Scientific Representation

Syntactic approaches in the philosophy of science, which are based on formalizations in predicate logic, are often considered in principle inferior to semantic approaches, which are based on formalizations with the help of structures. To compare the two kinds of approach, I identify some ambiguities in common semantic accounts and explicate the concept of a structure in a way that avoids hidden references to a specific vocabulary. From there, I argue that contrary to common opinion (i) unintended models do not (...) pose a significant problem for syntactic approaches to scientific theories, (ii) syntactic approaches can be at least as language-independent as semantic ones, and (iii) in syntactic approaches, scientific theories can be as well connected to the world as in semantic ones. Based on these results, I argue that syntactic and semantic approaches fare equally well when it comes to (iv) ease of application, (v) accommodating the use of models in the sciences, and (vi) capturing the theory-observation relation. (shrink)

This article critically evaluates Itzhak Gilboa, Andrew Postlewaite, Larry Samuelson, and David Schmeidler’s account of economic models. First, it gives a selective overview of their argument, highlighting its emphasis on similarity and their oversight of the role of idealizations in economics. Second, it proposes a sketch of an account of models as arguments and argumentative devices. This account not only sheds light on Gilboa et al.’s approach, including its shortcomings, but also identifies key challenges in model-based inference, suggesting a fresh (...) perspective on the uses of models in economics for diverse objectives. (shrink)

In the philosophy of science, increasing attention has been given to the methodological novelties associated with the study of complex systems. However, there is little agreement on exactly what complex systems are. Although many characterizations of complex systems are available, they tend to be either impressionistic or overly formal. Formal definitions rely primarily on ideas from the study of computational complexity, but the relation between these formal ideas and the messy world of empirical phenomena is unclear. Here, I give a (...) definition of complex systems that draws on algorithmic complexity theory, but also provides a way of interpreting the formal idea in an empirical setting. I then use the definition to show that two canonical forms of scientific idealization are empirically inadequate when applied to complex systems. The inadequacy of these forms of idealization is shown to be the primary reason that complex systems require novel methods. Moreover, this demand for novel methods helps explain the rise of complex systems science as an autonomous discipline. (shrink)

We argue that quantum mechanics is not an ontological description of microscopic reality but a frame-dependent epistemic structure. Building upon the Aperture Stack architecture, we interpret quantum formalism as a topology of observational constraint rather than a reflection of physical entities. Wavefunction collapse, uncertainty, and measurement are reframed as structural transformations within epistemic geometry. Interpretations of QM are thus slices of frame geometry, not metaphysical claims. We further explore a structural homology between quantum formalism and reflexive cognitive architectures, suggesting a (...) deep alignment between knowing and being. (shrink)

In this paper I argue against a deflationist view that as representational vehicles symbols and models do their jobs in essentially the same way. I argue that symbols are conventional vehicles whose chief function is denotation while models are epistemic vehicles whose chief function is showing what their targets are like in the relevant aspects. It is further pointed out that models usually do not rely on similarity or some such relations to relate to their targets. For that referential relation (...) they reply instead on symbols (names and labels) given to them and their parts. And a Goodmanian view on pictures of fictional characters reveals the distinction between symbolic and model representations. (shrink)

The philosophical literatures on models and thought experiments have been developing exponentially, and independently, for decades. This independence is surprising, given how similar models and thought experiments are. They each have “lives of their own,” they sit between theory and experience, they are important for both pedagogy and cutting-edge science, they galvanize conceptual changes and paradigm shifts, and they involve entertaining imaginary scenarios and working out what happens. Recently, philosophers have begun to highlight these similarities. This entry aims at taking (...) the idea further, by trying to systematically identify places where insights from one literature can be taken up in the other. Along the way, important differences will also be highlighted. (shrink)

Contemporary action theory is generally concerned with giving theories of action ontology. In this paper, we make the novel proposal that the standard view in action theory—the Causal Theory of Action—should be recast as a “model”, akin to the models constructed and investigated by scientists. Such models often consist in fictional, hypothetical, or idealized structures, which are used to represent a target system indirectly via some resemblance relation. We argue that recasting the Causal Theory as a model can not only (...) accomplish the goals of causal theorists, but also give the theory greater flexibility in responding to common objections. (shrink)

