This 1983 book is a lively and clearly written introduction to the philosophy of natural science, organized around the central theme of scientific realism. It has two parts. 'Representing' deals with the different philosophical accounts of scientific objectivity and the reality of scientific entities. The views of Kuhn, Feyerabend, Lakatos, Putnam, van Fraassen, and others, are all considered. 'Intervening' presents the first sustained treatment of experimental science for many years and uses it to give a new direction to debates about (...) realism. Hacking illustrates how experimentation often has a life independent of theory. He argues that although the philosophical problems of scientific realism can not be resolved when put in terms of theory alone, a sound philosophy of experiment provides compelling grounds for a realistic attitude. A great many scientific examples are described in both parts of the book, which also includes lucid expositions of recent high energy physics and a remarkable chapter on the microscope in cell biology. (shrink)

The law of gravitation, an example of physical law The relation of mathematics to physics The great conservation principles Symmetry in physical law The distinction of past and future Probability and uncertainty: the quantum mechanical view of nature Seeking new laws.

Many philosophers have claimed that evidence for a theory is better when multiple independent tests yield the same result, i.e., when experimental results are robust. Little has been said about the grounds on which such a claim rests, however. The present essay presents an analysis of the evidential value of robustness that rests on the fallibility of assumptions about the reliability of testing procedures and a distinction between the strength of evidence and the security of an evidence claim. Robustness can (...) enhance the security of an evidence claim either by providing what I call second-order evidence, or by providing back-up evidence for a hypothesis. (shrink)

Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, I argue that model (...) building is successful even when models are predictively inaccurate for at least three reasons: models allow scientists to explore the possible behaviors of ecological systems; models give scientists simplified means by which they can investigate more complex systems by determining how the more complex system deviates from the simpler model; and models give scientists conceptual frameworks through which they can conduct experiments and fieldwork. Critics often mistake the purposes of model building, and once we recognize this, we can see their complaints are unjustified. Even though models in ecology are not always accurate in their assumptions and predictions, they still contribute to successful science. (shrink)

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and (...) showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth. (shrink)

This book offers a comprehensive overview of the structure, strategy and methods of assessment of orthodox theoretical economics. In Part I Professor Hausman explains how economists theorise, emphasising the essential underlying commitment of economists to a vision of economics as a separate science. In Part II he defends the view that the basic axioms of economics are 'inexact' since they deal only with the 'major' causes; unlike most writers on economic methodology, the author argues that it is the rules that (...) economists espouse rather than their practice that is at fault. Part III links the conception of economics as a separate science to the fact that economic theories offer reasons and justifications for human actions, not just their causes. With its lengthy appendix introducing relevant issues in philosophy of science, this book is a major addition to philosophy of economics and of social science. (shrink)

A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closing, another application of (...) my account to the problem of evidential diversity is also discussed. (shrink)

The partial structures program of da Costa, French and others offers a unified framework within which to handle a wide range of issues central to contemporary philosophy of science. I argue that the program is inadequately equipped to account for simple cases where idealizations are used to construct abstract, mathematical models of physical systems. These problems show that da Costa and French have not overcome the objections raised by Cartwright and Suárez to using model‐theoretic techniques in the philosophy of science. (...) However, my concerns arise independently of the more controversial assumptions that Cartwright and Suárez have employed. (shrink)

Can social phenomena be understood by analyzing their parts? Contemporary economic theory often assumes that they can. The methodology of constructing models which trace the behavior of perfectly rational agents in idealized environments rests on the premise that such models, while restricted, help us isolate tendencies, that is, the stable separate effects of economic causes that can be used to explain and predict economic phenomena. In this paper, I question both the claim that models in economics supply claims about tendencies (...) and also the view that economics, when successful, necessarily follows this method. When economics licenses successful policy interventions, as it did in the case of the Federal Communications Commission spectrum auctions, its method is not to study tendencies but rather to study the phenomenon as a whole. Key Words: economic models • tendencies • economic experiments • policy making • John Stuart Mill. (shrink)

Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. (...) Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines. (shrink)

The study of economics has been a going concern among philosophers for the better part of twenty years without very many people even noticing that economics has a metaphysics. Indeed, among economists the term ‘metaphysical’ is probably an epithet of opprobrium, employed to suggest that a claim is untestable or otherwise without cognitive significance. Philosophers of economics will admit to the existence of an epistemology of economics—the study of the nature, extent and justification of economic knowledge. But even this is (...) controversial among some economists. The notion that economics might have a metaphysics as well as an epistemology will be scoffed at by those economists doubtful about the enterprise of the philosophy of economics altogether. Nevertheless, philosophers of economics should have no trouble identifying a metaphysics in some of the theoretical commitments of economics. (shrink)

Challenges the widely held view that good models must necessarily be simplifications and hence cannot be true. This is done by distinguishing between whole truth (complete description) and truth (essential description, attained by the method of isolation).

The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

Economics today cannot predict the likely outcome of specific events any better than it could in the time of Adam Smith. This is Alexander Rosenberg's controversial challenge to the scientific status of economics. Rosenberg explains that the defining characteristic of any science is predictive improvability--the capacity to create more precise forecasts by evaluating the success of earlier predictions--and he forcefully argues that because economics has not been able to increase its predictive power for over two centuries, it is not a (...) science. (shrink)

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...) purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding. (shrink)