Models are of central importance in many scientific contexts. The centrality of models such as inflationary models in cosmology, general-circulation models of the global climate, the double-helix model of DNA, evolutionary models in biology, agent-based models in the social sciences, and general-equilibrium models of markets in their respective domains is a case in point (the Other Internet Resources section at the end of this entry contains links to online resources that discuss these (...) class='Hi'>models). Scientists spend significant amounts of time building, testing, comparing, and revising models, and much journal space is dedicated to interpreting and discussing the implications of models. In short, models are one of the principal instruments of modern science. (shrink)

The estrangement between genetic scientists and theologians originating in the 1960s is reflected in novel combinations of human thought (subject) and genes (investigational object), paralleling each other through the universal process known in chaos theory as self-similarity. The clash and recombination of genes and knowledge captures what Philip Hefner refers to as irony, one of four voices he suggests transmit the knowledge and arguments of the religion-and-science debate. When viewed along a tangent connecting irony to leadership, journal dissemination, and (...) the activities of the "public intellectual" and the public at large, the sequence of voices is shown to resemble the passage of genetic information from DNA to mRNA, tRNA, and protein, and from cell nucleus to surrounding environment. In this light, Hefner's inquiry into the voices of Zygon is bound up with the very subject matter Zygon covers. (shrink)

This text addresses the problems of complex systems in understanding natural phenomena and the behaviour of systems related to human activity, from a science and humanities perspective. It discusses molecular behaviour and structures, and offers examples of ecological and environmental modelling.

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)

This paper outlines the demographic characteristics of the Baiga tribe, one of the most primitive of the aboriginal tribal groups of Central India. The Baiga population has grown steadily since the first anthropological study of the tribe in the 1930s. Age at menarche, age at marriage, breast-feeding, and time interval between marriage and first conception are natural. There are more females than males. Sub-tribe endogamy is common; consanguineous marriage is favoured (34% of marriages are between first cousins) and marital distance (...) is low (mean 7·1 km). Though the mean menarcheal age is high (15·2 years), mean age at first marriage is low (16·6 years), giving a long reproductive period. (shrink)

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)

In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While (...) idealization and approximation is key to the making of models for the observable systems, they are in fact inoperable, at least not straightforwardly so, regarding models for the unobservable. And because of this point, which is so far neglected in the literature, I speculate that factionalism may have a better chance with models for the unobservable. (shrink)

This paper examines the form of mental representation of scientific theories in scientists and nonscientists. It concludes that images and schemas are not the appropriate form of mental representation for scientific theories but that mental models and perceptual symbols do seem appropriate for representing physical/mechanical phenomena. These forms of mental representation are postulated to have an analogical relation with the world and it is this relationship that gives them strong explanatory power. It is argued that the construct of naïve (...) theories as used in developmental psychology may be the appropriate form of mental representation for non physical/mechanical domains. The paper adopts a strong form of psychologism in the philosophy of science and argues that model-based approaches to scientific theories are more appropriate forms of representation for scientific theories than the formalist approaches that dominate current philosophy of science. (shrink)

Computer models provide formal techniques that are highly relevant to philosophical issues in epistemology, metaphysics, and ethics. Such models can help philosophers to address both descriptive issues about how people do think and normative issues about how people can think better. The use of computer models in ways similar to their scientific applications substantially extends philosophical methodology beyond the techniques of thought experiments and abstract reflection. For formal philosophy, computer models offer a much broader range of (...) representational techniques than are found in traditional logic, probability, and set theory, taking into account the important roles of imagery, analogy, and emotion in human thinking. Computer models make possible investigation of the dynamics of inference, not just abstract formal relations. (shrink)

