Globally networked risks and how to respond

Helbing, Dirk

doi:10.1038/nature12047

Cited by 952 publications

(695 citation statements)

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“…Yet most risk assessments ignore networked threats 2,3 . The annual Global Risks report of the World Economic Forum considers risks qualitatively, based on the views of experts 4 .…”

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confidence: 99%

Global change: Put people at the centre of global risk management

Brasseur

Ciais²,

Eekeren

et al. 2015

Nature

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“…Yet most risk assessments ignore networked threats 2,3 . The annual Global Risks report of the World Economic Forum considers risks qualitatively, based on the views of experts 4 .…”

mentioning

confidence: 99%

Global change: Put people at the centre of global risk management

Brasseur

Ciais²,

Eekeren

et al. 2015

Nature

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“…It is clearly seen that the sizes of impacts are very different from country to country and that as the first and the second waves become more serious (α and β become larger) the impact sizes also increase. We also list the ten countries that have the highest values in I (1) and I (2) in Table I for α = β = 0.5. The top countries which cause the biggest first impacts are somehow different from those countries with the biggest second impacts.…”

Section: Local Spreading Model a Model Description: Local Spreadingmentioning

confidence: 99%

“…The increasing globalization provides important advantages in terms of risk sharing and risk diversification in banking and financial markets, but it also facilitates the risk spreading among different nations [1]. The recent financial crisis or shock, originated from United States (US) and spread to other countries, has witnessed that many overseas banks cut back their loans to the local markets and withdrew their representations.…”

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confidence: 99%

International transmission of shocks and fragility of a bank network

Feng

Kim

2014

Physica A: Statistical Mechanics and its Applications

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The weighted and directed network of countries based on the number of overseas banks is analyzed in terms of its fragility to the banking crisis of one country. We use two different models to describe transmission of shocks, one local and the other global. Depending on the original source of the crisis, the overall size of crisis impacts is found to differ country by country. For the two-step local spreading model, it is revealed that the scale of the first impact is determined by the out-strength, the total number of overseas branches of the country at the origin of the crisis, while the second impact becomes more serious if the in-strength at the origin is increased. For the global spreading model, some countries named "triggers" are found to play important roles in shock transmission, and the importance of the feed-forward-loop mechanism is pointed out. We also discuss practical policy implications of the present work. I. BACKGROUNDThe increasing globalization provides important advantages in terms of risk sharing and risk diversification in banking and financial markets, but it also facilitates the risk spreading among different nations [1]. The recent financial crisis or shock, originated from United States (US) and spread to other countries, has witnessed that many overseas banks cut back their loans to the local markets and withdrew their representations. The withdrawals cause the liquidity shortage of the host countries which then lead to subsequent withdrawals of the host countries' overseas branches in the crisis attacked countries. This process can continue and the shocks in this situation are hence defined as sequential shocks in this study.There are abundant literature on transmission mechanism of shocks in global banking: [2] examines how Japanese asset bubbles have been transmitted to US via lending responses of Japanese overseas banks in US in the 80's; [3] explores the mechanism of 1998 Russian debt default as a negative liquidity shock to international banking and its impacts on the banking of Peru; [4] studies the effect of financial distress in foreign parent banks on the local SME (small and medium enterprises) financing in 14 central and eastern European countries during the early stage of the 2007 financial crisis.Within the framework of complex network theory, this paper introduces models for shock transmission and examines the different impacts of the sequential shocks on the global banking network. We find that the damage of the shocks is closely related to the directionality of the edges and the network topology plays an important role. * Corresponding author: beomjun@skku.edu II. LITERATURE REVIEW ON COMPLEX NETWORK METHODOLOGYThe research using complex system approach in banking has also experienced three stages of the development. In the earlier stage, [6] claims that the possibility of contagion effect depends on the structure of interbank linkages. They believe that a "complete structure of linkages" will share the risk more easily than "an incomplete structure", hence the risk sh...

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“…12 Health systems and hospital flow do not function like machines ("well-oiled," or not), and if we are stuck in this frame of reference, the problem will remain intractable. Patient flow is a wicked problem 17 and is better thought of in the context of complex adaptive systems methodology 12,17,18 (which work differently and are less predictable than machines). Patient flow in today's healthcare ecosystems involves multiple independent agents, following different and dynamic rules (not a single set of stable system rules), with interdependent impacts on outcomes, coevolving adaptations (work-arounds, gaming), nonlinear tipping points, negative and positive feedback loops, self-organizing and emergent patterns, and no single locus of control-all characteristics of a complex adaptive system.…”

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Crowding, Karenina, and Complexity: Patient Flow in Evolving Health Care Ecosystems

Petrie

2017

CJEM

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Tolstoy begins Anna Karenina with this observation: "Happy families are all alike; every unhappy family is unhappy in its own way." The retrospective observational study by Cheng et al. 1 in the current issue of this journal looking at the factors associated with failure to meet emergency department length of stay targets confirms again 2-5 that perhaps the opposite is true of patient flow in emergency departments (EDs): "Unhappy EDs are all alike, but every happy ED is happy in its own way." That is to say, the most common and most significant factor in what is known as overcrowding (a word on semantics in a second) is, by far, the boarding of admitted patients in the ED.In Cheng's study, 1 the odds ratio of missing ED length of stay targets for admission to the intensive care unit (ICU) was a whopping 364 (upper 95% confidence interval [CI] 3071) if the bed request duration was greater than 6 hours, and 217 (upper CI 1541) if there was "access block" in the ICU. During this time, the average hospital occupancy was 97.6% (so that almost half the time it was over 100%; and if this hospital is like others, the average occupancy of general/community medicine beds, often the destination of the majority of ED admits, may be higher than that). We know from a recent paper from Quebec 6 that increases in hospital bed occupancy are associated with an increase in 30-day adverse outcomes even after adjustment for patient and ED characteristics. Commenting on that paper, an editorial entitled "Just Another Crowding Paper" 7 laments that the finding of a 10% increase in ED bed occupancy ratio being associated with a 3% increase in death might be seen as old news. Yet, if this mortality rate was discovered to be due to an unrecognized complication of a new drug, or a new infectious disease outbreak, it would be front-page news.Cheng et al. 1 clearly described by observation what we already known with mathematical certainty given the nonlinear nature of queues: 8 if random variation in demand cannot be smoothed to any significant degree (i.e., in EDs), and capacity is fixed, and the ability to tolerate wait times is limited (another paper out of Ontario 9 showed that wait times correlate with mortality in a dose-response relationship that suggests causation), then high occupancy rates in the hospital (i.e., greater than 85% 8,10 ), leads to the ED being used as a holding area for admissions, displacing incoming ED patients into hallways and waiting rooms, which equals missed ED length of stay targets, unhappy EDs, and, most alarmingly, poor patient outcomes. We are all alike in that way.That is not to say that ED leaders and front-line staff can throw up their hands, point fingers, and blame all dysfunction on "the system." Happy EDs (in happy hospitals) have problem-solved and adapted to the myriad dynamic and interdependent factors that contribute to ED overcrowding. Like seeds that evolve to become drought-tolerant over time when there is a lack of water, EDs have evolved over time to be more efficient at rationalizing th...

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Globally networked risks and how to respond

Cited by 952 publications

References 65 publications

Global change: Put people at the centre of global risk management

Global change: Put people at the centre of global risk management

International transmission of shocks and fragility of a bank network

Crowding, Karenina, and Complexity: Patient Flow in Evolving Health Care Ecosystems

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