Scientific modelling, model prescription and the lightness of data (original) (raw)

Scientific Models

in: S. Sarkar et al. (eds.), The Philosophy of Science: An Encyclopedia, New York: Routledge, 2005

Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.

Models and Theories in Science

Oxford Bibliographies Online Datasets

Simulation the last two decades of the 20th century. In more recent years, new questions have come into focus, in particular the issues of scientific representation, the use of data, and the role of computer simulation in both modeling and theorizing. This entry provides a guide to these intellectual traditions. In doing so, it sets aside a number of related issues, in particular scientific realism, explanation, confirmation, and the application of mathematics. General Overviews Novices to the subject can gain an overview of the different positions and problems in Frigg and

Models of Science and Models in Science

E. Ippoliti, F. Sterpetti & T. Nickles (eds.), Models and Inferences in Science, Springer, Cham 2016, pp. 95-122, 2016

With regard to science, one may speak of models in different senses. The two main ones are models of science and models in science. A model of science is a representation of how scientists build their theories, a model in science is a representation of empirical objects, phenomena, or processes of some area of science. In this article I will describe and compare four models of science: the analytic-synthetic model, the hypothetico-deductive model, the semantic model, and the analytic model. Then I will discuss to what extent each of these models of science is capable of accounting for models in science.

Making Sense of Modeling: Beyond Representation

European Journal for Philosophy of Science, 2009

It has recently been aptly emphasized that how models are used is essential to what scientific models are. But the explanations of why and how a model is used or why a model is scientifically valuable are still merely in terms of the relation between the model and its target, just as they were before the explicit mention of uses and users. To use a model is to perform an action, and as for any action, different accounts can be given depending on the perspective that is

Recent Semantic Developments on Models

Science & Education, 2015

The importance of models and modelling in the contexts of science and science education can hardly be overrated: 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. (Frigg and Hartmann 2012, n/p.) Hence the philosophy of science, for at least six decades now, has devoted careful attention to the nature and role of models in the scientific enterprise. Accordingly, it is against the backdrop of the existing plethora of philosophical attempts at elucidating what models are that any new academic contributions seeking to study this construct are heartily welcome and should be critically analysed and assessed. In my opinion, this is especially the case when such contributions can be located within the socalled semanticist perspective on models, which (arguably) is the most developed, well established, and widely accepted in current philosophy of science (cf. Thomson-Jones 2006). Simulation and Similarity: Using Models to Understand the World by Michael Weisberg deals with physical, mathematical and computational models, revisiting a classical, analytic distinction, and amplifying and refining it with the aid of semantic constructs and tools. The book elaborately develops a well-founded account on the nature of scientific models and on the practice of scientific modelling, in which much attention is paid-as the title wants to indicate-to the semantic relation of ''similarity'' that holds between a model and its intended target(s).

Models and Modeling in Science: the role of metamathematics

Principia: an international journal of epistemology

The use of models of scientific theories should not be done without qualifications about the mathematics being used to build the models. This looks obvious, at least for logicians, but generally, it is not to the philosopher of science. Thus, some details about this point seem useful for both. Since any quick revision in the literature shows that in most cases, mainly after the raising of the semantic approach (to scientific theories), the models are taken to be set-theoretical structures, in discussing the issue we shall be concerned more with set theories, the locus where the play is usually developed (yet sometimes unconsciously).

How to Do Science with Models: A Philosophical Primer

Taking scientific practice as its starting point, this book charts the complex territory of models used in science. It examines what scientific models are and what their function is. Reliance on models is pervasive in science, and scientists often need to construct models in order to explain or predict anything of interest at all. The diversity of kinds of models one finds in science – ranging from toy models and scale models to theoretical and mathematical models – has attracted attention not only from scientists, but also from philosophers, sociologists, and historians of science. This has given rise to a wide variety of case studies that look at the different uses to which models have been put in specific scientific contexts. By exploring current debates on the use and building of models via cutting-edge examples drawn from physics and biology, the book provides broad insight into the methodology of modelling in the natural sciences. It pairs specific arguments with introductory material relating to the ontology and the function of models, and provides some historical context to the debates as well as a sketch of general positions in the philosophy of scientific models in the process.

What Scientists Say: Scientists' Views of Models

Online Submission, 2005

This paper focuses on scientists' views of scientific models and their use in authentic practice. Participants were 24 scientists, averaging 25 years research experience, representing four discipline areas. Views of scientific models were assessed through an open-ended questionnaire (VNOS-Sci) and interviews. The scientists described models relative to their research in a variety of ways, from model development to model use through testing of predictions. Model development and model use were described as distinct practices. Those who emphasized model use had a greater tendency to emphasize prediction in scientific research. The analysis revealed multiple descriptions of the purpose of models in authentic practice. The majority of the scientists reported that models explain or organize observations/predict/test. Other descriptions included: models provide understanding of system/complexity made simple/abstract made visual, models are mathematical representations, models are representations of physical systems, and models provide a directing framework for research. Variations in frequency of these descriptions amongst the scientists are discussed. Several responses demonstrated a connection between views of models and views of certainty and hierarchy of scientific knowledge. Results also suggest scientists' descriptions of model purpose and use may differ based on scientific discipline and investigative approach utilized in scientific research. Paper presented as part of the symposium, "Learning about models and modeling in science: International views of research issues" at the annual meeting of the