Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.

This paper examines, from the point of view of a philosopher of science, what it is that introductory science textbooks say and do not say about 'scientific method'. Seventy introductory texts in a variety of natural and social sciences provided the material for this study. The inadequacy of these textbook accounts is apparent in three general areas: (a) the simple empiricist view of science that tends to predominate; (b) the demarcation between scientific and non-scientific inquiry and (c) the avoidance of (...) controversy-in part the consequence of the tendency toward textbook standardization. Most importantly, this study provides some evidence of the gulf that separates philosophy of science from science instruction, and examines some important aspects of the demarcation between science and non-science-an important issue for philosophers, scientists, and science educators. (shrink)

Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may not be sufficient for (...) justified belief in results, the successful application of a slightly elaborated version of the Sherlock Holmes strategy is sufficient. (shrink)

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)

Feyerabrend argues that intellectual progress relies on the creativity of the scientist, against the authority of science.

Thomas S. Kuhn's classic book is now available with a new index.

The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. It is (...) argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models. Introduction Models in statistics—‘of what population is this a random sample?’ The fundamental lemma Controversy about models Exploratory data analysis as a model-critical approach. (shrink)

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation--an experimentation which is not, as in the "standard view", driven by specific theories. It is typically practiced in periods in which no theory or--even more fundamentally--no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

Outstanding translations by leading contemporary scholars--many commissioned especially for this volume--are presented here in the first single edition to include the entire surviving corpus of works attributed to Plato in antiquity. In his introductory essay, John Cooper explains the presentation of these works, discusses questions concerning the chronology of their composition, comments on the dialogue form in which Plato wrote, and offers guidance on approaching the reading and study of Plato's works. Also included are concise introductions by Cooper and Hutchinson (...) to each translation, meticulous annotation designed to serve both scholar and general reader, and a comprehensive index. This handsome volume offers fine paper and a high-quality Smyth-sewn cloth binding in a sturdy, elegant edition. (shrink)

The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This 2002 book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference during the 1920s and 1930s. Through the examination of a (...) dispute between two British scientists, the author argues that a choice between the two interpretations is not forced by pure logic or the mathematics of the situation, but depends on the experiences and aims of the individuals involved. The book should be of interest to students and scientists interested in statistics and probability theories and to general readers with an interest in the history, sociology and philosophy of science. (shrink)

How should the concept of evidence be understood? And how does the concept of evidence apply to the controversy about creationism as well as to work in evolutionary biology about natural selection and common ancestry? In this rich and wide-ranging book, Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology. Drawing on a set of fascinating examples, he analyzes whether claims about intelligent design (...) are untestable; whether they are discredited by the fact that many adaptations are imperfect; how evidence bears on whether present species trace back to common ancestors; how hypotheses about natural selection can be tested, and many other issues. His book will interest all readers who want to understand philosophical questions about evidence and evolution, as they arise both in Darwin's work and in contemporary biological research. (shrink)

In Systematicity, Paul Hoyningen-Huene answers the question "What is science?" by proposing that scientific knowledge is primarily distinguished from other forms of knowledge, especially everyday knowledge, by being more systematic. "Science" is here understood in the broadest possible sense, encompassing not only the natural sciences but also mathematics, the social sciences, and the humanities. The author develops his thesis in nine dimensions in which it is claimed that science is more systematic than other forms of knowledge: regarding descriptions, explanations, predictions, (...) the defense of knowledge claims, critical discourse, epistemic connectedness, an ideal of completeness, knowledge generation, and the representation of knowledge. He compares his view with positions on the question held by philosophers from Aristotle to Nicholas Rescher. The book concludes with an exploration of some consequences of Hoyningen-Huene's view concerning the genesis and dynamics of science, the relationship of science and common sense, normative implications of the thesis, and the demarcation criterion between science and pseudo-science. (shrink)

This chapter defends a pluralistic view of science: the various projects of enquiry that fall under the general rubric of science share neither a methodology nor a subject matter. Ontologically, it is argued that sciences need have nothing in common beyond an antipathy to the supernatural. Epistemically one central virtue is defended, empiricism, meaning just that scientific knowledge must ultimately be answerable to experience. Prima facie science is as diverse as the world it studies; and rejection of this prima facie (...) diversity in favour of a spurious aspiration to unity is grounded in a priori assumption not experience. Disunity, however, does not mean disconnectedness, and the connections between different areas of knowledge are an important theme in the book. (shrink)

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

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)

Chapter 1 FROM ORDER TO DISORDER 5 mins. John enters and goes into his office. He says something very quickly about having made a bad mistake. He had sent the review of a paper. . . . The rest of the sentence is inaudible. 5 mins.

Inviting a reappraisal of the status of scientific knowledge, Andrew Pickering suggests that scientists are not mere passive observers and reporters of nature.

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)

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)

The Old Deferentialism, taking science to enjoy a privileged epistemic standing because of its uniquely rational and objective method, is over-optimistic. But there is no need to conclude, like the New Cynics, that appeals to evidence, rationality, objectivity are mere rhetorical bullying. A new theory of scientific method and knowledge is developed, which combines logical and social elements, and reveals science to be not epistemologically privileged, but epistemologically distinguished.

