Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

Making sense of modeling: beyond representation Content Type Journal Article Category Original paper in Philosophy of Science Pages 335-352 DOI 10.1007/s13194-011-0032-8 Authors Isabelle Peschard, Philosophy Department, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3.

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)

Multiply realizable kinds are scientifically problematic, for it appears that we should not expect discoveries about them to hold of other members of that kind. As such, it looks like MR kinds should have no place in the ontology of the special sciences. Many resist this conclusion, however, because we lack a positive account of the role that certain realization-unrestricted terms play in special science explanations. I argue that many such terms actually pick out idealizing models. Idealizing explanation has many (...) of the features normally associated with explanation by MR kinds. As idealized models are usually mere possibilia, such explanations do not run afoul of the metaphysical problems that plague MR kinds. (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)

En oposición al escepticismo declarado por Callender y Cohen respecto de la existencia de algún problema en torno a la representación científica, el objetivo de este artículo es mostrar un conjunto de características peculiares de la representación científica, aunque no exclusivas, que una teoría satisfactoria debe explicar. Estas son: la predicación, la posibilidad de ser una representación incorrecta, el razonamiento subrogatorio y la independencia entre el contenido y la denotación. Además, se argumenta que una teoría satisfactoria de la representación debe (...) ofrecer una definición de sus medios representacionales, es decir, de lo que es un modelo científico. Finalmente, se propone que para dar cuenta de estas características basta responder la pregunta: ¿Cómo nos permiten los modelos científicos ganar información sobre el objeto representado? (shrink)

Scientific research is constrained by limited resources, so it is imperative that it be conducted efficiently. This paper introduces the notion of epistemic expression, a kind of representation that expedites the solution of research problems. Epistemic expressions are representations that (i) contain information in a way that enables more reliable information to place the most stringent constraints on possible solutions and (ii) make new information readily extractible by biasing the search through that space. I illustrate these conditions using historical and (...) contemporary examples of biomolecular structure determination. Then, I argue that the notion of epistemic expression parts ways with pragmatic accounts of scientific representation and an understanding of models as artifacts, neither of which require models to accurately represent. Explicating epistemic expression thus fills a gap in our understanding of scientific practice, extending Morrison and Morgan's (1999) conception of models as investigative instruments. (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)

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)

Formal criteria of theoretical equivalence are mathematical mappings between specific sorts of mathematical objects, notably including those objects used in mathematical physics. Proponents of formal criteria claim that results involving these criteria have implications that extend beyond pure mathematics. For instance, they claim that formal criteria bear on the project of using our best mathematical physics as a guide to what the world is like, and also have deflationary implications for various debates in the metaphysics of physics. In this paper, (...) I investigate whether there is a defensible view according to which formal criteria have significant non-mathematical implications, of these sorts or any other, reaching a chiefly negative verdict. Along the way, I discuss various foundational issues concerning how we use mathematical objects to describe the world when doing physics, and how this practice should inform metaphysics. I diagnose the prominence of formal criteria as stemming from contentious views on these foundational issues, and endeavor to motivate some alternative views in their stead. (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.

What are scientific models? Philosophers of science have been trying to answer this question during the last three decades by putting forward a number of different proposals. Some say that models are best understood as abstract Platonic objects or fictional entities akin to Sherlock Holmes, while others focus on their mathematical nature and see them as set theoretical structures. Although each account has its own strengths in offering various insights on the nature of models, several objections have been raised against (...) these views which still remain unanswered, making the debate on the ontology of models seem unresolvable. The primary aim of this paper is to show that a large part of these difficulties stems from an inappropriate reading of the main question on the ontology of models as a purely metaphysical question. Building on Carnap, it is argued that the question of the ontology of scientific models is either an internal theoretical question within an already accepted linguistic framework or an external practical question regarding the choice of the most appropriate form of language in order to describe and explain the practice of scientific modelling. The main implication of this view is that the question of the ontology of models becomes a means of probing other related questions regarding the overall practice of scientific modelling, such as questions on the capacity of models to provide knowledge and the relation of models with background theories. (shrink)

The purpose of this article is to establish a connection between modelling practices and interpretive approaches in quantum mechanics, taking as a starting point the literature on scientific representation. Different types of modalities play different roles in scientific representation. I postulate that the way theoretical structures are interpreted in this respect affects the way models are constructed. In quantum mechanics, this would be the case in particular of initial conditions and observables. I examine two formulations of quantum mechanics, the standard (...) wave-function formulation and the consistent histories formulation, and show that they correspond to opposite stances, which confirms my approach. Finally, I examine possible strategies for deciding between these stances. (shrink)

