In this paper, by means of a novel use of insights from the literature on scientific modelling, I will argue in favour of an instrumentalist approach to the models that are crucially involved in the study of adaptive systems within the Free-Energy Principle (FEP) framework. I will begin (§2) by offering a general, informal characterisation of FEP. Then (§3), I will argue that the models involved in FEP-theorising are plausibly intended to be isomorphic to their targets. This will allow (§4) (...) to turn the criticisms moved against isomorphism-based accounts of representation towards the FEP modelling practice. Since failure to establish an isomorphism between model and target would result in the former’s failure to represent the latter, and given that it is highly unlikely that FEP-models are ever isomorphic to their targets, maintaining that FEP-models represent their targets as they are, in a realist sense, is unwarranted. Finally (§5), I will consider what implications my argument in favour of an instrumentalist reading of FEP-models has for attempts at making use of the FEP to elaborate an account of what cognition exactly is. My conclusion is that we should not dismiss FEP-based accounts of cognition, as they would still be informative and would further our understanding of the nature of cognition. Nonetheless, the prospects of settling the philosophical debates that sparked the interest in having a “mark of the cognitive” are not good. (shrink)

This paper aims to meet an objection that has been raised against structural empiricism known as the “loss of reality objection.” I argue that an inferential approach to scientific representation allows the structural empiricist to account for the representation of phenomena by data models and ensures that such a representation is not arbitrary. By the notions of immersion, derivation, and interpretation, I show how data models are able to represent phenomena in a non-arbitrary manner. I conclude this paper with a (...) programmatic outline of a view that arises from the theses defended throughout the article and that I would like to call “semantic structuralism.”. (shrink)

A central goal of the scientific endeavor is to explain phenomena. Scientists often attempt to explain a phenomenon by way of representing it in some manner—such as with mathematical equations, models, or theory—which allows for an explanation of the phenomenon under investigation. However, in developing scientific representations, scientists typically deploy simplifications and idealizations. As a result, scientific representations provide only partial, and often distorted, accounts of the phenomenon in question. Philosophers of science have analyzed the nature and function of how (...) scientists construct representations, deploy idealizations, and provide explanations. As such, our aim in this special issue is to bring these three pillars of research into closer contact with the contributions to it focusing on three main themes. -/- The first set of papers, Alan Baker (2021) and Marc Lange (2021), address mathematical explanations in science. Baker (2021), a proponent of mathematical Platonism, examines its capacity to evade the critique that the so-called Enhanced Indispensability Argument is circular. Lange (2021) examines distinctively mathematical explanations, arguing that neither Platonism nor representationalism are successful paths, and instead argues in favor of Aristotelian realism. A second theme emerging from the papers in this special issue is the impact that various conceptualizations of idealization have on our abilities to offer scientific explanations, to pro- duce an analysis of what explanations are or should be, and to understand scientific representation. Peter Tan (2021) suggests amending inferentialist accounts of scientific representation to account for inconsistent idealizations. Michael Strevens (2021) advocates a view wherein the introduction of idealizations into a model is legitimate so long as it pertains to non-difference-making factors, arguing for a logical reading of the notion of difference-making. Natalia Carrillo and Tarja Knuuttila (2022) offer an alternative account to the idealization-as-distortion view, emphasizing instead the holistic nature of idealization. -/- Finally, contributions by Carrillo and Knuuttila (2022), Terzian (2021), Valente (2021), and Rodriguez (2021) illustrate how issues regarding idealization, representation, and explanation are applied to specific contexts and across various sciences. Carrillo and Knuuttila (2022) examine conceptions of idealization in the context of models of the nerve impulse. Giulia Terzian (2021) extends the discussion of idealizations to the context of generative linguistics. Giovanni Valente (2021) examines how idealizations and evaluations of accurate representation impact capacities to explain phenomena in the context of statistical thermodynamics. Quentin Rodriguez (2021) examines the role of idealizations and analogies in various strategies to explain critical phenomena. -/- In what follows, we offer a brief overview of important philosophical issues connected to representation, idealization, and explanation in science. We then provide short summaries of the eight papers in this special issue. (shrink)

