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)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

The object of this paper is to look at the extent and nature of the uses of analogy during the ªrst century following the so-called scientiªc revolution. Using the research tool provided by JSTOR we systematically analyze the uses of “analog” and its cognates (analogies, analogous, etc.) in the Philosophical Transactions of the Royal Society of London for the period 1665–1780. In addition to giving the possibility of evaluating quantitatively the proportion of papers explicitly using analogies, this approach makes it (...) possible to go beyond the maybe idiosyncratic cases of Descartes, Kepler, Galileo, and other much studied giants of the so-called Scientiªc Revolution. As a result a classiªcation of types of uses is proposed. Relations between types of analogies and research ªelds are also established. In this paper we are less interested in discussing the “real nature” or “essence” or even the cognitive limitations of analogical thinking than in describing its various uses and different meanings as they changed over the course of a century. (shrink)

Although there has been a recent swell of interest in theories of truth that attempt solutions to the liar paradox and the other paradoxes affecting our concept of truth, many of these theories have been criticized for generating new paradoxes, called revenge paradoxes. The criticism is that the theories of truth in question are inadequate because they only work for languages lacking in the resources to generate revenge paradoxes. Theorists facing these objections offer a range of replies, and the matter (...) seems now to be at a standoff. I aim, first, to bolster the revenge objections by considering a relation, internalizability, between languages and theories of truth. A theory of truth is internalizable for a language iff there is an extension of that language in which the theory is expressible and for which the theory provides an accurate and complete assignment of semantic values. There are good reasons to think that acceptable theories of truth are internalizable for any language. With this internalizability requirement in hand, I argue that properly formulated revenge objections are decisive and that the replies to them are inadequate. Second, I show that the internalizability requirement can be met by a certain theory of truth. The central claim of this theory is that truth is an inconsistent concept and should be replaced with a pair of consistent concepts that can then be used to provide a semantics for our truth predicates. This theory is compatible with classical logic, does not give rise to revenge paradoxes of any kind, and satisfies the internalizability requirement. (shrink)

Our aim in this article is to show how the theory of conceptual spaces can be useful in describing diachronic changes to conceptual frameworks, and thus useful in understanding conceptual change in the empirical sciences. We also compare the conceptual space approach to Moulines’s typology of intertheoretical relations in the structuralist tradition. Unlike structuralist reconstructions, those based on conceptual spaces yield a natural way of modeling the changes of a conceptual framework, including noncumulative changes, by tracing the changes to the (...) dimensions that reconstitute a conceptual framework. As a consequence, the incommensurability of empirical theories need not be viewed as a matter of conceptual representation. (shrink)

Epistemology and informal logic have overlapping and broadly similar subject matters. A principle of methodological symmetry is: philosophical theories of sufficiently similar subject matters should engage similar methods. Suppose the best way to do epistemology is in highly formalized ways, with a large role for mathematical methods. The symmetry principle suggests this is also the best way to do the logic of the reasoning and argument, the subject matter of informal logic. A capitulation to mathematics is inimical to informal logicians, (...) yet formal methods and mathematical models are an emerging force in epistemology. What is to be done? What’s sauce for the goose of epistemology is sauce for the gander of informal logic. (shrink)

I provide an explicit formulation of empirical adequacy, the central concept of constructive empiricism, and point out a number of problems. Based on one of the inspirations for empirical adequacy, I generalize the notion of a theory to avoid implausible presumptions about the relation of theoretical concepts and observations, and generalize empirical adequacy with the help of approximation sets to allow for lack of knowledge, approximations, and successive gain of knowledge and precision. As a test case, I provide an application (...) of these generalizations to a simple interference phenomenon. (shrink)

Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two groups (...) of mathematical modelers in plant biology. Scientific debate can arise from different modeling philosophies. (shrink)

This paper offers a novel way of reconstructing conceptual change in empirical theories. Changes occur in terms of the structure of the dimensions—that is to say, the conceptual spaces—underlying the conceptual framework within which a given theory is formulated. Five types of changes are identified: (1) addition or deletion of special laws, (2) change in scale or metric, (3) change in the importance of dimensions, (4) change in the separability of dimensions, and (5) addition or deletion of dimensions. Given this (...) classification, the conceptual development of empirical theories becomes more gradual and rationalizable. Only the most extreme type—replacement of dimensions—comes close to a revolution. The five types are exemplified and applied in a case study on the development within physics from the original Newtonian mechanics to special relativity theory. (shrink)

