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)

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)

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)

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)

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 and helps advance them. (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)

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)

Geographic Information Science (GIS) is an interdisciplinary science aiming to detect and visually represent patterns in spatial data. GIS is used by businesses to determine where to open new stores and by conservation biologists to identify field study locations with relatively little anthropogenic influence. Products of GIS include topographic and thematic maps of the Earth’s surface, climate maps, and spatially referenced demographic graphs and charts. In addition to its social, political, and economic importance, GIS is of intrinsic philosophical interest due (...) to its methodological richness and because it is an instructive analogue to other sciences. This chapter works towards a philosophy of GIS and cartography, or PGISC. In particular, it examines practices of classifying geographic space, objects, and relations. By focusing on the use of natural kinds in data modeling and map generalization practices, I show how the making and using of kinds is contextual, fallible, plural, and purposive. (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)

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)

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.

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)

How does the compositional arrangement of elements in a complex image, like a diagram, a picture or a map, represent the structural features of its content? In this paper I argue that they do so iconically, through the exploitation of relations of visual similarity and dissimilarity. I develop the general claim that our interpretation of this sort of images is guided by the implicit defeasible assumption that things that are patently related represent things that are relevantly related in a similar (...) way. I also identify the usual intrinsic and extrinsic mechanisms we use to represent structural features and illustrate them with an example from real life: a recent infographic from a popular science magazine. (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 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 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)

On entend généralement par « théorie des modèles » autant la métamathématique (ou sémantique formelle) que la sémantique des modèles des sciences non formelles. Cet article a pour objet la théorie des modèles scientifiques que Mario Bunge a développée dans Method, Models and Matter (1973). J’y analyse l’intégration théorique qu’opère Bunge des sciences formelles et des sciences expérimentales ou observationnelles, laquelle prend appui sur sa philosophie des sciences. Je la compare sommairement à la théorie des modèles de Gilles-Gaston Granger dans (...) le but évident d’en dégager les ressemblances et les dissimilitudes, mais aussi leur commun point d’achoppement : l’une comme l’autre usent en effet d’un concept non analysé dont la fonction épistémologique est pourtant capitale et produit les mêmes effets. Au centre de la théorie des modèles de Bunge se trouve le concept de simulation que je comparerai à celui qui est en usage dans les sciences de l’ordinateur et qui est de nos jours largement appliqué à diverses sciences, tant sociales que naturelles. Je conclurai sur les conséquences méthodologiques et métaphysiques de la théorie bungéenne des modèles. (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)

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)

While the predominant focus of the philosophical literature on scientific modeling has been on single-scale models, most systems in nature exhibit complex multiscale behavior, requiring new modeling methods. This challenge of modeling phenomena across a vast range of spatial and temporal scales has been called the tyranny of scales problem. Drawing on research in the geosciences, I synthesize and analyze a number of strategies for taming this tyranny in the context of conceptual, physical, and mathematical modeling. This includes several strategies (...) that can be deployed in physical modeling, even when strict dynamical scaling fails. In all cases, I argue that having an adequate conceptual model—given both the nature of the system and the particular purpose of the model—is essential. I draw a distinction between depiction and representation, and use this research in the geosciences to advance a number of debates in the philosophy of modeling. (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)

-/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...) the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

Mathematics is often taken to play one of two roles in the empirical sciences: either it represents empirical phenomena or it explains these phenomena by imposing constraints on them. This article identifies a third and distinct role that has not been fully appreciated in the literature on applicability of mathematics and may be pervasive in scientific practice. I call this the “bridging” role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and (...) how two different descriptions of an empirical phenomenon relate to each other. I discuss two bridging roles appearing in biological and physical explanations. (shrink)

This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach (...) focuses on epistemic affordances of different kinds of external representational and other tools employed in model construction. In doing so, the artifactual account gives a unified treatment of different model types as it circumvents the tendency of the fictional and other representational approaches to separate model systems from their “model descriptions”. (shrink)

