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ETH Competence Center for
 
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Mission Statement

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Motivation and Scope

Social systems typically feature crises, that is unstable and dangerous situations that are characterized by abrupt and large-scale changes. Such disruptions are very hard to predict with any precision and even harder to control. Indeed, crises often convey an impression that key decision makers have lost control and that events unfold in an unstoppable and even catastrophic way. Examples include environmental crises, traffic congestion, as well as financial and social crises, such as poverty, social conflicts or wars.

  Despite the very real risks posed by the aforementioned crises, the conventional social sciences usually steer clear of studying "extreme events" by assumption. When postulating the existence of an equilibrium, the main effort concerns characterizing such stable states and studying moderate deviations from them. However, most crises represent dynamics far away from any equilibrium.
  Inspired by non-equilibrium physics and interdisciplinary research on complex systems, researchers have begun focusing directly on critical phenomena and extreme events in social systems. These phenomena are often characterized by power laws and other skew distributions that differ dramatically from the Gaussian curve. Under such circumstances, extreme outcomes occur much more often than is usually assumed based on the normal distribution.
  Instead of assuming that the social system in question always manages to balance itself through self-correcting processes, this research makes no a-priori commitments as to whether the system will reach, or remain in, equilibrium. Based on innovative methods in the natural and computational sciences, such as statistical analysis of extreme events and computer simulation, researchers have been able to shed new light on as diverse phenomena as crises in stock markets, political conflict, mass panic, and traffic congestion. While all these examples are unique to some extent, it has proven possible to transfer insights from one applied area to another. It is our belief that we are quickly approaching a point at which these research efforts will take off and lead to a new wave of substantive and potentially actionable findings.
  Subscribing to these principles, the Competence Center Coping with Crises in Complex Socio-Economic Systems (CCSS) aims at coordinating collaborative research on non-equilibrium processes in such settings. Ultimately, we want to contribute to a better understanding of the causes and cures of crises in large socio-economic systems in selected problem areas, for example:
  • crises in financial markets
  • crises in societal infrastructure
  • crises involving political violence
  In these areas, we strive to:
  • collect empirical data of crisis scenarios
  • develop a better understanding of systemic risks
  • create virtual laboratories as a way to explore alternative scenarios
  • merge theories and methods from the science of complex systems, statistical physics and network theory with traditional approaches in the social sciences
  • determine the degree of predictability of social crises

Examples

  Crises in societal infrastructure

What does congestion spreading have in common with cascading disaster spreading? How can one characterize the breakdown dynamics of traffic flows in urban networks? Why does the traffic situation vary largely from one day to another, even when the travel demands are almost the same? What variables would allow better prediction of the severity of developing traffic congestion? Can models of self-organized criticality or percolation provide additional insights into the size distribution of congestion events? How vulnerable is the critical infrastructure "transportation network"? How does the sensitivity of traffic flows to local perturbations (such as accidents) depend on the network topology? We are planning to respond to these challenges with a series of models that capture grid lock events, integration of self-organized signal control, cascading congestion events, and mental maps of activity generation in traffic systems.

  Crises in financial markets

Our basic research carried out in this work area is motivated by boom-and-bust cycles in the global economy and instabilities in financial systems and markets in general. Why do markets crash? Can we design markets such that bubbles and crashes do not happen? Under which conditions does the global network of firms and banks either stabilize markets or imply a higher risk of the global spread of local crisis? In the presence of increasing globalization, financial engineering innovations, and disintermediation due to the Internet, are the financial and credit markets becoming more stable or unstable? We intend to develop a new framework to address these types of questions. We look at market crises as an emergent behavior resulting from the presence of mechanisms that lead to positive feedback. These mechanisms include herding and over-reaction to information.

  Crises involving political violence

Why do wars occur? How can they be ended, or even better, entirely prevented? What are the social consequences of such disruptions? Although the risk and intensity of political violence has been declining since the mid-1990s, civil wars, terrorism, and other types of conflict still represent a considerable risk in many parts of the world. We propose a new approach to conflict research that is based on the principles of non-equilibrium theory. Our goal is to achieve a conceptual breakthrough by viewing conflict as a dynamical process rather than as an isolated event caused by static conditions. There are several reasons why such an approach promises to break new ground. First, by adopting an explicitly systemic perspective, the researcher can avoid artificial ceteris-paribus assumptions. Second, the actors and their interactions can be dynamically modeled, including their identities and boundaries. Finally, conflict can be viewed as a non-equilibrium phenomenon that results from a dynamic process when the system transits from one meta-stable state to another.

 

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© 2015 ETH Zurich | Imprint | Disclaimer | 6 June 2010
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