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 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)

Models as Mediators discusses the ways in which models function in modern science, particularly in the fields of physics and economics. Models play a variety of roles in the sciences: they are used in the development, exploration and application of theories and in measurement methods. They also provide instruments for using scientific concepts and principles to intervene in the world. The editors provide a framework which covers the construction and function of scientific models, and explore the ways in which they (...) enable us to learn about both theories and the world. The contributors to the volume offer their own individual theoretical perspectives to cover a wide range of examples of modelling, from physics, economics and chemistry. These papers provide ideal case-study material for understanding both the concepts and typical elements of modelling, using analytical approaches from the philosophy and history of science. (shrink)

In this powerful work of conceptual and analytical originality, the author argues for the primacy of the material arrangements of the laboratory in the dynamics of modern molecular biology. In a post-Kuhnian move away from the hegemony of theory, he develops a new epistemology of experimentation in which research is treated as a process for producing epistemic things. A central concern of the book is the basic question of how novelty is generated in the empirical sciences. In addressing this question, (...) the author brings French poststructuralist thinking—notably Jacques Derrida’s concepts of “différance” and “historiality”—to bear on the construction of epistemic things. Historiographical perspective shifts from the actors’ minds to their objects of manipulation. These epistemological and historical issues are illuminated in a detailed case study of a particular laboratory, that of the oncologist and biochemist Paul C. Zamecnik and his colleagues, located in a specific setting—the Collis P. Huntington Memorial Hospital of Harvard University at the Massachusetts General Hospital of Boston. The author traces how, between 1945 and 1965, this group developed an experimental system for synthesizing proteins in the test tube that put Zamecnik’s research team at the forefront of those who led biochemistry into the era of molecular biology. (shrink)

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)

Philosophy of Experimental Biology explores some central philosophical issues concerning scientific research in experimental biology, including genetics, biochemistry, molecular biology, developmental biology, neurobiology, and microbiology. It seeks to make sense of the explanatory strategies, concepts, ways of reasoning, approaches to discovery and problem solving, tools, models and experimental systems deployed by scientific life science researchers and also integrates developments in historical scholarship, in particular the New Experimentalism. It concludes that historical explanations of scientific change that are based on local laboratory (...) practice need to be supplemented with an account of the epistemic norms and standards that are operative in science. This book should be of interest to philosophers and historians of science as well as to scientists. (shrink)

Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterra's and (...) Pauling's work involved modeling; much of Darwin's and Mendeleev's did not. In order to capture this distinction, I consider two examples of theory construction in detail: Volterra's treatment of post-WWI fishery dynamics and Mendeleev's construction of the periodic system. I argue that modeling can be distinguished from other forms of theorizing by the procedures modelers use to represent and to study real-world phenomena: indirect representation and analysis. This differentiation between modelers and non-modelers is one component of the larger project of understanding the practice of modeling, its distinctive features, and the strategies of abstraction and idealization it employs. (shrink)

Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the (...) representational arts. Harvard University Press, Cambridge/MA) has the resources to answer these questions. I introduce this account, outline the answers that it offers, and develop a general picture of scientific modelling based on it. (shrink)

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)

This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...) the characteristics of the communities who use these two types of models, including their research goals, disciplinary affiliations, and preferred practices to show how these have contributed to the conceptualization of a model organism. We conclude that model organisms are a specific subgroup of organisms that have been standardized to fit an integrative and comparative mode of research, and that it must be clearly distinguished from the broader class of experimental organisms. In addition, we argue that model organisms are the key components of a unique and distinctively biological way of doing research using models.Keywords: Experimental organism; Genetics; Model organism; Modeling; Philosophy of biology; Representation. (shrink)

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...) Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)

Simulations (both digital and analog) and experiments share many features. But what essential features distinguish them? I discuss two proposals in the literature. On one proposal, experiments investigate nature directly, while simulations merely investigate models. On another proposal, simulations differ from experiments in that simulationists manipulate objects that bear only a formal (rather than material) similarity to the targets of their investigations. Both of these proposals are rejected. I argue that simulations fundamentally differ from experiments with regard to the background (...) knowledge that is invoked to argue for the “external validity” of the investigation. (shrink)

Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the (...) representational arts. Harvard University Press, Cambridge/MA) has the resources to answer these questions. I introduce this account, outline the answers that it offers, and develop a general picture of scientific modelling based on it. (shrink)

