Scientific representation is a currently booming topic, both in analytical philosophy and in history and philosophy of science. The analytical inquiry attempts to come to terms with the relation between theory and world; while historians and philosophers of science aim to develop an account of the practice of model building in the sciences. This article provides a review of recent work within both traditions, and ultimately argues for a practice-based account of the means employed by scientists to effectively achieve representation (...) in the modelling sciences. (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.

[full article, abstract in English; only abstract in Lithuanian] This article presents the results of a broader research project which aims to argue for the normativity of scientific laws. Usually scientific laws are regarded as descriptive, which contrasts them to prescriptive norms. To show their normativity, I utilize the logical account of explicitly normative systems by Carlos Alchourrón and Eugenio Bulygin. I identify the characteristic elements of normativity and analyse accounts of implicit normativity in science using those terms to show (...) the affinities of explicit and implicit normativities. The research project continues with the substantiation of the normativity of scientific laws in detail and the results will be presented in Normativity of Scientific Laws. (shrink)

General Relativity and the Standard Model often are touted as the most rigorously and extensively confirmed scientific hypotheses of all time. Nonetheless, these theories appear to have consequences that are inconsistent with evidence about phenomena for which, respectively, quantum effects and gravity matter. This paper suggests an explanation for why the theories are not disconfirmed by such evidence. The key to this explanation is an approach to scientific hypotheses that allows their actual content to differ from their apparent content. This (...) approach does not appeal to ceteris-paribus qualifiers or counterfactuals or similarity relations. And it helps to explain why some highly idealized hypotheses are not treated in the way that a thoroughly refuted theory is treated but instead as hypotheses with limited domains of applicability. (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)

This article adopts a bottom-up approach to theory interpretation, following the slogan “meaning is use”, and applies it to quantum mechanics. I argue that it fits very well with the Consistent Histories formulation of quantum mechanics, interpreted in a particular way that is not the interpretation favoured by original proponents of the formulation. I examine the difficulties and advantages of this interpretation.

Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this “hypothetical modeling”. This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account of the epistemology of (...) hypothetical models seems to cohere with the most characteristic function of scientific modeling in general, i.e., surrogative representation. The second epistemological challenge is a version of “reliability challenges” familiar from other areas. There is a challenge to explain how hypothetical models could be a reliable guide to what is possible, given that they are not and cannot be compared against their nonactual targets and updated accordingly. I close with some brief remarks on possible solutions to these challenges. (shrink)

The complex societal challenges of the twenty-first Century require scientific researchers and academically educated professionals capable of conducting scientific research in complex problem contexts. Our central claim is that educational approaches inspired by a traditional empiricist epistemology insufficiently foster the required deep conceptual understanding and higher-order thinking skills necessary for epistemic tasks in scientific research. Conversely, we argue that constructivist epistemologies provide better guidance to educational approaches to promote research skills. We also argue that teachers adopting a constructivist learning theory (...) do not necessarily embrace a constructivist epistemology. On the contrary, in educational practice, novel educational approaches that adopt constructivist learning theories often maintain traditional empiricist epistemologies. Philosophers of science can help develop educational designs focused on learning to conduct scientific research, combining constructivist learning theory with constructivist epistemology. We illustrate this by an example from a bachelor’s program in Biomedical Engineering, where we introduce conceptual models and modeling as an alternative to the traditional focus on hypothesis testing in conducting scientific research. This educational approach includes the so-called B&K method for constructing scientific models to scaffold teaching and learning conceptual modeling. (shrink)

Measurement results are stated in terms of sentences ascribing measured values, as obtained via measurement processes, to measurands, as defined by measuring agents. Since both the definition of the measurands and the characterization of the processes depend on models constructed on the basis of relevant theories, the issue arises of the theory dependence of the truth of those sentences. This paper aims at assessing the question by introducing suitable distinctions about the sense and reference of the terms used to refer (...) to individual and general quantities, in a framework where measurement is construed as an agent-dependent, model-based, purpose-driven activity. (shrink)

