Thomas Nickles

I discuss changes of perspective of four kinds in science and about science. Section 2 defends a perspectival nonrealism—something akin to Giere’s perspectival realism but not a realism—against the idea of complete, “Copernican” objectivity. Section 3 contends that there is an inverse relationship between epistemological conservatism and scientific progress. Section 4 casts doubt on strong forms of scientific realism by taking a long-term historical perspective that includes future history. Section 5 defends a partial reversal in the status of so-called context of discovery and context of justification. Section 6 addresses the question of how we can have scientific progress without scientific realism—how progress is possible without the accumulation of representational truth. The overall result is a pragmatic instrumentalist perspective on the sciences and how to study them philosophically, one that contains a kernel of realism—instrumental realism.

A serious problem for covering law explanation is raised and its consequences for the Hempelian theory of explanation are discussed. The problem concerns an intensional feature of explanations, involving the manner in which theoretical law statements are related to the events explained. The basic problem arises because explanations are not of events but of events under descriptions; moreover, in a sense, our linguistic descriptions outrun laws. One form of the problem, termed the problem of weak intensionality, is apparently solved by a simple logical move, but in fact the problem arises in a new, strong form. It is found that Hempel's model for deductive explanation (to which this discussion is confined) requires modification to handle the weak intensionality problem but then is faced with the problem of strong intensionality. In consequence, it is suggested that Hempel's important concept of explanation sketch is not as widely applicable as usually claimed, especially for explanations in the behavioral and social sciences and history. Reason is found to reject the covering law thesis that every scientific explanation must contain at least one law statement. An important feature of the discussion is that some of the main reasons given for altering the deductive model and for considering other forms of explanation are internal to the covering law theory

Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain controversial in many respects. The attempt to understand the ways models and inferences are made basically opens two roads. The first one is to produce an analysis of the role that models and inferences play in science. The second one is to produce an analysis of the way models and inferences are constructed, especially in the light of what science tells us about our cognitive abilities. The papers collected in this volume go both ways.

The history of science is articulated by moments of discovery. Yet, these 'moments' are not simple or isolated events in science. Just as a scientific discovery illuminates our understanding of nature or of society, and reveals new connections among phenomena, so too does the history of scientific activity and the analysis of scientific reasoning illuminate the processes which give rise to moments of discovery and the complex network of consequences which follow upon such moments. Understanding discovery has not been, until recently, a major concern of modem philosophy of science. Whether the act of discoyery was regarded as mysterious and inexplicable, or obvious and in no need of explanation, modem philosophy of science in effect bracketed the question. It concentrated instead on the logic of scientific explanation or on the issues of validation or justification of scientific theories or laws. The recent revival of interest in the context of discovery, indeed in the acts of discovery, on the part of philosophers and historians of science, represents no one particular method'ological or philosophical orientation. It proceeds as much from an empiricist and analytical approach as from a sociological or historical one; from considerations of the logic of science as much as from the alogical or extralogical contexts of scientific tho'¢tt and practice. But, in general, this new interest focuses sharply on the actual historical and contem porary cases of scientific discovery, and on an examination of the act or moment of discovery in situ.

Pure consequentialists hold that all theoretical justification derives from testing the consequences of hypotheses, while generativists maintain that reasoning (some feature of) the hypothesis from we already know is an important form of justification. The strongest form of justification (they claim) is an idealized discovery argument. In the guise of H-D methodology, consequentialism is widely supposed to have defeated generativism during the 19th century. I argue that novel prediction fails to overcome the logical weakness of consequentialism or to render generative methodology superfluous. Specifically, Bayesian consequentialism is not an alternative to generativism but reduces to an instance of it.

The last forty years have produced a dramatic reversal in leading accounts of science. Once thought necessary to (explain) scientific progress, a rigid method of science is now widely considered impossible. Study of products yields to study of processes and practices, .unity gives way to diversity, generality to particularity, logic to luck, and final justification to heuristic scaffolding. I sketch the story, from Bacon and Descartes to the present, of the decline and fall of traditional scientific method, conceived as The Central Planning Bureau for Science or as Rationality Czar. I defend a deflationary account of method and of rational judgment,. with emphasis on heuristic appraisal and cognitive economy.

Reduction was once a central topic in philosophy of science. I claim that it remains important, especially when applied to problems and problem-solutions rather than only to large theory-complexes. Without attempting a comprehensive classification, I discuss various kinds of problem reductions and similar relations, illustrating them, inter alia, in terms of the blackbody problem and early quantization problems. Kuhn's early work is suggestive here both for structuralist theory of science and for the line I prefer to take. My central claims in the paper are (1) that problem reduction is important in its own right and does not "reduce" to theory reduction and (2) that problem reduction is generally more important than theory reduction to methodology as the "control theory" of inquiry.

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science.

Looking at Thomas Kuhn's work from a cognitive science perspective helps to articulate and to legitimize, to some degree, his rejection of traditional views of concepts, categorization, theory structure, and rule-based problem solving. Whereas my colleagues focus on the later Kuhn of the MIT years, I study the early Kuhn as an anticipation of case-based reasoning and schema theory. These recent developments in cognitive psychology and artificial intelligence may point toward a more computational version of Kuhn's ideas, but they also expose ambiguities in his work, notably in his understanding of exemplars