Making
Uncertainty
Valuable
not Risky
Making
Uncertainty
Valuable
About KCA
 Management Consultancy focused on Energy,
Technology, and Related Markets.
 Work with clients to develop and implement gamechanging strategies, improve operational efficiencies,
and reduce costs through long-term competitive
advantage.
 Principals have worked with start-ups to Fortune 100
clients on over $400 billion in investments.
 Headquartered in Houston, TX
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Wise advice…
“To be absolutely certain
about something, one must
know everything or nothing
about it.”
- Olin Miller
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Agenda
 Some basics
 Good decisions do not guarantee good outcomes
 There is a process for ensuring decision quality
 Our own risk profiles and how they change and why
 Ebola and money
 Stopping our biggest handicaps
 Biases
 Thinking the future is certain
 Gaining confidence in our decisions
 Embracing uncertainty
 Use appropriate tools and processes
 Creating value from uncertainty
 The Clairvoyant and the Wizard
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The Decision Spectrum
 Routine decisions
 Made frequently
 Usually low ambiguity, uncertainty
and risk
 Usually low materiality or impact
 High confidence in the outcome
 Non-routine decisions
 In frequent
 Often have lots of ambiguity,
uncertainty, and risk
 Usually high business materiality
 Confidence level in outcome ranges
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Decision making becomes difficult
when…
 The real decision-maker is hidden
 Dealing with multiple decision-makers
 Identifying and clarifying objectives
 Making trade-offs
 Understanding the key uncertainties
 Developing and quantifying unique options
 Agreeing on the measures of merit
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Traditional decision making works for
routine decisions…
Situation
Analysis
Decision
Proposed
Assumptions
& Forecasts
Discount
Factor
Value
Calculated
Decision
Review
What can go wrong
with this approach?
Why does it so often lead to a lack
of buy-in, unresolved ambiguities,
lingering uncertainties or
frustrating analysis paralysis?
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Non-routine decisions benefit from
Decision Analysis
 “Decision Analysis is a methodology
and set of probabilistic frameworks
for facilitating high quality, logical
discussions; illuminating difficult
decisions, and leading to clear and
compelling action by the decision
maker.”*
 We can know the quality of the
decision before it is made.
 The best you can do is to
incorporate:
 What you want
 What you can do
 What you know
* Skinner, “Introduction to Decision Analysis, 2nd Edition,” pages 11-13, 16.
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Decision analysis provides the tools to
effectively deal with uncertainty & risk
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Good decisions don’t guarantee
good outcomes
A process for making
quality decisions improves
the chance of a good
outcome.
Poor Decision
Good Outcome
Luck
Good Decision
Good Outcome
Poor Decision
Poor Outcome
Good Decision
Poor Outcome
Decision Quality
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Is Decision Analysis Effective?
 Paul Nutt (London Business School) studied 127 major
decisions in North America.
 He found that the probability of success almost doubled
(from about 40% to about 80%) when DA process were
utilized.
 He saw dramatic improvement in understanding,
participant buy-in, use of creative ideas and
achievement of business results.
 Other studies show similar results.
Paul Nutt, London Business School, Business Strategy Review 1997, Volume 8, Issue 4, PP 44-52.
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Decision making without the right
tools lead to higher risk & lower value
Uncertainty
“Gut Feel”
“Decision Paralysis”
Both
“Just Do It”
Ambiguity
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Quickest way to better decisions
 Use a DA approach
 Gather “unbiased”
information
 Harness the power
of uncertainty
Unclear
Future
Increasing Uncertainty
 Eliminate ambiguity
first
Clear
Future
Analysis
Tools
Make
Decision
Full
Decision
Analysis
Framing
Tools
Increasing Ambiguity
Clear Goals
Unclear and
Conflicting Goals
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A bit of psychology
 When it comes to potential gains, people are risk-averse
 When it comes to potential losses, people are gamblers
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Example: Ebola
 Ebola is spreading in Houston,
and it is estimated that 600
people will die as a result.
Two alternative programs
have been proposed to
combat it:
 With Program A, 200 people
will be saved.
 With Program B, there is a 33%
chance that 600 people will
be saved, and a 67% chance
that no one will be saved.
 Which would you choose?
Reference: Tversky and Kahneman
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Ebola continued
 Of the two programs, 72% of
those tested chose A, 28%, B.
 However, 2 new alternatives
arise:
 With Program C, 400 people
will die.
 With Program D, there is a 33%
chance that nobody will die,
and a 67% chance that 600
people will die.
 With these choices, 78%
chose D, 22%, C.
Reference: Tversky and Kahneman
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The Framing phenomenon
 If a project, decision, choice, situation, etc. is framed in
terms of potential gains, most people are risk-averse
 If the exact same project, decision, etc. is framed in
terms of potential losses, most people become riskseeking
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Same is true in financial situations
 Offered a choice between:
 A: A sure-fire gain of $240
 B: A 25% chance of
receiving $1000
 The vast majority choose A.
