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Failure dynamics of the global risk network.

Szymanski BK, Lin X, Asztalos A, Sreenivasan S - Sci Rep (2015)

Bottom Line: Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data.Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability.The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.

View Article: PubMed Central - PubMed

Affiliation: 1] Social and Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy NY 12180 [2] Dept. of Computer Science, RPI, 110 8th Street, Troy, NY 12180 [3] Dept. of Computer Science &Management, Wroclaw University of Technology, 50-370 Wroclaw, Poland.

ABSTRACT
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.

No MeSH data available.


Related in: MedlinePlus

Global risk network intra-group connectivity and nodes congestion potentials derived from optimal model parameters:Each node is sized proportionally to its internal failure probability while node color corresponds to its total contagion potential. The number of edges in each group shows the intra-group connectivity. The nodes with the highest congestion potential are identified by name and include risks 8 “Severe income disparity”, 17 “Rising greenhouse gas emissions”, 1 “Chronic fiscal imbalances”, 40 “Water supply crises”, 25 “Global governance failure” and 27 “Pervasive entrenched corruption”.
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f2: Global risk network intra-group connectivity and nodes congestion potentials derived from optimal model parameters:Each node is sized proportionally to its internal failure probability while node color corresponds to its total contagion potential. The number of edges in each group shows the intra-group connectivity. The nodes with the highest congestion potential are identified by name and include risks 8 “Severe income disparity”, 17 “Rising greenhouse gas emissions”, 1 “Chronic fiscal imbalances”, 40 “Water supply crises”, 25 “Global governance failure” and 27 “Pervasive entrenched corruption”.

Mentions: The entire global risk graph can be modeled as a Stochastic Block Model22 with probability pg of the edge existing between two risks in the same group g (this probability differs from group to group) while connections between nodes from two different risk groups, (g1, g2), are drawn with unique probability pg1,g2. The values of these probabilities for the WEF global risk graph are shown in Figs. 1 and 2. The adjacency matrix of this graph is denoted A, and its binary element ai,j is 1 if and only if edge (i, j) exists. We express the probabilities of risk materialization in terms of the Lis and bijs obtained from the WEF dataset and the parameters of our model.


Failure dynamics of the global risk network.

Szymanski BK, Lin X, Asztalos A, Sreenivasan S - Sci Rep (2015)

Global risk network intra-group connectivity and nodes congestion potentials derived from optimal model parameters:Each node is sized proportionally to its internal failure probability while node color corresponds to its total contagion potential. The number of edges in each group shows the intra-group connectivity. The nodes with the highest congestion potential are identified by name and include risks 8 “Severe income disparity”, 17 “Rising greenhouse gas emissions”, 1 “Chronic fiscal imbalances”, 40 “Water supply crises”, 25 “Global governance failure” and 27 “Pervasive entrenched corruption”.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4471900&req=5

f2: Global risk network intra-group connectivity and nodes congestion potentials derived from optimal model parameters:Each node is sized proportionally to its internal failure probability while node color corresponds to its total contagion potential. The number of edges in each group shows the intra-group connectivity. The nodes with the highest congestion potential are identified by name and include risks 8 “Severe income disparity”, 17 “Rising greenhouse gas emissions”, 1 “Chronic fiscal imbalances”, 40 “Water supply crises”, 25 “Global governance failure” and 27 “Pervasive entrenched corruption”.
Mentions: The entire global risk graph can be modeled as a Stochastic Block Model22 with probability pg of the edge existing between two risks in the same group g (this probability differs from group to group) while connections between nodes from two different risk groups, (g1, g2), are drawn with unique probability pg1,g2. The values of these probabilities for the WEF global risk graph are shown in Figs. 1 and 2. The adjacency matrix of this graph is denoted A, and its binary element ai,j is 1 if and only if edge (i, j) exists. We express the probabilities of risk materialization in terms of the Lis and bijs obtained from the WEF dataset and the parameters of our model.

Bottom Line: Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data.Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability.The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.

View Article: PubMed Central - PubMed

Affiliation: 1] Social and Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy NY 12180 [2] Dept. of Computer Science, RPI, 110 8th Street, Troy, NY 12180 [3] Dept. of Computer Science &Management, Wroclaw University of Technology, 50-370 Wroclaw, Poland.

ABSTRACT
Risks threatening modern societies form an intricately interconnected network that often underlies crisis situations. Yet, little is known about how risk materializations in distinct domains influence each other. Here we present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of the global risk network dynamics. The modeled risks range from environmental to economic and technological, and include difficult to quantify risks, such as geo-political and social. Using the maximum likelihood estimation, we find the optimal model parameters and demonstrate that the model including network effects significantly outperforms the others, uncovering full value of the expert collected data. We analyze the model dynamics and study its resilience and stability. Our findings include such risk properties as contagion potential, persistence, roles in cascades of failures and the identity of risks most detrimental to system stability. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network.

No MeSH data available.


Related in: MedlinePlus