Williams and J. Fraser have recently argued that effective field theory methods enable scientific realists to make more reliable ontological commitments in quantum field theory than those commonly made. In this paper, I show that the interpretative relevance of these methods extends beyond the specific context of QFT by identifying common structural features shared by effective theories across physics. In particular, I argue that effective theories are best characterized by the fact that they contain intrinsic empirical limitations, and I extract (...) from their structure one central interpretative constraint for making more reliable ontological commitments in different subfields of physics. While this is in principle good news, this constraint still raises a challenge for scientific realists in some contexts, and I bring the point home by focusing on Williams’s and J. Fraser’s defense of selective realism in QFT. (shrink)

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, (...) which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies. (shrink)

Mathematics appears to play an explanatory role in science. This, in turn, is thought to pave a way toward mathematical Platonism. A central challenge for mathematical Platonists, however, is to provide an account of how mathematical explanations work. I propose a property-based account: physical systems possess mathematical properties, which either guarantee the presence of other mathematical properties and, by extension, the physical states that possess them; or rule out other mathematical properties, and their associated physical states. I explain why Platonists (...) should accept that physical systems have mathematical properties, and why a property based account is better than existing accounts of mathematical explanation. I close by considering whether nominalists can accept the view I propose here. I argue that they cannot. (shrink)

Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own (...) solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus’s parts and hence violates representational “gameplay.”. (shrink)

Callender and Cohen have proposed to apply a “Gricean strategy” to the constitution problem of scientific representation, taking inspiration from Grice’s reduction of linguistic meaning to mental states. They suggest that scientific representation can be reduced to stipulation by epistemic agents. This account has been criticised for not making a distinction between symbolic and epistemic representation and not taking into account the communal aspects of scientific representation. I argue that these criticisms would not apply if Grice’s actual strategy were properly (...) employed. I present Grice’s account of linguistic meaning and transpose his actual strategy and method to epistemic representation. This results in a reduction of epistemic representation to mental states that does not fall prey to the same criticisms. The main novelty of the resulting account is a distinction between contextual representational use and general representational status, which I address using the notion of indexicality. (shrink)

The claim that there can be relations without relata, submitted by the radical ontic structural realist, mounts a serious challenge to her: on the one hand, the world is constituted, according to this sort of realism, just by structures and relations, and on the other hand, relations depend, mathematics says, on individual objects as relata. To resolve the problem, Steven French has argued that while the dependency of relations on relata is conceivable concerning the structure associated with the source of (...) representation, radical ontic structural realism assumes the existence of relations devoid of relata regarding the target of representation. In this article, I will argue this solution considered together with his semantics for the sentences that contain terms referring apparently to individual objects seems incoherent. (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)

Semantic realism can be characterised as the idea that scientific theories are truth-bearers, and that they are true or false in virtue of the world. This notion is often assumed, but rarely discussed in the literature. I examine how it fares in the context of the semantic view of theories and in connection with the literature on scientific representation. Making sense of semantic realism requires specifying the conditions of application of theoretical models, even for models that are not actually used, (...) which leads to several difficulties. My conclusion is that semantic realism is far more demanding than one would expect. Finally, I briefly examine some pragmatist alternatives. (shrink)

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...) higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)

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)

It is commonly claimed that the universality of critical phenomena is explained through particular applications of the renormalization group. This article has three aims: to clarify the structure of the explanation of universality, to discuss the physics of such RG explanations, and to examine the extent to which universality is thus explained. The derivation of critical exponents proceeds via a real-space or a field-theoretic approach to the RG. Building on work by Mainwood, this article argues that these approaches ought to (...) be distinguished: while the field-theoretic approach explains universality, the real-space approach fails to provide an adequate explanation. (shrink)

Callender and Cohen argue that there is no need for a special account of the constitution of scientific representation. I argue that scientific representation is communal and therefore deeply tied to the practice in which it is embedded. The communal nature is accounted for by licensing, the activities of scientific practice by which scientists establish a representation. A case study of the Lotka-Volterra model reveals how licensure is a constitutive element of the representational relationship. Thus, any account of the constitution (...) of scientific representation must account for licensing, meaning that there is a special problem of scientific representation. (shrink)

Van Fraassen argues that data provide the target-end structures required by structuralist accounts of scientific representation. But models represent phenomena not data. Van Fraassen agrees but argues that there is no pragmatic difference between taking a scientific model to accurately represent a physical system and accurately represent data extracted from it. In this article I reconstruct his argument and show that it turns on the false premise that the pragmatic content of acts of representation include doxastic commitments.

