Develop Lexicon
Build a vocabulary
of sources of
uncertainty
Calibrate
Experts’
Knowledge
Reducing Complexity
Modeling Uncertainty
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3
2
5
Elicit Change Drivers
and Alternatives
Reduce Complexity
with Cause-and-Effect
Analysis
Assign Conditional
Probabilities
Apply Uncertainty to
Cost Formula Inputs
for Scenarios
Perform Monte
Carlo Simulation to
Compute Cost
Distribution
Driver State Table
Dependency
Structure Matrix
Bayesian Belief
Network
Cost Factor
Distributions by
Scenario of Change
Monte Carlo with
Cost Estimation
Tools
Gather experts. Identify
change drivers, nominal state,
and off-nominal states.
Nominal
State
Gather experts. Identify
change drivers, nominal state,
and off-nominal states.
Off-Nominal States
Deviation Deviation Deviation Deviation
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2
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5
6
7
8
9
Develop nominal and alternative
scenarios of the cascading
effects of change.
Determine "glue nodes" to feed
the estimating formulas.
Simulate cost model inputs to
calculate what-if possibilities.
…how does
that change
affect these?
Effects
If these
change…
Causes
Highlighting Tool identifies and
marks change drivers.
Machine-learning enabled.
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1
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5
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QUELCE Change Repository
Queries of historical MDAP
experience and content
1
2
1
0.0
1.0
2
1 3
2
0.0
1.0
20.0
31.0
10,000 iterations
62.0
38.0
69.0
0.0
1.0
0.0
1.0
Prune away the
unlikely and
weak drivers
0.0
1.0
80.0
12.0
88.0
50.0
50.0
Nominal Scenario
0.0
1.0
20.0
80.0
Output for Tools
Gluing our outputs
to their inputs
Displays cost
and confidence
Alternative Scenario 1
Done in subgroups
of experts
Alternative Scenario 2
Executive Summary
Scope, DAES, etc.
Source documents
• Scenario cost estimates
and summaries
• What we did
• Impacts