Transcript 06 Probabilistic Scenario Analysis

Institute for Water Resources
2009
Probabilistic Scenario Analysis
Charles Yoe, PhD
[email protected]
Why Are Decisions Hard?
•
•
•
•
Complex
Inherent uncertainty
Conflicting objectives
Differences in perspectives, i.e., risk
attitudes
• Scenarios can address these aspects
Bundle of Tools and Techniques
• Probabilistic scenario analysis is not
scenario planning
– Two different techniques for addressing
uncertainty
• HEC FDA, Beach FX, Harbor Sim are all
examples of PSA
• We’ll use event trees to better understand
the idea
Scenarios
• Literally an outline or
synopsis of a play
• Scenarios can be
used to describe
present
• Most often used to
describe possible
futures
• Corps scenarios
–
–
–
–
Without condition(s)
With conditions
Base year
Existing condition
Scenario Comparison
HUs
Cost
Without condition
5,000
0
With condition
Plan A
Change due Plan
A
With condition
Plan B
Change due Plan
B
7,500
One Million
+2,500
+1,000,000
25,000
One billion
+20,000
+1,000,000,000
Scenario Analysis
• Deterministic scenario analysis
– Examine specific scenarios
– Organize and simplify avalanche of data into
limited number of possible future states of the
study area or infrastructure
• Probabilistic scenario analysis
– Characterize range of potential futures and
their likelihoods
Some New Scenario Types
• As-planned scenario
• Failure scenarios
• Improvement scenarios
“As-Planned Scenario
• Surprise free scenario--free of any failures
• Risk free scenario--every feature of
system functions as planned—no
exposure to hazard
As planned
Yes
Yes
Terrorist
Attack on
Infrastructure
As planned
No
Plot
Detected
As planned
Yes
No
Attack
Foiled
No
Structure
Undamaged
Successful Attack
Failure Scenarios
• Tell story how various elements of system
might interact under certain conditions
• Challenge notion system will function as
planned
• Any aspect of as-planned scenario may be
challenged
• One common failure scenarios is “worstcase” scenario
Worst-Case Scenario
• Introduces conservatism into analysis--a
deliberate error
• Given any worst case an even worse
case can, paradoxically, be defined
• Possible is not necessarily probable
• Failure in the better than worst-case
world is still possible
Improvement Scenarios
• Risk analysis often results in new risk
management options to reduce risks
• Develop an improvement scenario for
each management option considered
– Used to evaluate risk management options
– Used to select the best option.
Scenario Comparisons
• Most likely future condition absent risk
management,
– Status quo or "without condition“--basic failure
scenario
– Every new risk management option evaluated against
this
• Most likely future condition with specific risk
management option
– “With condition“--improvement scenarios
– Each option has its own unique with condition
• Compare "with" and "without" conditions for
each new risk management option
Methods of Comparison
Risk Effect of Interest
With & Without
Option Comparison
Baseline
Before & After
Comparison
Existing
Target
Gap Analysis
Time
Deterministic Limits
• Limited number can be considered
• Likelihoods are difficult to estimate
• Cannot address full range of outcomes
Scenario Tools
• Event trees
– Forward logic
• Fault trees
– Backward logic
• Decision trees
– Decision, chance, decision, chance
• Probability trees
– All branches are probabilities
Event Tree
2.0%
48.5
Stall Occurs
Yes
Lockage
0.02
48.5
10.0%
10.0
Delay Occurs
2.125
90.0%
No
1.3
Yes
No
98.0%
0
0.098
10.0
0.882
1.3
Constructing Trees
• Keep it simple
– Rainfall  Dam failure
– Does that answer your questions?
• Don’t attempt complex model all at once
• Rapid iteration prototyping
• Analyze pros and cons of individual scenarios
only after considering all alternatives
– Avoid temptation to become enamored of one or a
few scenarios early in the process
Constructing Trees (cont.)
• Use Yes and No branches when possible
– Not always possible or desirable
• Separates elements of problem in
structured way
• Different trees yield different insights
What is Uncertain?
• Knowledge
• Model Uncertainty
• Quantity Uncertainty
– Parameters
– Empirical quantities
• Natural Variability
Many Scenarios
• Because of variability and uncertainty
there are many possible scenarios
• It is not possible to describe them all
• Some may be important to the decision
process
• Probability can be added to a scenario in a
variety of ways
– Monte Carlo process
Probability Is Not Intuitive
Learn It
Pick a door.
What is the probability you picked the winning door?
What is the probability you did not?
76.3%
Yes
0.000171615
0
0.8%
Yes
0
Grounding?
1
48.1%
Yes
2.56255E-05
0
23.7%
No
0
Allision?
1
1.0%
Yes
1
51.9%
No
2.76973E-05
1
Collision
1
Yes
4.0%
Casualty Occurs?
82
99.2%
No
0.029198219
81
Yes
72.7%
81
Draft > Controlling Depth?
1818
No
96.0%
2502
Vessels per Year
0.697278585
1736
1736
Contain Oil?
74.9%
Yes
4.92731E-06
0
0.0%
Yes
0
Grounding?
0
Yes
44.1%
7.29167E-07
0
25.1%
No
0
Allision?
0
Yes
1.0%
0
No
55.9%
0
Yes
4.9%
Casualty Occurs?
39
100.0%
No
39
No
27.3%
Draft > Controlling Depth?
684
No
95.1%
645
0.259915008
645
0.013376669
39
Collision
9.23368E-07
0
Data and Distributions
Checklist for Choosing a
Distributions From Some Data
1. Can you use your
data?
2. Understand your
variable
a)
b)
c)
d)
e)
f)
Source of data
Continuous/discrete
Bounded/unbounded
Meaningful parameters
Univariate/multivariate
1st or 2nd order
3. Look at your data—
plot it
4. Use theory
5. Calculate statistics
6. Use previous
experience
7. Distribution fitting
8. Expert opinion
9. Sensitivity analysis
Model Uncertainty
• Help people understand your model
• Be the first to point out its weaknesses
• Don’t be afraid to be creative
Take Away Points
• PSA is a class of tools that relies on
– Scenarios
– Probabilities
• PSA’s take many forms
– Most IWR tools are PSA’s
– Event trees & fault trees
– Process models & Flow diagrams
• PSA’s are very powerful and useful tools
Questions?
Charles Yoe, Ph.D.
[email protected]