This paper first argues that evolutionary models of conceptual development which are patterned on Darwinian selection are unlikely to solve the demarcation problem. The persistence of myths shows that in most social environments unfalsifiable ideas are more likely to survive than ones which can be subjected to empirical scrutiny. I then analyze Hull's claims about how the credit system operates in science and conclude with him that it can perform a surprising variety of functions. However I argue (...) that the credit system must be constantly tempered by internalized norms which encapsulate the traditional ultimate aims of science. (shrink)

We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. (...) We also claim that our account could be fruitfully used in appraising other models in philosophy and science. (shrink)

The aim of this article is to examine the scientific and public functions of two- andthree-dimensional models in the context of three episodes from nineteenth-century biology. Iargue that these models incorporate both data and theory by presenting theoretical assumptions inthe light of concrete data or organizing data through theoretical assumptions. Despite their diverseroles in scientific practice, they all can be characterized as mediators between data and theory.Furthermore, I argue that these different mediating functions often reflect their (...) different audiencesthat included specialized scientists, students, and the general public. In this sense, models innineteenth-century biology can be understood as mediators between theory, data, and their diverseaudiences. (shrink)

I. The function of models in the empirical sciencesII. Structure and purpose: conditions of a structural nature which models should satisfy in order to accomplish their function.III. Generalisation and specialisation of the classical definition of model, in view of the above requirements:the algebraic model conceptthe semantic model conceptthe syntactical model conceptIV. Attempt towards reunification: the concept of model on a pragmatic basis.

The central question of this thesis is how one can learn about particular targets by using models of those targets. A widespread assumption is that models have to be representative models in order to foster knowledge about targets. Thus the thesis begins by examining the concept of representation from an epistemic point of view and supports an account of representation that does not distinguish between representation simpliciter and adequate representation. Representation, understood in the sense of a representative (...) model, is regarded as a success term. That is, a representative model is one relatum in a relation of adequate representation (Chapter 2). When a representative model represents a target, it allows users of this model to learn something about the target. It is argued that a representative model has this epistemic function because it shares relevant features with the target. This presupposes a similarity view of representation. Similarity views of representation face serious objections, which will be rebutted (Chapter 3). One way of spelling out a similarity view of representation is to defend an indirect view of representation. In this thesis, which does not argue for an indirect view, it is assumed that the indirect view is a good option, if not the best, for articulating the similarity view. It is demonstrated how such an indirect view can be expanded to account for cases of technological modeling. A case study in bioengineering is used to show that the indirect view of representation must acknowledge a distinction between two directions of fit in relations between vehicles and targets. In this context the notion of design is interpreted as a relation between a vehicle and a target, thereby connecting ideas from philosophy of science with ideas from philosophy of technology (Chapter 4). Fictionalist accounts of models are intended to tackle the issue of the ontology of models. In this thesis, however, two prominent fictionalist accounts are discussed from an epistemological point of view in light of the central question regarding how one can learn about targets by using models. This question is addressed from the standpoint of Waltonian fictionalism. The result of the discussion is that the two discussed Waltonian fictionalist accounts cannot sufficiently answer the question. These accounts are criticized for their inability to deliver a satisfactory epistemology of representation (Chapter 5). Although Waltonian fictionalism is criticized, the present thesis also shows that the foundational theory of Waltonian fictionalism, the theory of make-believe can nevertheless be used to account for the distinction between projections and predictions that is made by the Intergovernmental Panel on Climate Change (Chapter 6). (shrink)

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the (...) nature and function of representation in computational cognitive models. (shrink)

Taking Brian Cantwell Smith’s study, “Limits of Correctness in Computers,” as its point of departure, this article explores the role of models in computer science. Smith identifies two kinds of models that play an important role, where specifications are models of problems and programs are models of possible solutions. Both presuppose the existence of conceptualizations as ways of conceiving the world “in certain delimited ways.” But high-level programming languages also function as models of virtual (...) (or abstract) machines, while low-level programming languages function as models of causal (or physical) machines. The resulting account suggests that sets of models embedded within models are indispensable for computer programming. (shrink)

