CCSS: Coping with Crises in Complex SocioEconomic Systems
This project is related to our research lines: Models of systemic risk and Financial networks
Duration: 44 months (September 2008  April 2012)
Funding source: ETH Grant
Project partners: Six chairs from three different deparments of ETH Zürich: Prof. Kay Axhausen (DBAUG), Prof. LarsErik Cederman (DGESS), Prof. Dirk Helbing (DGESS), Prof. Hans J. Herrmann (DBAUG), Prof. Didier Sornette (DMTEC), Prof. Frank Schweitzer (DMTEC)
The project supports the activities of our ETH Competence Center "Coping with Crises in Complex SocioEconomic Systems" (CCSS), which started in September 2008. By means of theoretical and empirical analysis, CCSS aims at understanding the causes of and cures to crises in selected problem areas, for example in financial markets, in societal infrastructure, or crises involving political violence. While these different crises may have their own time scale and evolution, they can be seen as the unintentional result of the interaction among millions of social actors. In this project, we look at these crises as emergent phenomena in complex systems and we investigate the feedback mechanisms that generate them as well as the possible strategies to prevent or mitigate them.
The project consists of 3 working packages: (i) crises in financial markets, (ii) crises in societal infrastructure, (iii) crises involving political violence.
Our contribution is mainly in the work package: "Crises in financial markets''. Our scientific goal is to investigate possible mechanisms for the control of systemic risk and for the mitigation of crises. We develop a dynamic model of financial fragility that is based on the literature on liability networks in financial economics. We are able to analytically investigate the relation between individual risk diversification and emerging systemic risk, in the presence of financial acceleration. Understanding this relation is a first important step towards the possible control of systemic risk. Ongoing work focuses on extending the model to more complex situations and network structures.
Selected Activities
8  13 June, 2009, ETH Zurich, Zurich, Switzerland
20  24 June, 2011, ETH Zurich, Zurich, Switzerland
The Structure of Financial Networks

[2010]

Battiston, Stefano;
Glattfelder, James B.;
Garlaschelli, Diego;
Lillo, F;
Caldarelli, Guido

Network Science
pages: 131163

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Abstract We present here an overview of the use of networks in Finance and Economics. We show how this approach enables us to address important questions as, for example, the structure of control chains in financial systems, the systemic risk associated with them and the evolution of trade between nations. All these results are new in the field and allow for a better understanding and modelling of different economic systems.
Power law signature of media exposure in human response waiting time distributions

[2010]

Crane, Riley;
Schweitzer, Frank;
Sornette, Didier

Physical Review E,
pages: 16,
volume: 81,
number: 5

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Abstract We study the humanitarian response to the destruction brought by the tsunami generated by the Sumatra earthquake of December 26, 2004, as measured by donations, and find that it decays in time as a power law ~ 1/t^(alpha) with alpha = 2.5 + /0.1. This behavior is suggested to be the rare outcome of a priority queuing process in which individuals execute tasks at a rate slightly faster than the rate at which new tasks arise. We believe this to be the first empirical evidence documenting this recently predicted regime, and provide additional independent evidence that suggests it arises as a result of the intense focus placed on this donation "task" by the media.
Economic networks: The new challenges

[2009]

Schweitzer, Frank;
Fagiolo, Giorgio;
Sornette, Didier;
Vega  Redondo, Fernando;
Vespignani, Alessandro;
White, Douglas R.

Science (New York, N.Y.),
pages: 422425,
volume: 325,
number: 5939

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Abstract The current economic crisis illustrates a critical need for new and fundamental understanding of the structure and dynamics of economic networks. Economic systems are increasingly built on interdependencies, implemented through transnational credit and investment networks, trade relations, or supply chains that have proven difficult to predict and control. We need, therefore, an approach that stresses the systemic complexity of economic networks and that can be used to revise and extend established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust.
Systemic risk in a unifying framework for cascading processes on networks

[2009]

Lorenz, Jan;
Battiston, Stefano;
Schweitzer, Frank

The European Physical Journal B,
pages: 441460,
volume: 71,
number: 4

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Abstract We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, X∗,and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of X∗ as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class leads to a firstorder phase transition in systemic risk, i.e. situations where small changes in the initial conditions determine a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalizations.
Systemic risk in a network fragility model analyzed with probability density evolution of persistent random walks

[2008]

Lorenz, Jan;
Battiston, Stefano

Networks and Heterogeneous Media,
pages: 185,
volume: 3,
number: 2

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Abstract We study the mean field approximation of a recent model of cascades on networks relevant to the investigation of systemic risk control in financial networks. In the model, the hypothesis of a trend reinforcement in the stochastic process describing the fragility of the nodes, induces a tradeoff in the systemic risk with respect to the density of the network. Increasing the average link density, the network is first less exposed to systemic risk, while above an intermediate value the systemic risk increases. This result offers a simple explanation for the emergence of instabilities in financial systems that get increasingly interwoven. In this paper, we study the dynamics of the probability density function of the average fragility. This converges to a unique stable distribution which can be computed numerically and can be used to estimate the systemic risk as a function of the parameters of the model.
Riskseeking versus riskavoiding investments in noisy periodic environments

[2008]

Navarro  Barrientos, Jesus Emeterio;
Walter, Frank Edward;
Schweitzer, Frank

International Journal of Modern Physics C,
pages: 971994,
volume: 19,
number: 6

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Abstract We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, x(t), and at each time step invest a particular fraction, q(t), of their budget. The return on investment (RoI), r(t), is characterized by a periodic function with different types and levels of noise. Riskavoiding agents choose their fraction q(t) proportional to the expected positive RoI, while riskseeking agents always choose a maximum value qmax if they predict the RoI to be positive (“everything on red”). In addition to these different strategies, agents have different capabilities to predict the future r(t), dependent on their internal complexity. Here, we compare “zerointelligent” agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict r(t). The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the riskseeking strategy outperforms the riskavoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.
