There is no broadly accepted definition of ‘life.’ Suggested definitions face problems, often in the form of robust counter-examples. Here we use insights from philosophical investigations into language to argue that defining ‘life’ currently poses a dilemma analogous to that faced by those hoping to define ‘water’ before the existence of molecular theory. In the absence of an analogous theory of the nature of living systems, interminable controversy over the definition of life is inescapable.

Wittgenstein finished part 1 of the Philosophical Investigations in the spring of 1945. From 1946 to 1949 he worked on the philosophy of psychology almost without interruption. The present two-volume work comprises many of his writings over this period. Some of the remarks contained here were culled for part 2 of the Investigations ; others were set aside and appear in the collection known as Zettel . The great majority, however, although of excellent quality, have hitherto remained unpublished. This bilingual (...) edition of the Remarks on the Philosophy of Psychology presents the first English translation of an essential body of Wittegenstein's work. It elaborates Wittgenstein's views on psychological concepts such as expectation, sensation, knowing how to follow a rule, and knowledge of the sensations of other persons. It also shows strong emphasis on the "anthropological" aspect of Wittgenstein's thought. Philosophers, as well as anthropologists, psychologists, and sociologists will welcome this important publication. (shrink)

From Gaia to Selfish Genes is a different kind of anthology. Lively excerpts from the popular writings of leading theorists in the life sciences blend in a seamless presentation of the controversies and bold ideas driving contemporary biological research. Selections span scales from the biosphere to the cell and DNA, and disciplines from global ecology to behavior and genetics, and also reveals the links between biology and philosophy. They plunge the reader into debates about heredity and environment, competition and cooperation, (...) randomness and determinism, and the meaning of individuality. From Gaia to Selfish Genes conveys the technical and conceptual roots of current scientific theories beginning with the planetary perspective of James Lovelock and Lynn Margulis and concluding with the reductionist views of Richard Dawkins and E. 0. Wilson. The contrasting worldviews, coupled with excerpts drawn from critics of each theory, encourage readers to examine their own presuppositions. In addition to the scientists' portrayal of the Gaia hypothesis, symbiosis in cell evolution, hierarchy theory, systems theory, game theory, sociobiology, and the selfish gene, the text is rich in autobiographical passages and biographies. By presenting the human side of research, From Gaia to Selfish Genes reveals the social context and interactions, the motivations and range of cognitive styles that comprise the scientific endeavor. Concluding essays written expressly for this book by Lynn Margulis, John Maynard Smith, W. Ford Doolittle, and others underscore the importance of such diversity. Connie Barlow is a science writer currently living in New York City. The scientists include: Robert Axelrod. Richard D. Alexander. Ludwig von Bertalanffy. Leo W. Buss. Francis Crick. Richard Dawkins. W. Ford Doolittle. Douglas Hofstadter. Julian Huxley. Leon J. Kamin. Philip Kitcher. Richard C. Lewontin. James Lovelock. Lynn Margulis. Ashley Montagu. Leslie Orgel. Steven Rose. Carmen Sapienza. John Maynard Smith. Lewis Thomas. Gerald Weinberg. E. 0. Wilson. Robert Wright. The science writers include: Lawrence Joseph. Arthur Koestler. Francesca Lyman. Jeanne McDermott. Richard Monastersky. Dorion Sagan. (shrink)

Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks'' contain no explicit protocols, but are generic learningsystems built on the model of (...) the interconnections of neurons in thebrain. I describe two cases that take the connectionist approach a stepfurther by using genetic algorithms, a form of evolutionary computationthat explicitly models Darwinian mechanisms. These cases show thatDarwinian mechanisms can make novel discoveries of complex, previouslyunknown patterns. With some caveats, they lend support to evolutionaryepistemology. (shrink)

A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features. We examined this issue using digital organisms—computer programs that self-replicate, mutate, compete and evolve. Populations of digital organisms often evolved the ability to perform complex logic functions requiring the coordinated execution of many genomic instructions. Complex functions evolved by building on simpler functions that had evolved earlier, provided that these were also selectively favoured. However, no particular intermediate stage was essential for evolving (...) complex functions. The first genotypes able to perform complex functions differed from their non-performing parents by only one or two mutations, but differed from the ancestor by many mutations that were also crucial to the new functions. In some cases, mutations that were deleterious when they appeared served as stepping-stones in the evolution of complex features. These findings show how complex functions can originate by random mutation and natural selection. (shrink)

Journal of Experimental and Theoretical Artificial Intelligence What is the difference between a simulation of X and simply another instance of X? Is there a point at which the ‘‘virtual reality’’ of a model becomes the real thing? This paper examines these questions using cases taken from recent developments in evolutionary engineering and artificial life research. By implementing the Darwinian mechanism and setting it to work on a design problem, scientists and engineers find that evolution not only can improve prior (...) designs, but also produce novel technological solutions. Artificial life systems Tierra and Avida which operate at a higher level of abstraction than evolutionary engineering applications. I analyze simulation as a rational concept ‘‘S simulates R’’ and argue that it always includes some relevant property P, of R, that is captured but that there is always also some other that it omits, and that pragmatic factors fix what counts as relevant. The border between a simulation and an instance can change depending upon the context. I show that in one sense, evo-technology and artificial life simulate organic evolution, but in another relevant sense they are instances of evolution itself. Biologists can use such systems to experimentally test evolutionary hypotheses such as those involving the evolution of complex features and altruism. This analysis suggests lines for future research on broader questions about models classification and confirmation. (shrink)