The central aim of science is to make sense of the world. To move forward as a community endeavor, sense-making must be systematic and focused. The question then is how do scientists actually experience the sense-making process? In this chapter we examine the “practice turn” in science studies and in particular how as a result of this turn scholars have come to realize that models are the “functional unit” of scientific thought and form the center of the (...) reasoning/sense-making process. This chapter will explore a context-dependent view of models and modeling in science. From this analysis we present a framework for delineating the different aspects of model-based reasoning and describe how this view can be useful in educational settings. This framework highlights how modeling supports and focuses scientific practice on sense-making. (shrink)

Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains (...) are cases in point. Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools. In short, models are one of the principal instruments of modern science. -/- Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice. The result has been an incredible proliferation of model-types in the philosophical literature. Probing models, phenomenological models, computational models, developmental models, explanatory models, impoverished models, testing models, idealized models, theoretical models, scale models, heuristic models, caricature models, didactic models, fantasy models, toy models, imaginary models, mathematical models, substitute models, iconic models, formal models, analogue models and instrumental models are but some of the notions that are used to categorize models. While at first glance this abundance is overwhelming, it can quickly be brought under control by recognizing that these notions pertain to different problems that arise in connection with models. For example, models raise questions in semantics (what is the representational function that models perform?), ontology (what kind of things are models?), epistemology (how do we learn with models?), and, of course, in philosophy of science (how do models relate to theory?; what are the implications of a model based approach to science for the debates over scientific realism, reductionism, explanation and laws of nature?). (shrink)

I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...) allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

Prior to statistical mechanics and especially before the advent of the new quantum mechanics, it was traditionally held, following in the main the Kantian philosophy, that the task of science is to attain a unique quantitative representation of reality. It was thought—and with justifiable zeal—that a scientific discipline is exact to the extent to which a mathematical pattern yielding quantitative relations can be applied to its subject matter. This view was based on the implicit assumptions that functional relatedness conducive (...) to quantitative evaluation is bound to reveal the most generic, and hence, the most essential traits of events. (shrink)

Based on an examination of Galileo’s mechanics, Peter Machamer and Andrea Woody proposed the scientific use of what they call models of intelligibility. As they define it, a model of intelligibility is a concrete phenomenon that guides scientific understanding of problematic cases. This paper extends Machamer and Woody’s analysis by elaborating the semantic function of MOIs. MOIs are physical embodiments of theoretical representations. Therefore, they eliminate the interpretive distance between theory and phenomena, creating classes of concrete referents for theoretical (...) concepts. Meanwhile, MOIs also provide evidence for historical analyses of concepts, like ‘body’ or ‘motion’, that are otherwise thought to be too basic for explicit explication. These points are illustrated by two examples also drawn from Galileo. First, I show how the introduction of the balance as an MOI leads Galileo to reject the Aristotelian conception of elemental natures. Second, Galileo’s rejection of medieval MOIs of circular motion constrains the reference of ‘conserved motion’ to curvilinear translations, thereby excluding the rotations that had been included in its scope. Both uses of MOIs marked important steps toward modern classical mechanics. (shrink)

This handbook offers the first comprehensive reference guide to the interdisciplinary field of model-based reasoning. It highlights the role of models as mediators between theory and experimentation, and as educational devices, as well as their relevance in testing hypotheses and explanatory functions. The Springer Handbook merges philosophical, cognitive and epistemological perspectives on models with the more practical needs related to the application of this tool across various disciplines and practices. The result is a unique, reliable source of (...) information that guides readers toward an understanding of different aspects of model-based science, such as the theoretical and cognitive nature of models, as well as their practical and logical aspects. The inferential role of models in hypothetical reasoning, abduction and creativity once they are constructed, adopted, and manipulated for different scientific and technological purposes is also discussed. Written by a group of internationally renowned experts in philosophy, the history of science, general epistemology, mathematics, cognitive and computer science, physics and life sciences, as well as engineering, architecture, and economics, this Handbook uses numerous diagrams, schemes and other visual representations to promote a better understanding of the concepts. This also makes it highly accessible to an audience of scholars and students with different scientific backgrounds. All in all, the Springer Handbook of Model-Based Science represents the definitive application-oriented reference guide to the interdisciplinary field of model-based reasoning. (shrink)