In this paper we study in details a system of two weakly coupled harmonic oscillators from the point of view of Bohm's interpretation of quantum mechanics. This system may be viewed as a simple model for the interaction between a photon and a photodetector. We obtain exact solutions for the general case. We then compute approximate solutions for the case where one oscillator is initially in its first excited state (a single photon) reaching the other oscillator in its ground state (...) (the photodetector). The approximate solutions represent the state of both oscillators after the interaction, which is not an eigenstate of the individual hamiltonians for each oscillator, and therefore the energies for each oscillator do not exist in the Copenhagen interpretation of Quantum Mechanics. We use the approximate solutions that we obtained to compute Bohmian trajectories and to study the energy transfer between the oscillators. We conclude that, even using the Bohmian view, the energy of each individual oscillator is not well defined, as the nonlocal quantum potential is not negligible even after the coupling is turned off. (shrink)

The thesis of this article is that the nature of probability is centered on its formal properties, not on any of its standard interpretations. Section 2 is a survey of Bayesian applications. Section 3 focuses on two examples from physics that seem as completely objective as other physical concepts. Section 4 compares the conflict between subjective Bayesians and objectivists about probability to the earlier strident conflict in physics about the nature of force. Section 5 outlines a pragmatic approach to the (...) various interpretations of probability. Finally, Sect. 6 argues that the essential formal nature of probability is expressed in the standard axioms, but more explicit attention should be given to the concept of randomness. (shrink)

In this paper we argue that quantum mechanics provides a genuine kind of structural explanations of quantum phenomena. Since structural explanations only rely on the formal properties of the theory, they have the advantage of being independent of interpretative questions. As such, they can be used to claim that, even in the current absence of one agreed-upon interpretation, quantum mechanics is capable of providing satisfactory explanations of physical phenomena. While our proposal clearly cannot be taken to solve all interpretive issues (...) raised by quantum theory, we will argue that it can be successfully applied to some of its most puzzling phenomena, such Heisenberg's uncertainty relations and quantum non-locality. The discussion of these two case studies will also serve to illustrate the main properties of structural explanations and compare them to the DN and the unificationist models. Finally, we briefly discuss how structural explanations might relate to structural realism. (shrink)

My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...) be determined by the forces they will be subjected to between the moment the toboggan will be released at the top of the hill and the moment it will reach its highest speed. The problem is that, throughout their downhill journey, my daughters and the toboggan will be subjected to an incredibly large number of forces—from the gravitational pull of any massive object in the universe to the weight of the snowflake that is sitting on the tip of one of my youngest daughter’s hairs—so that any attempt to apply the theory directly to the real-world system in all its complexity seems to be doomed to failure. (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)

We inquire into the question whether the Aristotelean or classical \emph{ideal} of science has been realised by the Model Revolution, initiated at Stanford University during the 1950ies and spread all around the world of philosophy of science --- \emph{salute} P.\ Suppes. The guiding principle of the Model Revolution is: \emph{a scientific theory is a set of structures in the domain of discourse of axiomatic set-theory}, characterised by a set-theoretical predicate. We expound some critical reflections on the Model Revolution; the conclusions (...) will be that the philosophical problem of what a \emph{scientific theory} is has \emph{not} been solved yet --- \emph{pace} P.\ Suppes. While reflecting critically on the Model Revolution, we also explore a proposal of how to complete the Revolution and briefly address the intertwined subject of \emph{scientific representation}, which has come to occupy center stage in philosophy of science over the past decade. (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)

According to the semantic view, a theory is characterized by a class of models. In this paper, we examine critically some of the assumptions that underlie this approach. First, we recall that models are models of something. Thus we cannot leave completely aside the axiomatization of the theories under consideration, nor can we ignore the metamathematics used to elaborate these models, for changes in the metamathematics often impose restrictions on the resulting models. Second, based on a parallel between van Fraassen’s (...) modal interpretation of quantum mechanics and Skolem’s relativism regarding set-theoretic concepts, we introduce a distinction between relative and absolute concepts in the context of the models of a scientific theory. And we discuss the significance of that distinction. Finally, by focusing on contemporary particle physics, we raise the question: since there is no general accepted unification of the parts of the standard model, we have no theory, in the usual sense of the term. This poses a difficulty: if there is no theory, how can we speak of its models? What are the latter models of? We conclude by noting that it is unclear that the semantic view can be applied to contemporary physical theories. (shrink)