In this work, I attempt to develop what I call a co-evolutionary model of scientific change, which I expect to afford a more balanced view on both the continuous and discontinuous aspects of scientific change. Supposing that scientific goals, methods and theories constitute the main components of scientific inquiry, I focus on the relationships among these components and their changing patterns. First of all, I identify explanatory power and empirical adequacy as primary goals of science and explore the possibility (...) of evaluating scientific goals. Then I try to bring out the major features of how the main components of science are related to each other. One major feature is that they mutually constrain each other, and as such each main component operates as a selective force on the other components. Another major feature is that the main components of science induce changes reciprocally, but with certain intervals. Other important features are the modes and tempos of changes in the main components of scientific inquiry. All these features together, I conclude, suggest that scientific change is evolutionary, as well as co-evolutionary. Finally I argue that this co-evolutionary model of scientific change does not yield to what I call the problems of circularity and scientific progress. (shrink)

Biologists, climate scientists, and economists all rely on models to move their work forward. In this book, I explore the use of models in these and other fields to introduce readers to the various philosophical issues that arise in scientific modeling. I show that paying attention to models plays a crucial role in appraising scientific work. -/- After surveying a wide range of models from a number of different scientific disciplines, I demonstrate how focusing on (...) class='Hi'>models sheds light on many perennial issues in philosophy of science and in philosophy in general. For example, reviewing the range of views on how models represent their targets introduces readers to the key issues in debates on representation, not only in science but in the arts as well. Also, standard epistemological questions are cast in new and interesting ways when we confront the question, "What makes for a good (or bad) model?". (shrink)

The intention and the result of a scientific inquiry is to obtain an understanding and a control of some part of the universe. This statement implies a dualistic attitude on the part of scientists. Indeed, science does and should proceed from this dualistic basis. But even though the scientist behaves dualistically, his dualism is operational and does not necessarily imply strict dualistic metaphysics.

The book "Idealization XIV: Models in Science" offers a detailed ontological, epistemological and historical account of the role of models in scientific practice. The volume contains contributions of different international scholars who developed many aspects of the use of idealizations and models both in the natural and the social sciences. This volume is particularly relevant because it offers original contributions concerning one of the main topic in philosophy of science: the role of models in (...) such branches of the sciences and the humanities like comparative historical sociology, economics, history, linguistics and political philosophy. -/- Contributors are: Giacomo Borbone, Krzysztof Brzechczyn, Mieszko Ciesielski, Adam Czerniak, Xavier de Donato Rodríguez, José L. Falguera, Adolfo García de la Sienra, Lidia Godek, Igor Hanzel, Łukasz Hardt, Krzysztof Kiedrowski, Barbara Konat, Zenonas Norkus, Piotr Przybysz, Piotr Szwochert. (shrink)

Abstract Three main concepts of model in science are distinguished: (1) semantical model of a theory; (2) real model of another real thing; (3) mathematical model of a real thing. The last concept is the most important for the empirical sciences. The mathematical model is not identical with a theory: it is an ideal object which is directly described by the theory. We have here an intermediate level between reality and theory.

Debunks cherished theories of mainstream consensus and reveals the deeper mysteries of the science of the unseen.

Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to (...) use. She examines early mechanical models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes their roots in the mathematical principles of Newton and others, and compares them to contemporary mechanistic approaches. Bailer-Jones then views the use of analogy in the late nineteenth century as a means of understanding models and to link different branches of science. She reveals how analogies can also be models themselves, or can help to create them. The first half of the twentieth century saw little mention of models in the literature of logical empiricism. Focusing primarily on theory, logical empiricists believed that models were of temporary importance, flawed, and awaiting correction. The later contesting of logical empiricism, particularly the hypothetico-deductive account of theories, by philosophers such as Mary Hesse, sparked a renewed interest in the importance of models during the 1950s that continues to this day. Bailer-Jones analyzes subsequent propositions of: models as metaphors; Kuhn's concept of a paradigm; the Semantic View of theories; and the case study approaches of Cartwright and Morrison, among others. She then engages current debates on topics such as phenomena versus data, the distinctions between models and theories, the concepts of representation and realism, and the discerning of falsities in models. (shrink)

Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis (...) has a long way to go if it is to account for the epistemic merits of toy models. (shrink)