I articulate and defend a new theory of what it is for a physical system to implement an abstract computational model. According to my descriptivist theory, a physical system implements a computational model just in case the model accurately describes the system. Specifically, the system must reliably transit between computational states in accord with mechanical instructions encoded by the model. I contrast my theory with an influential approach to computational implementation espoused by Chalmers, Putnam, and others. I deploy my theory (...) to illuminate the relation between computation and representation. I also rebut arguments, propounded by Putnam and Searle, that computational implementation is trivial. (shrink)

Since the publication of his seminal monograph "The scientific image", Bas van Fraassen is a key figure in philosophy of science. In this book, other philosophers with various outlooks critically discuss his work on theories, empiricism and philosophical stances. The book starts with a new article by van Fraassen on his preferred account of theories, the so-called semantic view. This account is now 50 years old, and van Fraassen takes this anniversary as an opportunity to review the account, its history (...) and the philosophical discussion about it. In the main part of the book, Nancy Cartwright, Finnur Dellsén, Matthias Egg, Steven French, Michael Friedman, Milena Ivanova and Michela Massimi discuss van Fraassen's contributions to philosophy. Three chapters focus on his engagement with realism (French, Friedman, Ivanova). Others study his voluntarism (Cartwright) and his view on representation (Massimi). Finally, there are contributions about his elaboration of empiricism (Dellsén) and his proposal to consider philosophical positions as stances (Egg). The volume includes a laudatio written by Steven French and finishes with a reply by van Fraassen to his critics. (shrink)

In There Are No Such Things As Theories (French 2020), the reification of theories is critically analysed and rejected. My aim here is to tease out some of the implications of this approach first of all, for how we, philosophers of science, should view the history of science; secondly, for how we should understand the devices that we use in our own philosophical practices; and thirdly, for how we might think about the relationship between the history of science and the (...) philosophy of science. (shrink)

While many recent accounts of scientific representation have given a central role to the agency and intentions of scientists in explaining representation, they have left these agential concepts unanalyzed. An account of scientific, representational actions will be a useful piece in offering a more complete account of the practice of representation in science. Drawing on an Anscombean approach to the nature of intentional actions, the Means-End Account of Scientific, Representational Actions describes three features of scientific, representational actions: (I) the final (...) description in the means-end ordering of descriptions is some scientific aim; (II) that interaction with a vehicle distinct from its target stands as an earlier description which is ordered toward the final description as means to end; and (III) the means-end structure is licensed by scientific practice. After describing each of the components of the Means-End Account in greater detail through the example of the representational use of a mathematical model, I explain how it can demarcate scientific, representational actions from other sorts of actions. I close by describing some payoffs of the account, showing how it contributes to a more thorough understanding of the practice of representation in science and how it can be of use in understanding the close connection between representation and other forms of scientific activity. (shrink)

How does mathematics apply to something non-mathematical? We distinguish between a general application problem and a special application problem. A critical examination of the answer that structural mapping accounts offer to the former problem leads us to identify a lacuna in these accounts: they have to presuppose that target systems are structured and yet leave this presupposition unexplained. We propose to fill this gap with an account that attributes structures to targets through structure generating descriptions. These descriptions are physical descriptions (...) and so there is no such thing as a solely mathematical account of a target system. (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)

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 book is a sustained defense of the compatibility of the presence of idealizations in the sciences and scientific realism. So, the book is essentially a detailed response to the infamous arguments raised by Nancy Cartwright to the effect that idealization and scientific realism are incompatible.

