Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...) on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism. * Both of us gratefully acknowledge support from the Graduate School at the University of Wisconsin-Madison, and NSF grant DIR-8822278 (M.F.) and NSF grant SBE-9212294 (E.S.). Special thanks go to A. W. F. Edwards.William Harper. Martin Leckey. Brian Skyrms, and especially Peter Turney for helpful comments on an earlier draft. (shrink)

In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes' theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory's goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship (...) between simplicity of a theory and its predictive accuracy. (shrink)

In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit pull in opposite directions. To solve this problem, two proposals, the first one based on Bayes's theorem criterion (BTC) and the second one advocated by Forster and Sober based on Akaike's Information Criterion (AIC) are discussed. We show that AIC, which is frequentist in spirit, is logically equivalent to BTC, provided that a suitable choice of priors is made. We evaluate the charges against Bayesianism and contend that AIC approach (...) has shortcomings. We also discuss the relationship between Schwarz's Bayesian Information Criterion and BTC. (shrink)

I show that van Fraassen's empiricism leads to mutually incompatible claims with regard to empirical theories. He is committed to the claim that reasons for accepting a theory and believing it are always identical, insofar as the theory in question is an empirical theory. He also makes a general claim that reasons for accepting a theory are not always reasons for believing it irrespective of whether the theory is an empirical theory.

Constructive empiricism is supposed to offer a positive alternative to scientific realism that dispenses with the need for metaphysics. I first review the terms of the debate before arguing that the standard objections to constructive empiricism are not decisive. I then explain van Fraassen's views on modality and counterfactuals, and argue that, because constructive empiricism recommends on epistemological grounds belief in the empirical adequacy rather than the truth of theories, it requires that there be an objective modal distinction between the (...) observable and the unobservable. This conclusion is incompatible with van Fraassen's empiricism. Finally I explain some further problems for constructive empiricism that arise when we consider modal matters. (shrink)

One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for (...) a new generation of readers. (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)

Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships. For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that (...) aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science. Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments. (shrink)

Instrumentalism is usually understood as a semantic thesis: scientific theories are neither true nor false, but are merely instruments for making predictions. Scientific realists are on firm ground when they reject this semantic claim. This paper focuses on epistemological rather than semantic instrumentalism. This form of instrumentalism claims that theories are to be judged by their ability to make accurate predictions, and that predictive accuracy is the only consideration that matters in the end. I consider how instrumentalism is related to (...) a quite different proposal concerning how theories should be evaluated—scientific realism. Instrumentalism allows for the fact that a false model can get one closer to the truth than a true one. (shrink)

Striving to boldly redirect the philosophy of science, this book by renowned philosopher Philip Kitcher examines the heated debate surrounding the role of science in shaping our lives. Kitcher explores the sharp divide between those who believe that the pursuit of scientific knowledge is always valuable and necessary--the purists--and those who believe that it invariably serves the interests of people in positions of power. In a daring turn, he rejects both perspectives, working out a more realistic image of the sciences--one (...) that allows for the possibility of scientific truth, but nonetheless permits social consensus to determine which avenues to investigate. He then proposes a democratic and deliberative framework for responsible scientists to follow. Controversial, powerful, yet engaging, this volume will appeal to a wide range of readers. Kitcher's nuanced analysis and authorititative conclusion will interest countless scientists as well as all readers of science--scholars and laypersons alike. (shrink)

Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

During the last three decades, reflections on the growth of scientific knowledge have inspired historians, sociologists, and some philosophers to contend that scientific objectivity is a myth. In this book, Kitcher attempts to resurrect the notions of objectivity and progress in science by identifying both the limitations of idealized treatments of growth of knowledge and the overreactions to philosophical idealizations. Recognizing that science is done not by logically omniscient subjects working in isolation, but by people with a variety of personal (...) and social interests, who cooperate and compete with one another, he argues that, nonetheless, we may conceive the growth of science as a process in which both our vision of nature and our ways of learning more about nature improve. Offering a detailed picture of the advancement of science, he sets a new agenda for the philosophy of science and for other "science studies" disciplines. (shrink)

Metaphysicians speak of laws of nature in terms of necessity and universality; scientists, in terms of symmetry and invariance. In this book van Fraassen argues that no metaphysical account of laws can succeed. He analyzes and rejects the arguments that there are laws of nature, or that we must believe there are, and argues that we should disregard the idea of law as an adequate clue to science. After exploring what this means for general epistemology, the author develops the empiricist (...) view of science as a construction of models to represent the phenomena. (shrink)