Based on an examination of Galileo’s mechanics, Peter Machamer and Andrea Woody (and Machamer alone in subsequent articles) proposed the scientific use of what they call models of intelligibility. As they define it, a model of intelligibility (MOI) is a concrete phenomenon that guides scientific understanding of problematic cases. This paper extends Machamer and Woody’s analysis by elaborating the semantic function of MOIs. MOIs are physical embodiments of theoretical representations. Therefore, they eliminate the interpretive distance between theory and phenomena, (...) creating classes of concrete referents for theoretical concepts. Meanwhile, MOIs also provide evidence for historical analyses of concepts, like ‘body’ or ‘motion’, that are otherwise thought to be too basic for explicit explication. These points are illustrated by two examples also drawn from Galileo. First, I show how the introduction of the balance as an MOI leads Galileo to reject the Aristotelian conception of elemental natures. Second, Galileo’s rejection of medieval MOIs of circular motion constrains the reference of ‘conserved motion’ to curvilinear translations, thereby excluding the rotations that had been included in its scope. Both uses of MOIs marked important steps toward modern classical mechanics. (shrink)

This book explains the relationship between intelligence and environmental complexity, and in so doing links philosophy of mind to more general issues about the relations between organisms and environments, and to the general pattern of 'externalist' explanations. The author provides a biological approach to the investigation of mind and cognition in nature. In particular he explores the idea that the function of cognition is to enable agents to deal with environmental complexity. The history of the idea in the work of (...) Dewey and Spencer is considered, as is the impact of recent evolutionary theory on our understanding of the place of mind in nature. (shrink)

In this article it is argued that a complex model that includes Toulmin's functional account of argument, the pragma-dialectical stage analysis of argumentation offered by the Amsterdam School, and criteria developed in critical thinking theory, can be used to account for the normativity and field-dependence of argumentation in science. A pragma-dialectical interpretation of the four main elements of Toulmin's model, and a revised account of the double role of warrants, illuminates the domain specificity of scientific argumentation and the restrictions (...) to which the confrontation and opening stages of scientific critical discussions are subjected. In regard to the argumentation stage, examples are given to show that a general account of argumentation, as advocated by informal logicians, is not applicable to arguments in science. Furthermore, although patterns of inference differ in various scientific practices, deductive validity is argued to be a crucial notion in the assessment of scientific arguments. Finally, some remarks are made concerning the burden of proof and the concluding stage of scientific argumentation. (shrink)

The assessment of models in an experiment depends on their material nature and their function in the experiment. Models that are used to make the phenomenon under investigation visible - sensors - are assessed by calibration. However, calibration strategies assume material intervention. The experiment discssed in this paper is an experiment in economics to measure the influence of technology shocks on business cycles. It uses immaterial, mathematical instruments. It appears that calibration did not work for these kinds of (...) models, it did not provide reliable evidence for the facts of the business cycle. (shrink)

This paper considers the role of mathematics in the process of acquiring new knowledge in physics and astronomy. The defining of the notions of continuum and discreteness in mathematics and the natural sciences is examined. The basic forms of representing the heuristic function of mathematics at theoretical and empirical levels of knowledge are studied: deducing consequences from the axiomatic system of theory, the method of generating mathematical hypotheses, “pure” proofs for the existence of objects and processes, mathematical modelling, the formation (...) of mathematics on the basis of internal mathematical principles and the mathematical theory of experiment. (shrink)

The investigation of a method for postulating counterfactual histories of science has led to the development of a theory of science based on general units of knowledge, which are called “advances”. Advances are passed on from scientist to scientist, and may be seen as “causing” the appearance of other advances. This results in networks which may be analyzed in terms of probabilistic causal models, which are readily encodable in computer language. The probability for a set of advances (...) to give rise to another advance is taken to be invariant through history, but depends on a typical time span which is an inverse function of the degree of development of science. Examples are given from the early science of magnetism and from the 19th century physics. (shrink)

The purpose of the paper is to present basic principles of connectionism and its position within contemporary cognitive science. Connectionist paradigm postulates thinking as a parallel processing of non-structured information by simple calculations performed by neurons that are deeply mutually interconnected. The basic numerical tools of connectionism are represented by so-called artificial neural networks, which are immediately applicable to the study of many cognitive functions at different levels of complexity and sophistication. Connectionism has brought with it a number (...) of important philosophical issues and concerns. Connectionism as a dramatic shift from more traditional accounts of cognition has forced philosophers to reconsider many assumptions based upon earlier theories. (shrink)