The problem of plant individuality is something which has vexed botanists throughout the ages, with fashion swinging back and forth from treating plants as communities of individuals (Darwin 1800 ; Braun and Stone 1853 ; Münch 1938 ) to treating them as organisms in their own right, and although the latter view has dominated mainstream thought most recently (Harper 1977 ; Cook 1985 ; Ariew and Lewontin 2004 ), a lively debate conducted mostly in Scandinavian journals proves that the issues (...) are far from being resolved (Tuomi and Vuorisalo 1989b ; Fagerström 1992 ; Pan and Price 2001 ). In this paper I settle the matter once and for all, by showing which elements of each side are correct. (shrink)

There has been relatively little effort to provide a systematic overview of different forms of exploratory experimentation (EE). The present paper examines the growing subdiscipline of nanotoxicology and suggests that it illustrates at least four ways that researchers can engage in EE: searching for regularities; developing new techniques, simulation models, and instrumentation; collecting and analyzing large swaths of data using new experimental strategies (e.g., computer-based simulation and "high-throughput" instrumentation); and structuring an entire disciplinary field around exploratory research agendas. In order (...) to distinguish these and other activities more effectively, the paper proposes a taxonomy that includes three dimensions along which types of EE vary: (1) the aim of the experimental activity, (2) the role of theory in the activity, and (3) the methods or strategies employed for varying experimental parameters. (shrink)

Exploratory experimentation and high-throughput molecular biology appear to have considerable affinity for each other. Included in the latter category is metagenomics, which is the DNA-based study of diverse microbial communities from a vast range of non-laboratory environments. Metagenomics has already made numerous discoveries and these have led to reinterpretations of fundamental concepts of microbial organization, evolution, and ecology. The most outstanding success story of metagenomics to date involves the discovery of a rhodopsin gene, named proteorhodopsin, in marine bacteria that were (...) never suspected to have any photobiological capacities. A discussion of this finding and its detailed investigation illuminates the relationship between exploratory experimentation and metagenomics. Specifically, the proteorhodopsin story indicates that a dichotomous interpretation of theory-driven and exploratory experimentation is insufficient and that an interactive understanding of these two types of experimentation can be usefully supplemented by another category, "natural history experimentation". Further reflection on the context of metagenomics suggests the necessity of thinking more historically about exploratory and other forms of experimentation. (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)

Model organisms are central to contemporary biology and studies of embryogenesis in particular. Biologists utilize only a small number of species to experimentally elucidate the phenomena and mechanisms of development. Critics have questioned whether these experimental models are good representatives of their targets because of the inherent biases involved in their selection (e.g., rapid development and short generation time). A standard response is that the manipulative molecular techniques available for experimental analysis mitigate, if not counterbalance, this concern. But the most (...) powerful investigative techniques and molecular methods are applicable to single-celled organisms (‘microbes’). Why not use unicellular rather than multicellular model organisms, which are the standard for developmental biology? To claim that microbes are not good representatives takes us back to the original criticism leveled against model organisms. Using empirical case studies of microbes modeling ontogeny, we break out of this circle of reasoning by showing: (a) that the criterion of representation is more complex than earlier discussions have emphasized; and, (b) that different aspects of manipulability are comparable in importance to representation when deciding if a model organism is a good model. These aspects of manipulability harbor the prospect of enhancing representation. The result is a better understanding of how developmental biologists conceptualize research using experimental models and suggestions for underappreciated avenues of inquiry using microbes. More generally, it demonstrates how the practical aspects of experimental biology must be scrutinized in order to understand the associated scientific reasoning. (shrink)

This paper uses a number of examples of diverse types and functions of models in evolutionary biology to argue that the demarcation between theory and practice, or "theory model" and "data model," is often difficult to make. It is shown how both mathematical and laboratory models function as plausibility arguments, existence proofs, and refutations in the investigation of questions about the pattern and process of evolutionary history. I consider the consequences of this for the semantic approach to theories and theory (...) confirmation. The paper attempts to reconcile the insights of both critics and advocates of the semantic approach to theories. (shrink)

This paper uses a number of examples of diverse types and functions of models in evolutionary biology to argue that the demarcation between theory and practice, or “theory model” and “data model,” is often difficult to make. It is shown how both mathematical and laboratory models function as plausibility arguments, existence proofs, and refutations in the investigation of questions about the pattern and process of evolutionary history. I consider the consequences of this for the semantic approach to theories and theory (...) confirmation. The paper attempts to reconcile the insights of both critics and advocates of the semantic approach to theories. (shrink)