This fascinating study in the sociology of science explores the way scientists conduct, and draw conclusions from, their experiments. The book is organized around three case studies: replication of the TEA-laser, detecting gravitational rotation, and some experiments in the paranormal. "In his superb book, Collins shows why the quest for certainty is disappointed. He shows that standards of replication are, of course, social, and that there is consequently no outside standard, no Archimedean point beyond society from which we can lever (...) the intellects of our fellows. "- -Donald M. McCloskey, Journal of Economic Psychology "Collins is one of the genuine innovators of the sociology of scientific knowledge.... Changing Order is a rich and entertaining book. "- - Isis "The book gives a vivid sense of the contingent nature of research and is generally a good read. "- -Augustine Brannigan, Nature "This provocative book is a review of [Collins's] work, and an attempt to explain how scientists fit experimental results into pictures of the world.... A promising start for new explorations of our image of science, too often presented as infallibly authoritative. "- -Jon Turney, New Scientist. (shrink)

Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.

The anthropological approach is the central focus of this study. Laboratories are looked upon with the innocent eye of the traveller in exotic lands, and the societies found in these places are observed with the objective yet compassionate eye of the visitor from a quite other cultural milieu. There are many surprises that await us if we enter a laboratory in this frame of mind... This study is a realistic enterprise, an attempt to truly represent the social order of life (...) in laboratories and institutes of research, just as they are. By bringing the philosophical issues to the surface as matters not of prejudgement but as matters of concern, Karin Knorr-Cetina has developed the first really positive challenge to the philosophy of science since the days of paradigms and internal definitions of meanings. (shrink)

How does science create knowledge? Epistemic cultures, shaped by affinity, necessity, and historical coincidence, determine how we know what we know. In this book, Karin Knorr Cetina compares two of the most important and intriguing epistemic cultures of our day, those in high energy physics and molecular biology. Her work highlights the diversity of these cultures of knowing and, in its depiction of their differences--in the meaning of the empirical, the enactment of object relations, and the fashioning of social relations--challenges (...) the accepted view of a unified science. By many accounts, contemporary Western societies are becoming "knowledge societies"--which run on expert processes and expert systems epitomized by science and structured into all areas of social life. By looking at epistemic cultures in two sample cases, this book addresses pressing questions about how such expert systems and processes work, what principles inform their cognitive and procedural orientations, and whether their organization, structures, and operations can be extended to other forms of social order. The first ethnographic study to systematically compare two different scientific laboratory cultures, this book sharpens our focus on epistemic cultures as the basis of the knowledge society. (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)

The ArgumentThis paper sets out a framework for understanding how the scientific community constructs computer simulation as an epistemically and pragmatically useful methodology. The framework is based on comparisons between simulation and the loosely-defined categories of “theoretical work” and “experimental work.” Within that framework, the epistemological adequacy of simulation arises from its role as a mathematical manipulation of a complex, abstract theoretical model. To establish that adequacy demands a detailed “theoretical” grasp of the internal structure of the computer program. Simultaneously, (...) the pragmatic usefulness of simulation arises from its role as a “virtual laboratory.” That role is made possible by black-boxing the internal structure of the program, such that the scientist can interact with the computer in an intuitive, “experimental” manner. Thus simulation is rendered authoritative, opening up encoded theories to a novel, “experimental” type of manipulation. (shrink)

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only (...) from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth. (shrink)

In several accounts of what models are and how they function a specific view dominates. This view contains the following characteristics. First, there is a clear-cut distinction between theories, models and data and secondly, empirical assessment takes place after the model is built. This view in which discovery and justification are disconnected is not in accordance with several practices of mathematical business-cycle model building. What these practices show is that models have to meet implicit criteria of adequacy, such as satisfying (...) theoretical, mathematical and statistical requirements, and be useful for policy. In order to be adequate, models have to integrate enough items to satisfy such criteria. These items include besides theoretical notions, policy views, mathematisations of the cycle and metaphors also empirical data and facts. So, the main thesis of this chapter is that the context of discovery is the successful integration of those items that satisfy the criteria of adequacy. Because certain items are empirical data and facts, justification can be built-in. (shrink)

Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The (...) primary value of models is heuristic. (shrink)

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a “number crunching” technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories and more (...) emphasis on the role of theory in guiding the construction of models. (shrink)

Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a "number crunching" technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes (...) that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...) models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated by a case study of (...) the general circulation models of meteorology, the major simulation models in climate research. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models (...) and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

This article explores the practice of simulation modeling by investigating how parameterizations are constructed and integrated into existing frameworks. Parameterizations are simplified process descriptions adapted for simulation models. On the basis of a study of meteorological research, the article presents predictive and representative construction as two different ways of developing parameterizations and the trade-offs involved in this work. Because the overall aim in predictive construction is to improve weather forecasts, the most practical solutions are chosen over the best theoretical solutions. (...) In representative construction, the situation is reversed, but while discourse focuses on theory and models, the everyday work is often tied to computer programs. These different ways of construction work are closely related to the role of the simulation models as epistemic or technical objects, and this characterization is also used to compare the results with previous research. (shrink)

Radical uncertainty is a concept currently debated, for example, in the economics literature to theorize the impossibility of foreseeing the outcomes of scientific and technological development work. The purpose of this study is to extend the concept to articulate and theorize the minute-to-minute transactions in scientific laboratories. Empirical materials resulting from five years of ethnographic work in one laboratory focusing on fish vision are used to show how scientists produce a material continuity between some natural phenomena and the way they (...) are represented in scientific discourse. Because the outcomes of scientists' actions sometimes turn out to be uncertain, the material actions that produce this continuity themselves retroactively become uncertain. Scientists may at any one point determine that what they had done is not what they thought and said to have done. Actions and the objects they produce therefore stand in a dialectical relationship: they produce, mutually presuppose, and in their respective materiality, stabilize one another. (shrink)