Separatists about grounding take explanations to be separate from their corresponding grounding-facts. Grounding-facts are supposed to underlie, or back, such explanations. However, the backing relation hasn’t received much attention in the literature. The aim of this paper is to provide an informative definition of backing. First, I examine two prominent proposals: backing as explaining (Kovacs 2017; 2019a) and backing as grounding (see Sjölin Wirling 2020). Finally, I put forward my own proposal. I argue that under plausible assumptions about the role (...) of backing and the nature of explanation, backing should be understood as a form of truthmaking, minimally construed. (shrink)

Philosophers of science have recently taken care to highlight different modeling practices where scientific models primarily contribute modal information, in the form of for example possibility claims, how-possibly explanations, or counterfactual conditionals. While examples abound, comparatively little attention is being paid to the question of under what conditions, and in virtue of what, models can perform this epistemic function. In this paper, we firstly delineate modal modeling from other modeling practices, and secondly reviewattempts to spell out and explain the epistemic (...) success conditions of modal modeling. The aim is to more clearly expose the respective justificatory strategies of these accounts, and secondly, to identify lacunae where further work is needed. (shrink)

Metaphors play a crucial role in the understanding of science. Since antiquity, metaphors have been used in technical texts to describe structures unknown or unnamed; besides establishing a terminology of science, metaphors are also important for the expression of concepts. However, a concise terminology to classify metaphors in the language of science has not been established yet. But in the context of studying the history of a science and its concepts, a precise typology of metaphors can be helpful. Metaphors have (...) a lot in common with models in science, as has been observed already. In this paper, therefore, I suggest a typology of metaphor in ancient science to fill this terminological gap by using concepts applied to the classification of models in science, as coined by Rom Harré. I propose to differentiate between homeoconceptual metaphors and paraconceptual metaphors. Furthermore, functional and structural aspects of metaphors in ancient science are taken into account. Case studies from ancient texts displaying metaphors in ancient science are presented and classified following the outlined typology of metaphors. (shrink)

This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...

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.

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)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides insight into the common (...) but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

This thesis starts with three challenges to the structuralist accounts of applied mathematics. Structuralism views applied mathematics as a matter of building mapping functions between mathematical and target-ended structures. The first challenge concerns how it is possible for a non-mathematical target to be represented mathematically when the mapping functions per se are mathematical objects. The second challenge arises out of inconsistent early calculus, which suggests that mathematical representation does not require rigorous mathematical structures. The third challenge comes from renormalisation group (...) (RG) explanations of universality. It is argued that the structural mapping between the world and a highly abstract minimal model adds little value to our understanding of how RG obtains its explanatory force. I will address the first and second challenges from the similarity perspective. The similarity account captures representations as similarity relations, providing a more flexible and broader conception of representation than structuralism. It is the specification of the respect and degree of similarity that forges mathematics into a context of representation and directs it to represent a specific system in reality. Structuralism is treatable as a tool for explicating similarity rela-tions set-theoretically. The similarity account, combined with other approaches (e.g., Nguyen and Frigg’s extensional abstraction account and van Fraassen’s pragmatic equivalence), can dissolve the first challenge. Additionally, I will make a structuralist response to the second challenge, and suggestions regarding the role of infinitesimals from the similarity perspective. In light of the similarity account, I will propose the “hotchpotch picture” as a method-ological reflection of our study of representation and explanation. Its central insight is to dissect a representation or an explanation into several aspects and use different theories (that are usually thought of competing) to appropriate each of them. Based on the hotchpotch picture, RG explanations can be dissected to the “indexing” and “inferential” conceptions of explanation, which are captured or characterised by structural mappings. Therefore, structuralism accommodates RG explanations, and the third challenge is resolved. (shrink)

Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of generative models function to generate new homogenous (...) models in the sense that the latter is a straightforward derivative of the former, both of which share many similar features. Unfortunately, both accounts are implicit about the generative capacity of generative models. Using a case study, I articulate that a two-staged process of abstraction and idealization in modeling may contribute to the generative capacity of a scientific model. I also demonstrate that this two-staged process may go beyond the capacity of generating new homogenous models to generating new heterogeneous models. (shrink)