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. (shrink)

Designing models of complex phenomena is a difficult task in engineering that can be tackled by composing a number of partial models to produce a global model of the phenomena. We propose to embed the partial models in software agents and to implement their composition as a cooperative negotiation between the agents. The resulting multiagent system provides a global model of a phenomenon. We applied this approach in modelling two complex physiological processes: the heart rate regulation and the glucose-insulin metabolism. (...) Beyond the effectiveness demonstrated in these two applications, the idea of using models associated to software agents to give reason of complex phenomena is in accordance with current tendencies in epistemology, where it is evident an increasing use of computational models for scientific explanation and analysis. Therefore, our approach has not only a practical, but also a theoretical significance: agents embedding models are a technology suitable both to representing and to investigating reality. (shrink)

A standard formalization of a scientific theory is a system of axioms for that theory in a first-order language (possibly many-sorted; possibly with the membership primitive $$\in$$ ). Suppes (in: Carvallo M (ed) Nature, cognition and system II. Kluwer, Dordrecht, 1992) expressed skepticism about whether there is a “simple or elegant method” for presenting mathematicized scientific theories in such a standard formalization, because they “assume a great deal of mathematics as part of their substructure”. The major difficulties amount to these. (...) First, as the theories of interest are mathematicized, one must specify the underlying applied mathematics base theory, which the physical axioms live on top of. Second, such theories are typically geometric, concerning quantities or trajectories in space/time: so, one must specify the underlying physical geometry. Third, the differential equations involved generally refer to coordinate representations of these physical quantities with respect to some implicit coordinate chart, not to the original quantities. These issues may be resolved. Once this is done, constructing standard formalizations is not so difficult—at least for the theories where the mathematics has been worked out rigorously. Here we give what may be claimed to be a simple and elegant means of doing that. This is for mathematicized scientific theories comprising differential equations for $$\mathbb{R}$$ -valued quantities Q (that is, scalar fields), defined on n (“spatial” or “temporal”) dimensions, taken to be isomorphic to the usual Euclidean space $$\mathbb{R}^n$$. For illustration, I give standard (in a sense, “text-book”) formalizations: for the simple harmonic oscillator equation in one-dimension and for the Laplace equation in two dimensions. (shrink)

We shall address from a conceptual perspective the duality between algebra and geometry in the framework of the refoundation of algebraic geometry associated to Grothendieck’s theory of schemes. To do so, we shall revisit scheme theory from the standpoint provided by the problem of recovering a mathematical structure A from its representations \ into other similar structures B. This vantage point will allow us to analyze the relationship between the algebra-geometry duality and the structure-semiotics duality. Whereas in classical algebraic geometry (...) a certain kind of rings can be recovered by considering their representations with respect to a unique codomain B, Grothendieck’s theory of schemes permits to reconstruct general rings by considering representations with respect to a category of codomains. The strategy to reconstruct the object from its representations remains the same in both frameworks: the elements of the ring A can be realized—by means of what we shall generally call Gelfand transform—as quantities on a topological space that parameterizes the relevant representations of A. As we shall argue, important dualities in different areas of mathematics can be understood as particular cases of this general pattern. In the wake of Majid’s analysis of the Pontryagin duality, we shall propose a Kantian-oriented interpretation of this pattern. We shall use this conceptual framework to argue that Grothendieck’s notion of functor of points can be understood as a “relativization of the a priori” that generalizes the relativization already conveyed by the notion of domain extension to more general variations of the corresponding domains. (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)

Suppose that a decision-maker's aim, under certainty, is to maximise some continuous value, such as lifetime income or continuous social welfare. Can such a decision-maker rationally satisfy what has been called "continuity for easy cases" while at the same time satisfying what seems to be a widespread intuition against the full-blown continuity axiom of expected utility theory? In this note I argue that the answer is "no": given transitivity and a weak trade-off principle, continuity for easy cases violates the anti-continuity (...) intuition. I end the note by exploring an even weaker continuity condition that is consistent with the aforementioned intuition. (shrink)

