This document summarizes a research paper that proposes a methodology to identify extreme risks in critical infrastructure systems. The methodology models interdependent infrastructure sectors as networks and uses multi-objective optimization techniques to analyze how unknown interdependencies could exacerbate failures and increase disruption impacts. Experimental tests on a sample network identify potential high-impact failure scenarios involving unknown interdependencies. The results suggest the approach could help anticipate "black swan" infrastructure events and inform policymaker decision making.

1. ENDORSING PARTNERS
Identifying Extreme Risks
in Critical Infrastructure
Interdependencies
The following are confirmed contributors to the business and policy dialogue in Sydney:
•
Rick Sawers (National Australia Bank)
•
Nick Greiner (Chairman (Infrastructure NSW)
Monday, 30th September 2013: Business & policy Dialogue
3rd
www.isngi.org
Tuesday 1 October to Thursday,
October: Academic and Policy
Dialogue
Presented by: A/Prof Kang Tai, Nanyang Technological University
www.isngi.org

2. Identifying Extreme Risks in Critical
Infrastructure Interdependencies
K. Tai
School of Mechanical and Aerospace Engineering, NTU
A. Kizhakkedath
School of Mechanical and Aerospace Engineering, NTU
J. Lin
School of Mechanical and Aerospace Engineering, NTU
R.L.K. Tiong
Institute of Catastrophe Risk Management &
School of Civil and Environmental Engineering, NTU
M.S. Sim
Information Division, DSO National Laboratories, Singapore
International Symposium for Next Generation Infrastructure
SMART Infrastructure Facility, University of Wollongong
Wollongong, Australia, 1 – 4 October 2013