When Ron Giere introduced perspectivism into philosophy of science, he provided a perspectivist analysis of both scientific instruments and scientific theorizing. Today, there is a burgeoning literature that extends Giere’s analysis of theorizing, with many philosophers examining the perspectivist approach to aspects of theorizing such as models, laws, explanations, and so on. However, relatively little attention has been paid to Giere’s analysis of instruments. In this article, I hope to fill this gap. I argue that the perspectivist analysis of instruments (...) should be rejected. First, I give a general account of the kind of instrumentation relevant to instrumental perspectivism, drawing on Cartwright’s notion of nomological machines, which forms the basis of my criticism of Giere’s argument for instrumental perspectivism. Next, I present the case against instrumental perspectivism. I argue that instrumental perspectivism is dependent on theoretical perspectivism in a way that robs it of philosophical significance, and that it introduces new puzzles while adding little to our understanding of instruments. (shrink)

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...

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)

A persistent criticism of radical embodied cognitive science is that it will be impossible to explain “real cognition” without invoking mental representations. This paper provides an account of explicit, real-time thinking of the kind we engage in when we imagine counter-factual situations, remember the past, and plan for the future. We first present a very general non-representational account of explicit thinking, based on pragmatist philosophy of science. We then present a more detailed instantiation of this general account drawing on nonlinear (...) dynamics and ecological psychology. (shrink)

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

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational (...) literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science, which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced. In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge. (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)

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to (...) the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories. (shrink)

One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly (...) explanations establish claims of possible explanations, how-actually explanations establish claims of actual ones. Models, in turn, simply provide evidence for these claims. (shrink)

The goal of this article is to address the problem of inconsistent models and the challenge it poses for perspectivism. I analyze the argument, draw attention to some hidden premises behind it, and deflate them. Then I introduce the notion of perspectival models as a distinctive class of modeling practices whose primary function is exploratory. I illustrate perspectival modeling with two examples taken from contemporary high-energy physics at the Large Hadron Collider at the European Organization for Nuclear Research, which are (...) designed to show how a plurality of seemingly incompatible models is methodologically crucial to advance the realist quest in cutting-edge areas of scientific inquiry. (shrink)

South Korean high school students are being taught Einstein’s Special Theory of Relativity. In this article, I examine the portrayal of this theory in South Korean high school physics textbooks and discuss an alternative method used to solve the analyzed problems. This examination of how these South Korean textbooks present this theory has revealed two main flaws: First, the textbooks’ contents present historically fallacious backgrounds regarding the origin of this theory because of a blind dependence on popular undergraduate textbooks, which (...) ignore the revolutionary aspects of the theory in physics. And second, the current ingredients of teaching this theory are so simply enumerated and conceptually confused that students are not provided with good opportunities to develop critical capacities for evaluating scientific theories. Reviewing textbooks used in South Korea, I will, first, claim that the history of science contributes to understand not merely the origins but also two principles of this theory. Second, in addition to this claim, I argue that we should distinguish not only hypotheses from principles but also phenomena from theoretical consequences and evidence. Finally, I suggest an alternative way in which theory testing occurs in the process of evaluation among competitive theories on the basis of data, not in the simple relation between a hypothesis and evidence. (shrink)

The inclusion of the practice of “developing and using models” in the Framework for K-12 Science Education and in the Next Generation Science Standards provides an opportunity for educators to examine the role this practice plays in science and how it can be leveraged in a science classroom. Drawing on conceptions of models in the philosophy of science, we bring forward an agent-based account of models and discuss the implications of this view for enacting modeling in science classrooms. Models, according (...) to this account, can only be understood with respect to the aims and intentions of a cognitive agent, not solely in terms of how they represent phenomena in the world. We present this contrast as a heuristic—models of versus models for—that can be used to help educators notice and interpret how models are positioned in standards, curriculum, and classrooms. (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)