Three metascientific concepts that have been object of philosophical analysis are the concepts oflaw, model and theory. The aim ofthis article is to present the explication of these concepts, and of their relationships, made within the framework of Sneedean or Metatheoretical Structuralism (Balzer et al. 1987), and of their application to a case from the realm of biology: Population Dynamics. The analysis carried out will make it possible to support, contrary to what some philosophers of science in general and of (...) biology in particular hold, the following claims: a) there are "laws" in biological sciences, b) many of the heterogeneous and different "models" of biology can be accommodated under some "theory", and c) this is exactly what confers great unifying power to biological theories. (shrink)

Two metascientific concepts that have been ― and still are ― object of philosophical analysis are the concepts of model and theory. But while the concept of scientific theory was one of the concepts to which philosophers of science devoted most attention during the 20th century, it is only in recent decades that the concept of scientific model has come to occupy a central position in philosophical reflection. However, it has done so in such a way that, at present, as (...) Jim Bogen states in the back cover of the book Scientific Models in the Philosophy of Science, by Daniela Bailer-Jones, “[t]he standard philosophical literature on the role of models in scientific reasoning is voluminous, disorganized, and confusing”. In spite of this, one of the axes that would allow us to organize at least part of this literature, and with which Bailer-Jones’ book closes, is that which is identified as one of the “contemporary philosophical issues: how theories and models relate to each other” (Bailer-Jones 2009, p. 208).That is why, in this introduction to the special issues of Metatheoria devoted to the topic of “Models and Theories in Biology”, we will present the main advances that have been made in the philosophical analysis of the concepts of model and theory in general and in biology in particular, and we will also do the same with the answers that have been given to the problem of "how theories and models relate to each other”. (shrink)

It is well known that Felix Klein took a decisive step in investigating the invariants of transformation groups. However, less attention has been given to Klein’s considerations on the epistemological implications of his work on geometry. This paper proposes an interpretation of Klein’s view as a form of mathematical structuralism, according to which the study of mathematical structures provides the basis for a better understanding of how mathematical research and practice develop.

In this article, I develop three conceptual innovations within the area of formal metatheory, and present a computer program, called Reconstructor, that implements those developments. The first development consists in a methodology for testing formal reconstructions of scientific theories, which involves checking both whether translations of paradigmatically successful applications into models satisfy the formalisation of the laws, and also whether unsuccessful applications do not. I show how Reconstructor can help carry this out, since it allows the end-user to specify a (...) formal language, input axioms and models formulated in that language, and then ask if the models satisfy the axioms. The second innovation is the introduction of incomplete models (for which the denotation of some terms is missing) into scientific metatheory, in order to represent cases of missing information. I specify the paracomplete semantics built into Reconstructor to deal with sentences where denotation failures occur. The third development consists in a new way of explicating the structuralist notion of a determination method, by equating them with algorithms. This allows determination methods to be loaded into Reconstructor and then executed within a model to find out the value of a previously non-denoting term (i.e. it allows the formal reconstruction to make predictions). This, in turn, can help test the reconstruction in a different way. Finally, I conclude with some suggestions about additional uses the program may have. (shrink)

The problem of understanding how statistical inference is, and can be, applied in empirical sciences is important for the methodology of science. It is the objective of this paper to gain a better understanding of the role of statistical methods in scientific modeling. The important question of whether the applicability reduces to the representational properties of statistical models is discussed. It will be shown that while the answer to this question is positive, representation in statistical models is not purely structural. (...) In spite of the fact that representation in statistical models is based on the structural similarities between the statistical model and the empirical systems under study, these relationships are shown to be appropriate for representing relations in the target system by agent function, too. A second aspect of the paper involves the claim that agent-based components of statistical modeling are: a) interpretation of random variables, b) selection of the goal of statistical research, and c) selection of estimator properties. To justify these claims, a preliminary discussion will be presented on the role of statistics in modeling, as in regression and other structural models. This role will be explored and realized using a structural viewpoint. Also the role of statistical estimation in statistical modeling is discussed to explain the representational role of models and the inferential role of the agent in modeling. (shrink)