Was macht wissenschaftliches Verstehen aus? Und inwiefern können visuelle Repräsentationen, wie sie vielfach in der (Ergebnis-)Präsentation (Publikationen, Vorträgen etc.) in unterschiedlichen wissenschaftlichen Disziplinen Verwendung finden, zum Verstehen untersuchter Fragestellungen beitragen? Diesen Themen soll im folgenden Beitrag genauer nachgegangen werden. Den Ausgangspunkt bildet dabei Henk W. de Regts Studie (2017) zum wissenschaftlichen Verstehen. De Regt plädiert dafür, Kriterien des wissenschaftlichen Verstehens aus der aktuellen wissenschaftlichen Praxis zur Anwendung zu bringen – was auch bedeutet, ihre historische Variabilität ernst zu nehmen. Ein Punkt (...) seiner Untersuchung betrifft den Zusammenhang zwischen Visualisierbarkeit und wissenschaftlichem Verstehen. Erörtert wird von ihm die Frage, inwiefern Anschaulichkeit eine notwendige Bedingung für das Verstehen von Theorien und Phänomenen ist. De Regt bezieht dabei letztlich eine kritische Position, indem er sich gegen den Status einer solchen notwendigen Bedingung ausspricht, da sich in der Wissenschaftshistorie zeigen lasse, dass Anschaulichkeit nicht durchgängig als wesentliches Kriterium angesehen wurde. Dieses Votum contra eine notwendig bedeutungsvolle Rolle von Visualisierungen im Verstehensprozess scheint allerdings de Regts eigener Argumentation für eine Kontextabhängigkeit des Verstehens zuwiderzulaufen. Im vorliegenden Text soll diese Spannung aufgezeigt und zu Gunsten der Rolle von Visualisierungen im Prozess wissenschaftlichen Verstehens gewendet werden. Am Beispiel der maritimen Raumplanung (MRP) wird veranschaulicht, inwiefern die Redeweise von der Einsehbarkeit von Gründen mehr als eine bloße Metapher im Kontext des wissenschaftlichen Verstehens darstellen kann. (shrink)

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...) as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

Big data is opening new angles on old questions about scientific progress. Is scientific knowledge cumulative? If yes, how does it make progress? In the life sciences, what we call the Consensus Principle has dominated the design of data discovery and integration tools: the design of a formal classificatory system for expressing a body of data should be grounded in consensus. Based on current approaches in biomedicine and systematic biology, we formulate and compare three types of the Consensus Principle: realist, (...) contextual-best, and coordinative. Contrasted with the realist program of the Open Biomedical Ontologies Foundry, we argue that historical practices in systematic biology provide an important and overlooked alternative based on coordinative consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or “settled” science. (shrink)

The notion of shape space was introduced in the second half of the 20th Century as a useful analytical tool for tackling problems related to the intrinsic spatial configuration of material systems. In recent years, the geometrical properties of shape spaces have been investigated and exploited to construct a totally relational description of physics (classical, relativistic, and quantum). The main aim of this relational framework—originally championed by Julian Barbour and Bruno Bertotti—is to cast the dynamical description of material systems in (...) dimensionless and scale-invariant terms only. As such, the Barbour-Bertotti approach to dynamics represents the technical implementation of the famous Leibnizian arguments against the reality of space and time as genuine substances. The question then arises about the status of shape space itself in this picture: Is it an actual physical space in which the fundamental relational dynamics unfolds, or is it just a useful mathematical construction? The present paper argues for the latter answer and, in doing so, explores the possibility that shape space is a peculiar case of a conceptual space. (shrink)

Maps and mapping raise questions about models and modeling and in science. This chapter archives map discourse in the founding generation of philosophers of science (e.g., Rudolf Carnap, Nelson Goodman, Thomas Kuhn, and Stephen Toulmin) and in the subsequent generation (e.g., Philip Kitcher, Helen Longino, and Bas van Fraassen). In focusing on these two original framing generations of philosophy of science, I intend to remove us from the heat of contemporary discussions of abstraction, representation, and practice of science and thereby (...) see in a more distant and neutral light the many productive ways in which maps can stand in analytically for scientific theories and models. The chapter concludes by complementing the map analogy – i.e., a scientific theory is a map of the world – with a model analogy, viz., a scientific model is a vehicle for understanding. (shrink)