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

In both theoretical and applied modeling in behavioral sciences, it is common to choose a mathematical specification of functional form and distribution of unobservables on grounds of analytic convenience without support from explicit theoretical postulates. This article discusses the issue of deriving particular qualitative hypotheses about functional form restrictions in structural models from intuitive theoretical axioms. In particular, we focus on a family of postulates known as dimensional invariance. Subsequently, we discuss how specific qualitative postulates can be reformulated so (...) as to allow for conventional statistical hypothesis testing, and we also derive details of a particular likelihood ratio test procedure. An empirical application of the testing procedure is carried out, based on data from a Stated Preference survey. (shrink)

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

Scientific fields frequently need to exchange data to advance their own inquiries. Data unification is the process of stabilizing these forms of interfield data exchange. I present an account of the epistemic structure of data unification, drawing on case studies from model-based cognitive neuroscience (MBCN). MBCN is distinctive because it shows that modeling practices play an essential role in mediating these data exchanges. Models often serve as interfield evidential integrators, and models built for this purpose have their own (...) representational and inferential functions. This form of data unification should be seen as autonomous from other forms, particularly explanatory unification. (shrink)

This book provides a comprehensive overview of the nature of explanations as given in both natural and social sciences. It discusses models of explanation adopted in natural and social sciences. The author also elaborates upon naturalistic and anti-naturalistic views and other types of explanations such as functional, purposive, etc in social science. The volume elaborates upon themes like bridge principle; functional explanation; purposive explanation; teleological explanation; prediction; methodological individualism; methodological collectivism; illocutionary redescription; principle of action; and dispositional explanations, (...) to understand whether the explanations given in the realm of social sciences are the same or different from the explanations that are given in the field of natural sciences. This introductory book is a must read for students and scholars of philosophy of science, logic, science and technology studies, social sciences, and philosophy in general. (shrink)

In this essay I argue against I. Bernard Cohen's influential account of Newton's methodology in the Principia: the 'Newtonian Style'. The crux of Cohen's account is the successive adaptation of 'mental constructs' through comparisons with nature. In Cohen's view there is a direct dynamic between the mental constructs and physical systems. I argue that his account is essentially hypothetical-deductive, which is at odds with Newton's rejection of the hypothetical-deductive method. An adequate account of Newton's methodology needs to show how Newton's (...) method proceeds differently from the hypothetical-deductive method. In the constructive part I argue for my own account, which is model based: it focuses on how Newton constructed his models in Book I of the Principia. I will show that Newton understood Book I as an exercise in determining the mathematical consequences of certain force functions. The growing complexity of Newton's models is a result of exploring increasingly complex force functions (intra-theoretical dynamics) rather than a successive comparison with nature (extra-theoretical dynamics). Nature did not enter the scene here. This intra-theoretical dynamics is related to the 'autonomy of the models'. (shrink)

In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other (...) hand, we suggest that if ABMs of science underwent two types of robustness analysis, they could indeed have a clear epistemic function, namely by providing evidence for philosophical and historical hypotheses. In this sense, ABMs can obtain evidential and explanatory properties and thus be a useful tool for integrated history and philosophy of science. We illustrate our point with an example of a model—building on the work by Kevin Zollman—which we apply to a concrete historical case study. (shrink)

The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China (...) but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations). Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY/X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY/X(t), which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances. (shrink)

The article deals with a topic of the role of science in current society and focuses on the role of political economy. There has been a critical feedback to the fact that the social sciences subordinate themselves to the interests of power instead of looking for the models of healthy society. These models should correspond with the changes in the society and should not allow to be tied with unchanging paradigms and various ‘-isms‘. Political economy should deal (...) with the topic of human freedom and increasing social inequalities, which threaten the society. It is important to work with sustainable relationship of social-economical and natural environment and with the relationship of culture and nature.) It is a new way of understanding what production means and how the society should cope with it. It is not just about classic waste but also about negative externalities. The human cannot be reduced to ‘human resources‘ or ‘homo economicus‘. The emphasis is laid on humanity and taking human as an indivisible individual. The article emphasizes the importance of full responsibility preference over limited responsibility and preference of the individual and community over an economical corporation. The society cannot be reduced or administrated as an economical or accounting unit. Good governance includes taking care of reproduction, justice and safety. It is all about sufficiency, dignity and trust among people. The most important is the fight for human freedom and independent life, which is the meaning of itself. People who are free and can persist in such fight, which is held by a spirit of unity, are important but also more and more rare. Political economy should look for the possibilities how to follow up the Jewish-Christian tradition without the God. (shrink)