3. Critical Infrastructure
• Critical infrastructure refers to the assets, systems and networks
comprising identifiable industries, institutions and distribution
capabilities that provide a reliable flow of goods and services
essential to the functioning of the economy, the government at
various levels, and society as a whole (Clinton 1996).
Clinton, W.J. (1996) “Executive order 13010 - Critical infrastructure protection”, Federal Register, Vol.61, No.138,
pp.37347-37350
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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4. Critical Infrastructure for a
Modern Society/Economy
Agriculture
and Food
Drinking Water
and Treatment
Plants
Health Care
and Civil
Defence
Banking and
Finance
Energy
Transportation
Systems
Communication
and Information
Technology
Military
Installations
and Defence
Commercial
and Industry
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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5. Critical Infrastructures form
Interconnected Networks with
Complex Interdependencies
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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6. Classification of Infrastructure
Interdependencies
•
Physical
– A physical or engineering reliance between infrastructures, e.g. material flow from one
infrastructure to another
•
Information / Cyber
– An informational or control requirement between infrastructures, e.g. a reliance on
information transfer between infrastructures
•
Geospatial / Geographic
– A relationship that exists entirely because of the proximity of infrastructures, e.g. a local
environmental event affects components across multiple infrastructures due to physical
proximity
•
Policy / Procedural
– An interdependency that exists due to policy or procedure that relates a state or event
change in one infrastructure sector to a subsequent effect on another sector, e.g.
government’s emergency mandatory orders on a particular area due to the influence of an
event
•
Societal / Logical
– An interdependency that an infrastructure event may have on societal factors, e.g. public
opinion, public confidence, fear, and culture issues
(Rinaldi et al. 2001, Pederson et al. 2006)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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7. Complex Interdependencies Lead
to Infrastructure Disruptions with
Widespread Consequences
9/11 terrorist attacks
2011 Tohoku
earthquake/tsunami
2008 global
financial crisis
2011 floods in Thailand
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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8. Unforeseen Interdependencies
Banking and Financial
Infrastructures
9/11 terrorist attacks
Transportation
Infrastructures
Military Infrastructures
Global Impact
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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9. Black Swans
• The idea that such high impact but highly unexpected events could
actually have been expected if the relevant available data had been
accounted for was put forth by Taleb in his book “The Black Swan”.
• Black Swan events are highly improbable events (outliers), and
highly impactful, and can be caused and/or exacerbated by their
being unexpected (Taleb 2007).
• However, in spite of being highly unexpected, it is natural that
experts (and even casual observers) will retrospectively be able to
construct explanations for their occurrences after they have
occurred, making them explainable and expected.
Taleb, N.N. (2007) The Black Swan: The Impact of the Highly Improbable, Random House
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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10. Analyzing Vulnerabilities in Critical
Infrastructure Networks by
Network Modelling/Analysis
sector 1
sector 2
sector 3
interdependency (known)
interdependency (unforeseen)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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11. Multiobjective Optimization of Risk
•
Risk Analysis Framework
–
risk = f (probability, impact)
•
Multiobjective Optimization Problem
– searching for maximum probability of occurrence of failure/hazard/threat
– searching for maximum impact of disruption (minimum giant component
size)
•
Decision Variables
– unforeseen interdependencies
– failure point(s)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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12. Decision Variables in
Multiobjective Optimization
Problem
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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13. Multiobjective Optimization by
Genetic Algorithms (GA)
begin
initialize
population
of
networks
selection and
recombination/mutation to
populate next generation
compute failure probabilities
& compute disruption
termination/
convergence
criteria
no
yes
stop
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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14. kS
wa
n
Pareto front
Bl
ac
probability
Anticipating Extreme Risks by
Multiobjective Optimization
wan
S
ack
Bl
disruption/impact
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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15. Experimental Test Problem
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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16. Experimental Test Problem –
Agent-Based Modelling/Simulation
Using NetLogo
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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17. Experimental Results – Single
Objective Opt. (Maximize Impact
with Single Node Failure/Attack)
Node failed No. of unforeseen Unforeseen interdependencies added
interdependencies
added
Giant
component
size
28
0
Nil
36
28
1
One of (7→3, 8→3, 11→3, 15→3,
17→3, 28→3, 31→3)
32
28
2
One of (7→3, 8→3, 11→3, 15→3,
17→3, 28→3, 31→3) and one of
(3→19, 4→19, 13→19, 15→19,
21→19, 28→19)
30
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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18. Experimental Results – Single
Objective Opt. (Maximize Impact
with Single Node Failure/Attack)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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19. Experimental Results – Multiobj.
Opt. (2 Unforeseen Interdepend. &
Single Node Failure/Attack)
Node Probability Giant
failed
comp.
size
28
0.1
30
9
0.26
33
15
0.36
34
31
0.41
35
1, 5,
12, 43
0.5
36
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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20. Experimental Results – Multiobj.
Opt. (2 Unforeseen Interdepend. &
Single Node Failure/Attack)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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21. Experimental Results – Multiobj.
Opt. (2 Unforeseen Interdepend. &
Double Node Failure/Attack)
Nodes failed
Probability
28, 41
0.0080
Giant
comp.
size
19
28, 36
0.0130
21
28, 14
0.0200
22
28, 16
0.0320
27
28, 1
0.0500
28
9, 15
0.0936
30
27, 15
0.1152
31
27, 43
0.1600
32
(1,15),(5,15),
(12,15),(43,15)
(1,31),(5,31),
(12,31),(43,31)
(12,43),(1,12),
(1,5),(1,43),
(5,43),(5,12)
0.1800
33
0.2050
34
0.2500
35
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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22. Experimental Results – Multiobj.
Opt. (2 Unforeseen Interdepend. &
Double Node Failure/Attack)
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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23. Concluding Remarks
• The experiments show that unforeseen interdependencies can
indeed exacerbate the disruption consequences/impact, with the
extreme disruptions interpreted as Black Swan events.
• The methodology can serve as a tool for scenario planning, by
helping policymakers to anticipate and thereby focus on the “worst
case” scenarios.
• The multiobjective optimization approach also provides a way for
policymakers to analyze the “trade-off” between the highprobability/low-impact events and the low-probability/high-impact
events.
Tai et al. – Identifying Extreme Risks in Critical Infrastructure Interdependencies (ISNGI 2013)
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24. Contact :
Associate Professor K. Tai
School of Mechanical and Aerospace Engineering, Nanyang Technological University
Phone : 67904444 Email : mktai@ntu.edu.sg URL : http://www.ntu.edu.sg/home/mktai/