Following the proposal of a new kind of selective structural realism that uses as a basis the distinction between framework and interaction theories, this work discusses relevant applications in fundamental physics. An ontology for the different entities and properties of well-known theories is thus consistently built. The case of classical field theories—including general relativity as a classical theory of gravitation—is examined in detail, as well as the implications of the classification scheme for issues of realism in quantum mechanics. These applications (...) also shed light on the different range of applicability of the ontic and epistemic versions of structural realism. (shrink)

In this article my primary intention is to engage in a discussion on the inherent constraints of models, taken as models of theories, that reaches beyond the epistemological level. Naturally the paper takes into account the ongoing debate between proponents of the syntactic and the semantic view of theories and that between proponents of the various versions of scientific realism, reaching down to the most fundamental, subjective level of discourse. In this approach, while allowing for a limited discussion of physical (...) and positive science models, I am primarily focused on the structure and ontology of mathematical models, in particular Cohen’s forcing models and to a lesser extent Gödel’s constructible universe, to the extent that these were designed to answer questions bearing on the scope, the capacity and ultimately the ontology of models themselves, therefore influencing in one or the other way the status of models in general. This status, it is argued, is largely defined by the way models subsume a set-theoretical structure whose constraints, reducible to an extra-linguistic level of discourse, may implicitly condition the epistemic status of models as representations of axiomatic theories. In the last section I deal extensively with the inner constraints of theories as subjectively originated in a less technical philosophically oriented discussion with certain prompts from phenomenology. (shrink)

We propose a formal framework to examine the relationship between models and observations. To make our analysis precise,models are reduced to first-order theories that represent both terminological knowledge – e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations – and assertional knowledge – e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that describes their nature (...) and their organization and takes track of the way they are experimentally acquired or intentionally elaborated. A model mainly represents the theoretical knowledge or hypotheses on a domain, while the theory of observations mainly represents the empirical knowledge and the given experimental practices. We propose a precise identity criterion for observations and we explore different links between models and observations by assuming a degree of independence between them. By exploiting some techniques developed in the field of social choice theory and judgment aggregation, we sketch some strategies to solve inconsistencies between a given set of observations and the assumed theoretical hypotheses. The solutions of these inconsistencies can impact both the observations – e.g., the theoretical knowledge and the analysis of the way observations are collected or produced may highlight some unreliable sources – and the models – e.g. empirical evidences may invalidate some theoretical laws. (shrink)

I propose a new account of homology, according to which homology is a correspondence of developmental mechanisms due to common ancestry, formally defined as an isomorphism of causal graphs over lineages. The semiformal definition highlights the role of homology as a higher-order principle unifying evolutionary models and also provides definite meanings to concepts like constraints, evolvability, and novelty. The novel interpretation of homology suggests a broad perspective that accommodates evolutionary developmental biology and traditional population genetics as distinct but complementary approaches (...) to understand evolution, prompting further empirical and theoretical research. (shrink)

A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...) models. I present and critically assess two leading accounts of model assessment, arguing that neither is sufficient to provide a complete understanding of model evaluation. I introduce a more powerful framework incorporating elements of the two views, which can help answer these three questions: What is the target of evaluation in model assessment? How does that evaluation proceed? What licenses us in making inferences about the real world, based on the evaluation of our models as successful? The framework identifies two distinct targets of model evaluation: representational similarity between the model and target system, and the adequacy of the model as a tool to answer questions. Both assessments must be relativized to a purpose, of which there are three general kinds: descriptive, predictive, and explanatory. These purposes differ in the way they inform the similarity relation, which is relevant for the similarity assessment, and the output they produce, which is relevant for the adequacy assessment. Any model can be assessed relative to any purpose, however a model encodes certain decisions made during the model’s construction, which impact its ability to be applied to a new purpose or new domain. My framework shows that extending a model, and drawing inferences from it, depends on its representational similarity. I apply this framework to several examples taken from astrophysics showing in detail how it can help illuminate the structure of the models, as well as make the justification for inferences made from them clear. The final chapter is a detailed analysis of a contemporary debate surrounding the use of models in astrophysics, between proponents of MOND and the standard ΛCDM model. (shrink)

