| Description |
1 online resource : illustrations |
| Contents |
Cover -- Modern Risk Quantification in Complex Projects: Non-linear Monte Carlo and System Dynamics Methodologies -- Copyright -- Dedication -- Foreword -- Preface -- Contents -- List of Figures -- List of Tables -- List of Abbreviations -- List of Models -- Introduction -- Part One: Risk Quantification in Simple Projects -- 1: High-level Overview of Project Risk Management (PRM) -- 1.1 Introduction -- 1.2 Two Fundamental Goals of Risk Management -- 1.3 Inanimate, Animate, and Mixed Systems and Risks -- 1.4 PRM Context-PRM Method Mismatch -- 1.4.1 A Role of PRM Planning |
|
1.4.2 Project Phases and Selection of PRM Methodologies -- 1.4.3 Bias and PRM Zealotry -- 1.4.4 Conventional and Unconventional PRM Methods -- 1.4.5 Sampling Theory Basics -- 1.4.5.1 Two kinds of statistics -- 1.4.5.2 A general approach towards sampling -- 1.4.5.3 Sampling in PRM -- 1.4.5.4 Parametric sampling and inference -- 1.4.5.5 Monte Carlo sampling and inference -- 1.4.5.6 Implications of the small sample theory -- 1.4.6 A List of methods Used In or In Place of Inferential Statistics -- 1.5 A High-level overview of Conventional PRM Methodologies |
|
1.5.1. Two Important Conventional PRM Methodologies -- 1.5.2 Advantages and Limitations of Conventional PRM Methodologies -- 1.6 A High-level Overview of Regression-based Methodologies -- 1.6.1 Flaws of Averages -- 1.6.1.1 A first limitation: predictions are limited by the used sample -- 1.6.1.2 A second limitation: a proclivity to use convenience and judgement sampling -- 1.6.1.3 A third limitation: ignored historic aspect of data collection -- 1.6.1.4 A fourth limitation: misalignment with mathematics fundamenta |
|
1.6.2 Low Accuracy of Parametric Inferential Methods and a Room for Their Applicability -- 1.6.3 Remarkable Achievements of Regression-based Methods in Descriptive Statistics -- 1.7 High-level Introduction of Emerging Unconventional Risk Quantification Methods: System Dynamics and ANN -- 1.8 Chapter's Markup and Takeaway -- 2: Conventional Project Risk Management (PRM) Methodologies -- 2.1 A Definition of Project Risk -- 2.1.1 Occurrence and Probability Uncertainty -- 2.1.2 Uncertainty of Impact -- 2.1.3 Uncertainty of Favourability -- 2.1.4 Uncertainty of Manageability |
|
2.1.5 Uncertainty of Identification -- 2.2 Three Components of a Conventional Risk Management System -- 2.2.1 PRM Context -- 2.2.2 PRM Process -- 2.2.3 PRM Tools -- 2.2.3.1 Risk breakdown structure -- 2.2.3.2 Bowtie diagram for risk identification -- 2.2.3.3 Bowtie diagram for risk addressing -- 2.2.3.4 Risk assessment tools -- 2.2.3.5 Project risk registers -- 2.3 Role of Bias in PRM -- 2.3.1 Psychological Bias -- 2.3.2 Organizational bias -- 2.4 Chapter's markup and takeaway -- 3: Overview of Conventional Risk Quantification Methods |
| Summary |
A modified non-linear Monte Carlo methodology is developed to dramatically increase the accuracy of contingency development in complex project. It is achieved through counting of non-linear risk interactions in complex projects consistently that have been completely missed out by the traditional methods |
| Bibliography |
Includes bibliographical references and index |
| Notes |
3.1 A Challenge to Select Adequate Risk Quantification Methods |
|
Online resource; title from PDF title page (ProQuest Ebook Central, viewed on November 04, 2020) |
| Subject |
Monte Carlo method.
|
|
Electronic books.
|
|
Monte Carlo Method
|
|
e-books.
|
|
Monte Carlo method
|
| Form |
Electronic book
|
| ISBN |
9780192582645 |
|
019258264X |
|
9780192582652 |
|
0192582658 |
|
9780191879883 |
|
0191879886 |
|
