Includes bibliographical references and index. ISBN 0-226-595 19-6 (cloth : alk. paper) . A . 1. Environmental policy. 2. Environmental management — Decision making. 3. Interdisciplinary research. 4. Communication in science. 5. Sustainable ...

The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice. The practice has also been severely criticized by Steven Orzack and Elliott Sober, who claim that it is a nonempirical form of confirmation, effective only under unusual circumstances. This paper addresses Orzack and Sober's criticisms by giving a new account of robustness analysis and (...) showing how the practice can identify robust theorems. Once the structure of robust theorems is clearly articulated, it can be shown that such theorems have a degree of confirmation, despite the lack of direct empirical evidence for their truth. (shrink)

Ambitiously identifying fresh issues in the study of complex systems, Peter J. Taylor, in a model of interdisciplinary exploration, makes these concerns accessible to scholars in the fields of ecology, environmental science, and science studies. Unruly Complexity explores concepts used to deal with complexity in three realms: ecology and socio-environmental change; the collective constitution of knowledge; and the interpretations of science as they influence subsequent research. For each realm Taylor shows that unruly complexity-situations that lack definite boundaries, where what goes (...) on "outside" continually restructures what is "inside," and where diverse processes come together to produce change-should not be suppressed by partitioning complexity into well-bounded systems that can be studied or managed from an outside vantage point. Using case studies from Australia, North America, and Africa, he encourages readers to be troubled by conventional boundaries-especially between science and the interpretation of science-and to reflect more self-consciously on the conceptual and practical choices researchers make. (shrink)

Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, I argue that model (...) building is successful even when models are predictively inaccurate for at least three reasons: models allow scientists to explore the possible behaviors of ecological systems; models give scientists simplified means by which they can investigate more complex systems by determining how the more complex system deviates from the simpler model; and models give scientists conceptual frameworks through which they can conduct experiments and fieldwork. Critics often mistake the purposes of model building, and once we recognize this, we can see their complaints are unjustified. Even though models in ecology are not always accurate in their assumptions and predictions, they still contribute to successful science. (shrink)