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Managing in the Presence of Uncertainty requires
making decision with Models of that Uncertainty
Monte Carlo Simulation and some related approaches can be the basis of making informed decisions in
the presence of Uncertainty
MONTE CARLO
SIMULATION AND
ESTIMATING TRADITIONAL
AND AGILE
DEVELOPMENT
V1.0 Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
2. + The Motivation for Monte Carlo
Simulation
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
2
A rough translation of the planning algorithm
from Aristotle’s De Moti Animalium, c. 400 BC
But how does it happen that thinking is
sometimes accompanied by action and
sometimes not, sometimes by motion, and
sometimes not?
It looks as if almost the same thing happens as
in the case of reasoning and making inferences
about unchanging objects.
But in that case the end is a speculative
proposition ... whereas here the conclusion
which results from the two premises is an
action. ... I need covering; a cloak is a
covering. I need a cloak. What I need, I have to
make; I need a cloak. I have to make a cloak.
And the conclusion, the “I have to make a
cloak,” is an action.
3. Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017 3
Uncertainties are
things we can not
be certain about.
Uncertainty is
created by our
incomplete
knowledge; not
by our ignorance
5. + Some Words about Uncertainty
When we say uncertainty, we speak about a future state of an system that
is not fixed or determined.
Uncertainty is related to three aspects in our program management
domain:
The external world – the activities of the program
Our knowledge of this world – the planned and actual behaviors of the program
Our perception of this world – the data and information we receive about these
behaviors
Managing in the presence of uncertainty is part of each success factor
What does Done Look Like?
What’s the Plan to reach Done
What resources do we need to reach Done?
What are the Impediments to reaching Done?
How are we measuring progress to plan toward Done?
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
5
6. + Taxonomy of Uncertainty
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
6
Uncertainty
Irreducible
(Aleatory)
Reducible
(Epistemic)
Natural Variability
Ambiguity
Ontological
Uncertainty
Probabilistic Events
Probabilistic
Impacts
Periods of Exposure
7. + Aleatory & Epistemic Uncertainty
Aleatory Pertaining to stochastic (non-deterministic) events, the outcome
of which is described using probability.
From the Latin alea
For example in a game of chance stochastic variability's are the natural
randomness of the process and are characterized by a probability density
function (PDF) for their range and frequency
Since these variability's are natural they are therefore irreducible.
Epistemic (subjective or probabilistic) uncertainties are event based
probabilities, are knowledge-based, and are reducible by further gathering
of knowledge.
Pertaining to the degree of knowledge about models and their parameters.
From the Greek episteme (knowledge).
Separating these classes helps in design of assessment calculations and in
presentation of results for the integrated program risk assessment.
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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8. + 3 Conditions of Aleatory Uncertainty
An aleatory model contains a single unknown parameter.
Duration
Cost
The prior information for this parameter is homogeneous and is known
with certainty.
Reference Classes
Past Performance
The observed data are homogeneous and are known with certainty.
A set of information that is made up of similar constituents.
A homogeneous population is one in which each item is of the same type.
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9. + Measurement Uncertainty
Precision – how small is the variance of the estimates
Accuracy – how close is the estimate to the actual values
Bias – what impacts on precision and accuracy come from the human
judgments (or misjudgments)
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Accuracy
Precision
Accuracy
Precision
Accuracy
Precision
Accuracy
Precision
10. + Precision and Accuracy
Credible estimates of progam variables require both Accuracy and
Precision
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11. + Cost Probability Distributions
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
11
$
Cost Driver (Weight)
Cost = a + bXc
Cost
Estimate
Historical data point
Cost estimating relationship
Standard percent error boundsTechnical Uncertainty
Combined Cost
Modeling and Technical
Uncertainty
Cost Modeling
Uncertainty
† NRO Cost Group Risk Process, Tim Anderson, The Aerospace Corporation, 2003
12. +
Monte Carlo
Simulation in the
Presence of
Uncertainty
George Louis Leclerc, Comte
de Buffon, asked what was
the probability that the needle
would fall across one of the
lines, marked here in green.