The fundamental tenet of modern empiricism is the view that all non-analytic knowledge is based on experience. Let us call this thesis the principle of empiricism. [1] Contemporary logical empiricism has added [2] to it the maxim that a sentence makes a cognitively meaningful assertion, and thus can be said to be either true or false, only if it is either (1) analytic or self-contradictory or (2) capable, at least in principle, of experiential test. According to this so-called empiricist criterion (...) of cognitive meaning, or of cognitive significance, many of the formulations of traditional metaphysics and large parts of epistemology are devoid of cognitive significance--however rich some of them may be in non-cognitive import by virtue of their emotive appeal or the moral inspiration they offer. Similarly certain doctrines which have been, at one time or another, formulated within empirical science or its border disciplines are so contrived as to be incapable of test by any conceivable evidence; they are therefore qualified as pseudo- [p. 42:] hypotheses, which assert nothing, and which therefore have no explanatory or predictive force whatever. This verdict applies, for example, to the neo-vitalist speculations about entelechies or vital forces, and to the "telefinalist hypothesis" propounded by Lecomte du Noüy. (shrink)

: The increasing attention on experiment in the last two decades has led to important insights into its material, cultural and social dimensions. However, the role of experiment as a tool for generating knowledge has been comparatively poorly studied. What questions are asked in experimental research? How are they treated and eventually resolved? And how do questions, epistemic situations, and experimental activity cohere and shape each other? In my paper, I treat these problems on the basis of detailed studies of (...) research practice. After presenting several cases from the history of electricity—Dufay, Ampère, and Faraday—I discuss a specific type of experiment—the "exploratory experiment"—and analyze how it works in concept formation. I argue that a fuller understanding of experiment can only be achieved by intertwining historical and philosophical perspectives in such a way that the very separation of the two become questioable. (shrink)

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.

My aim in this article is to introduce readers to the topic of exploratory experimentation and briefly explain how the three articles that follow, by Richard Burian, Kevin Elliott, and Maureen O'Malley, advance our understanding of the nature and significance of exploratory research. I suggest that the distinction between exploratory and theory-driven experimentation is multidimensional and that some of the dimensions are continuums. I point out that exploratory experiments are typically theory-informed even if they are not theory-driven. I also distinguish (...) between research programs and experiments. Research programs that are largely exploratory, such as the ones discussed in these case studies, can involve both exploratory and theory-driven experimentation. (shrink)

Scientific articles exemplify standard functional units constraining argumentative structures. Severe space limitations demand every paragraph and illustration contribute to establishing the paper's claims. Philosophical testing and confirmation models should take into account each paragraph, table, and illustration. Hypothetico-Deductive, Bayesian Inductive, and Inference-to-the-Best-Explanation models do not, garbling the logic of papers. Micro-analysis of the fundamental paper in plate tectonics reveals an argumentative structure commonplace in science but ignored by standard philosophical accounts that cannot be dismissed as mere rhetorical embellishment. Papers with (...) illustrations often display a second argumentative structure differing from the text's. Constraints on adequate testing and confirmation analyses are adduced. "Experiments are about the assembly of persuasive arguments, ones that will stand up in court.... The task at hand is to capture the building-up of a persuasive argument about the world even in the absence of the logician's certainty." --Galison, How Experiments End, 277. (shrink)

This paper is devoted to an examination of the discovery, characterization, and analysis of the functions of microRNAs, which also serves as a vehicle for demonstrating the importance of exploratory experimentation in current (post-genomic) molecular biology. The material on microRNAs is important in its own right: it provides important insight into the extreme complexity of regulatory networks involving components made of DNA, RNA, and protein. These networks play a central role in regulating development of multicellular organisms and illustrate the importance (...) of epigenetic as well as genetic systems in evolution and development. The examination of these matters yields principled arguments for the historicity of the functions of key biological molecules and for the indispensability of exploratory experimentation in contemporary molecular biology as well as some insight into the complex interplay between exploratory experimentation and hypothesis-driven science. This latter result is not only important for philosophy of science, but also of practical importance for the evaluation of grant proposals, although the elaboration of this latter claim must be left for another occasion. (shrink)

This paper addresses the question of what the nature of science is. I will first make a few preliminary historical and systematic remarks. Next, I shall give an answer to the question that has to be qualified, clarified and justified. Finally, I will compare my answer with alternative answers and draw consequences for the demarcation problem.

Andrea Falcon's work is guided by the exegetical ideal of recreating the mind of Aristotle and his distinctive conception of the theoretical enterprise. In this concise exploration of the significance of the celestial world for Aristotle's science of nature, Falcon investigates the source of discontinuity between celestial and sublunary natures and argues that the conviction that the natural world exhibits unity without uniformity is the ultimate reason for Aristotle's claim that the heavens are made of a special body, unique to (...) them. This book presents Aristotle as a totally engaged, systematic investigator whose ultimate concern was to integrate his distinct investigations into a coherent interpretation of the world we live in, all the while mindful of human limitations to what can be known. Falcon reads in Aristotle the ambition of an extraordinarily curious mind and the confidence that that ambition has been largely fulfilled. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

This article identifies a fundamental distinction in scientific practice: the mismatch between what scientists do and what they state they did when they communicate their findings in their publications. The insight that such a mismatch exists is not new. It was already implied in Hans Reichenbach's distinction between the contexts of discovery and justification, and it is taken for granted across the board in philosophy of science and science studies. But while there is general agreement that the mismatch exists, the (...) epistemological implications of that mismatch are not at all clear. Philosophers, historians, and sociologists of different stripes have expressed widely different views about how one should understand and interpret the relation between what scientists do and what they state they did. This article surveys a number of approaches to the mismatch. Based on this survey, I offer an assessment of the epistemological significance of the mismatch and identify the major meta-epistemological challenges that it poses for the analysis of scientific practice. *Received May 2007; revised April 2008. †To contact the author, please write to: Department of History and Philosophy of Science, 1011 East Third Street, Goodbody Hall 130, Indiana University, Bloomington, IN 47405; e-mail: [email protected]. (shrink)