“Planning for an uncertain future”
Overcoming the challenges we face in handling uncertainty
Dickie Whitaker- Director and joint founder of
Lighthill Risk Network, Financial Services KTN and Oasis Loss Modelling
Framework
“It is hard to overstate the damage done in the recent
past by people who thought they knew more about the
world than they really did.”
John Kay in “Obliquity” 2010
Examples of where we have gone wrong
•
Financial crash, Efficient market hypothesis, VAR ++
If you marked your position to market every day for a million years, there
would still be a less than one in a million chance of experiencing a 25standard deviation event.*
•
Sustainable cod management in Grand Banks Newfoundland -1968
810,000 tons of Cod caught by 1992 fishing collapsed
•
Japan assessment of seismicity and Tsunami wall height
Cognitive Bias
Mario Weick,
School of Psychology,
University of Kent
Anchoring
Anchoring describes the process of using a starting point for
evaluating or estimating unknown values
Gambler’s fallacy effect
It describes the tendency of decision makers to underestimate the
probability of a repetition of an event that has just happened
(Cohen, Etner, & Jeleva, 2008).
Mean-reversion bias
Whereby decision makers assume that over time, a trend has to
return to the mean. For example, after an ‘unusually’ high rate of
a natural disaster striking, decision makers may succumb to the
false believe that the rate of occurrence will return to ‘normal’.
Complex problems
Hypothesis weakness
‘good participants differed from the bad ones … in how often
they tested their hypotheses. The bad participants failed to do
this. For them, to propose a hypothesis was to understand
reality; testing that hypothesis was unnecessary. Instead of
generating hypotheses, they generated “truths” ’
The Logic of Failure -Dorner
Losers:
Acted without prior analysis
Didn’t test against evidence
Assumed absence of negative meant correct decisions made
Blind to emerging circumstances
Focused on the local not the global
Avoided uncertainty
Practical challenges
1. Its very challenging, we go from a simple to a complex (all possible theories and outcomes)
question
2. We often do not have the resources, Time, Money Compute power or good enough statistical
skills
3. Multi Disciplinary nature of the problem
4. Communication especially visual
How does insurance deal with uncertainty?
•
Regulation didn't help that much
•
We cant compute (or get given) all of the uncertainty
•
We struggle to visualise and explain uncertainty
•
We struggle to combine models and results
•
But the market and Oasis is driving change
Oasis Loss Modelling Framework
Not for profit owned and funded by 23 global Insurers Reinsurers and Brokers
Identification of uncertainty
Model Uncertainty covers the uncertainties that exist within the scope of the model.
Model inadequacy represents the unsuitability of the underlying physical hazard and vulnerability
models, as well as the effect of ignoring secondary perils such as demand surge
Model risk is the risk that the particular model is wrong or provides an incomplete and
misleading picture.
Parameter risk is the risk that the model’s parameters (often expressing assumptions or calibration
factors) are wrong, and calculation error the risk that the model has been insufficiently discretised
or sampled.
Mean
Underwriting decisions made nowadays on the basis of an “EP curve” from which three
statistics are typically used:
Mean
Standard Deviation
1 in 200 year VaR
Idealised use of models
Seek transparency and ease of interrogation of any model, with clear
expression of the provenance of assumptions.
Communicate the estimates with humility, communicate the uncertainty with
confidence.
Fully acknowledge the role of judgement.”
D. J. Spiegelhalter and H. Riesch in “Don’t know, can’t know: embracing deeper
uncertainties when analysing risks” Phil. Trans. R. Soc. A (2011) 369, 4730–4750
What lessons for other sectors and other areas
•
Academia needs to have application of best practices in statistics for climate science?
•
We need to have more multi disciplinary approaches for science
•
Peer review process reinforces status quo rather than stimulating innovation and multi disciplinary approaches
•
Can be too much focus on “Impact” and not enough on utility
•
Communication and visualisation better approaches
•
Understanding of Bias in judgement
•
Computation is and will play big part as data can be Big
•
Use statistics in peer review
Thank You
&
to Dr Peter Taylor & * Prof. Lenny Smith
Dickie Whitaker, Director
Financial Services Knowledge Transfer Network
Tel:
+44(0)7920 502302
email:
dickie.whitaker@fs-net.org
Web:
http://ktn.innovateuk.org/web/financialservicesktn
Dickie Whitaker- Project Director
Oasis Loss Modelling Framework
http://oasislmf.org/
Tel: +44(0)7920 502 302
dickie.whitaker@oasislmf.org
Models and uncertainty Lenny Smith LSE; http://www.lse.ac.uk/CATS/Publications/CATS%20Papers%20-%20Chronological.aspx
“Somewhere along the way, appreciation
for the inherent uncertainty in risk has been diminished or even lost.
Instead of models helping users to become more deeply risk-aware, the opposite ca
occur.
Indeed, some now feel more vulnerable to a change in model versions than to the ve
catastrophes that these models were intended to mitigate.”
Hemant Shah, CEO