2017
DOI: 10.1007/978-3-319-72150-7_91
|View full text |Cite
|
Sign up to set email alerts
|

Evolution of the Global Risk Network Mean-Field Stability Point

Abstract: 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 Econo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2018
2018
2021
2021

Publication Types

Select...
4
1

Relationship

5
0

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 12 publications
0
3
0
Order By: Relevance
“…We model the network activity dynamics with an adaptation of a stochastic process that is based on an alternating renewal process [8,41,50,21] and is continuous. Instead of each risk transitioning to a discrete state indicating activity, we have the state of each node represent the expected value of risk i being active at time step k + 1, x i (k + 1) using the following model [41,18,29,27,30].…”
Section: The Dynamic Global Risk Networkmentioning
confidence: 99%
“…We model the network activity dynamics with an adaptation of a stochastic process that is based on an alternating renewal process [8,41,50,21] and is continuous. Instead of each risk transitioning to a discrete state indicating activity, we have the state of each node represent the expected value of risk i being active at time step k + 1, x i (k + 1) using the following model [41,18,29,27,30].…”
Section: The Dynamic Global Risk Networkmentioning
confidence: 99%
“…In general, compared with the independent model, the accuracy of the network model is significantly higher as evidenced by having the mean simulated activity closer to historical data than independent model does and by requiring 47% smaller multiple of standard deviation bound to cover all historical data than the independent model needs. Some other network effect analyses were presented in [21]. The results show that the isolated risks (nodes with low degrees) have extremely low external activation fractions and thus are unlikely to be influenced by other risks in the network.…”
Section: Network Effectsmentioning
confidence: 99%
“…In the following sections of the paper we analyze evolution of risks over the years 2013-2017. Some of these results were presented in [21] but they were limited to two points in time, year 2013 and year 2017. Hence, the number of the results and points of evolution presented here more than doubled.…”
Section: Introductionmentioning
confidence: 99%