Conventionally, agent-based models are specified in a combination of natural language and mathematical terms, and their implementation seen as an afterthought. I challenge this view and argue that it is the source code that represents the model best, with natural language and mathematical descriptions serving as documentation. This modeling paradigm is inspired by agile software development and adopting it leads to various - mostly beneficial - consequences. First, discrepancies between the specification documents and what the model actually does are eliminated by definition as the code becomes the specification. Second, replicability is greatly improved. Third, object-oriented programming is recognized as an integral part of a modeler?s skill set. Forth, tools and methods from software engineering can support the modeling process, making it more agile. Fifth, increased modularity allows to better manage complexity and enables the collaborative construction of large models. Sixth, the way models are published needs to be reconsidered, with source code ideally being part of the peer review. Seventh, the quality of source code in science is improved as it enjoys more importance, attention and scrutiny.
Using high-frequency data we document that episodes of market turmoil in the European sovereign bond market are on average associated with large decreases in trading volume. The response of trading volume to market stress is conditional on transaction costs. Low transaction cost turmoil episodes are associated with volume increases (investors rebalance), while high transaction cost turmoil periods are associated with abnormally low volume (market freezes). We find suggestive evidence of market freezes in response to shocks to the risk bearing capacity of market makers while investor rebalancing is triggered by wealth shocks. Overall, our results show that the recent sovereign debt crisis was not associated with large-scale investor rebalancing.
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
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.