In this sequence of philosophical essays about natural science, the author argues that fundamental explanatory laws, the deepest and most admired successes of modern physics, do not in fact describe regularities that exist in nature. Cartwright draws from many real-life examples to propound a novel distinction: that theoretical entities, and the complex and localized laws that describe them, can be interpreted realistically, but the simple unifying laws of basic theory cannot.

This paper argues against the deductive reconstruction of scientific prediction, that is, against the view that in prediction the predicted event follows deductively from the laws and initial conditions that are the basis of the prediction. The major argument of the paper is intended to show that the deductive reconstruction is an inaccurate reconstruction of actual scientific procedure. Our reason for maintaining that it is inaccurate is that if the deductive reconstruction were an accurate reconstruction, then scientific prediction would be (...) impossible. (shrink)

_Popper’s Critical Rationalism_ presents Popper’s views on science, knowledge, and inquiry, and examines the significance and tenability of these in light of recent developments in philosophy of science, philosophy of probability, and epistemology. It develops a fresh and novel philosophical position on science, which employs key insights from Popper while rejecting other elements of his philosophy. Central theses include: Crucial questions about scientific method arise at the level of the group, rather than that of the individual. Although criticism is vital (...) for science, dogmatism is important too. Belief in scientific theories is permissible even in the absence of evidence in their favour. The aim of science is to eliminate false theories. Critical rationalism can be understood as a form of virtue epistemology. (shrink)

Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.

A scientific community cannot practice its trade without some set of received beliefs. These beliefs form the foundation of the "educational initiation that prepares and licenses the student for professional practice". The nature of the "rigorous and rigid" preparation helps ensure that the received beliefs are firmly fixed in the student's mind. Scientists take great pains to defend the assumption that scientists know what the world is like...To this end, "normal science" will often suppress novelties which undermine its foundations. Research (...) is therefore not about discovering the unknown, but rather "a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education". (shrink)

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

Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its (...) members are less aware of their colleagues' experimental results. Second, there is a robust tradeoff between the reliability of a community and the speed with which it reaches a correct conclusion. ‡The author would like to thank Brian Skyrms, Kyle Stanford, Jeffrey Barrett, Bruce Glymour, and the participants in the Social Dynamics Seminar at University of California–Irvine for their helpful comments. Generous financial support was provided by the School of Social Science and Institute for Mathematical Behavioral Sciences at UCI. †To contact the author, please write to: Department of Philosophy, Baker Hall 135, Carnegie Mellon University, Pittsburgh, PA 15213-3890; e-mail: [email protected]. (shrink)

Science's priority rule rewards those who are first to make a discovery, at the expense of all other scientists working towards the same goal, no matter how close they may be to making the same discovery. I propose an explanation of the priority rule that, better than previous explanations, accounts for the distinctive features of the rule. My explanation treats the priority system, and more generally, any scheme of rewards for scientific endeavor, as a device for achieving an allocation of (...) resources among different research programs that provides as much benefit as possible to society. I show that the priority system is especially well suited to finding an efficient allocation of resources in those situations, characteristic of scientific inquiry, in which any success in an endeavor subsequent to the first success brings little additional benefit to society. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their “veritistic value”, i.e., their tendency to promote the acquisition of true beliefs in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because we often don't know what beliefs are actually true, and even if we did, (...) the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment is presented and put to some preliminary tests. (shrink)

This paper compares and contrasts the concept of a stance with that of a paradigm qua disciplinary matrix, in an attempt to illuminate both notions. First, it considers to what extent it is appropriate to draw an analogy between stances and disciplinary matrices. It suggests that despite first appearances, a disciplinary matrix is not simply a stance writ large. Second, it examines how we might reinterpret disciplinary matrices in terms of stances, and shows how doing so can provide us with (...) a better insight into non-revolutionary science. Finally, it identifies two directions for future research: “Can the rationality of scientific revolutions be understood in terms of the dynamic between stances and paradigms?” and “Do stances help us to understand incommensurability between disciplinary matrices?”. (shrink)

This paper compares and contrasts the concept of a stance with that of a paradigm qua disciplinary matrix, in an attempt to illuminate both notions. First, it considers to what extent it is appropriate to draw an analogy between stances (which operate at the level of the individual) and disciplinary matrices (which operate at the level of the community). It suggests that despite first appearances, a disciplinary matrix is not simply a stance writ large. Second, it examines how we might (...) reinterpret disciplinary matrices in terms of stances, and shows how doing so can provide us with a better insight into non-revolutionary science. Finally, it identifies two directions for future research: “Can the rationality of scientific revolutions be understood in terms of the dynamic between stances and paradigms?” and “Do stances help us to understand incommensurability between disciplinary matrices?”. (shrink)

Popper’s Critical Rationalism presents Popper’s views on science, knowledge, and inquiry, and examines the significance and tenability of these in light of recent developments in philosophy of science, philosophy of probability, and epistemology. It develops a fresh and novel philosophical position on science, which employs key insights from Popper while rejecting other elements of his philosophy.

