Contributions in Merlin

Trust pervades human societies. Trust is indispensable in friendship, love, families and organizations, and plays a key role in economic exchange and politics. In the absence of trust among trading partners, market transactions break down. In the absence of trust in a country's institutions and leaders, political legitimacy breaks down. Much recent evidence indicates that trust contributes to economic, political and social success. Little is known, however, about the biological basis of trust among humans. Here we show that intranasal administration of oxytocin, a neuropeptide that plays a key role in social attachment and affiliation in non-human mammals, causes a substantial increase in trust among humans, thereby greatly increasing the benefits from social interactions. We also show that the effect of oxytocin on trust is not due to a general increase in the readiness to bear risks. On the contrary, oxytocin specifically affects an individual's willingness to accept social risks arising through interpersonal interactions. These results concur with animal research suggesting an essential role for oxytocin as a biological basis of prosocial approach behaviour.

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric hypotheses are obtained. We propose a feasible and efficient algorithm for the computation of our robust estimators, which makes use of analytical Laplace approximations to estimate the auxiliary recentering vectors ensuring Fisher consistency in robust estimation. This strongly reduces the necessary computation time by avoiding the simulation of multidimensional integrals, a task that has typically to be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR 1)-ARCH(1) process we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. On the contrary, classical Pseudo Maximum Likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for ARCH.

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Health care may be similar to Sisyphus work: When the task is about to be completed, work has to start all over again. To see the analogy, consider an initial decision to allocate more resources to health. The likely consequence is an increased number of survivors, who will exert additional demand for health care. With more resources allocated to health, the cycle starts over again. The objective of this paper is to improve on earlier research that failed to find evidence of a Sisyphus syndrome in industrialized countries. This time, there are signs of such a cycle, which however seems to have faded away recently.

It is very important for us to understand the functions and structures of terrorist networks to win the battle against terror. However, previous studies of terrorist network structure have generated little actionable results. This is mainly due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we employed several advance network analysis techniques ranging from social network analysis to Web structural mining on a Global Salafi Jihad network dataset collected through a large scale empirical study. Our study demonstrated the effectiveness and usefulness of advanced network techniques in terrorist network analysis domain. We also introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been used in this domain. More importantly, the results from our analysis provide not only insights for terrorism research community but also empirical implications that may help law-reinforcement, intelligence, and security communities to make our nation safer.

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Large and complex software systems are confronted with continuous changes during all stages in their life comprising development, maintenance, migration, and retirement. On the one side these changes are mandatory to guarantee the success of a software system but on the other side changes affect the architecture and design of a software system. Therefore, a continuous observation and analysis of the architecture and the design is needed to early identify shortcomings and resolve them. In this dissertation we propose the ArchView approach that focuses on the analysis and evaluation of software modules regarding their structural and evolutionary characteristics. Software modules are architectural elements that are implemented in source files, classes, or aggregations of them. The primary objective of our approach is to extract higher-level views of software modules and their dependency relationships that allow the spectator to identify structural and evolutionary shortcomings. For the analysis of the structural and evolutionary characteristics of software modules ArchView uses software metrics and coupling relationships. Software metrics quantify the size, complexity, coupling degree, modification and problem frequency of software modules. Coupling relationships show change as well as implemented dependency relationships between modules. Both, metrics and coupling relationships are computed for a number of subsequent source code releases giving insights into the evolution of modules. For the identification of structural and evolutionary shortcomings ArchView introduces a graph representation technique that is based on the principle of measurement mapping. Metric values are mapped to graphical attributes highlighting in particular modules and dependency relationships with noticeable structural and evolutionary characteristics. To handle the various characteristics we present a number of different view configurations that we implemented in a prototype tool. They can be extended and used by engineers in everyday analysis tasks. The evaluation and validation of the ArchView approach and its different view configurations is done with the large open source project Mozilla. We focus on the analysis of the content and layout modules with different higher-level views. Resulting views clearly show the usefulness of ArchView to visualize structural and evolutionary characteristics of Mozilla modules and point out a number of shortcomings in their design.