Scientific progress is achieved not only by continuous accumulation of knowledge but also by paradigm shifts. These shifts are often necessitated by anomalous findings that cannot be incorporated in accepted models. Two important methodological principles regulate this process and complement each other: Ockham's Razor as the principle of parsimony and Plato's Life Boat as the principle of the necessity to 'save the appearances' and thus incorporate conflicting phenomenological data into theories. We review empirical data which are in conflict with (...) some presuppositions of accepted mainstream science: Clinical and experimental effects of prayer and healing intention, data from telepathy, psychokinesis experiments and precognition, and anecdotal reports of macro-psychokinesis. Taken together, the now well documented possibility of these events suggests that such phenomena are anomalies that challenge some widely held beliefs in mainstream science. On the other hand, scientists often fear that by accepting the reality of these phenomena they also have to subscribe to world-models invoking ontological dualism or idealism. We suggest accepting the phenomena as real, but without questionable ontologies commonly associated with them. We outline how this might work. (shrink)

This book provides both an introduction to the philosophy of scientific modeling and a contribution to the discussion and clarification of two recent philosophical conceptions of models: artifactualism and fictionalism. These can be viewed as different stances concerning the standard representationalist account of scientific models. By better understanding these two alternative views, readers will gain a deeper insight into what a model is as well as how models function in different sciences. Fictionalism has been a traditional epistemological (...) stance related to antirealist construals of laws and theories, such as instrumentalism and inferentialism. By contrast, the more recent fictional view of models holds that scientific models must be conceived of as the same kind of entities as literary characters and places. This approach is essentially an answer to the ontological question concerning the nature of models, which in principle is not incompatible with a representationalist account of the function of models. The artifactual view of models is an approach according to which scientific models are epistemic artifacts, whose main function is not to represent the phenomena but rather to provide epistemic access to them. It can be conceived of as a non-representationalist and pragmatic account of modeling, which does not intend to focus on the ontology of models but rather on the ways they are built and used for different purposes. The different essays address questions such as the artifactual view of idealization, the use of information theory to elucidate the concepts of abstraction and idealization, the deidealization of models, the nature of scientific fictions, the structural account of representation and the ontological status of structures, the role of surrogative reasoning with models, and the use of models for explaining and predicting physical phenomena. (shrink)

What gets integrated in integrative scientific practices has been a topic of much discussion. Traditional views focus on theories and explanations, with ideas of reduction and unification dominating the conversation. More recent ideas focus on disciplines, fields, or specialties; models, mechanisms, or methods; phenomena, problems. How integration works looks different on each of these views since the objects of integration are ontologically and epistemically various: statements, boundary conditions, practices, protocols, methods, variables, parameters, domains, laboratories, and questions all have their (...) own structures, functions and logics. I focus on one particular kind of scientific practice, integration of “approaches” in the context of a research system operating on a special kind of “platform.” Rather than trace a network of interactions among people, practices, and theoretical entities to be integrated, in this essay I focus on the work of a single investigator, David Wake. I describe Wake’s practice of integrative evolutionary biology and how his integration of approaches among biological specialties worked in tandem with his development of the salamanders as a model taxon, which he used as a platform to solve, re-work and update problems that would not have been solved so well by non-integrative approaches. The larger goal of the project to which this paper contributes is a counter-narrative to the story of 20th century life sciences as the rise and march of the model organisms and decline of natural history. (shrink)

The classic logical positivist account of historical explanation, putting forward what is variously called the "regularity interpretation" (#Gardiner, The Nature of Historical Explanation), the "covering law model" (#Dray, Laws and Explanation in History), or the "deductive model" (Michael #Scriven, "Truisms as Grounds for Historical Explanations"). See also #Danto, Narration and Knowledge, for further criticisms of the model. Hempel formalizes historical explanation as involving (a) statements of determining (initial and boundary) conditions for the event to be explained, and (b) statements of (...) general laws that imply that whenever events of the kind described in (a) occur, an event of the kind to be explained will take place. He admits that in practice historians rarely explain historical events in this way, but instead give only fragmentary "explanation sketches." Such sketches can, however, be "scientifically acceptable," providing it points to where "more specific statements" are to be found (351). An interesting aspect of the article is its "late" positivist, proto‑Kuhnian assertion that "the separation of 'pure description' and 'hypothetical generalization and theory construction' in empirical science is unwarranted" (356). But Hempel does not work out the wider implications of this important conclusion. It is also worth noting that he himself was not much interested in the philosophy of history; other authors took up his model and discussed it within that context. Usefully discussed by #Ricoeur, Time and Narrative, 1:112‑17. (shrink)

