The topic of disaster relief has become very active in Japan since Fukushima in 2011. Various projects are conducted to achieve higher resilience in various areas, from evacuations to damage control to network service to supply chains, etc. The common approach in most areas is centered around disaster scenarios, response to which is improved, resulting in higher resilience. This paper will discuss a new approach in which there is only one scenario represented as a distribution of Black Swans where each swan is a combination of primitive events or conditions.

2. .
Post-Fukushima Disaster Research
01 "Disaster Information WG" http://www.bousai.go.jp/jishin/nankai/taisaku_wg (current)
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 2/22
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2/22

3. .
The Scenario Method
hand-made scenarios
region- and event-specific
scenarios are improved via local drills
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 3/22
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3/22

4. .
Problems
rare events are completely ignored
BigData does not help -- so it is not used
very little room for automation -- mostly manual work
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 4/22
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4/22

5. .
Blackswan Scenarios
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 5/22
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5/22

6. .
BlackSwan : The Definition
.
Black Swans
.
... are extremely rare but also extremely heavy impact events
02
.
other names: low predictability events, rare events, etc.
BlackSwans are already defined in engineering 03 04
BlackSwan scenarios focus on rare events
03 L.McGinty+1 Black Swans, Gray Cygnets and Other Rare Birds Springer LNAI vol.5650 (2009)
04 A.Nafday Consequence-based structural design approach for black swan events Elsevier J. Structural Safety (2011)
02 N.Taleb The black swan: the impact of highly improbable Penguin (2008)
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 6/22
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6/22

7. .
The BlackSwan Scenario : Basics
Occurrence Frequency
Increasing Impact
1 2
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 7/22
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7/22

8. .
Scenario (1) Combine and Review
.
Event ...
.
....can be primitive or complex (consist of other events)
1 2
Occurrence Frequency Increasing Impact
fix a swan method is used in
construction -- resilient design
04
the main problem is to
discover BlackSwans
04 A.Nafday Consequence-based structural design approach for black swan events Elsevier J. Structural Safety (2011)
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 8/22
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8/22

9. .
Scenario (1) Event Order
complexity of BlackSwan events should have finite order
Order
3
2
1
Enough!
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 9/22
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9/22

10. .
Components
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 10/22
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10/22

11. .
Components (1) Rating
w is weight, F(v; t) is the evaluation
of an event within a time window,
occurrence probability (rank) then is:
Rk =
Σ
i=1::t
wiFk(v; t); (1)
w is a distribution (matrix math)
potential (risk) of a given event:
Pk = jRk;i Rk;i1j: (2)
the entire blackswan scenario can be
evaluated as.
E = var(fPkg): (3)
evolution of that evaluation in time:
EVO = var(fEtg): (4)
the conventional bin packing problem
can be applied to potential (of risk):
minimize
Σ
i=1;n
Σ
j=1;m
Pij: (5)
06 T.Aven Identification of safety and security critical systems and activities J of Rel. Eng. Sys. Safety (2009)
07 R.Kennet+1 Quality, Risk and the Taleb Quadrants IBM T.J.Watson Research Chapter (2009)
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 11/22
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11/22

12. .
Components (2) Hotpots
4 sets: normal versus hot and baseline hot versus Flash Crowd
hot
0 10 20 30 40 50
List of traffic sources
2.8
2.4
2
1.6
1.2
0.8
0.4
0
log( traffic volume)
0 10 20 30 40 50
List of traffic sources
2.8
2.4
2
1.6
1.2
0.8
0.4
0
log( traffic volume)
09 M.Zhanikeev+1 Popularity-Based Modeling of Flash Events in Synthetic Packet Traces CQ研(2012)
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 12/22
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12/22

13. .
System Design
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 13/22
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13/22

14. .
BlackSwan Scenario : Specs
create and maintain BigData
discover BlackSwans by processing BigData
use automation as much as possible
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 14/22
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14/22

15. .
BlackSwan Scenario : Design
at least 3 classes of textual soup that makes events
Accident Something happened at Site A
Causes Part A, Part B, Part C, … Human Factors…
All Parts Part Z, Part Y, …, Human Manuals, … Rating
Blackswan
scenario
management
platform
Human
judgment
Storage,
Database
Auto
judgement
Report
on site
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 15/22
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15/22

16. .
BlackSwan Discovery
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 16/22
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16/22

17. .
BlackSwan Discovery Automation
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 17/22
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17/22

18. .
BlackSwan Automation (2)
complexity is reduced by using humans as trainers
Search
the space
A very
complex
system
A less
complex
system
Robot
Tell
what
to do
Human
Robot
Human
Search
What the space
should
I do?
Guide through
feedback
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 18/22
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18/22

19. .
BlackSwan Autumation (3)
(careless) Input
Human
Rebot
(pinpoint) Select Browse (or use otherwise)
Human
{structure}
Some
Knowledge
(folksonomies,
knowledge bases,
databases, indexes,
ontologies, etc.)
(metromaps )
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 19/22
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19/22

20. .
BlackSwan Automation = Context
← NOTE! the two previous slides came from my research on context
management
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 20/22
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20/22

21. .
BlackSwan Automation : Software
improved Bayesian classifiers that learn via feedback
I use metromaps as visual interface between human and machine
already have/published some prototypes
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 21/22
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21/22

22. .
Wrapup
decent Disaster Relief can only happen if the BigData from previous
disasters is digested
predictable events are easy, BlackSwans are hard
with automation, proper context management and social design it can
be done
M.Zhanikeev -- maratishe@gmail.com -- Black Swan Disaster Scenarios -- http://bit.do/marat141211 -- 22/22
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22/22