This thesis considers the following problem: What methods should the epistemology of science use to gain insight into the structure and behaviour of scientific knowledge and method in actual scientific practice? After arguing that the elucidation of epistemological and methodological phenomena in science requires a method that is rooted in formal methods, I consider two alternative methods for epistemology of science. One approach is the classical approaches of the syntactic and semantic views of theories. I show that typical approaches of (...) this sort are inadequate and inaccurate in their representation of scientific knowledge by showing how they fail to account for and misrepresent important epistemological structure and behaviour in science. The other method for epistemology of science I consider is modeled on the methods used to construct valid models of natural phenomena in applied mathematics. This new epistemological method is itself a modeling method that is developed through the detailed consideration of two main examples of theory application in science: double pendulum systems and the modeling of near-Earth objects to compute probability of future Earth impact. I show that not only does this new method accurately represent actual methods used to apply theories in applied mathematics, it also reveals interesting structural and behavioural patterns in the application process and gives insight into what underlies the stability of methods of application. I therefore conclude that for epistemology of science to develop fully as a scientific discipline it must use methods from applied mathematics, not only methods from pure mathematics and metamathematics as traditional formal epistemology of science has done. (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 explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.

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

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)

Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...) in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion. (shrink)

This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.

The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by (...) drawing analogies between the different fields of inquiry. We will study analogical reasoning in synthetic biology through the emergence of the functional meaning of noise, which marks an important shift in how engineering concepts are employed in this field. The notion of noise serves also to highlight the differences between the two branches of synthetic biology: the basic science-oriented branch and the engineering-oriented branch, which differ from each other in the way they draw analogies to various other fields of study. Moreover, we show that fixing the mapping between a source domain and the target domain seems not to be the goal of analogical reasoning in actual scientific practice. (shrink)

Scientific inquiry has led to immense explanatory and technological successes, partly as a result of the pervasiveness of scientific theories. Relativity theory, evolutionary theory, and plate tectonics were, and continue to be, wildly successful families of theories within physics, biology, and geology. Other powerful theory clusters inhabit comparatively recent disciplines such as cognitive science, climate science, molecular biology, microeconomics, and Geographic Information Science (GIS). Effective scientific theories magnify understanding, help supply legitimate explanations, and assist in formulating predictions. Moving from their (...) knowledge-producing representational functions to their interventional roles (Hacking 1983), theories are integral to building technologies used within consumer, industrial, and scientific milieus. -/- This entry explores the structure of scientific theories from the perspective of the Syntactic, Semantic, and Pragmatic Views. Each of these views answers questions such as the following in unique ways. What is the best characterization of the composition and function of scientific theory? How is theory linked with world? Which philosophical tools can and should be employed in describing and reconstructing scientific theory? Is an understanding of practice and application necessary for a comprehension of the core structure of a scientific theory? How are these three views ultimately related? (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)

Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, I conclude that his (...) approach involves a version of reasoning from variety of evidence, enabling this robustness to be a confirmatory virtue.. (shrink)

Sciences are often regarded as providing the best, or, ideally, exact, knowledge of the world, especially in providing laws of nature. Ilya Prigogine, who was awarded the Nobel Prize for his theory of non-equilibrium chemical processes—this being also an important attempt to bridge the gap between exact and non-exact sciences [mentioned in the Presentation Speech by Professor Stig Claesson (nobelprize.org, The Nobel Prize in Chemistry 1977)]—has had this ideal in mind when trying to formulate a new kind of science. Philosophers (...) of science distinguish theory and reality, examining relations between these two. Nancy Cartwright’s distinction of fundamental and phenomenological laws, Rein Vihalemm’s conception of the peculiarity of the exact sciences, and Ronald Giere’s account of models in science and science as a set of models are deployed in this article to criticise the common view of science and analyse Ilya Prigogine’s view in particular. We will conclude that on a more abstract, philosophical level, Prigogine’s understanding of science doesn’t differ from the common understanding. (shrink)

I critically examine the assumption that the theoretical structure that varies under theoretical symmetries is redundant and should be eliminated from a metaphysical picture of the universe, following a ‘symmetry to reality’ inference. I do so by analysing the status of coordinate change symmetries taking a pragmatic approach. I argue that coordinate systems function as indexical devices, and play an important pragmatic role for representing concrete physical systems. I examine the implications of considering this pragmatic role seriously, taking what I (...) call a perspectivist stance. My conclusion is that under a perspectivist stance, all symmetries (including local gauge symmetries) potentially have a direct empirical status: they point to dynamical aspects that are invariant under changes of operationalisation, and they constitute a guide not to reality, but to nomology and kinship. (shrink)