Representationalists and anti-representationalists disagree about whether a naturalisation of mental content is possible and, hence, whether positing mental representations in cognitive science is justified. Here, I develop a novel way to think about mental representations based on a philosophical description of (cognitive) science inspired by cognitive instrumentalism. On this view, our acceptance of theories positing mental representations and our beliefs in (something like) mental representations do not depend on the naturalisation of content. Thus, I conclude that if we endorse cognitive (...) instrumentalism about mental representations, then we can finally leave the dispute between representationalism and anti-representationalism behind. (shrink)

Agent-based accounts of scientific representation all agree that the representational relationship is constituted by the actions of scientists. Despite this agreement, there are several differences in how agent-based accounts describe scientific representation. In this essay, I argue that these differences do not undercut the compatibility between the accounts. I make my argument by examining the nature of human agency and demonstrating that scientific, representational actions are multiply describable. I then argue that the differences between the accounts are valuable because they (...) help to bring different parts of the representational practices of science into greater focus. (shrink)

Theories of scientific representation, following Chakrawartty's categorization, are divided into two groups. Whereas cognitive-functional views emphasize agents' intentions, informational theories stress the objective relation between represented and representing. In the first part, a modified structuralist theory is introduced that takes into account agents' intentions. The second part is devoted to dismissing a criticism against the structural account of representation on which similarity as the backbone of representation raises serious problems, since it has definite logical features, i.e. reflexivity, symmetry and transitivity, (...) which representation lacks. Drawing on the representational relation between quantum and statistical field theories, I argue that scientific representation displays these logical features, although depending on the context they may be used or not. (shrink)

In recent years, the use of historical cases in philosophy of science has become a proper topic of reflection. In this article I will contribute to this research by means of a discussion of one very famous example of case-based philosophy of science, namely the debate on the London & London model of superconductivity between Cartwright, Suárez and Shomar on the one hand, and French, Ladyman, Bueno and Da Costa on the other. This debate has been going on for years, (...) without any satisfactory resolution. I will argue here that this is because both sides impose on the historical case a particular philosophical conception of scientific representation that does not do justice to the historical facts. Both sides assume, more specifically, that the case concerns the discovery of a representational connection between a given experimental insight – the Meissner effect – and the diamagnetic meaning of London and London’s new equations of superconductivity. I will show, however, that at the time of the Londons’ publication, neither the experimental insight nor the meaning of the new equations was established: both were open for discussion and they were stabilized only later. On the basis of this historical discussion, I will then propose an alternative approach to the case study: the case should not be seen as a site of confrontation between pre-existing philosophical accounts, but rather as a way to historically elaborate and develop philosophical concepts. I will then show how approaching the historical episode in this way suggests an alternative approach to the philosophical study of representation, according to which it involves the establishment, over time, of a stable connection between constellations of different elements that, through discussion and engagement with alternative views and approaches, come to constitute phenomenon and meaning. (shrink)

In this essay, I examine the role of dissimilarity in scientific representation. After briefly reviewing some of the philosophical literature which places a strong emphasis on the role of similarity, I turn to examine some work from Carroll and Borges which demonstrates that perfect similarity is not valuable in the representational use of maps. Expanding on this insight, I go on to argue that this shows that dissimilarity is an important part of the representational use of maps—a point I then (...) extend to the case of scientific representation. Relying on some work from Latour, I argue that dissimilarity plays an essential role in representational practice, by providing novel forms of manipulation and use which affords the achievement of various epistemic and nonepistemic aims. After showing how this point connects to some other literature on scientific representation, I discuss some examples of the value of dissimilarity in the use of representational vehicles. Overall, I argue that to understand scientific representation, we will need to consider more than just similarity. We will need to explore dissimilarities as well. (shrink)

Styles of reasoning are important devices to understand scientific practice. As I use the concept, a style of reasoning is a pattern of inferential relations that are used to select, interpret, and support evidence for scientific results. In this paper, I defend the view that there is a plurality of styles of reasoning: different domains of science often invoke different styles. I argue that this plurality is an important source of disunity in scientific practice, and it provides additional arguments in (...) support of the disunity claim. I also contrast Ian Hacking’s broad characterization of styles of reasoning with a narrow understanding that I favor. Drawing on examples from molecular biology, chemistry and mathematics, I argue that differences in style of reasoning lead to differences in the way the relevant results are obtained and interpreted. The result is a pluralist view about styles of reasoning that is sensitive to nuances of inferential relations in scientific activity. (shrink)