It is plausible that the models of scientific theories correspond to possibilities. But how do we know which models of which scientific theories so correspond? This paper provides a novel proposal for guiding belief about possibilities via scientific theories. The proposal draws on the notion of an effective theory: a theory that applies very well to a particular, restricted domain. We argue that it is the models of effective theories that we should believe correspond, at least in part, to possibilities. (...) It is thus effective theories that should guide modal reasoning in science. (shrink)

Craver's diagram, comprising symbols such as X (entity), S (mechanism), Φ (activity), and Ψ (phenomenon), is widely used to represent biological mechanisms in the New Mechanism. However, this paper demonstrates that Craver’s framework lacks the formal capacity to adequately capture the organizational structures and functional dynamics essential for mechanistic explanations, particularly the temporal interplay among entities and activities or the relational nature of enzymatic state transitions. To address these limitations, this paper proposes a supplementary framework based on category theory, enabling (...) the abstraction of mechanisms as temporally organized structures of state transitions. By integrating key features such as order, frequency, and duration, categorical diagrams provide a cohesive representation of type mechanisms, supporting the deficiencies of token-centric approaches. Using protein synthesis as a case study, the paper illustrates how categorical abstractions enhance the formal representation of biological mechanisms while emphasizing the philosophical importance of organizational dimensions. (shrink)

Scientific realists with traditional semantic inclinations are often pressed to explain away the distinguished series of referential failures that seem to plague our best past science. As recent debates make it particularly vivid, a central challenge is to find a reliable and principled way to assess referential success at the time a theory is still a live concern. In this paper, I argue that this is best done in the case of physics by examining whether the putative referent of a (...) term is specifiable within the limited domain delineated by the range of parameters over which the theory at stake is empirically accurate. I first implement this selective principle into a general account of reference, building on Stathis Psillos’s works. Then, I show that this account offers a remarkably reliable basis to assess referential success before theory change in the case of effective theories. Finally, I briefly show that this account still works well with other physical examples and explain how it helps us to handle problematic cases in the history of physical sciences. (shrink)

Accounts of scientific representation typically assume that there is a single sense of “represent”, and they attempt to develop a theory that can account for all its features. The aim of this article is to draw the consequences of a distinction between two senses of “represent” that has been proposed recently. Taking inspiration from the distinction between speaker-meaning and expression-meaning in philosophy of language, a first sense is analysed in terms of the mental states of the user of a vehicle (...) in context, and a second sense in terms of communal norms constraining contextual uses. I argue that making this distinction, and thus understanding the representation relation as essentially indexical and normative, can help us move beyond the controversies between various accounts of scientific representation, notably what have been dubbed informational and functional accounts, as well as debates regarding the ontology of scientific models. (shrink)

Several philosophers of science have drawn attention to a number of modeling practices where scientific models primarily contribute modal information. Examples now abound, and, recently, there have also been some preliminary attempts to address questions of under what conditions, and by virtue of what, models can perform this modal epistemic function. This paper sets out to constructively review those attempts through a prism of the more general literature on the epistemology of modality. One aim of this exercise is to expose (...) the underlying justificatory strategies of the modal modeling accounts – which are strikingly convergent with those already identified by modal epistemologists. Another aim is to highlight some challenges familiar to modal epistemology and discuss the form they take in the context of modal modeling as well as the resources that modal modeling accounts have for dealing with them. This is all done with an eye to pointing out ways forward in further developing the work on the epistemology of modal modeling. (shrink)

Conceptions about the nature of scientific models held by science students frequently involve distorted views, with a tendency to consider them as mere copies of reality. Besides encompassing an untenable view about the nature of science itself, this misconstruction can effectively be a pedagogical impediment to learning. Objectives: We evaluate whether Mario Bunge’s epistemology might contribute to tackling issues related to the nature of models in science education contexts. De-sign: After identifying Bunge’s main model categories, we employ them to examine (...) aspects of the historical development of atomic models and contrast the resulting framework with issues about model conceptions in science education, as pointed out in the literature. Setting and participants: Due to this research’s theoretical nature, this study did not include human participants other than authors from the literature and the theoretical framework. Data collection and analysis: We per-formed a constant comparative analysis to identify patterns of meanings shared between the historical case and the theoretical framework. Results: Features of models pointed out by Bunge were identified in the development of atomic models and could provide consistent and explanatory viewpoints about key issues related to model conceptions in science education. Conclusions: Bunge’s framework might help to clarify aspects of the nature of models relevant to science education contexts. -/- . (shrink)

