Failure dynamics of the global risk network

Szymański, Bolesław K.; Lin, Xin; Asztalos, Andrea; Sreenivasan, Sameet

doi:10.1038/srep10998

Cited by 22 publications

(56 citation statements)

References 24 publications

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“…In [11], authors investigate multiple event resources including articles, news, Wikipedia. Here, we only focus on the Wikipedia Current Events Portal (WCEP) [14], which comprehensively includes daily summaries of news events edited by crowdsourcing.…”

Section: Risk Detectionmentioning

confidence: 99%

“…A high-quality dataset combined with the validated models are a foundation for global risks reliable discovery, analysis and predic-tion. The first systematic approach for such data collection effort was done and described in [11], in which the researchers manually collected monthly data points for each risk using online event resources such as articles, news, and Wikipedia to annotate events relevant to any global risk activation. The covered period ranged from beginning of year 2000 to the end of year 2016.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Learning of the Global Risk Network Activation from Media Event Reports

2019

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The World Economic Forum (WEF) publishes annual reports of global risks which have the high impact on the world's economy. Currently, many researchers analyze the modeling and evolution of risks. However, few studies focus on validation of the global risk networks published by the WEF. In this paper, we first create a risk knowledge graph from the annotated risk events crawled from the Wikipedia. Then, we compare the relational dependencies of risks in the WEF and Wikipedia networks, and find that they share over 50% of their edges. Moreover, the edges unique to each network signify the different perspectives of the experts and the public on global risks. To reduce the cost of manual annotation of events triggering risk activation, we build an auto-detection tool which filters out over 80% media reported events unrelated to the global risks. In the process of filtering, our tool also continuously learns keywords relevant to global risks from the event sentences. Using locations of events extracted from the risk knowledge graph, we find characteristics of geographical distributions of the categories of global risks.

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Section: Risk Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised Learning of the Global Risk Network Activation from Media Event Reports

2019

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“…Incorporating the effect of interdependence into the risk management process has attracted much recent interest (Battiston, Caldarelli, D'Errico, & Gurciullo, 2016;Battiston, Puliga, Kaushik, Tasca, & Caldarelli, 2012;DasGupta & Kaligounder, 2014;Helbing, 2013;Roukny, Bersini, Pirotte, Caldarelli, & Battiston, 2013;Szymanski, Lin, Asztalos, & Sreenivasan, 2015) partly due to the 2007 -2008 global financial crisis and the way in which traditional risk models, also grounded on the assumption of risk independence, failed to foresee it (Battiston, Caldarelli, et al, 2016;Battiston, Farmer, et al, 2016;Besley & Hennessy, 2009;Schweitzer, Fagiolo, Sornette, Vega-Redondo, & White, 2009).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the role of risk networks in risk identification, classification and emergence

Ellinas¹,

Allan²,

Coombe³

2017

JNTF

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Modern society heavily relies on strongly connected, socio-technical systems. As a result, distinct risks threatening the operation of individual systems can no longer be treated in isolation. Consequently, risk experts are actively seeking for ways to relax the risk independence assumption that undermines typical risk management models. Prominent work has advocated the use of risk networks as a way forward.Yet, the inevitable biases introduced during the generation of these survey-based risk networks limit our ability to examine their topology, and in turn challenge the utility of the very notion of a risk network. To alleviate these concerns, we proposed an alternative methodology for generating weighted risk networks. We subsequently applied this methodology to an empirical dataset of financial data. This paper reports our findings on the study of the topology of the resulting risk network. We observed a modular topology, and reasoned on its use as a robust risk classification framework. Using these modules, we highlight a tendency of specialization during the risk identification process, with some firms being solely focused on a subset of the available risk classes. Finally, we considered the independent and systemic impact of some risks and attributed possible mismatches to their emerging nature. Keywords risk network, network topology, horizon scanning, emerging risk, risk management Key messages The authors introduce a novel methodology to generate risk networks, which relaxes some assumptions of past related work, including prominent work by the World Economic Forum. The risk network can serve as a robust risk classification framework, free from externallyimposed artificial constructs (e.g. risk classification based on regulatory requirements). 2 By applying it to a (re)insurance dataset, the authors use the resulting risk network to evaluate the 'horizon scanning' capacity of every included firm. The risk network uncovers a general mismatch between independent and systemic impact of risks, with authors arguing that this mismatch can be used to classify certain risks as 'emerging'.

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“…Recently, cascading failures have been extensively studied [11,7,6,12,1,10]. Most studies focus on financial institutions, internet and infrastructure systems [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Here, we further subdivide the activation transition into internal activation and external activation transitions. The three transitions are represented as Poisson processes defined as follows [11].…”

Section: Introductionmentioning

confidence: 99%

Evolution of the Global Risk Network Mean-Field Stability Point

Niu

Moussawi

Derzsy

et al. 2017

Complex Networks &Amp; Their Applications VI

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With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global governance, etc. A quantitative understanding of the mechanisms guiding this global network is necessary for proper operation and maintenance of the global infrastructure. Each year the World Economic Forum publishes an authoritative report on global risks, and applying this data to a CARP model, we answer critical questions such as how the network evolves over time. In the evolution, we compare not the current states of the global risk network at different time points, but its steady state at those points, which would be reached if the risk were left unabated. Looking at the steady states show more drastically the differences in the challenges

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Failure dynamics of the global risk network

Cited by 22 publications

References 24 publications

Supervised Learning of the Global Risk Network Activation from Media Event Reports

Supervised Learning of the Global Risk Network Activation from Media Event Reports

Evaluating the role of risk networks in risk identification, classification and emergence

Evolution of the Global Risk Network Mean-Field Stability Point

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