Recent years have seen a series of intense, increasingly acrimonious debates over the status and legitimacy of the natural sciences. These “science wars” take place in the public arena--with current battles over evolution and global warming--and in academia, where assumptions about scientific objectivity have been called into question. Given these hostilities, what makes a scientific claim merit our consideration? In Cogent Science in Context, William Rehg examines what makes scientific arguments cogent--that is, strong and convincing--and how we should assess that cogency.
Information shapes biological organization in fundamental ways and at every organizational level. Because organisms use information--including DNA codes, gene expression, and chemical signaling--to construct, maintain, repair, and replicate themselves, it would seem only natural to use information-related ideas in our attempts to understand the general nature of living systems, the causality by which they operate, the difference between living and inanimate matter, and the emergence, in some biological species, of cognition, emotion, and language.
How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken.
In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent explanatory models.
The notion of function is an integral part of thinking in both biology and technology; biological organisms and technical artifacts are both ascribed functionality. Yet the concept of function is notoriously obscure (with problematic issues regarding the normative and the descriptive nature of functions, for example) and demands philosophical clarification.
Building a person has been an elusive goal in artificial intelligence. This failure, John Pollock argues, is because the problems involved are essentially philosophical; what is needed for the construction of a person is a physical system that mimics human rationality.
Emergence, largely ignored just thirty years ago, has become one of the liveliest areas of research in both philosophy and science. Fueled by advances in complexity theory, artificial life, physics, psychology, sociology, and biology and by the parallel development of new conceptual tools in philosophy, the idea of emergence offers a way to understand a wide variety of complex phenomena in ways that are intriguingly different from more traditional approaches.
Genetically modified food, art in the form of a phosphorescent rabbit implanted with jellyfish DNA, and robots that simulate human emotion would seem to be evidence for the blurring boundary between the natural and the artificial. Yet because the deeply rooted concept of nature functions as a cultural value, a social norm, and a moral authority, we cannot simply dismiss the distinction between art and nature as a nostalgic relic.
The twentieth century’s conceptual separation of the process of evolution (changes in a population as its members reproduce and die) from the process of development (changes in an organism over the course of its life) allowed scientists to study evolution without bogging down in the “messy details” of development. Advances in genetics produced the modern synthesis, which cast the gene as the unit of natural selection.
Abstract and conceptual models have become an indispensable tool for analyzing the flood of highly detailed empirical data generated in recent years by advanced techniques in the biosciences. Scientists are developing new modeling strategies for analyzing data, integrating results into the conceptual framework of theoretical biology, and formulating new hypotheses. In Modeling Biology, leading scholars investigate new modeling strategies in the domains of morphology, development, behavior, and evolution.