front cover of The Experimental Side of Modeling
The Experimental Side of Modeling
Isabelle F. Peschard
University of Minnesota Press, 2018

An innovative, multifaceted approach to scientific experiments as designed by and shaped through interaction with the modeling process


The role of scientific modeling in mediation between theories and phenomena is a critical topic within the philosophy of science, touching on issues from climate modeling to synthetic models in biology, high energy particle physics, and cognitive sciences. Offering a radically new conception of the role of data in the scientific modeling process as well as a new awareness of the problematic aspects of data, this cutting-edge volume offers a multifaceted view on experiments as designed and shaped in interaction with the modeling process.

Contributors address such issues as the construction of models in conjunction with scientific experimentation; the status of measurement and the function of experiment in the identification of relevant parameters; how the phenomena under study are reconceived when accounted for by a model; and the interplay between experimenting, modeling, and simulation when results do not mesh. Highlighting the mediating role of models and the model-dependence (as well as theory-dependence) of data measurement, this volume proposes a normative and conceptual innovation in scientific modeling—that the phenomena to be investigated and modeled must not be precisely identified at the start but specified during the course of the interactions arising between experimental and modeling activities.

Contributors: Nancy D. Cartwright, U of California, San Diego; Anthony Chemero, U of Cincinnati; Ronald N. Giere, U of Minnesota; Jenann Ismael, U of Arizona; Tarja Knuuttila, U of South Carolina; Andrea Loettgers, U of Bern, Switzerland; Deborah Mayo, Virginia Tech; Joseph Rouse, Wesleyan U; Paul Teller, U of California, Davis; Michael Weisberg, U of Pennsylvania; Eric Winsberg, U of South Florida.

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front cover of Learning More from Social Experiments
Learning More from Social Experiments
Evolving Analytic Approaches
Howard S. Bloom
Russell Sage Foundation, 2005
Policy analysis has grown increasingly reliant on the random assignment experiment—a research method whereby participants are sorted by chance into either a program group that is subject to a government policy or program, or a control group that is not. Because the groups are randomly selected, they do not differ from one another systematically. Therefore any differences between the groups at the end of the study can be attributed solely to the influence of the program or policy. But there are many questions that randomized experiments have not been able to address. What component of a social policy made it successful? Did a given program fail because it was designed poorly or because it suffered from low participation rates? In Learning More from Social Experiments, editor Howard Bloom and a team of innovative social researchers profile advancements in the scientific underpinnings of social policy research that can improve randomized experimental studies. Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random assignment studies can be overcome by combining data from these studies with statistical methods from other research designs. Carolyn Hill, James Riccio, and Bloom profile a new statistical model that allows researchers to pool data from multiple randomized-experiments in order to determine what characteristics of a program made it successful. Lisa Gennetian, Pamela Morris, Johannes Bos, and Bloom discuss how a statistical estimation procedure can be used with experimental data to single out the effects of a program's intermediate outcomes (e.g., how closely patients in a drug study adhere to the prescribed dosage) on its ultimate outcomes (the health effects of the drug). Sometimes, a social policy has its true effect on communities and not individuals, such as in neighborhood watch programs or public health initiatives. In these cases, researchers must randomly assign treatment to groups or clusters of individuals, but this technique raises different issues than do experiments that randomly assign individuals. Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that arise from its use. He pays particular attention to the way in which the movement of individuals into and out of clusters over time complicates the design, execution, and interpretation of a study. Learning More from Social Experiments represents a substantial leap forward in the analysis of social policies. By supplementing theory with applied research examples, this important new book makes the case for enhancing the scope and relevance of social research by combining randomized experiments with non-experimental statistical methods, and it serves as a useful guide for researchers who wish to do so.
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front cover of Philosophical Instruments
Philosophical Instruments
Minds and Tools at Work
Daniel Rothbart. Foreword by Rom Harré
University of Illinois Press, 2006

The surprising roles of instruments and experimentation in acquiring knowledge

In Philosophical Instruments Daniel Rothbart argues that our tools are not just neutral intermediaries between humans and the natural world, but are devices that demand new ideas about reality. Just as a hunter's new spear can change their knowledge of the environment, so can the development of modern scientific equipment alter our view of the world.

Working at the intersections of science, technology, and philosophy, Rothbart examines the revolution in knowledge brought on by recent advances in scientific instruments. Full of examples from historical and contemporary science, including electron scanning microscopes, sixteenth-century philosophical instruments, and diffraction devices used by biochemical researchers, Rothbart explores the ways in which instrumentation advances a philosophical stance about an instrument's power, an experimenter's skills, and a specimen's properties. Through a close reading of engineering of instruments, he introduces a philosophy from (rather than of) design, contending that philosophical ideas are channeled from design plans to models and from model into the use of the devices.

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front cover of What Makes a Good Experiment?
What Makes a Good Experiment?
Reasons and Roles in Science
Allan Franklin
University of Pittsburgh Press, 2015
What makes a good experiment? Although experimental evidence plays an essential role in science, as Franklin argues, there is no algorithm or simple set of criteria for ranking or evaluating good experiments, and therefore no definitive answer to the question. Experiments can, in fact, be good in any number of ways: conceptually good, methodologically good, technically good, and pedagogically important. And perfection is not a requirement: even experiments with incorrect results can be good, though they must, he argues, be methodologically good, providing good reasons for belief in their results. Franklin revisits the same important question he posed in his 1981 article in the British Journal for the Philosophy of Science, when it was generally believed that the only significant role of experiment in science was to test theories. But experiments can actually play a lot of different roles in science—they can, for example, investigate a subject for which a theory does not exist, help to articulate an existing theory, call for a new theory, or correct incorrect or misinterpreted results. This book provides details of good experiments, with examples from physics and biology, illustrating the various ways they can be good and the different roles they can play.
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