Popper repeatedly emphasised the significance of a critical attitude, and a related critical method, for scientists. Kuhn, however, thought that unquestioning adherence to the theories of the day is proper; at least for ‘normal scientists’. In short, the former thought that dominant theories should be attacked, whereas the latter thought that they should be developed and defended. Both seem to have missed a trick, however, due to their apparent insistence that each individual scientist should fulfil similar functions. The trick is (...) to consider science at the group level; and doing so shows how puzzle solving and ‘offensive’ critical activity can simultaneously have a legitimate place in science. This analysis shifts the focus of the debate. The crucial question becomes ‘How should the balance between functions be struck?’. (shrink)

Popper repeatedly emphasised the significance of a critical attitude, and a related critical method, for scientists. Kuhn, however, thought that unquestioning adherence to the theories of the day is proper; at least for ‘normal scientists’. In short, the former thought that dominant theories should be attacked, whereas the latter thought that they should be developed and defended (for the vast majority of the time). -/- Both seem to have missed a trick, however, due to their apparent insistence that each individual (...) scientist should fulfil similar functions (at any given point in time). The trick is to consider science at the group level; and doing so shows how puzzle solving and ‘offensive’ critical activity can simultaneously have a legitimate place in science. This analysis shifts the focus of the debate. The crucial question becomes ‘How should the balance between functions be struck?’. (shrink)

Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, (...) but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models' robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their , i.e., their tendency to promote the acquisition of true beliefs (and impede the acquisition of false beliefs) in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because (1) we often don't know what beliefs are (...) actually true, and (2) even if we did, the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment (Laputa) is presented and put to some preliminary tests. (shrink)

Plausibly, when we adopt a probabilistic standpoint any measure Cb of the degree to which evidence e confirms hypothesis h relative to background knowledge b should meet these five desiderata: Cb > 0 when P > P < 0 when P < P; Cb = 0 when P = P. Cb is some function of the values P and P assume on the at most sixteen truth-functional combinations of e and h. If P < P and P = P then (...) Cb ≤ Cb; if P = P and P < P then Cb ≥ Cb. Cb – Cb is fully determined by Cb and Cbe – Cbe; if Cb = 0 then Cb + Cbe = 0. If P = P then Cb = Cb. (shrink)

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)

Paul Feyerabend's globally acclaimed work, which sparked and continues to stimulate fierce debate, examines the deficiencies of many widespread ideas about scientific progress and the nature of knowledge. Feyerabend argues that scientific advances can only be understood in a historical context. He looks at the way the philosophy of science has consistently overemphasized practice over method, and considers the possibility that anarchism could replace rationalism in the theory of knowledge. -- Amazon.com.

Much of the literature on "ceteris paribus" laws is based on a misguided egalitarianism about the sciences. For example, it is commonly held that the special sciences are riddled with ceteris paribus laws; from this many commentators conclude that if the special sciences are not to be accorded a second class status, it must be ceteris paribus all the way down to fundamental physics. We argue that the (purported) laws of fundamental physics are not hedged by ceteris paribus clauses and (...) provisos. Furthermore, we show that not only is there no persuasive analysis of the truth conditions for ceteris paribus laws, there is not even an acceptable account of how they are to be saved from triviality or how they are to be melded with standard scientific methodology. Our way out of this unsatisfactory situation to reject the widespread notion that the achievements and the scientific status of the special sciences must be understood in terms of ceteris paribus laws. (shrink)

Over recent decades, computer simulations have become a common tool among practitioners of the social sciences. They have been utilized to study such diverse phenomena as the integration and segregation of different racial groups, the emergence and evolution of friendship networks, the spread of gossip, fluctuations of housing prices in an area, the transmission of social norms, and many more. Philosophers of science and others interested in the methodological status of these studies have identified a number of distinctive virtues of (...) the use of computer simulations. For instance, it has been generally appreciated that as simulations require the formulation of an explicit algorithm, they foster precision and clarity about whatever conceptual issues are involved in the study. The value of computer simulations as a heuristic tool for developing hypotheses, models, and theories has also been recognized, as has been the fact that they can serve as a substitute for real experiments. This is especially useful in the social domain, given that human beings cannot be freely manipulated at the discretion of the experimenter . However, the main virtue of computer simulations is generally believed to be that they are able to deal with the complexities that arise when many elements interact in a highly dynamic system and which often evade an exact formal analysis. (shrink)

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

It's uncontroversial that notions of idealization and approximation are central to understanding computer simulations and their rationale. What's not so clear is what exactly these notions come to. Two distinct forms of approximation will be distinguished and their features contrasted with those of idealizations. These distinctions will be refined and closely tied to computer simulations by means of Scott-Strachey denotational programming semantics. The use of this sort of semantics also provides a convenient format for argumentation in favor of several theses (...) I shall propose concerning the role computer implemented approximations and idealizations play in fixing what the acceptance of an underlying scientific theory is or should be. (shrink)