This article concerns the split between syntactic and semantic approaches to scientific theories. It aims at showing that an axiomatic representation of a scientific theory is a precondition of comprehending if the models of contain infinite entities. This result is established on the basis of the proposition that the human mind—which is finitely bounded for all we know—is not capable of directly grasping infinite entities. In view of this cognitive limitation, an indirect and finite representation of possibly infinite components of (...) the models of a scientific theory proves to be indispensable. Sets of axioms and sets of axiom schemes provide such a representation. These considerations will be cast into an argument for an axiomatic conception of scientific theories. The article concludes with a case study of the ideal gas model. (shrink)

In “The Toolbox of Science” (1995) together with Towfic Shomar we advocated a form of instrumentalism about scientific theories. We separately developed this view further in a number of subsequent works. Steven French, James Ladyman, Otavio Bueno and Newton Da Costa (FLBD) have since written at least eight papers and a book criticising our work. Here we defend ourselves. First we explain what we mean in denying that models derive from theory – and why their failure to do so should (...) be lamented. Second we defend our use of the London model of superconductivity as an example. Third we point out both advantages and weaknesses of FLBD’s techniques in comparison to traditional Anglophone versions of the semantic conception. Fourth we show that FLBD’s version of the semantic conception has not been applied to our case study. We conclude by raising doubts about FLBD’s overall project. (shrink)

In this paper a theory of finitistic and frequentistic approximations — in short: f-approximations — of probability measures P over a countably infinite outcome space N is developed. The family of subsets of N for which f-approximations converge to a frequency limit forms a pre-Dynkin system $${{D\subseteq\wp(N)}}$$. The limiting probability measure over D can always be extended to a probability measure over $${{\wp(N)}}$$, but this measure is not always σ-additive. We conclude that probability measures can be regarded as idealizations of (...) limiting frequencies if and only if σ-additivity is not assumed as a necessary axiom for probabilities. We prove that σ-additive probability measures can be characterized in terms of so-called canonical and in terms of so-called full f-approximations. We also show that every non-σ-additive probability measure is f-approximable, though neither canonically nor fully f-approximable. Finally, we transfer our results to probability measures on open or closed formulas of first-order languages. (shrink)

Since the beginning of the 20th century, philosophers of science have asked, "what kind of thing is a scientific theory?" The logical positivists answered: a scientific theory is a mathematical theory, plus an empirical interpretation of that theory. Moreover, they assumed that a mathematical theory is specified by a set of axioms in a formal language. Later 20th century philosophers questioned this account, arguing instead that a scientific theory need not include a mathematical component; or that the mathematical component need (...) not be specified by a set of axioms in a formal language. We survey various accounts of scientific theories entertained in the 20th century -- removing some misconceptions, and clearing a path for future research. (shrink)

The debate between critics of syntactic and semantic approaches to the formalization of scientific theories has been going on for over 50 years. I structure the debate in light of a recent exchange between Hans Halvorson, Clark Glymour, and Bas van Fraassen and argue that the only remaining disagreement concerns the alleged difference in the dependence of syntactic and semantic approaches on languages of predicate logic. This difference turns out to be illusory.

Review Artcile of an anthology of writings inspired by Patrick Suppes, "Foundations and Methods from Mathematics to Neuroscience" examined in the context of Suppes' life and philosophical development.