The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these (...) heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation, held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004. (shrink)

One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of (...) their function in the mechanism. This paper examines the notion of ‘function’ within structural modelling. We argue that ‘functions’ ought to be understood as the theoretical underpinnings of the causes, namely as the role that causes play in the functioning of the mechanism. (shrink)

There has recently been a great interest in models in the natural sciences. Models are used mainly for their representational functions: they help to concretize certain relationships between parameters in studying physical systems. For instance, we might be interested in representing how the planets orbit around the sun—a scale model of the solar system is an ideal tool for achieving this end. We are free to leave out one or two planets or ignore the moons which many (...) of the planets have. Alternatively, we might be interested in studying the relationship between two particular parameters—how one may be dependent on the other. Then we construct a functional model and determine the functional relationship between them. For instance, the orbital period of a planet is functionally dependent on the average distance of the planet from the sun.1 Models are rather widespread in the social sciences, especially in economics where functional models are used to study relationships between, say, supply and demand. Economics, however, also uses a different kind of model which is also used in the natural sciences: the hypothetical or as if model. Economists employ as if constructions when they assume economic agents to be perfectly rational beings who always seek to maximize their utilities. It is common knowledge that economic agents are not perfectly rational and that the assumptions of perfect rationality and optimal information are at best idealizations which do not, strictly speaking, correspond to the economic reality of how economic agents behave in the market-place. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of (...) potential explanations, and as a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

The rationality debate centers on the meaning of deviations of decision makers' responses from the predictions/prescriptions of normative models. But for the debate to have significance, the meaning and functions of normative analysis must be clear. Presently, they are not, and the debate's persistence owes much to conceptual blur. An attempt is made here to clarify the concept of normative analysis.

Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides (...) a selective and critical overview of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers (...) of science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry. (shrink)

Propositions alone are not constitutive of science. But is the "non-propositional" side of science theoretically superfluous: must philosophy of science consider it in order to adequately account for science? I explore the boundary between the propositional and non-propositional sides of biological theory, drawing on three cases: Grinnell's remnant models of faunas, Wright's path analysis, and Weismannism's role in the generalization of evolutionary theory. I propose a picture of material model-building in biology in which manipulated systems (...) of material objects function as theoretical models. In each of the cases, material systems such as diagrams play important generative as well as presentational roles. (shrink)

There are three ways in which bridges may be built between science and theology: spirituality, methodology, and content. Spirituality is the power which drives each to address reality and the expectations with which each approaches the pursuit of truth. The methodology of science is summarized in terms of three activities: taxonomy; the hypothetico‐deductive cycle; derivative technology. The content of science, especially with respect to the phenomena of givenness, connectedness and openness in the life sciences, is correlated with (...) theological constructs. Attention is drawn to the role of the double helix in biology and a possible parallel is proposed to the function of the icon in religion and theology. (shrink)

I develop a theory of counterfactuals about relative computability, i.e. counterfactuals such as 'If the validity problem were algorithmically decidable, then the halting problem would also be algorithmically decidable,' which is true, and 'If the validity problem were algorithmically decidable, then arithmetical truth would also be algorithmically decidable,' which is false. These counterfactuals are counterpossibles, i.e. they have metaphysically impossible antecedents. They thus pose a challenge to the orthodoxy about counterfactuals, which would treat them as uniformly true. What’s more, I (...) argue that these counterpossibles don’t just appear in the periphery of relative computability theory but instead they play an ineliminable role in the development of the theory. Finally, I present and discuss a model theory for these counterfactuals that is a straightforward extension of the familiar comparative similarity models. (shrink)