Interpretation plays a central role in using scientific models to explain natural phenomena: Meaning must be bestowed upon a model in terms of what it is and what it represents to be used for model explanations. However, it remains unclear how capacious and complex interpretation in models can be, particularly when conducted by the same group of scientists in the context of one explanatory project. This paper sheds light upon this question by examining modelling and explanatory practices related to the (...) Olami-Feder-Christensen model of earthquakes. This case study shows that various interpretations are intricately connected in the overall meaning of a model used for model explanations. This leads to a manifold picture of interpretation, according to which scientific models are construed as networks of interconnected meanings. As scientists ponder and integrate these various interpretations, guided by locally attended epistemic interests, they achieve model explanations with layers of content, both in their explanantia and explananda. (shrink)

The structuralist approach represents the relation between a model and physical system as a relation between two mathematical structures. However, since a physical system is _prima facie_ _not_ a mathematical structure, the structuralist approach seemingly fails to represent the fact that science is about concrete, physical reality. In this paper, I take up this _problem of lost reality_ and suggest how it may be solved in a purely structuralist fashion. I start by briefly introducing both the structuralist approach and the (...) problem of lost reality and discussing the various (non-structuralist) solutions that have been proposed in the literature. Following this, I decompose the problem into the _ontological mismatch_ and _specification_ problems. In response to the former, I present a _metascientific dissolution argument_, according to which the difference in kind between mathematical structures and physical systems poses no deep obstacle to the structuralist approach, and consider some upshots of this argument for our views on representation. By way of conclusion, I argue that the metascientific dissolution argument paves the way for a solution to the specification problem as well. (shrink)

In this article I connect two debates in the philosophy of science: the questions of scientific representation and both model and theoretical equivalence. I argue that by paying attention to how a model is used to draw inferences about its target system, we can define a notion of theoretical equivalence that turns on whether models license the same claims about the same target systems. I briefly consider the implications of this for two questions that have recently been discussed in the (...) context of the formal philosophy of science. (shrink)

This article defends a pragmatic and structuralist account of scientific representation of the kind recently proposed by Bas van Fraassen against criticisms of both the structuralist and the pragmatist plank of the account. I argue that the account appears to have the unacceptable consequence that the domain of a theory is restricted to phenomena for which we actually have constructed a model—a worry arising from the account’s pragmatism, which is exacerbated by its structuralism. Yet, the account has the resources, at (...) least partially, to address the worry. What remains as implication is a strong anti-foundationalism. 1 Introduction2 ‘No Representation without Representer’3 Representational Structuralism3.1 Do structural models need to be concretely fitted out?3.2 The triviality objection4 Anti-foundationalism5 Conclusion. (shrink)

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)

Two of the most important outcomes of The Contradictory Christ include: identifying Christ as an unproblematically contradictory being as well as laying the foundations of an investigation of the logical consequences of the existence of Christ, qua contradictory, within a particular 'theory'. In light of the enormous reluevance of Beall’s The contradictory Christ for the study of inconsistency, my main concern here is to explore the effect of some methodological choices behind Beall’s proposal -this in order to recognize in more (...) detail the scope of Beall’s contribution. To do so, I will focus on three main questions: 1. What is required for the identification of a contradiction? 2. How can we recognize a true contradiction from either an apparent or a temporal contradiction? 3. If we identify a true contradiction within a theory, where can we actually go from there? (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.

Inconsistency toleration is the phenomenon of working with inconsistent information without threatening one’s rationality. Here I address the role that ignorance plays for the tolerance of contradictions in the empirical sciences. In particular, I contend that there are two types of ignorance that, when present, can make epistemic agents to be rationally inclined to tolerate a contradiction. The first is factual ignorance, understood as temporary undecidability of the truth values of the conflicting propositions. The second is what I call “ignorance (...) of theoretical structure”, which is lack of knowledge of relevant inference patterns within a specific theory. I argue that these two types of ignorance can be explanatory of the scientists’ rational disposition to be tolerant towards contradictions, and I illustrate this with a case study from neutrino physics. (shrink)