Over the years, mathematics and statistics have become increasingly important in the social sciences1 . A look at history quickly confirms this claim. At the beginning of the 20th century most theories in the social sciences were formulated in qualitative terms while quantitative methods did not play a substantial role in their formulation and establishment. Moreover, many practitioners considered mathematical methods to be inappropriate and simply unsuited to foster our understanding of the social domain. Notably, the famous Methodenstreit also concerned (...) the role of mathematics in the social sciences. Here, mathematics was considered to be the method of the natural sciences from which the social sciences had to be separated during the period of maturation of these disciplines. All this changed by the end of the century. By then, mathematical, and especially statistical, methods were standardly used, and their value in the social sciences became relatively uncontested. The use of mathematical and statistical methods is now ubiquitous: Almost all social sciences rely on statistical methods to analyze data and form hypotheses, and almost all of them use (to a greater or lesser extent) a range of mathematical methods to help us understand the social world. Additional indication for the increasing importance of mathematical and statistical methods in the social sciences is the formation of new subdisciplines, and the establishment of specialized journals and societies. Indeed, subdisciplines such as Mathematical Psychology and Mathematical Sociology emerged, and corresponding journals such as The Journal of Mathematical Psychology (since 1964), The Journal of Mathematical Sociology (since 1976), Mathematical Social Sciences (since 1980) as well as the online journals Journal of Artificial Societies and Social Simulation (since 1998) and Mathematical Anthropology and Cultural Theory (since 2000) were established. What is more, societies such as the Society for Mathematical Psychology (since 1976) and the Mathematical Sociology Section of the American Sociological Association (since 1996) were founded. Similar developments can be observed in other countries. The mathematization of economics set in somewhat earlier (Vazquez 1995; Weintraub 2002). However, the use of mathematical methods in economics started booming only in the second half of the last century (Debreu 1991). Contemporary economics is dominated by the mathematical approach, although a certain style of doing economics became more and more under attack in the last decade or so. Recent developments in behavioral economics and experimental economics can also be understood as a reaction against the dominance (and limitations) of an overly mathematical approach to economics. There are similar debates in other social sciences. It is, however, important to stress that problems of one method (such as axiomatization or the use of set theory) can hardly be taken as a sign of bankruptcy of mathematical methods in the social sciences tout court. This chapter surveys mathematical and statistical methods used in the social sciences and discusses some of the philosophical questions they raise. It is divided into two parts. Sections 1 and 2 are devoted to mathematical methods, and Sections 3 to 7 to statistical methods. As several other chapters in this handbook provide detailed accounts of various mathematical methods, our remarks about the latter will be rather short and general. Statistical methods, on the other hand, will be discussed in-depth. (shrink)

In this contribution, I comment on Raffaella Campaner’s defense of explanatory pluralism in psychiatry (in this volume). In her paper, Campaner focuses primarily on explanatory pluralism in contrast to explanatory reductionism. Furthermore, she distinguishes between pluralists who consider pluralism to be a temporary state on the one hand and pluralists who consider it to be a persisting state on the other hand. I suggest that it would be helpful to distinguish more than those two versions of pluralism – different understandings (...) of explanatory pluralism both within philosophy of science and psychiatry – namely moderate/temporary pluralism, anything goes pluralism, isolationist pluralism, integrative pluralism and interactive pluralism. Next, I discuss the pros and cons of these different understandings of explanatory pluralism. Finally, I raise the question of how to implement or operationalize explanatory pluralism in scientific practice; how to structure the “genuine dialogue” or shape “the pluralistic attitude” Campaner is referring to. As tentative answers, I explore a question-based framework for explanatory pluralism as well as social-epistemological procedures for interaction among competing approaches and explanations. (shrink)

This paper aims to set forth Carnapian structuralism, i.e., a syntactic view of the structuralist approach which is deeply inspired by Carnap’s dual level conception of scientific theories. At its core is the axiomatisation of a metatheoretical concept AE(T) which characterises those extensions of an intended application that are admissible in the sense of being models of the theory-element T and that satisfy all links, constraints and specialisations. The union of axiom systems of AE(T) (where T is an element of (...) the theory-net N) will allow us to present scientific theories in an axiomatic fashion so that deductive reasoning in science can be formalised. Thereupon defeasible and paraconsistent means of reasoning will be introduced. The logical study of scientific reasoning is the key motivation of Carnapian structuralism. A further motivation is to help overcome the syntactic-semantic split in the philosophy of science. (shrink)

Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...) comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)

‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...) analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (shrink)

In this first paper of a series of works on the foundations of science, we examine the significance of logical and mathematical frameworks used in foundational studies. In particular, we emphasize the distinction between the order of a language and the order of a structure to prevent confusing models of scientific theories with first-order structures, and which are studied in standard model theory. All of us are, of course, bound to make abuses of language even in putatively precise contexts. This (...) is not a problem—in fact, it is part of scientific and philosophical practice. But it is important to be sensitive to the dierent uses that structure, model, and language have. In this paper, we examine these topics in the context of classical logic; only in the last section we touch upon briefly on non-classical ones. (shrink)

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)