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(RISK-2142) The Monte Carlo Method for Modeling & Mitigating Systemic Risk

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Primary Author: Dr. David T. Hulett FAACE
Co-Author(s): Waylon T. Whitehead

Audience Focus: Advanced
Application Type: Research
Venue: 2016 AACE International Annual Meeting, Toronto, ON, Canada

Abstract: A number of references have been made about the supposed inability of Monte Carlo simulation (MCS) methods to represent systemic risks to project cost and schedule. The bottom-up approach of data collection in some MCS-based analyses does not incorporate systemic risks; this is problematic given the large overruns in schedule and cost that can occur on mega-projects partially as a result of these risks.

Systemic risks can relate to the level of new technology, degree of project definition, project complexity, project size and the organization’s ability to manage large projects. Systemic risks interact with stress factors such as aggressiveness of requirements, stakeholder mismanagement and lack of clarity in decision making. Together these explain why some mega-projects exhibit serious overruns.

This paper combines typical MCS methods used to measure uncertainty and capture the effects of project-specific risks, but it goes farther. Using MCS, it also accounts for systemic risks, and shows the suitability of the using the Risk Drivers Methodology for prioritizing risks for mitigation, and understanding when mitigations must begin and conclude for optimum impact.