This article is an introduction to and advertisement of Erwin Schrödinger’s little-known real-valued wave equation, the first published time dependent Schrödinger equation. I argue that this equation is not merely a historical curiosity. Not only does it show that quantum mechanics need not be viewed as essentially complex-valued, but the real formalism also provides a deep insight into the puzzling nature of time reversal in a quantum world. It is hoped that this observation will stimulate the discovery of other areas (...) where ‘keeping it real’ will prove pedagogically or foundationally useful. (shrink)

I offer an explicit account of the underdetermination thesis as well as of the many challenges it poses to scientific realism; a way to answer to these challenges is explored and outlined, by shifting attention to the content of theories. I argue that, even if we have solid grounds (as I contend we do) to support that some varieties of the underdetermination thesis are true, scientific realism can still offer an adequate picture of the aims and achievements of science.

Perspectival realism combines two apparently contradictory aspects: the epistemic relativity of perspectives and the mind-independence of realism. This paper examines the prospects for a coherent perspectival realism, taking the literature on scientific representation as a starting point. It is argued that representation involves two types of norms, referred to as norms of relevance and norms of accuracy. Norms of relevance fix the domain of application of a theory and the way it categorises the world, and norms of accuracy give the (...) conditions for the theory to be true. Perspectival realism could be made coherent by taking a realist stance towards one type of norm, and a perspectival stance towards the other, by assuming they are relative to a community. This provides two versions of perspectival realism, called relevance perspectivism and accuracy perspectivism. How each option fares with respect to the challenge of incompatible models is examined. Finally, the prospects of full perspectivism are evaluated. (shrink)

Brief overview of the debates held in the workshop on scientific representation, in Prague, May 2018.

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)

In recent decades, philosophers of science have devoted considerable efforts to understand what models represent. One popular position is that models represent fictional situations. Another position states that, though models often involve fictional elements, they represent real objects or scenarios. Though these two positions may seem to be incompatible, I believe it is possible to reconcile them. Using a threefold distinction between different signs proposed by Peirce, I develop an argument based on a proposal recently made by Kralemann and Lattman (...) (in Synthese 190:3397–3420, 2013) that shows that the two aforementioned positions can be reconciled by distinguishing different ways in which a model representation can be used. In particular, on the basis of Peirce’s distinction between icons, indices and symbols, I argue that models can sometimes function as icons, sometimes as indexes and sometimes as symbols, depending on the context in which they are considered and the use that they are developed for because they all have iconic, indexical and symbolic features. In addition, I show that conceiving models as signs enables us to develop an account of scientific representation that meets the main desiderata that Shech (in Synthese 192:3463–3485, 2015) presents. (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)

In this paper we argue that the different positions taken by Dyson and Feynman on Feynman diagrams’ representational role depend on different styles of scientific thinking. We begin by criticizing the idea that Feynman Diagrams can be considered to be pictures or depictions of actual physical processes. We then show that the best interpretation of the role they play in quantum field theory and quantum electrodynamics is captured by Hughes' Denotation, Deduction and Interpretation theory of models (DDI), where “models” are (...) to be interpreted as inferential, non-representational devices constructed in given social contexts by the community of physicists. (shrink)

In this paper, I characterize visual epistemic representations as concrete two- or three-dimensional tools for conveying information about aspects of their target systems or phenomena of interest. I outline two features of successful visual epistemic representation: that the vehicle of representation contain sufficiently accurate information about the phenomenon of interest for the user’s purpose, and that it convey this information to the user in a manner that makes it readily available to her. I argue that actual epistemic representation may involve (...) tradeoffs between these and is successful to the extent that they are present. (shrink)

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

In 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.

What is it for a picture to depict a scene? The most orthodox philosophical theory of pictorial representation holds that depiction is grounded in resemblance. A picture represents a scene in virtue of being similar to that scene in certain ways. This essay presents evidence against this claim: curvilinear perspective is one common style of depiction in which successful pictorial representation depends as much on a picture's systematic differences with the scene depicted as on the similarities; it cannot be analyzed (...) in terms of similarity alone. The same problem arises for many other kinds of depiction. The essay concludes that depiction in general is not grounded in resemblance but geometrical transformation. (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)

This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.

