Problem Solving Policing: Views of Citizens and Citizens Expectations in Germany

For the last two decades American police experts developed new police philosophies in order to tackle more successful the increasing crime problems. Community Policing tries to improve the cooperation between the population and the police and to increase the trust in the police. A crucial factor is a meaningful cooperation between the police and the citizens. Problem Oriented Policing aims at structural changes in the organisation and the procedures of the police in public. The police have to investigate the hidden problems and conflicts of an individual offence and to create proactive and long term concepts for the social area of conflicts beyond the specific case. It is doubtful whether these philosophies can be implemented in Germany since the legality principle prohibits meaningful, trustworthy relationships between citizens and police officers. However, if one examines the results of surveys on citizens views and expectations towards the police one finds that the majority of the German citizens favour the postulates of community and problem oriented policing. They expect through these measures an improvement of their life situation in the community and the feelings of safety. If one takes these results seriously one has to question if the legality principle is still appropriate. It seems to hamper new, more promising policing styles which seem to improve life of it's citizens and reflect what the citizens want and expect from their police force.

Generating Dutiful Members in Communities of 'Rational Choice'

The study 'Youth Welfare as Prevention - The reconfiguration of support and control in advanced liberalism' is part of the Graduate program 'Youth Welfare in Transition' funded by the 'German Research Association'. In empirical terms it is based on both interviews with professionals, reconstructive discourse analysis based on documents and programs as well as a secondary analysis of a range of existing studies.

Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision

This article addresses the issue of kriging-based optimization of stochastic simulators. Many of these simulators depend on factors that tune the level of precision of the response, the gain in accuracy being at a price of computational time. The contribution of this work is two-fold: first, we propose a quantile-based criterion for the sequential design of experiments, in the fashion of the classical expected improvement criterion, which allows an elegant treatment of heterogeneous response precisions. Second, we present a procedure for the allocation of the computational time given to each measurement, allowing a better distribution of the computational effort and increased efficiency. Finally, the optimization method is applied to an original application in nuclear criticality safety. This article has supplementary material available online. The proposed criterion is available in the R package DiceOptim.

A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.