Surveying the welfare state: challenges, policy development and causes of resilience

This paper takes a closer look at the supposed mechanisms behind German welfare state resilience, which are given in mainstream institutionalist theories. I focus on reform hurdles associated with the make-up of the German welfare state, both at the macro-level (principles) as well as at the meso-level (programmes), exposing systematically mechanisms of path-dependence and other sources of policy stickiness. In addition, I zoom in on the political- institutional sources of resilience. This survey of institutional reform obstacles is complemented by a review of reform pressures and policy developments in the main social policy areas, old age pensions, health care, and unemployment policy between 1975 and 2004. The main conclusion is that despite numerous sources of pressure for far-reaching welfare state reform, the various mechanisms of resilience have made for a pattern of incremental reform, although a number of structural reforms have occurred fairly recently. -- Dieses Arbeitspapier befasst sich mit den Mechanismen, die gemäß institutionellen Wohlfahrtstaatstheorien die Reformresistenz des deutschen Sozialstaats erklären sollen. Es richtet sich dabei auf Reformhindernisse, die auf der institutionellen Struktur des Wohlfahrtsstaats beruhen, sowohl auf Makroniveau (sozialstaatliche Prinzipien) als auch auf Mesoniveau (Teilbereiche). Dabei legt es systematisch dar, wo sich tatsächlich Pfadabhängigkeiten und andere Ursachen von Veränderungsresistenz verbergen. Darüber hinaus wird auf die politisch-institutionellen Quellen der Reformresistenz eingegangen. Die Analyse wird ergänzt durch eine Besprechung der Ursachen des wachsenden Reformdrucks und einer Übersicht der Policy-Entwicklung der letzten 30 Jahre in den Teilbereichen Rente, Arbeitslosenversicherung und Gesundheitspolitik. Das Papier kommt zu dem Schluss, dass trotz des erheblichen Reformdrucks die zahlreichen Resistenzmechanismen ein Muster inkrementeller Veränderungen zur Folge hatten und dieses erst in den letzten Jahren durch eine Reihe von Strukturreformen unterbrochen wurde.

Centrality in children's best friend networks: the role of social behaviour

Centrality is an indicator of an individual's relative importance within a social group. Predictors of centrality in best friendship networks were examined in 146 children (70 boys, 76 girls, Mage= 9.95). Children completed measures of social confidence, social desirability, friendship quality, school liking, and loneliness, and nominated their best friends from within their class at two time points, 3 months apart. Multigroup path analysis revealed gender differences in the antecedents of centrality. Social confidence, social desirability, and friendship quality predicted changes in the indicators of centrality in best friend networks over time. In boys’ social behaviour positively predicted changes in centrality whereas in girls’ social behaviour negatively predicted changes in centrality. Together, these findings suggest that some aspects of social behaviour are influential for centrality in best friend groups

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic

While motion planning approaches for automated driving often focus on safety and mathematical optimality with respect to technical parameters, they barely consider convenience, perceived safety for the passenger and comprehensibility for other traffic participants. For automated driving in mixed traffic, however, this is key to reach public acceptance. In this paper, we revise the problem statement of motion planning in mixed traffic: Instead of largely simplifying the motion planning problem to a convex optimization problem, we keep a more complex probabilistic multi agent model and strive for a near optimal solution. We assume cooperation of other traffic participants, yet being aware of violations of this assumption. This approach yields solutions that are provably safe in all situations, and convenient and comprehensible in situations that are also unambiguous for humans. Thus, it outperforms existing approaches in mixed traffic scenarios, as we show in simulation