According to the standard no miracles argument, science’s predictive success is best explained by the approximate truth of its theories. In contemporary science, however, machine learning systems, such as AlphaFold2, are also remarkably predictively successful. Thus, we might ask what best explains such successes. Might these AIs accurately represent critical aspects of their targets in the world? And if so, does a variant of the no miracles argument apply to these AIs? We argue for an affirmative answer to these questions. (...) We conclude that if the standard no miracles argument is sound, an AI-specific no miracles argument is also sound. (shrink)

Scientists regularly make possibility claims. While philosophers of science are well aware of the distinction between epistemic and objective notions of possibility, we believe that they often fail to apply this distinction in their analyses of scientific practices that employ modal concepts. We argue that heeding this distinction will help further progress in current debates in the philosophy of science, as it shows that the debaters talk about different things, rather than disagree on the same issue. We first discuss how (...) the two notions differ with respect to their epistemology and show that these differences are sometimes ignored in the philosophy of science. We then revisit four current philosophy of science debates about modelling, that are framed in modal terms, to showcase how the distinction significantly clarifies these debates and thereby helps advance them. (shrink)

As Nelson Goodman highlighted, there are two main senses of “abstract” that can be found in discussions about abstract art. On the one hand, a representation is abstract if it leaves out certain features of its target. On the other hand, something can be abstract to the extent that it does not represent a concrete subject. The first sense of “abstract” is well-known in philosophy of science. For example, philosophers discuss mathematical models of physical, biological, and economic systems as being (...) abstract in this sense. However, it is the second sense that dominates discussions of abstract art in aesthetics. For example, abstract art was (and is) considered revolutionary precisely for being non-figurative. Through an analysis of artists including Kandinsky, Malevich, and Mondrian, we develop a reading of this second sense, which we call “generative abstraction,” as opposed to “subtractive” abstraction. Generative abstraction is a process in which a new artifact is created which does not represent the initial concrete target system that inspired it (if there was one), where the artifact’s features are explored for their own sake, and where the “language” of the new artifact is in some way more “universal.” Focusing on this sense of abstraction is helpful in revealing the complexity of the process of crafting an abstract artifact, in problematizing the notion that abstraction can always be un-done (or concretized), as well as revealing new ways for abstractions to be epistemically (un)successful. (shrink)

To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...) mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon’s approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. (shrink)

Why study notations, diagrams, or more broadly the variety of nonverbal “representations” or “signs” that are used in mathematical practice? This chapter maps out recent work on the topic by distinguishing three main philosophical motivations for doing so. First, some work (like that on diagrammatic reasoning) studies signs to recover norms of informal or historical mathematical practices that would get lost if the particular signs that these practices rely on were translated away; work in this vein has the potential to (...) broaden our view of the specific normativity of mathematics beyond logical proof. Second, some work focuses on signs to highlight the open-ended and “nonrigorous” aspects of mathematical practice, and the role of these aspects in the historical development of mathematics. The third motivation is based on the following observation: the reason differences in signs matter in mathematics is that humans are finite agents, with cognitive and computational limitations. Accordingly, studying signs can be a way to study how human computational constraints shape the mathematics that we do, and to tackle the topic of mathematical understanding. (shrink)

James Nguyen & Roman Frigg (2022). Scientific Representation. Cambridge University Press, 90pp., €21.23 (Paperback), ISBN: 9781009009157.

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 objective of this article is to approach the way in which Kant considers the “analogies of experience” necessary connections for the scientific representation of the physical world to occur. The method consists of carrying out a conceptual analysis of selected excerpts from theCritique of pure reasonthat may serve to support the interpretation that the analogies of experience are, for Kant, rules that determine the necessary links between perceptions and the ability to understand phenomena. from them. In this way, we (...) seek to explain the divergence between pure knowledge and empirical knowledge, as well as the way in which physics manages to maintain its a priori concepts. -/- O objetivo deste artigo é abordar a maneira como Kant considera as “analogias da experiência” ligações necessárias para ocorrer a representação científica do mundo físico. O método consiste em analisar trechos selecionados da Crítica da razão pura, sobretudo do texto “Analítica dos princípios”, que apresentem as analogias da experiência como regras que determinam as ligações necessárias entre as percepções e a capacidade de compreensão dos fenômenos. Kant argumenta que embora os objetos sejam em si mesmos inacessíveis a nós isto não implica a impossibilidade de pensarmos neles. O resultado é uma perspectiva sobre o conhecimento científico capaz de considerar as analogias da experiência para manter alguns conceitos a priori sobre o mundo físico. (shrink)

