This paper distinguishes several different models of the relation between philosophical ontology and applied (scientific) ontology that have been advanced in the history of philosophy. Adoption of a strong participation model for the philosophical ontologist in science is urged, and requirements and consequences of the participation model are explored. This approach provides both a principled view and justification of the role of the philosophical ontologist in contemporary empirical science as well as guidelines for integrating philosophers and philosophical contributions into the practice of science.
Within the last five years, a number of research papers and two important books have appeared on scientific discovery (see, Langley, Simon, Bradshaw, & Zytkow, 1987; Shrager & Langley, 1990), one on the computational philosophy of science (Thagard, 1988), and another one on creativity (Boden, 1990). Langley et al.'s (1987) posed the first serious challenge to the conventional study of science by proposing that, far from being mysterious and unexplainable, scientific discovery (and by implication scientific creativity), can be explained in a series of processes. They also described several computational models in support of their view. Shrager and Langley's (1990) later book introduced a new framework for the study of scientific development, and explained how the methods of the computational study of science were superior to the methods of conventional philosophy of science. Boden's (1990) work extended some of these views and discussed, from a cognitive scientist's perspecfive, how creativity in arts and literature, as well as in science could be studied within a computational context in a more systematic way. Nevertheless, previous work leaves some of the important issues in discovery untouched, such as the elements of scientific creativity, the types of scientific discovery and creativity, and the dimensions of scientific research. In thispaper, we examine the basic cognitive concepts of creativity, and describe how these concepts are connected, and then discuss the role of background knowledge and the kinds of knowledge necessary for scientific research. Finally, we discuss the types of scientific discovery and the elements of scientific research.
It's an interesting time to be making a case for philosophy in science. On the one hand, some scientists working on ideas such as string theory or the multiverse--ideas that reach far beyond our current means to test them--are forced to make a philosophical defense of research that can't rely on traditional hypothesis testing. On the other hand, some physicists, such as Richard Feynman and Stephen Hawking, were notoriously dismissive of the value of the philosophy of science. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. That value is asserted with gentle but firm assurance by Michela Massimi, the recent recipient of the Wilkins-Bernal-Medawar Medal, an award given annually by the UK's Royal Society.
This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) animal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psychology through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and to computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these problems and domains, empirical, experimental, and methodological studies will also appear from time to time. The present volume offers a broad and imaginative approach to the study of the mind, which emphasizes several themes, namely: the importance of functional organization apart from the specific material by means of which it may be implemented; the use of modeling to simulate these functional processes and subject them to certain kinds of tests; the use of mentalistic language to describe and predict the behavior of artifacts; and the subsumption of processes of adaptation, learning, and intelligence by means of explanatory principles. The author has produced a rich and complex, lucid and readable discussion that clarifies and illuminates many of the most difficult problems arising within this difficult domain.
The Second International Workshop on Human and Machine Cognition was held on 9-11 May 1991. Participation was limited to 40 researchers who are principally involved in computer science, philosophy, and psychology. The workshop focused on the foundational and methodological concerns of those who want to forge a robust and scientifically respectable AI and cognitive science. The debate between the traditional AI and the situated cognition types and the connnectionists was a focal point for discussion during the workshop.