This paper aims to make progress in understanding the nature of reasoning. Its primary goal is to spell out and defend a novel account of what reasoning might be, in terms of how reasoning contributes to settling (practical and theoretical) inquiries. Prior to spelling out this constructive proposal, however, the paper problematizes a very common picture of reasoning in an attempt to demonstrate the need for an alternative approach. The overarching argument of the paper is comprised of three stages. The (...) first attacks the predominant conception of reasoning for its specious restriction to fully conscious and explicit episodes. The second stage offers a replacement for this faulty conception, according to which a reasoning agent is one who may be represented as if she were undergoing a fully conscious and explicit process. Finally, the last stage proposes one way to fill in the above schema, articulating and defending conditions for representing agents as reasoning fully consciously and explicitly. Each of the above stages is independent of the others: One may accept that reasoning need not be conscious and explicit but reject the schema proposed as a replacement; and again, the schema may be accepted but not the particular proposal for filling it in. (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)

A novel account of semantic information is proposed. The gist is that structural correspondence, analysed in terms of similarity, underlies an important kind of semantic information. In contrast to extant accounts of semantic information, it does not rely on correlation, covariation, causation, natural laws, or logical inference. Instead, it relies on structural similarity, defined in terms of correspondence between classifications of tokens into types. This account elucidates many existing uses of the notion of information, for example, in the context of (...) scientific models and structural representations in cognitive science. It is poised to open a new research program concerned with various kinds of semantic information, its functions, and its measurement. (shrink)

We advocate an account of dualities between physical theories: the basic idea is that dual theories are isomorphic representations of a common core. We defend and illustrate this account, which we call a Schema, in relation to symmetries. Overall, the account meshes well with standard treatments of symmetries. But the distinction between the common core and the dual theories prompts a distinction between three kinds of symmetry: which we call ‘stipulated’, ‘accidental’ and ‘proper’.

After a brief presentation of Feynman diagrams, we criticizise the idea that Feynman diagrams can be considered to be pictures or depictions of actual physical processes. We then show that the best interpretation of the role they play in quantum field theory and quantum electrodynamics is captured by Hughes' Denotation, Deduction and Interpretation theory of models, where “models” are to be interpreted as inferential, non-representational devices constructed in given social contexts by the community of physicists.

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)

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 scientific models are representations. Building on Goodman and Elgin’s notion of representation-as we analyse what this claim involves by providing a general definition of what makes something a scientific model, and formulating a novel account of how they represent. We call the result the DEKI account of representation, which offers a complex kind of representation involving an interplay of, denotation, exemplification, keying up of properties, and imputation. Throughout we focus on material models, and we illustrate our claims with the (...) Phillips-Newlyn machine. In the conclusion we suggest that, mutatis mutandis, the DEKI account can be carried over to other kinds of models, notably fictional and mathematical models. (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)

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)

Theories of action tend to require agents to have mental representations. A common trope in discussions of artificial intelligence (AI) is that they do not, and so cannot be agents. Properly understood there may be something to the requirement, but the trope is badly misguided. Here we provide an account of representation for AI that is sufficient to underwrite attributions to these systems of ownership, action, and responsibility. Existing accounts of mental representation tend to be too demanding and unparsimonious. We (...) offer instead a minimalist account of representation that ascribes only those features necessary for explaining action, trimming the “extra” features in existing accounts (e.g., representation as a “mental” phenomenon). Our account makes ‘representation’ whatever it is that, for example, the thermostat is doing with the thermometer. The thermostat is disposed to act as long as the thermometer is outside a given range of parameters.. Our account allows us to offer a new perspective on the ‘responsibility gap’, a problem raised by the actions of sophisticated machines: because nobody has enough control over the machine’s actions to be able to assume responsibility, conventional approaches to responsibility ascription are inappropriate. We argue that there is a distinction between finding responsible and holding responsible and, in order to resolve the responsibility gap, we must first clarify the conceptual terrain on which agent is in fact responsible. (shrink)

