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Cambridge Centre for Risk Studies
In search of Black Swans?
Professor Danny Ralph
University of Cambridge
Presented at the 3rd International Symposium on Catastrophe Risk Management
ICRM, Nanyang Technological University, Singapore
21 – 22 February 2012
VIEWING THE FUTURE
3rd annual meeting of Centre for Risk Studies
� Themes
– Unsustainability
– Speculations on the Future
– Future-proof Decisions
� Breakouts
– Threat Assessment
– Systemic Risk
– Using Scenarios
Statistical forecasts
� Bank of England estimates
of UK DGP, Nov 2007
– Shows % increase in output
on a year earlier
... across “structural breaks”
Long term forecasts
� 1980 AT&T’s new wonder “Mobile Phone”
� McKinsey estimated size of mobile phone
market in 2000 as < 1Million subscribers
� Actual market in 2000 > 120Million with
4Billion worldwide in 2008
Expert elicitation
� Delphi
– Groups are better than individuals
– Groupthink
– Convergence minimises extremes
� Horizon scanning workshops
– Searching for wildcards
– Missing the obvious
Up to this point ... generating scenarios
� Scenarios for the future from– Data driven models
– Consultants
– Subject experts
� I won’t cover quantitative tools:– estimating impact/ value / cost of a scenario
– estimating probability / chance / likelihood of a scenario
FALSE 0%
€ 123,867 € 123,867
30% Decision
0 € 125,484
50% 5%
€ 195,173 € 195,173
33% Chance
€ 159,520
50% 5%
€ 123,867 € 123,867
TRUE Chance
€ 125,484
67% 20%
€ 108,467 € 108,467
Chance
€ 115,946
FALSE 0%
€ 114,067 € 114,067
45% Decision
0 € 117,022
CaseOfRiesling
Early good weather
Harvest
Early fair weather
Harvest
Don't harvest
Late poor weather
Late fair weather
Noble rot
No noble rot
SEARCHING FOR BLACK SWANS?
We need to search for black swan scenarios
But have little confidence in what we find
(or what it is that we don’t find)
I. FALSIFICATION
Confirmation Bias
� Confirmation bias is tendency to seek evidence that supports your view
The policy was set…the war in Iraq was coming. And
they were looking for intelligence to fit into the policy,
to justify the policy.
….the White House promoted intelligence it liked and
ignored intelligence it didn’t.
Tyler Drumheller, Ex-CIA Officer, 2006
� And worse than confirmation basis
Falsification
� How do you test the validity of
an idea?
... when you have eliminated the
impossible, whatever remains,
however improbable, must be the truth
Sherlock Holmes, The Sign of the Four (1890), Arthur Conan Doyle
� It is often more informative to search for situations
where an idea fails than when it succeeds
“Baconian algorithm”
for decision maker
A process traceable back to Francis Bacon
� Start with a best guess, base scenario
� ...look for evidence in respect of ... scenarios that are
more attractive [than the base scenario]
� In doing so: his chances of discovering something that
he would not have otherwise, and which would have
the potential to eliminate … [the base scenario] are
increased
A Feduzi, J Runde, Uncovering Unknown Unknowns (2010)
University of Cambridge
Black Swans arise from adversarial thinking
SEARCHING FOR BLACK SWANS?
We need to search for black swan scenarios
But have little confidence in what we find
(or what it is that we don’t find)
II. REVERSE STRESS TESTING
Stress testing
� Value at Risk, VaR, estimates the potential decline in
the value of a position or a portfolio.
� How sensitive is the bank’s VaR to changes in
volatilities and correlations of uncertain factors?
� Stress testing attempts to model the effect on VaR of
historical or hypothetical events
Reverse Stress Testing
Reverse Stress Testing
� What would break the house?
– In a large UK retailer, what decrease in sales volume would
presage the end of the business?
� This is like searching for black swan outcomes rather
than black swan events
� Reverse stress testing is prescribed for UK banks by the
Financial Services Authority.
SEARCHING FOR BLACK SWANS?
We need to search for black swan scenarios
But have little confidence in what we find
(or what it is that we don’t find)
III. ADMITTING THE OBVIOUS
We don’t know what we don’t know
� Looking for Unknown
unknowns
– Fat tails
– Knightian uncertainty
– Black Swans
� Known unknowns?
– Black Turkeys Laurence B. Siegel
– Things we knew could happen
but thought wouldn’t
Admitting the obvious
� When do several black turkeys graduate into a
single black swan?
� Perfect Storm
� Example of complex systems
– Many possible failures
– Which combinations matter? Impact or severity estimates
– Which combinations are more likely than we might guess?
Probability estimates
� Cascading Failure
NE of USA/Canada: before
2003 blackout of New York City and surroundings(Janusz Bialek 2010)
NE of USA/Canada: after
How it all started: tree flashover at 3.05 pm Cascading failure
�Searching for black swans is aided by
challenge (not confirmation)
– Falsification
– Reverse stress testing
� BUT ... I’m suggesting a different
challenge
CONCLUSION
�Searching for black swans is aided by
challenge (not confirmation)
– Falsification
– Reverse stress testing
� BUT ... I’m suggesting a different
challenge
CONCLUSION
http://systemshock.org.uk/ joint project with ICRM
Understanding Risk Relationships
23
Air Travel Network
Global Financial Centres Trading Networks
Cargo Shipping Networks Communications Networks
Population Centres
Geography vs Topology
Cambridge Centre for Risk Studies
Thank you for listening
Look forward to hearing your thoughts
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