That outcome will occur only if
𝐴 < 𝑙 sin 𝜃
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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13. + Monte Carlo Simulation Provides one
Solution the Estimating Problem
Yes, Monte Carlo is named after the
country full of casinos located on the
French Rivera
Advantages of Monte Carlo
Examines all possible states of a
variable, not just the Mean and Variance
Provides an accurate (true) estimate of
completion
Overall duration distribution
Confidence interval (accuracy range)
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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Sensitivity analysis of interacting tasks
Varied activity distribution types
Dependency logic can include both probabilistic and conditional
When resource loaded plans are used – provides integrated cost and schedule
probabilistic model
14. + The Monte Carol Methods Starts in
WWII History
Any method which solves a problem
by generating suitable random
numbers and observing that fraction
of the numbers obeying some
property.
The Monte Carlo method provides
approximate solutions to a variety of
mathematical problems by
performing statistical sampling
experiments on a computer.
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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The method applies to problems with no probabilistic content as well as to
those with inherent probabilistic structure.
The method is named after the city of Monte Carlo in the principality of
Monaco, because of a roulette, a simple random number generator. The
name and the systematic development of Monte Carlo methods dates
from about 1944 and the Manhattan project.
15. + Monte Carlo Simulation Tools
@Risk – we use this on our programs
http://www.palisade.com/risk/
Risk Amp – an embedded Excel MCS simulator, used for cost modeling
https://www.riskamp.com/
Risky Project ‒ a MCS for cost and schedule using MSFT Project on our programs
http://intaver.com/
MonteCarlito – haven’t used
http://www.montecarlito.com/
SimTools – haven’t used
http://home.uchicago.edu/~rmyerson/addins.htm
Monte Carlo Simulation Tutorial
http://excelmontecarlo.com/
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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16. + Monte Carlo Simulation Tools
SimulAr – haven’t used
http://www.simularsoft.com.ar/SimulAr1e.htm
Barnecana – popular in our domain
https://www.barbecana.com/
Monte Carlo Simulation tool for JIRA – interesting plug in
https://agilemontecarlo.com/
Guesstimate – used for quick assessment of cost model
https://www.getguesstimate.com/
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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17. + References
Cost Risk Analysis Made Simple
https://www.aceit.com/docs/default-source/white-papers/cost-risk-analysis-
made-simple-(aiaa-sep-2004).pdf
An Implementation of the Lurie-Goldberg Algorithm in Schedule Risk Analysis
http://www.slideserve.com/Olivia/an-implementation-of-the-lurie-goldberg-
algorithm-in-schedule-risk-analysis
The Beginning of the Monte Carlo Method
http://library.lanl.gov/cgi-bin/getfile?00326866.pdf
The Basics of Monte Carlo Simulation
http://www.risksig.com/members/present/2001/21023.pdf
“The Mother of All Guesses: A User Friendly Guide to Statistical Estimation,”
Francois Melese and David Rose, Armed Forces Comptroller, 1998
http://www.nps.navy.mil/drmi/graphics/StatGuide–web.pdf
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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18. + References
Anchoring and Adjustment in Software Estimation
http://www.cs.toronto.edu/~sme/papers/2005/ESEC-FSE-05-Aranda.pdf
Managing in the Presence of Uncertainty
https://www.slideshare.net/galleman/managing-in-the-presence-of-
uncertainty
How to reduce Agile Risk with Monte Carlo Simulation
https://blog.versionone.com/how-to-reduce-agile-risk-with-monte-carlo-
simulation/
Agile project forecasting using Monte Carlo Simulation
http://scrumage.com/blog/2015/09/agile-project-forecasting-the-monte-carlo-
method/
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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19. + References
Effort Estimation in Agile Software Software Development: A Systematic Literature
Review
https://www.diva-portal.org/smash/get/diva2:881296/FULLTEXT01.pdf
Monte Carlo Basics
https://arxiv.org/pdf/cond-mat/0104215.pdf
Focused Objectives has many papers and a book
http://focusedobjective.com/forecast_agile_project_spreadsheet/
Monte Carlo Simulation in Agile Project Estimation
https://www.academia.edu/8939341/Monte-
Carlo_Simulation_in_Agile_Project_Estimation_Forecasting_Schedule_and
_Required_Velocity (log in may be required)
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