1.
+
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
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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

4.
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017 4
“Am I to understand that my estimate
is greeted by some skepticism?”

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
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6.
+ Taxonomy of Uncertainty
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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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.
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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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)
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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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
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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11.
+ Cost Probability Distributions
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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$
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)
Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017
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