 Offered a choice between:
 C: A sure-fire loss of $750
 D: A 75% chance of losing
$1000
 The majority choose D.
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Assessing the future is easy if you
suppress uncertainty.
 Confidence biases are a
part of our culture
 We are trained in school
to provide “the answer”
 Decision makers like
deterministic (precise
looking) forecasts
 Explicitly assessing
uncertainty
 allows contingency
planning
 opens up options on
upside potential
 can provide a higher
level of confidence
 Failing to deal with
uncertainty can lead to
surprises
 Lack of planning offers
little or no time to
respond
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Decision Trees and Expected Value
 A decision tree is evaluated or “rolled back” by summing
the product of each outcome times its associated
probability. This gives the Expected Value (mean), which
is the value that should be achieved if the game could
be played hundreds or thousands of times.
High
Successful?
0.900
Yes
Pursue Opportunity?
$641
No
0.300
Fails
Yes : $641
0.300
Medium
$740
0.400
Low
0.100
$1,500; P = 0.270
$800; P = 0.360
($100); P = 0.270
($250); P = 0.100
$0
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Expected value…
From an expected value perspective this guy is fine!
But really? One hand on fire, the other is frozen…
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Why can’t you just give me the
number?
 Feel uneasy
 Make overly biased
estimates
 Usually very
conservative estimates
 Give lots of caveats
 Won’t give you an
estimate
 Need time to build a
model before
committing
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Biases create surprises
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Anchoring is the worst bias
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We want an 80% confidence range
80% confidence
140
120
100
80
60
40
20
0
A
B
p10
C
p50
D
p90
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Quick Test:
.10
.50
.90
1. What was the average rig count
for North Dakota in 1980?
2. The year Attila the Hun died.
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A & D are management surprises
Normal Distribution vs Results
160
140
120
100
80
60
40
20
0
A
B
C
D
With over 1,000 tests administered the distribution rarely changes.
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Uncertainty and risk vary by industry
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What is the Difference between Risk
and Uncertainty?
Risk (Chance)
Uncertainty
 0 or 1; success or failure
outcome
 Many outcomes are
possible
 Assessed as a %
 Assessed as a 10-50-90
 Examples:
 Probability of finding
recoverable
hydrocarbons
 Probability of rain
tomorrow
 Probability of getting lost
on the way home
 Examples:
 Reserves
 Time to drill a well
 Price of gasoline
 Time it takes to drive
home
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Tornado Graph
 A tornado graph is developed
by:
 Stepping through each risk
and uncertainty and
recording the effect on the
measure of value, and
 Sorting from most important
to least important.
Measure of value (usually NPV) ->
 This gives insight as to which risks
and uncertainties merit further
study.
 The tornado graph is more
powerful than the traditional
sensitivity analysis, as there’s a
logical basis for each p10 and
p90 input.
Upside
Potential
Downside
Risk
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Cumulative Probability Graph
1
Cumulative
Probability
Interpretation:
• There is a 90% chance
that the blue project will
make less than 200.
• There is a 10% chance
the blue project will
make less than 100.
0.5
Which investment would
you prefer?
0
0
100 200 300 400
Measure of value (usually NPV or IRR) ->
See Blank and Tarquin, “Engineering Economy, 6th Edition,” pages 660 to 666.
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Would gathering more information
improve the decision?
 We all feel the need to gather more information when
we have an important decision to make.
 But does it really matter in a lot of cases?
 Does it just give us more of a comfort factor?
 What if you could determine its value before gathering or
buying new information?
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What is the Value of Information?
Competitor is Out
0.800
28.5 M
Competitor is In
0.200
Current Contracts
Beat the Competition
Current Contracts
Beat the Competition
Expected Value was $ 28.5 M
Value with Clairvoyance = $28.5 M
10.5 M
34.5 M; P = 0.800
(0.5 M)
4.3 M; P = 0.200
Value of Information = $ 0
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While it is good to know the future, it is
even better to control it.
Value of Information can help you to
understand the trade-offs of
gathering more information.
Value of Control can provide you
with a quantitative value for taking
certain actions to control your
outcome.
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What is the Value of Control?
Competitor is Out
1.000
34.5 M
Competitor is In
0.000
Current Contracts
Beat the Competition
Current Contracts
Beat the Competition
10.5 M
34.5 M; P = 1.000
(0.5 M)
4.3 M
Remember…for Value of Control, we are
just setting the probability for the desired
outcome equal to 1.0
Expected Value was $ 28.5 M
Value with Wizard = $ 34.5 M
Value of Control is $ 6.0 M
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Thank you
David Skinner
CEO
dskinner@kcarpenterassociates.com
832-338-4111
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