This paper is concerned with scientific reasoning in the engineering sciences. Engineering sciences aim at explaining, predicting and describing physical phenomena occurring in technological devices. The focus of this paper is on mathematical description. These mathematical descriptions are important to computer-aided engineering or design programs (CAE and CAD). The first part of this paper explains why a traditional view, according to which scientific laws explain and predict phenomena and processes, is problematic. In the second part, the reasons of these methodological (...) difficulties are analyzed. Ludwig Prandtl’s method of integrating a theoretical and empirical approach is used as an example of good scientific practice. Based on this analysis, a distinction is made between different types of laws that play a role in constructing mathematical descriptions of phenomena. A central assumption in understanding research methodology is that, instead of scientific laws, knowledge of capacities and mechanisms are primary in the engineering sciences. Another important aspect in methodology of the engineering sciences is that in explaining a phenomenon or process spatial regions are distinguished in which distinct physical behaviour occur. The mechanisms in distinct spatial regions are represented in a so-called diagrammatic model. The construction of a mathematical description of the phenomenon or process is based on this diagrammatic model. (shrink)

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; (...) class='Hi'>models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science. (shrink)

An important distinction is that between selective abductions, which select an optimal candidate from given multitude of possible explanations, and creative abductions, which introduce new theoretical concepts and models. The article focuses on creative abductions, which are essential for scientific progress, although they are rarely discussed in the literature. Scientifically, fruitful creative abductions are demarcated from purely speculative abductions by means of three virtues which are possessed by the former but not by the latter: (i) providing unification, (ii) detecting (...) common causes and (iii) generating novel predictions by means of which they can be independently tested. Based on historical examples it is demonstrated that common cause abduction from correlated dispositions is the fundamental abductive operation by which new theoretical concepts and models are scientifically generated. Statistical factor analysis can be regarded as a statistical generalization of common cause abduction. (shrink)

This book contains contributions presented during the international conference on Model-Based Reasoning (MBR'012), held on June 21-23 in Sestri Levante, Italy. Interdisciplinary researchers discuss in this volume how scientific cognition and other kinds of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. Some of the contributions analyzed the problem of model-based reasoning in technology and stressed the issues of scientific and technological innovation. The book is divided (...) in three main parts: models, mental models, representations; abduction, problem solving and practical reasoning; historical, epistemological and technological issues.. (shrink)

Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.

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 Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it (...) is shown how the representational adequacy of qualitative models is essential to evaluate reachability properties. Justification theory, if not playing an essential role in EOCS, is exhibited to be involved in the process of advancing and corroborating model-based hypotheses about empirical systems in ECB and ABM. Finally, the practice of evaluating model-based hypothesis by testing the simulated systems is shown to constitute an argument in favour of the thesis that computer simulations in ECB and ABM can be put on a par with scientific experiments. (shrink)

Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper briefly (...) describes a computational model of problem solving and learning that has been used to simulate several kinds of scientific reasoning. (shrink)

In Beauty and Revolution in Science, James McAllister advances a rationalistic picture of science in which scientific progress is explained in terms of aesthetic evaluations of scientific theories. Here I present a new model of aesthetic evaluations by revising McAllister’s core idea of the aesthetic induction. I point out that the aesthetic induction suffers from anomalies and theoretical inconsistencies and propose a model free from such problems. The new model is based, on the one hand, on McAllister’s original (...) model and on further developments by Theo Kuipers in his “Beauty, a Road to the Truth?”. On the other hand, it is based on empirical findings about affection and emotion, and a naturalistic aesthetic theory. The new model is thus a naturalistic model with a wider explanatory range and much more internal consistency that McAllister’s. (shrink)

Name der Zeitschrift: Jahrbuch für Wissenschaft und Ethik Jahrgang: 20 Heft: 1 Seiten: 293-302.

Name der Zeitschrift: Jahrbuch für Wissenschaft und Ethik Jahrgang: 20 Heft: 1 Seiten: 237-240.