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Managing in the Presence of Uncertanty

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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

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Managing in the Presence of Uncertanty

  1. 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. 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. 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. 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. 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. 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. 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 7
  8. 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 8
  9. 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 9  Accuracy  Precision  Accuracy  Precision  Accuracy  Precision  Accuracy  Precision
  10. 10. + Precision and Accuracy  Credible estimates of progam variables require both Accuracy and Precision Performance–Based Project Management®, Copyright © Glen B. Alleman, 2002 ― 2017 10
  11. 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. 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 12
  13. 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 13  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. 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 14  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. 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 15
  16. 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 16
  17. 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 17
  18. 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 18
  19. 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 19

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