In this essay, I will expand the philosophical discussion about the representational practice in science to examine its role in science education through four case studies. The cases are of what I call ‘educational laboratory experiments’, performative models used representationally by students to come to a better understanding of theoretical knowledge of a scientific discipline. The studies help to demonstrate some idiosyncratic features of representational practices in science education, most importantly a lack of novelty and discovery built into the ELEs (...) as their methodology is solidified when it becomes a widely spread educational tool within a discipline. There is thus an irreducible role for the historical development of ELEs in understanding their representational nature and use. The important role of the historical development of ELEs leads to an interesting way that educators can use ELEs as a means of connecting students to important historical developments within their disciplines. (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)

The thesis proposes an account of the means of scientific representation focused on similarity, or more specifically, on the notion of “creative similarity”. I first distinguish between two different questions regarding the problem of representation: the question about the constituents and the question about the means of representation (following Suárez 2003; van Fraassen 2008). I argue that, although similarity is not a good candidate for constituent of representation, it can satisfactorily answer the question about the means of representation if adequately (...) characterized. To motivate this position, I dispute the main arguments offered against similarity as means of representation, namely the arguments from variety, vagueness, and misrepresentation, and contend that similarity plays a central epistemic role in practices of representing in science. The study of the role of similarity in scientific practice, I argue, requires the analysis of the uses of judgments of similarity in the construction of scientific models. I examine the cases of the Mississippi Basin Model and the San Francisco Bay Model to illustrate how judgments of similarity are directly involved in the production of epistemically fruitful models. Informed by the practical investigation developed throughout the thesis, I finally outline what I call the creative similarity account of the means of representation. The notion of “creative similarity” helps to capture the way in which similarity, as means of representation, intervenes in actual practices of representing, namely, in the form of a productive interplay of judgments of similarity and distortions (i.e. idealizations, abstractions, simplifications), which is employed by resourceful agents with the aim of understanding aspects of the natural world. (shrink)

According to standard accounts of mathematical representations of physical phenomena, positing structure-preserving mappings between a physical target system and the structure picked out by a mathematical theory is essential to such representations. In this paper, I argue that these accounts fail to give a satisfactory explanation of scientific representations that make use of inconsistent mathematical theories and present an alternative, robustly inferential account of mathematical representation that provides not just a better explanation of applications of inconsistent mathematics, but also a (...) compelling explanation of mathematical representations of physical phenomena in general. 1Inconsistent Mathematics and the Problem of Representation2The Early Calculus3Mapping Accounts and the Early Calculus3.1Partial structures3.2Inconsistent structures3.3Related total consistent structures4A Robustly Inferential Account of the Early Calculus in Applications 4.1The robustly inferential conception of mathematical representation4.2The robustly inferential conception and inconsistent mathematics4.3The robustly inferential conception and mapping accounts5Beyond Inconsistent Mathematics. (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)

The notion of scientific representation plays a central role in current debates on modeling in the sciences. One or maybe the major epistemic virtue of successful models is their capacity to adequately represent specific phenomena or target systems. According to similarity views of scientific representation, models should be similar to their corresponding targets in order to represent them. In this paper, Suárez’s arguments against similarity views of representation will be scrutinized. The upshot is that the intuition that scientific representation involves (...) similarity is not refuted by the arguments. The arguments do not make the case for the strong claim that similarity between vehicles and targets is neither necessary nor sufficient for scientific representation. Especially, one claim that a similarity view wants to uphold, still, is the following thesis: only if a vehicle is similar to a target in relevant respects and to a specific degree of similarity then the vehicle is a scientific representation of that target. (shrink)

While many recent accounts of scientific representation have given a central role to the agency and intentions of scientists in explaining representation, they have left these agential concepts unanalyzed. An account of scientific, representational actions will be a useful piece in offering a more complete account of the practice of representation in science. Drawing on an Anscombean approach to the nature of intentional actions, the Means-End Account of Scientific, Representational Actions describes three features of scientific, representational actions: the final description (...) in the means-end ordering of descriptions is some scientific aim; that interaction with a vehicle distinct from its target stands as an earlier description which is ordered toward the final description as means to end; and the means-end structure is licensed by scientific practice. After describing each of the components of the Means-End Account in greater detail through the example of the representational use of a mathematical model, I explain how it can demarcate scientific, representational actions from other sorts of actions. I close by describing some payoffs of the account, showing how it contributes to a more thorough understanding of the practice of representation in science and how it can be of use in understanding the close connection between representation and other forms of scientific activity. (shrink)