in search of black swans?icrm.ntu.edu.sg/newsnevents/doc/icrm_sym/documents/3rdicrm/… · however...

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