We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.

How does segregation shape intergroup violence in contested urban spaces? Should nominal rivals be kept separate or instead more closely integrated? We develop an empirically grounded agent-based model to understand the sources and patterns of violence in urban areas, employing Jerusalem as a demonstration case and seeding our model with microlevel, geocoded data on settlement patterns. An optimal set of parameters is selected to best fit the observed spatial distribution of violence in the city, with the calibrated model used to assess how different levels of segregation, reflecting various proposed "virtual futures" for Jerusalem, would shape violence. Our results suggest that besides spatial proximity, social distance is key to explaining conflict over urban areas: arrangements conducive to reducing the extent of intergroup interactions-including localized segregation, limits on mobility and migration, partition, and differentiation of political authority-can be expected to dampen violence, although their effect depends decisively on social distance. R ecent outbreaks of violence in multiethnic cities across the world highlight the fragility of intergroup relations. Such conflict raises a fundamental issue: what can be done to foster harmonious coexistence in contested urban spaces? In particular, should nominal rivals be kept separate or instead more closely integrated? This question remains unresolved, given ambiguous empirical evidence and contrary theoretical perspectives about causal mechanisms, which together have engendered a vigorous, ongoing debate in the literature.On the one hand, observations from numerous cities around the world suggest that to mitigate intergroup

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