results that should be believed. (shrink)

I critically examine some recent work on the philosophy of scientific fictions, focusing on the work of Winsberg. By considering two case studies in fracture mechanics, the strip yield model and the imaginary crack method, I argue that his reliance upon the social norms associated with an element of a model forces him to remain silent whenever those norms fail to clearly match the characteristic of fictions or non-fictions. In its place, I propose a normative epistemology of fictions which clarifies (...) a model's ontological commitments when the community of scientists lack any clear, shared norms of use. Specifically, I will introduce a variational account of fictions as an extension of Laymon's monotonic improvability account of idealizations. I will conclude by connecting this variational account of fictions to didactic fictions in general. (shrink)

The goal of this paper is to show how scientific explanation functions in the context of idealized models. It argues that the aspect of explanation most urgently requiring investigation is the nature of the connection between global theories and explanatory local models. This aspect is neglected in traditional accounts of explanation. The paper examines causal, minimal model, and structural accounts of model-based explanation. It argues that they too fail to offer an account of the connection with global theory that can (...) justify the explanatory power of an idealized local model, and consequently these accounts are unable effectively to distinguish explanatory from non-explanatory models. On the account proposed here, scientific explanation requires theoretical integration between the local model described in the explanation and a global theory with independent explanatory power. (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)