Der Starökonom aus Zürich über den Vormarsch der Verhaltensökonomie, die wirtschaftlichen Folgen von Unehrlichkeit – und seine Forschung mit Kindern und Hirnscannern.
Die Regulierung der Mietpreise ist schädlich – für die Vermieter, die sozial Schwachen, den Baubestand und den Arbeitsmarkt. Ein Kommentar von Joachim Voth.
Many bipartite and unipartite real-world networks display a nested structure. Examples pervade different disciplines: biological ecosystems (e.g. mutualistic networks), economic networks (e.g. manufactures and contractors networks) to financial networks (e.g. bank lending networks), etc. A nested network has a topology such that a vertex’s neighbourhood contains the neighbourhood of vertices of lower degree; thus – upon vertex reordering – the adjacency matrix is step-wise. Despite its strictmathematical definition and the interest triggered by their common occurrence, it is not easy to measure the extent of nested graphs unequivocally. Among others, there exist three methods for detection and quantification of nestedness that are widely used: BINMATNEST, NODF, and fitness-complexity metric (FCM). However, thesemethods fail in assessing the existence of nestedness for graphs of low (NODF) and high (NODF, BINMATNEST) network density. Another common shortcoming of these approaches is the underlying assumption that all vertices belong to a nested component. However, many real-world networks have solely a sub-component (i.e. a subset of its vertices) that is nested. Thus, unveiling which vertices pertain to the nested component is an important research question, unaddressed by the methods available so far. In this contribution, we study in detail the algorithm Nestedness detection based on Local Neighbourhood (NESTLON). This algorithm resorts solely on local information and detects nestedness on a broad range of nested graphs independently of their nature and density. Further, we introduce a benchmark model that allows us to tune the degree of nestedness in a controlled manner and study the performance of different algorithms. Our results show that NESTLON outperforms both BINMATNEST and NODF.
The aim of this paper is to put forward a new family of risk measures that as the coherent/convex risk measures impose a preference order on random cash flows and can be interpreted as prices. But at the difference of the axiomatic approach of Artzner, Delbaen, Eber and Heath (1999) and the subsequent extensions of this model, our risk measures are associated with the optimal policies of shareholder value maximizing company. We study these optimal policies and the related risk measures that we call shareholder risk measures. We emphasize the fact that due to the specific corporate environment, in particular the limited shareholders’ liability and the possibility to pay out dividends from the cash reserves, these risk measures are not convex. Also, they depend on the specific economic situation of the firm, in particular its current cash level, and thus they are not translation invariant. This paper bridges the gap between two important branches of mathematical finance: risk measures and optimal dividends.
We propose a massively parallelized and optimized framework to solve high-dimensional dynamic stochastic economic models on modern GPU- and MIC-based clusters. First, we introduce a novel approach for adaptive sparse grid index compression alongside a surplus matrix reordering, which significantly reduces the global memory throughput of the compute kernels and maps randomly accessed data onto cache or fast shared memory. Second, we fully vectorize the compute kernels for AVX, AVX2 and AVX512 CPUs, respectively. Third, we develop a hybrid cluster oriented work-preempting scheduler based on TBB, which evenly distributes the time iteration workload onto available CPU cores and accelerators. Numerical experiments on Cray XC40 KNL “Grand Tave” and on Cray XC50 “Piz Daint” systems at the Swiss National Supercomputer Centre (CSCS) show that our framework scales nicely to at least 4,096 compute nodes, resulting in an overall speedup of more than four orders of magnitude compared to a single, optimized CPU thread. As an economic application, we compute global solutions to an annually calibrated stochastic public finance model with sixteen discrete, stochastic states with unprecedented performance. Index Terms—High-Performance Computing, Macroeconomics, Public Finance, Adaptive Sparse Grids, Heterogeneous Systems, CUDA, GPU, MIC
Armut beeinträchtigt das Denken und Handeln. Wer arm ist, fällt oft falsche Entscheidungen und bleibt deshalb arm. Der brasilianische Ökonom Guilherme Lichand will das ändern – per SMS. Ein Feldexperiment mit 19000 Schulkindern hat gezeigt, dass regelmässige SMS an die Eltern die Lerngeschwindigkeit erhöhen und Absenzen reduzieren.
Der klinische Alltag konfrontiert Fachpersonen aus Medizin und Pflege regelmässig mit ethischen Problemen, die gemeinsam mit Patienten und deren Angehörigen angegangen werden müssen. Entsprechend sollte der Umgang mit ethischen Fragen Teil der Aus- und Weiterbildung von Fachpersonen sein, wobei dies meist Deliberation umfasst. Psychologische Kompetenzen werden durch diesen Ansatz meist nur indirekt gefördert. Wir stellen in diesem Beitrag das Konzept der "moralischen Intelligenz" vor, das aktuelle Kenntnisse der Moralpsychologie mit ethischen Gesichtspunkten vereint und Kompetenzen definiert, die Gegenstand von Diagnose und Training sein können. Anhand der moralischen Sensitivität wird skizziert, wie solche Kompetenzen messbar gemacht werden und zur Weiterentwicklung der Aus- und Weiterbildung in Ethik sowie als Instrument zur Entwicklung diagnostischer Verfahren dienen können.
