Contributions in Merlin

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Although animal researchers established the role of testosterone as a social hormone decades ago, the investigation of its causal influence on human social behaviors has only recently begun. Here, we review and discuss recent studies showing the causal effects of testosterone on social interactions in animals and humans, and outline the basic neurobiological mechanisms that might underlie these effects. Based on these recent findings, we argue that the role of testosterone in human social behavior might be best understood in terms of the search for, and maintenance of, social status.

Neuroökonome Christian Ruff über die biologische Grundlage unseres Entscheidungsverhaltens. Der Mensch versteht viele Dinge – nur sein eigenes Gehirn kaum. Die Neuroökonomie will herausfinden, wieso wir gewisse Entscheidungen rational und anderer irrational fällen. Sie zeigt Wege auf, wie wir Entscheide optimieren könnten. Werden wir durch solche Erkenntnisse eines Tages zu besseren Investoren, Managern, Bürgern oder gar Menschen?

A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

When performed on a model, a set of operations (e.g., queries or model transformations) rarely uses all the information present in the model. Unintended underuse of a model can indicate various problems: the model may contain more detail than necessary or the operations may be immature or erroneous. Analyzing the footprints of the operations - i.e., the part of a model actually used by an operation - is a simple technique to diagnose and analyze such problems. However, precisely calculating the footprint of an operation is expensive, because it requires analyzing the operation's execution trace.In this paper, we present an automated technique to estimate the footprint of an operation without executing it. We evaluate our approach by applying it to 75 models and five operations. Our technique provides software engineers with an efficient, yet precise, evaluation of the usage of their models.

In this paper we consider a canonical stochastic overlapping generations economy with sequentially complete markets. We examine how aggregate and individual shocks translate to changes in the distribution of wealth and how these movements in the wealth distribution affect asset prices and the interest rate. We show that effects are generally small if agents have identical beliefs but that differences in opinion lead to large movements in the wealth distribution. The interplay of belief heterogeneity and life-cycle savings motives creates very large movements of asset prices and can potentially generate realistic moments of asset returns.