Catálogo de publicaciones - libros

We review the opinion dynamics in the computer models of Deffuant et al. (D), of Krause and Hegselmann (KH), and of Sznajd (S). All of these models allow for consensus (one final opinion), polarization (two final opinions), and fragmentation (more than two final opinions), depending on how tolerant people are to different opinions. We then simulate the reactions of people to Xevents, in that we modify the opinion of an individual and investigate how the dynamics of a consensus model diffuses this perturbation among the other members of a community. It often happens that the original shock induced by the Xevent influences the opinion of a large part of society.

In this chapter, after a short survey of recent developments in the theory of complex networks, we discuss a class of random graph models for complex networks where the exceptional and Xevents play a crucial role in the formation of network structures. Indeed, some vertices — the “hubs” — have an extremely high number of connections to other vertices, whereas most vertices have just a few. These networks are generally “scale-free”; in other words, they exhibit architectural and statistical stability as the degree distribution grows. We also relate some extremal properties of the diameters of random graphs to the thresholds of epidemic processes, and we discuss robustness against system damage.

Population growth and climate change cause rapid changes in mountainous regions resulting in increased risks of floods, avalanches, debris flows and other natural hazards. Xevents are of particular concern, since attempts to protect against them result in exponentially growing costs. In this contribution, we suggest an integral risk management approach to dealing with natural hazards that occur in mountainous areas. Using the example of a mountain pass road, which can be protected from the danger of an avalanche by engineering (galleries) and/or organisational (road closure) measures, we show the advantage of an optimal combination of both versus the traditional approach, which is to rely solely on engineering structures. Organisational measures become especially important for Xevents because engineering structures cannot be designed for those events. However, organisational measures need a reliable and objective forecast of the hazard. Therefore, we further suggest that such forecasts should be developed using physical numerical modelling. We present the status of current approaches to using physical modelling to predict snow cover stability for avalanche warnings and peak runoff from mountain catchments for flood warnings. While detailed physical models can already predict peak runoff reliably, they are only used to support avalanche warnings. With increased process knowledge and computer power, current developments should lead to a enhanced role for detailed physical models in natural mountain hazard prediction.

Today there is common agreement that human actions are resulting in increasingly large-scale — even global — risks. Yet there seems to be a universal inability to stop these human, environmental and economic effects. In this chapter we consider the management of surprise in the framework of a wide spectrum of hazard levels. For instance, a reduction in greenhouse gases might reduce the probability of extreme climate changes. We have developed a general model for controlling extreme hazards. We first examine the dynamic behavior of a single global society and derive various optimal response strategies to counter the hazard. However, in real life such a global hazard management system does not exist due to a lack of international cooperation among nation states. A gaming model is constructed to elaborate the implications of hazard management when more nations are involved, and when expectations about the hazard are imperfect. While the models involved in this analysis are simple, the results from our numerical experiments are instructive and yield interesting insights into the economics of various institutions governing the interaction of societies and their capacity to mitigate risks. We discuss the outcome of the models in terms of its bearing on modern politics as well as what it might mean to the dangers that await us in the future.

We discuss why disasters occur more frequently and are more serious than expected according to a normal distribution. Moreover, we investigate the interaction networks responsible for the cascade-like spreading of disasters. Such causality networks allow one to estimate the development of disasters with time, to give hints about when to take certain actions, to assess the suitability of alternative measures of emergency management, and to anticipate their side effects. Finally, we identify other fields where network theory could help to improve disaster response management.