Akaike’s framework for thinking about model selection in terms of the goal of predictive accuracy and his criterion for model selection have important philosophical implications. Scientists often test models whose truth values they already know, and they often decline to reject models that they know full well are false. Instrumentalism helps explain this pervasive feature of scientific practice, and Akaike’s framework helps provide instrumentalism with the epistemology it needs. Akaike’s criterion for model selection also throws light on the role of (...) parsimony considerations in hypothesis evaluation. I explain the basic ideas behind Akaike’s framework and criterion; several biological examples, including the use of maximum likelihood methods in phylogenetic inference, are considered. (shrink)

If a parsimony criterion may be used to choose between theories that make different predictions, may the same criterion be used to choose between theories that are predictively equivalent? The work of the statistician H. Akaike (1973) is discussed in connection with this question. The results are applied to two examples in which parsimony has been invoked to choose between philosophical theories-Shoemaker's (1969) discussion of the possibility of time without change and the discussion by Smart (1959) and Brandt and Kim (...) (1967) of mind/body dualism and the identity theory. (shrink)

Constructive empiricism is supposed to offer a positive alternative to scientific realism that dispenses with the need for metaphysics. I first review the terms of the debate before arguing that the standard objections to constructive empiricism are not decisive. I then explain van Fraassen's views on modality and counterfactuals, and argue that, because constructive empiricism recommends on epistemological grounds belief in the empirical adequacy rather than the truth of theories, it requires that there be an objective modal distinction between the (...) observable and the unobservable. This conclusion is incompatible with van Fraassen's empiricism. Finally I explain some further problems for constructive empiricism that arise when we consider modal matters. (shrink)

In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes’ theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory’s goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship (...) between simplicity of a theory and its predictive accuracy. (shrink)

In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes' theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory's goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship (...) between simplicity of a theory and its predictive accuracy. (shrink)

Elliott Sober is one of the leading philosophers of science and is a former winner of the Lakatos Prize, the major award in the field. This new collection of essays will appeal to a readership that extends well beyond the frontiers of the philosophy of science. Sober shows how ideas in evolutionary biology bear in significant ways on traditional problems in philosophy of mind and language, epistemology, and metaphysics. Amongst the topics addressed are psychological egoism, solipsism, and the interpretation of (...) belief and utterance, empiricism, Ockham's razor, causality, essentialism, and scientific laws. The collection will prove invaluable to a wide range of philosophers, primarily those working in the philosophy of science, the philosophy of mind, and epistemology. (shrink)

Richard Swinburne offers an original treatment of a question at the heart of epistemology: what makes a belief rational, or justified in holding? He maps the rival accounts of philosophers on epistemic justification ("internalist" and "externalist"), arguing that they are really accounts of different concepts. He distinguishes between synchronic justification (justification at a time) and diachronic justification (synchronic justification resulting from adequate investigation)--both internalist and externalist. He also argues that most kinds of justification are worth having because they are indicative (...) of truth; however, it is only justification of internalist kinds that can guide a believer's actions. Swinburne goes on to show the usefulness of the probability calculus in elucidating how empirical evidence makes beliefs probably true. (shrink)

there be an objective modal distinction between the observable and the unobservable.’ My intent is to counter Ladyman's claim that the irreducibly modal character of empirical adequacy is something that is ‘really wrong with constructive empiricism’. I argue that disposition concepts refer to non-modal properties of types rather than to modal properties of tokens of those types. Solubility, for example, is an ‘occurrent’, though unobservable, property of a type of substance (involving the structure of associated atoms); and observability is, similarly, (...) an ‘occurrent’, though unobservable, property of a type of event (involving the structure of associated physical systems). Empirical adequacy, like truth, is an objective, semantic notion; the empirical adequacy of a theory depends upon all actual tokens of the relevant observable type, not just upon the tokens that have actually been observed. Introduction The typical character of disposition concepts Confirmational versus semantic empirical adequacy Conclusion. (shrink)

The question is answered by introducing an ideal, the ideal of well‐ordered science In well‐ordered science the inquiries pursued are those that would have been selected by a well‐informed group of deliberators dedicated to working cooperatively with one another. Well‐ordered science is contrasted with vulgar democracy and with elitism. The chapter suggests various ways in which our current practice of the sciences falls short of the ideal.