First, the various dimensions of the empirical study of the sciences are clarified in a methodological analysis of theoretical traditions, including the sociology of scientific knowledge and neo-conventionalism in the philosophy of science. Second, the author argues why the mathematical theory of communication enables us to address crucial problems in science and technology studies, both on the qualitative side (e.g., the significance of a reconstruction) and on the quantitative side (e.g., the prediction of indicators).
A comprehensive set of probabilistic entropy measures for studying complex developments in networks is elaborated. In the third part of the study, applications to S&T policy questions (e.g., the emergence of a European R&D system), to problems of (Bayesian) knowledge representations, and to the study of the sciences in terms of 'self-organizing' paradigms of scientific communication are provided. A discussion of directions for further research concludes the study.