The local configuration of a science and innovation policy : a city into the nanoworld

Local trajectories and arrangements play a significant role because the development of a research field, such as nanoscience and nanotechnology, requires substantial investments in human and instrumental resources. But why are there often concentrated in a limited number of places? What dynamics lead to such concentration? The hypothesis is that there is an assemblage of heterogeneous resources through the action of local actors. The chapter will explore, from an Actor Network Theory (ANT) perspective, how the local emergence of research dynamics from: the revival of local traditions, the local and national action of institutional entrepreneurs, controversial dynamics, and researchers' arrangements to involve other actors. It will examine how they connect up with each other and mutually commit themselves to the development of new technologies. It will focus on the role of narratives in this assembling: how were the local narratives of the past mobilized and to what effect.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.