Previously, I (Boesch 2017) described a notion called “representational licensing”—the set of activities of scientific practice by which scientists establish the intended representational use of a vehicle. In this essay, I expand and develop this concept of representational licensing. I begin by showing how the concept is of value for both pragmatic and substantive approaches to scientific representation. Then, through the examination of a case study of the Mississippi River Basin Model, I point out and explain some of the activities (...) of representational licensing that help to establish the representational nature of this model. Throughout the exploration of the case study, I pause to identify some important lessons which apply more generally about the nature of representational licensing in science. (shrink)

Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” (...) tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn’t work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling. (shrink)

This paper is a critical response to Andreas Bartels’ sophisticated defense of a structural account of scientific representation. We show that, contrary to Bartels’ claim, homomorphism fails to account for the phenomenon of misrepresentation. Bartels claims that homomorphism is adequate in two respects. First, it is conceptually adequate, in the sense that it shows how representation differs from misrepresentation and non-representation. Second, if properly weakened, homomorphism is formally adequate to accommodate misrepresentation. We question both claims. First, we show that homomorphism (...) is not the right condition to distinguish representation from misrepresentation and non-representation: a “representational mechanism” actually does all the work, and it is independent of homomorphism – as of any structural condition. Second, we test the claim of formal adequacy against three typical kinds of inaccurate representation in science which, by reference to a discussion of the notorious billiard ball model, we define as abstraction, pretence, and simulation. We first point out that Bartels equivocates between homomorphism and the stronger condition of epimorphism, and that the weakened form of homomorphism that Bartels puts forward is not a morphism at all. After providing a formal setting for abstraction, pretence and simulation, we show that for each morphism there is at least one form of inaccurate representation which is not accommodated. We conclude that Bartels’ theory – while logically laying down the weakest structural requirements – is nonetheless formally inadequate in its own terms. This should shed serious doubts on the plausibility of any structural account of representation more generally. (shrink)

This article argues for an anti-deflationist view of scientific representation. Our discussion begins with an analysis of the recent Callender–Cohen deflationary view on scientific representation. We then argue that there are at least two radically different ways in which a thing can be represented: one is purely symbolic, and therefore conventional, and the other is epistemic. The failure to recognize that scientific models are epistemic vehicles rather than symbolic ones has led to the mistaken view that whatever distinguishes scientific models (...) from other representational vehicles must merely be a matter of pragmatics. It is then argued that even though epistemic vehicles also contain conventional elements, they do their job of demonstration in spite of such elements. (shrink)

Modelos representacionais são usados na prática científica para representar diferentes fenômenos. O propósito deste trabalho é examinar o uso de autômatos celulares para representar fenômenos emergentes, isto é, fenômenos com aspectos globais que não podem ser preditos apenas a partir de seus aspectos locais, procurando entender como se dá a representação nesse processo de modelagem. Uma abordagem sugerida é a acepção DEKI desenvolvida por Roman Frigg e James Nguyen, no qual o processo de representação envolve quatro aspectos: denotação do sistema-alvo (...) pelo modelo sob uma interpretação, exemplificação das propriedades relevantes no modelo, formulação de uma legenda que relaciona propriedades do modelo com propriedades do alvo e imputação das propriedades dadas pela chave ao alvo. Nessa acepção, AC representariam os sistemas-alvo denotados como sistemas complexos que podem ser interpretados em nível local e global, de modo que propriedades emergentes são exemplificadas em nível global. (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)

This paper defends the deflationary character of two recent views regarding scientific representation, namely RIG Hughes’ DDI model and the inferential conception. It is first argued that these views’ deflationism is akin to the homonymous position in discussions regarding the nature of truth. There, we are invited to consider the platitudes that the predicate “true” obeys at the level of practice, disregarding any deeper, or more substantive, account of its nature. More generally, for any concept X, a deflationary approach is (...) then defined in opposition to a substantive approach, where a substantive approach to X is an analysis of X in terms of some property P, or relation R, accounting for and explaining the standard use of X. It then becomes possible to characterize a deflationary view of scientific representation in three distinct senses, namely: a “no-theory” view, a “minimalist” view, and a “use-based” view – in line with three standard deflationary responses in the philosophical literature on truth. It is then argued that both the DDI model and the inferential conception may be suitably understood in any of these three different senses. The application of these deflationary ‘hermeneutics’ moreover yields significant improvements on the DDI model, which bring it closer to the inferential conception. It is finally argued that what these approaches have in common – the key to any deflationary account of scientific representation – is the denial that scientific representation may be ultimately reduced to any substantive explanatory property of sources, or targets, or their relations. (shrink)

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)