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)

Scientific realism is the optimistic view that modern science is on the right track: that the world really is the way our best scientific theories describe it. In his book, Stathis Psillos gives us a detailed and comprehensive study which restores the intuitive plausibility of scientific realism. We see that throughout the twentieth century, scientific realism has been challenged by philosophical positions from all angles: from reductive empiricism, to instrumentalism and to modern sceptical empiricism. _Scientific Realism_ explains that the history (...) of science does not undermine the arguments for scientific realism, but instead makes it reasonable to accept scientific realism as the best philosophical account of science, its empirical success, its progress and its practice. Anyone wishing to gain a deeper understanding of the state of modern science and why scientific realism is plausible, should read this book. (shrink)

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access to the (...) empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

Toward theory a homuncular of believing For years and years, philosophers took thoughts and beliefs to be modifications of incorporeal Cartesian egos. ...

Comparisons of rival explanations or theories often involve vague appeals to explanatory power. In this paper, we dissect this metaphor by distinguishing between different dimensions of the goodness of an explanation: non-sensitivity, cognitive salience, precision, factual accuracy and degree of integration. These dimensions are partially independent and often come into conflict. Our main contribution is to go beyond simple stipulation or description by explicating why these factors are taken to be explanatory virtues. We accomplish this by using the contrastive-counterfactual approach (...) to explanation and the view of understanding as an inferential ability. By combining these perspectives, we show how the explanatory power of an explanation in a given dimension can be assessed by showing the range of answers it provides to what-if-things-had-been-different questions and the theoretical and pragmatic importance of these questions. Our account also explains intuitions linking explanation to unification or to exhibition of a mechanism. (shrink)

Machamer, Darden, and Craver argue that causal explanations explain effects by describing the operations of the mechanisms which produce them. One of this paper’s aims is to take advantage of neglected resources of Mechanism to rethink the traditional idea that actual or counterfactual natural regularities are essential to the distinction between causal and non-causal co-occurrences, and that generalizations describing natural regularities are essential components of causal explanations. I think that causal productivity and regularity are by no means the same thing, (...) and that the Regularists are mistaken about the roles generalizations play in causal explanation. Humean, logical empiricist, and other Regularist accounts of causal explanation have had the unfortunate effect of distracting philosophers’ from important non-explanatory scientific uses of laws and lesser generalizations which purport to describe natural regularities. My second aim is to characterize some of these uses, illustrating them with examples from neuroscientific research. (shrink)

To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex models. I describe how (...) incompatible climate models are used together in ‘multi-model ensembles’ and explain why this practice is reasonable, given scientists’ inability to identify a ‘best’ model for predicting future climate. Finally, I characterize climate model pluralism as involving both an ontic competitive pluralism and a pragmatic integrative pluralism. (shrink)

Lloyd (2009) contends that climate models are confirmed by various instances of fit between their output and observational data. The present paper argues that what these instances of fit might confirm are not climate models themselves, but rather hypotheses about the adequacy of climate models for particular purposes. This required shift in thinking—from confirming climate models to confirming their adequacy-for-purpose—may sound trivial, but it is shown to complicate the evaluation of climate models considerably, both in principle and in practice.

This paper formulates what I think is the basic problem of any attempt to characterise the abstract structure of scientific method, viz., that it has to satisfy two conflicting desiderata: it should be ampliative (contentincreasing) and it should confer epistemic warrant on its outcomes. Then, after two extreme solutions to the problem of the method, viz., Enumerative Induction and the Method of Hypothesis, are examined, the paper argues that abduction, suitably understood as Inference to the Best Explanation, offers the best (...) description of scientific method and solves the foregoing problem in the best way: it strikes the best balance between ampliation and epistemic warrant. (shrink)

This article examines the role of experimental generalizations and physical laws in neuroscientific explanations, using Hodgkin and Huxley’s electrophysiological model from 1952 as a test case. I show that the fact that the model was partly fitted to experimental data did not affect its explanatory status, nor did the false mechanistic assumptions made by Hodgkin and Huxley. The model satisfies two important criteria of explanatory status: it contains invariant generalizations and it is modular (both in James Woodward’s sense). Further, I (...) argue that there is a sense in which the explanatory heteronomy thesis holds true for this case. †To contact the author, please write to: SNF‐Professorship for Philosophy of Science, University of Basel, Missionsstrasse 21, 4003 Basel, Switzerland; e‐mail: [email protected]. (shrink)

Second, there is a form of ampliative inference that has come to be called ‘inference to the best explanation,’ or more briefly ‘explanatory inference.’ Roughly: From the fact that a certain hypothesis would explain the data at hand better than any other available hypothesis, we infer with some degree of confidence that that leading hypothesis is correct. There is no question but that this inference is often performed. Arguably, every human being performs it many times in a day, perhaps without (...) letup. (shrink)

I want to examine a possible solution to the problem of induction-one which, as far as I know, has not been discussed elsewhere. The solution makes crucial use of the notion of objective natural necessity. For the purposes of this discussion, I shall assume that this notion is coherent. I am aware that this assumption is controversial, but I do not have space to examine the issue here.

John Foster; VI*—Induction, Explanation and Natural Necessity, Proceedings of the Aristotelian Society, Volume 83, Issue 1, 1 June 1983, Pages 87–102, https://d.

In this paper I put forward what I think is a new approach to the problem of induction. I sketched the approach in brief sections of a book published in 1983. The same idea had occurred to the English philosopher John Foster and he presented it in a paper at about the same time.