In this paper, I present a new framework supporting the claim that some elements in science play a constitutive function, with the aim of overcoming some limitations of Friedman's (2001) account. More precisely, I focus on what I consider to be the gradualism implicit in Friedman's interpretation of the constitutive a priori, that is, the fact that it seems to allow for degrees of 'constitutivity'. I tease out such gradualism by showing that the constitutive character Friedman aims to track (...) can be captured by three features - namely, quasi-axiomaticity (QA), generative potential (GP), and empirical shielding (ES) - which are exhibited to a maximal degree by the examples Friedman deploys, particularly in his analysis of Newtonian mechanics. I argue that not all varieties of 'constitutivity' can be captured by the kind of gradualism implicit in Friedman's view, although developing the gradualism itself might provide useful insights. To show this, I analyse the function of the Hardy-Weinberg principle (HWP) in population genetics in terms of its QA, GP, and ES. Whereas the HWP does not count as constitutive in classical philosophical interpretations (Sober 1984), nor does it within Friedman's framework, it does nonetheless perform a minimally constitutive function. By means of historical details and considerations on the prospects of replacing the HWP, I show that the HWP is minimally constitutive by being a counterfactual instantiation of a paradigmatically constitutive stability principle, where the latter might itself be regarded as an enabling condition for a variety of modelling practices across the sciences. (shrink)

There is little doubt that in the actual practice of science, models, metaphors, analogies, reasoning by similar cases, and other "parallel" forms of argument are often essential for the discovery of new phenomena and their theoretical interpretation. The author has assembled in five essays, culled and developed from previous ones, her ideas on some basic questions concerning models and analogies. The first chapter considers in dialogue form the role of models in science; the next section (...) is an exploration of the questions of what is an analogy and under what conditions analogical arguments are valid. The formal logic of analogy is then developed in the third part. The theory concerning analogy put forth by Aristotle is next discussed; the last chapter treats of the explanatory function of metaphor—we are brought back to our starting point, hopefully enlightened.—P. J. M. (shrink)

As well known, the exact sciences served Kant as a model of metaphysical cognition. There is also an opposite relation between science and metaphysics, however, namely the teleology of reason, as expressed at the end of the Critique of Pure Reason. There, Kant claims that the sciences are ultimately subordinate to the essential ends of humanity “through the mediation of a rational cognition from mere concepts”, i. e., metaphysical cognition.

Abstract The word Padaartha, used as a technical term by different Indian schools of thought with different senses will be brought out. The meaning and intonation of the word Padaartha as used in the Upanishads, Brahmajnaana, Advaitha Philosophy, Sabdabrahma Siddhanta (Vyaakarana), the Shaddarshanas will be discussed. A comprehensive gist of this discussion will be presented relating to human consciousness, mind and their functions. The supplementary and complementary nature of these apparently “different” definitions will be conformed from cognitive science (...) point of view in understanding and a modern scientific model of human cognition and communication, language acquisition and in terms of brain modulation and demodulation will be presented. (shrink)

This book provides a methodological perspective on understanding the essential roles of econometric models in the theory and practice. Offering a comprehensive and comparative exposition of the accounts of models in both econometrics and philosophy of science, this work shows how econometrics and philosophy of science are interconnected while exploring the methodological insight of econometric modelling that can be added to modern philosophical thought. The notion of structure is thoroughly discussed throughout the book. The studies of (...) the consumption function of Trygve Haavelmo, Richard Stone, Milton Friedman, David Hendry and Robert Lucas are taken as the case studies to investigate their methodological implications of model and structure. In addition to the semantic view of the scientific theories, various philosophical accounts concerning scientific models are used to shed light on the methodological nature of these consumption studies in economics. This book will be of great interest to scholars and students of methodology of economics and econometrics as well as anyone interested in the philosophy of science in an economic context. (shrink)

In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by (...) analyzing from a semiotic point of view the representational relation which is characteristic of models. We then corroborate our hypothesis (2) by discussing the conceptual differences between icons, i.e. models, and indexical and symbolic signs and (3) by putting forward a general classification of all icons into three functional subclasses (images, diagrams, and metaphors). Subsequently, we (4) integratively refine our results by resorting to two influential and, as can be shown, complementary philosophy of science approaches to models. This yields the following result: models are determined by a semiotic structure in which a subject intentionally uses an object, i.e. the model, as a sign for another object, i.e. the original, in the context of a chosen theory or language in order to attain a specific end by instituting a representational relation in which the syntactic structure of the model, i.e. its attributes and relations, represents by way of a mapping the properties of the original, which hence are regarded as similar in a relevant manner. (shrink)