The type-level abstraction is a formal way to represent molecular structures in biological practice. Graphical representations of molecular structures of biological objects are also used to identify functional processes of things. This paper will reveal that category theory is a formal mathematical language not only to visualize molecular structures of biological objects as type-level abstraction formally but also to understand how to infer biological functions from the molecular structures of biological objects. Category theory is a toolkit to understand biological knowledge (...) at the type-level formally, not individual token-level, as well as typical heuristic strategies in molecular biology. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

It is often claimed that one can avoid the kind of underdetermination that is a typical consequence of symmetries in physics by stipulating that symmetry-related models represent the same state of affairs (Leibniz Equivalence). But recent commentators (Dasgupta 2011; Pooley 2021; Pooley and Read 2021; Teitel 2021a) have responded that claims about the representational capacities of models are irrelevant to the issue of underdetermination, which concerns possible worlds themselves. In this paper I distinguish two versions of this objection: (1) that (...) a theory’s formalism does not (fully) determine the space of physical possibilities, and (2) that the relevant notion of possibility is not physical possibility. I offer a refutation of each. (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.

Le but de cet article est d'examiner le rôle joué par les valeurs dans les activités de représentation en science, notamment la construction ou utilisation de modèles, en distinguant représentation concrète et abstraite. Un modèle hiérarchique est proposé. La conclusion est que l'influence des valeurs sociales dans la représentation scientifique dépend du niveau d'abstraction considéré, et qu'elle n'est problématique que quand des valeurs locales sont considérées pour évaluer des représentations plus générales.

What has fiction to do with science? At first glance, the two activities seem to have entirely different aims and products. Science aims at truth, while fiction can deviate wildly from it. Science produces theories, which we are asked to believe. Fiction produces stories, which we are asked to imagine. Given these differences, associating science and fiction might seem like a serious mistake, or even a threat to science. And yet many authors have tried to understand science by looking to (...) fiction. This article will consider three strands of thought on the topic: Hans Vaihinger’s classic work on fictions; fictionalist views, especially van Fraassen’s constructive empiricism; and recent work on models and fiction. As we will see, none of these strands pose a threat to science. Instead, each tries to understand the nature of scientific knowledge by drawing on fiction in different ways, and for different theoretical purposes. (shrink)

Biologists often study particular biological systems as models of a phenomenon of interest even if they already know that the phenomenon is produced by diverse mechanisms and hence none of those systems alone can sufficiently represent it. To understand this modeling practice, the present paper provides an account of how multiple model systems can be used to study a phenomenon that is produced by diverse mechanisms. Even if generalizability of results from a single model system is significantly limited, generalizations concerning (...) specific aspects of mechanisms often hold across certain ranges of biological systems, which enables multiple model systems to jointly represent such a phenomenon. Comparing mechanisms that operate in different biological systems as examples of the same phenomenon also facilitates characterization and investigation of individual mechanisms. I also compare my account with two existing accounts of the use of multiple model systems and argue that my account is distinct from and complementary to them. (shrink)

The question “what is an interpretation?” is often intertwined with the perhaps even harder question “what is a scientific theory?”. Given this proximity, we try to clarify the first question to acquire some ground for the latter. The quarrel between the syntactic and semantic conceptions of scientific theories occupied a large part of the scenario of the philosophy of science in the 20th century. For many authors, one of the two currents needed to be victorious. We endorse that such debate, (...) at least in the terms commonly expressed, can be misleading. We argue that the traditional notion of “interpretation” within the syntax/semantic debate is not the same as that of the debate concerning the interpretation of quantum mechanics. As much as the term is the same, the term “interpretation” as employed in quantum mechanics has its meaning beyond (pure) logic. Our main focus here lies on the formal aspects of the solutions to the measurement problem. There are many versions of quantum theory, many of them incompatible with each other. In order to encompass a wider variety of approaches to quantum theory, we propose a different one with an emphasis on pure formalism. This perspective has the intent of elucidating the role of each so-called “interpretation” of quantum mechanics, as well as the precise origin of the need to interpret it. (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)

One of the problems that are often indicated as a criticism of different forms of representationalism is the difficulty to find definitions that are neither semantic nor realist in a simple sense. The present work tackles this class of critiques from a contextualist point of view, assuming those semantic aspects that are necessary for a concept of representation, but showing that semantic relations of representation should neither be static, nor referential in a classical and strictly realist sense. Two distinctions are (...) crucial for our proposal: On the one hand, we have the distinction between closed and open systems; on the other, a tripartite distinction between structural, informational and semantic representations. On this basis, we understand representations in terms of asymmetric variations between contextual models. (shrink)

This chapter gives an overview of the various themes and issues discussed in the volume. It includes summaries of all chapters and places the contributions, some of which are part of a critical conversation format, in the context of the larger literature and debates.