Reichenbach’s early solution to the scientific problem of how abstract mathematical representations can successfully express real phenomena is rooted in his view of coordination. In this paper, I claim that a Reichenbach-inspired, ‘layered’ view of coordination provides us with an effective tool to systematically analyse some epistemic and conceptual intricacies resulting from a widespread theorising strategy in evolutionary biology, recently discussed by Okasha (2018) as ‘endogenization’. First, I argue that endogenization is a form of extension of natural selection theory that (...) comprises three stages: quasi-axiomatisation, functional extension, and semantic extension. Then, I argue that the functional extension of one core principle of natural selection theory, namely, the principle of heritability, requires the semantic extension of the concept of inheritance. This is because the semantic extension of ‘inheritance’ is necessary to establish a novel form of coordination between the principle of heritability and the extended domain of phenomena that it is supposed to represent. Finally, I suggest that – despite the current lack of consensus on the right semantic extension of ‘inheritance’ – we can fruitfully understand the reconceptualization of ‘inheritance’ provided by niche construction theorists as the result of a novel form of coordination. (shrink)

A Série Investigação Filosófica, uma iniciativa do Núcleo de Ensino e Pesquisa em Filosofia do Departamento de Filosofia da UFPel e do Grupo de Pesquisa Investigação Filosófica do Departamento de Filosofia da UNIFAP, sob o selo editorial do NEPFil online e da Editora da Universidade Federal de Pelotas, com auxílio financeiro da John Templeton Foundation, tem por objetivo precípuo a publicação da tradução para a língua portuguesa de textos selecionados a partir de diversas plataformas internacionalmente reconhecidas, tal como a Stanford (...) Encyclopedia of Philosophy, por exemplo. O objetivo geral da série é disponibilizar materiais bibliográficos relevantes tanto para a utilização enquanto material didático quanto para a própria investigação filosófica. (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)

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)

This book proposes a novel position in the debate on scientific realism: Modal Empiricism. Modal empiricism is the view that the aim of science is to provide theories that correctly delimit, in a unified way, the range of experiences that are naturally possible given our position in the world. The view is associated with a pragmatic account of scientific representation and an original notion of situated modalities, together with an inductive epistemology for modalities. It purports to provide a faithful account (...) of scientific practice and of its impressive achievements, and defuses the main motivations for scientific realism. More generally, Modal Empiricism purports to be the precise articulation of a pragmatist stance towards science. This book is of interest to any philosopher involved in the debate on scientific realism, or interested in how to properly understand the content, aim and achievements of science. (shrink)

Fiora Salis compares the fictional and the artifactual views of models. She argues that both accounts contain several deep insights concerning the nature of scientific models but they also face some difficult challenges. She then puts forward an account of the ontology of models intended to incorporate the benefits of both views avoiding their main difficulties. Her key idea is that models are human-made artifacts that are akin to literary works of fiction. In this view, models are complex objects that (...) are constituted by a model description and the model content generated within a game of make-believe. As per the fiction view, model descriptions are construed as props in a game of make-believe, where props are concrete objects that prescribe certain imaginings. As per the artifactual view, model descriptions are construed as concrete representational tools that enable and constrain a scientist’s cognitive processes and provide intersubjective epistemic access to their imaginings. (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)

The IHME Covid-19 prediction model has been one of the most influential Covid models in the United States. Early on, it received heavy criticism for understating the extent of the epidemic. I argue that this criticism was based on a misunderstanding of the model. The model was best interpreted not as attempting to forecast the actual course of the epidemic. Rather, it was attempting to make a conditional projection: telling us how the epidemic would unfold, given certain assumptions. This misunderstanding (...) of the IHME’s model prevented the public from seeing how dire the model’s projections actually were. (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)

Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modeling in order to characterize the informational content of a scientific model. They argue that when represented as a communication channel, a model source conveys information about its target, and that such representations are therefore appropriate whenever modeling is employed for informational gain. They then extract two consequences. First, the introduction of idealizations is akin in informational terms to the introduction of noise in a signal; for in (...) an idealization we introduce ‘extraneous’ elements into the model that have no correlate in the target. Second, abstraction in a model is informationally equivalent to equivocation in the signal; for in an abstraction we “neglect” in the model certain features that obtain in the target. They conclude that it becomes possible in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice. (shrink)