Systemic risk in dynamical networks with stochastic failure criterion

Podobnik, Boris; Horvatić, Davor; Bertella, Mário Augusto; Liu, Feng; Huang, Xuqing; Li, Baowen

doi:10.1209/0295-5075/106/68003

Cited by 14 publications

(14 citation statements)

References 36 publications

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“…If at time t the fraction of active neighbours of node n i is smaller than or equal to T h , then at time t + 1 node n i will become externally inactive with a probability r . We use a fractional threshold 51 52 , which is more appropriate than the absolute threshold 40 53 when networks have heterogeneous degrees. Each node can internally fail, independently of other nodes, with a probability p quantifying the magnitude of the attack.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks

Podobnik

Lipić

Horvatić

et al. 2015

Sci Rep

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Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.

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Section: Resultsmentioning

confidence: 99%

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks

Podobnik

Lipić

Horvatić

et al. 2015

Sci Rep

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“…To address the issues involved, we build upon a recent argument that by drawing analogies with independent research areas it is possible to unravel the complexities of financial risks [ 13 – 17 ]. Specifically, we treated the network of borrowers served by an MFI as a dynamical network, a versatile concept that has proven relevant in studies of many real-world phenomena, including finance [ 18 – 24 ]. The dynamical network was set up to obey the stylized rules of microfinance, where the operations of one of the best established representatives of the industry, Grameen Bank in Bangladesh [ 1 ], constituted a working template.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Mapping of Credit Risk for Sustainable Microfinance

et al. 2015

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By drawing analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk---a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylized rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan payment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.

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“…In network science vertices represent agents in economic systems and links represent connections among them. The networks can be generated by using real data [15,16] or by developing theoretical networks in which properties are assumed, e.g., random networks or scale-free networks [17]. Two types of network failure mechanism are used in this research.…”

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confidence: 99%

“…The other is the liquidity approach in which each node is assigned a simplified balance sheet state, and a default in asset results in cascading failures through liquidations. When its asset value drops below its liability value it either fails or survives with an assumed tolerance probability [17,21,22].…”

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confidence: 99%

Exacerbated vulnerability of coupled socio-economic risk in complex networks

Zhang

Feng

Berman

et al. 2016

EPL

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The study of risk contagion in economic networks has most often focused on the financial liquidities of institutions and assets. In practice the agents in a network affect each other through social contagion, i.e., through herd behavior and the tendency to follow leaders. We study the coupled risk between social and economic contagion and find it significantly more severe than when economic risk is considered alone. Using the empirical network from the China venture capital market we find that the system exhibits an extreme risk of abrupt phase transition and large-scale damage, which is in clear contrast to the smooth phase transition traditionally observed in economic contagion alone. We also find that network structure impacts market resilience and that the randomization of the social network of the market participants can reduce system fragility when there is herd behavior. Our work indicates that under coupled contagion mechanisms network resilience can exhibit a fundamentally different behavior, i.e., an abrupt transition. It also reveals the extreme risk when a system has coupled socio-economic risks, and this could be of interest to both policy makers and market practitioners.

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Systemic risk in dynamical networks with stochastic failure criterion

Cited by 14 publications

References 36 publications

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks

Agent-Based Mapping of Credit Risk for Sustainable Microfinance

Exacerbated vulnerability of coupled socio-economic risk in complex networks

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