Risk, Opportunity, Uncertainty and Other Random Models

Risk, Opportunity, Uncertainty and Other Random Models by Alan Jones, published by Taylor & Francis Group on June 24, 2024, spans 316 pages and is presented in English. This volume, part of the Working Guides to Estimating and Forecasting series, addresses the misconceptions surrounding research and development costs, particularly through the lens of the Norden-Rayleigh Curve and the PERT-Beta Curve. It emphasizes the importance of Monte Carlo Simulation as a technique for evaluating risk, opportunity, and uncertainty by analyzing multiple interacting variables.
Readers will find a comprehensive exploration of Monte Carlo Simulation, which is often perceived as a ‘black box’ in estimation and forecasting. The book provides insights into the methodology, aiming to clarify the processes involved and reduce the reliance on uninformed inputs that can lead to misconceptions. With a focus on practical applications, this resource is designed for estimators, engineers, accountants, and project risk specialists, offering valuable guidance supported by numerous figures and tables.
Official synopsis Publisher
Risk, Opportunity, Uncertainty and Other Random Models (Volume V in the Working Guides to Estimating and Forecasting series) goes part way to debunking the myth that research and development cost are somewhat random, as under certain conditions they can be observed to follow a pattern of behaviour referred to as a Norden-Rayleigh Curve, which unfortunately has to be truncated to stop the myth from becoming a reality! However, there is a practical alternative in relation to a particular form of PERT-Beta Curve.
However, the major emphasis of this volume is the use of Monte Carlo Simulation as a general technique for narrowing down potential outcomes of multiple interacting variables or cost drivers. Perhaps the most common of these in the evaluation of Risk, Opportunity and Uncertainty. The trouble is that many Monte Carlo Simulation tools are ‘black boxes’ and too few estimators and forecasters really appreciate what is happening inside the ‘black box’. This volume aims to resolve that and offers tips into things that might need to be considered to remove some of the uninformed random input that often creates a misinformed misconception of ‘it must be right!’
Monte Carlo Simulation can be used to model variable determine Critical Paths in a schedule, and is key to modelling Waiting Times and cues with random arisings. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.
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