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Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.
Discussions about the opening of science to society have led to the emergence of new fields such as sustainability science and transformative science. At the same time, the megatrend of stakeholder participation reached the academic world and thus scientific research processes. This challenges the way science is conducted and the tools, methods and theories perceived appropriate. Although researchers involve stakeholders, the scientific community still lacks comprehensive theoretical analysis of the practical processes behind their integration - for example what kind of perceptions scientists have about their roles, their objectives, the knowledge to gather, their understanding of science or the science-policy interface. Our paper addresses this research gap by developing four ideal types of stakeholder involvement in science - the technocratic, the functionalist, the neoliberal-rational and the democratic type. In applying the typology, which is based on literature review, interviews and practical experiences, we identify and discuss three major criticisms raised towards stakeholder involvement in science: the legitimacy of stakeholder claims, the question whether bargaining or deliberation are part of the stakeholder involvement process and the question of the autonomy of science. Thus, the typology helps scientists to better understand the major critical questions that stakeholder involvement raises and enables them to position themselves when conducting their research. (C) 2016 Elsevier Ltd. All rights reserved.
This study models maximum temperatures in Switzerland monitored in twelve locations using the generalised extreme value (GEV) distribution. The parameters of the GEV distribution are determined within a Bayesian framework. We find that the parameters of the underlying distribution underwent a substantial change in the beginning of the 1980s. This change is characterised by an increase both in the level and the variability. We assess the likelihood of the heat wave of the summer 2003 using the fitted GEV distribution by accounting for the presence of a structural break. The estimation results do suggest that the heat wave of 2003 is not that statistically improbable if an appropriate methodology is used for dealing with nonstationarity.