This chapter gives an overview of the various themes and issues discussed in the volume. It includes summaries of all chapters and places the contributions, some of which are part of a critical conversation format, in the context of the larger literature and debates.

This essay presents a fully inferentialist-expressivist account of scientific representation. In general, inferentialist approaches to scientific representation argue that the capacity of a model to represent a target system depends on inferences from models to target systems. Inferentialism is attractive because it makes the epistemic function of models central to their representational capacity. Prior inferentialist approaches to scientific representation, however, have depended on some representational element, such as denotation or representational force. Brandom’s Making It Explicit provides a model of how (...) to fully discharge such representational vocabulary, but it cannot be applied directly to scientific representations. Pursuing a strategy parallel to Brandom’s, this essay begins with an account of how surrogative inference is justified. Scientific representation and the denotation of model elements are then explained in terms of surrogative inference by treating scientific representation and denotation as expressive, analogous to Brandom’s account of truth. The result is a thoroughgoing inferentialism: M is a scientific representation of T if and only if M has scientifically justified surrogative consequences that are answers to questions about T. (shrink)

Review: J. E. Wolff. The metaphysics of quantity. New York: Oxford University Press, 2020. 240 pp, $72.00 HB.

This paper proposes an account of accurate scientific representation in terms of techniques that produce data from a target phenomenon. I consider an approach to accurate representation that abstracts from such epistemic factors, justified by a thesis I call Ontic Priority. This holds that criteria for representational accuracy depend on a pre-established account of the nature of the relation between a model and its target phenomenon. I challenge Ontic Priority, drawing on the observation that many working scientists do not have (...) access to information allowing them to describe this relation between model and target or compare them accordingly. I critique the ability of an ontic-first approach to provide accuracy criteria in such cases and present historical support for an alternative according to which a model is accurate if and only if integrating the model into a theory of the data acquisition process yields well-fitting predictions of patterns in the data. (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)

One of the critical issues in the philosophy of science is to understand scientific knowledge. This paper proposes a novel approach to the study of reflection on science, called “cognitive metascience”. In particular, it offers a new understanding of scientific knowledge as constituted by various kinds of scientific representations, framed as cognitive artifacts. It introduces a novel functional taxonomy of cognitive artifacts prevalent in scientific practice, covering a huge diversity of their formats, vehicles, and functions. As a consequence, toolboxes, conceptual (...) frameworks, theories, models, and individual hypotheses can be understood as artifacts supporting our cognitive performance. It is also shown that by empirically studying how artifacts function, we may discover hitherto undiscussed virtues and vices of these scientific representations. This paper relies on the use of language technology to analyze scientific discourse empirically, which allows us to uncover the metascientific views of researchers. This, in turn, can become part of normative considerations concerning virtues and vices of cognitive artifacts. (shrink)

This Element presents a philosophical exploration of the notion of scientific representation. It does so by focussing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant (...) accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works. (shrink)

This paper aims to offer an alternative view for understanding scientific models based on metaphors. To accomplish this, we employ a special case of Darwin’s use of metaphors, such as the notion of powerful Being, in order to represent natural selection. Our proposal contributes to issues in the literature of scientific model, such as imprecisions in the understanding of scientific models, especially in models based on metaphors. Thus, our alternative view of models based on metaphors, and inspired by Darwin’s use (...) of metaphors, provides us with four features, a-simplification and selection; b-articulation of familiar-unfamiliar structures; c-accessibility and moderations of complexity, and finally d-local realism. We contrast these features with Darwin’s use of a metaphors. We conclude by saying that our proposal of metaphor’s approach of models does not only contribute to the clarification of how these types of scientific models can be understood but it shows that metaphors can also contain a realist element that explains why scientists often use it in their practices of modeling the world. (shrink)