The notion of (deterministic) chaos is frequently used in an increasing number of scientific (as well as non-scientific) contexts, ranging from mathematics and the physics of dynamical systems to all sorts of complicated time evolutions, e.g., in chemistry, biology, physiology, economy, sociology, and even psychology. Despite (or just because of) these widespread applications, however, there seem to fluctuate around several misunderstandings about the actual impact of deterministic chaos on several problems of philosophical interest, e.g., on matters of prediction and computability, (...) and determinism and the free will. In order to clarify these points a survey of the meaning variance of the concept(s) of deterministic chaos, or the various contexts in which it is applied, is given, and its actual epistemological implications are extracted. In summary, it turns out that the various concepts of deterministic chaos do not constitute a “new science”, or a “revolutionary” change of the “scientific world picture”. Instead, chaos research provides a sort of toolbox of methods which are certainly useful for a more detailed analysis and understanding of such dynamical systems which are, roughly speaking, endowed with the property of exponential sensitivity on initial conditions. Such a property, then, implies merely one, but quantitatively strong type of limitation of long-time computability and predictability, respectively. (shrink)

Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and (too) high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models which are computationally tractable. Such (...) activities are compared with reduction types (more) established in philosophy of science. (shrink)

Die Kernaufgaben von Hochschulen sind Studium und Lehre (Persönlichkeitsbildung; inhaltliche und methodische Ausbildung, insbesondere des akademischen Nachwuchses; Bildung zur Berufsfähigkeit), Forschung und Third Mission (z. B. Wissens- und Technologietransfer; regionales Engagement; Weiterbildungsangebote; transdisziplinäre und soziale Innovationen). Im Rahmen dieses breiten Auftrags zu Aufklärung, Bildung und Innovation in Wissensgesellschaften sehen sich Hochschulen als kreative lernende Organisationen und strategische offene Republiken von Akademikern und Studierenden gegenwärtig vielfältigen und komplexen Herausforderungen konfrontiert. Solchen Herausforderungen kann ohne systematische, aufklärerisch-kritische Evaluation nicht produktiv und proaktiv begegnet (...) werden. (shrink)

Evaluationen sind Formen angewandter Sozialforschung zur systematisch-wissenschaftlichen Generierung von Qualitätsbewertungen von Evaluationsgegenständen und gegebenenfalls Entwicklungsempfehlungen in Bezug auf Entscheidungs- und Handlungsprozesse, die zur Lösung praktischer, meta-wissenschaftlicher, gesellschaftlicher und politischer Probleme beitragen. Evaluationen sind also Bewertungen von Qualitäts- und Entwicklungsniveaus von Evaluationsgegenständen (z. B. materielle Ausstattung von Organisationen; Leistungen von Schülern, Studierenden oder Lehrenden; Wirkungen von Studienprogrammen oder Programmen der Entwicklungszusammenarbeit; Erfolge von Forschungsprogrammen; Effektivität und Effizienz von Organisationen; etc.). Angesichts der Vielfalt von Evaluationsgegenständen finden Evaluationen grundsätzlich in Spannungsfeldern verschiedener Stakeholderinteressen, (...) Werte, Institutionen und Überzeugungssysteme statt. (shrink)

Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models which are computationally tractable. Such activities (...) are compared with reduction types established in philosophy of science. (shrink)