Epistemic vs. Aleatory uncertainty
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The capability to quantify the impact of uncertainty in the decision context is critical. Uncertainty can be divided in several categories but the most dominant ones in uncertainty theory are '''epistemic''' and '''aleatory''' uncertainty <ref>A. D. Kiureghiana and O. Ditlevsen, "Aleatory or epistemic? Does it matter?," Structural Safety, vol. 31, no. 2, p. 105–112, March 2009.</ref>. Epistemic uncertainty derives from the lack of knowledge of a parameter, phenomenon or process, while aleatory uncertainty refers to uncertainty caused by probabilistic variations in a random event. Each of these two different types of uncertainty has its own unique set of characteristics that separates it from the other and can be quantified through different methods. Some of these methods include simulation, statistical analysis or measurements. There is still ongoing research for increasing the accuracy of a result and include more parameters in calculating an outcome. | The capability to quantify the impact of uncertainty in the decision context is critical. Uncertainty can be divided in several categories but the most dominant ones in uncertainty theory are '''epistemic''' and '''aleatory''' uncertainty <ref>A. D. Kiureghiana and O. Ditlevsen, "Aleatory or epistemic? Does it matter?," Structural Safety, vol. 31, no. 2, p. 105–112, March 2009.</ref>. Epistemic uncertainty derives from the lack of knowledge of a parameter, phenomenon or process, while aleatory uncertainty refers to uncertainty caused by probabilistic variations in a random event. Each of these two different types of uncertainty has its own unique set of characteristics that separates it from the other and can be quantified through different methods. Some of these methods include simulation, statistical analysis or measurements. There is still ongoing research for increasing the accuracy of a result and include more parameters in calculating an outcome. | ||
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==What is Uncertainty== | ==What is Uncertainty== | ||
Revision as of 22:03, 17 February 2019
Panagiotis Vounatsos - s182563
Contents |
Abstract
Uncertainty is embedded in many aspects of a project, program and portfolio management. It is present in decision making for project integration and complexity, scope management, schedule management, cost management and risk management as this is mentioned in PMI standards as well as in risk management given in AXELOS project management standards.
Uncertainty derives from not knowing for sure if a statement is true or false. More specifically, it is the absence of information and if put more scientifically, it is the difference between the amount of information required to perform a task and the amount of information already possessed[1]. Uncertainty is considered crucial to be identified and mitigated as it can contribute to severe consequences to the aforementioned aspects of a project, program or portfolio. Depending on the level of the uncertainty and the consequence it may result in jeopardizing the outcome of an action or even of the whole project. It is worth mentioning that uncertainty is not only a part of the project management but also a part of the technical implementation of a project.
The capability to quantify the impact of uncertainty in the decision context is critical. Uncertainty can be divided in several categories but the most dominant ones in uncertainty theory are epistemic and aleatory uncertainty [2]. Epistemic uncertainty derives from the lack of knowledge of a parameter, phenomenon or process, while aleatory uncertainty refers to uncertainty caused by probabilistic variations in a random event. Each of these two different types of uncertainty has its own unique set of characteristics that separates it from the other and can be quantified through different methods. Some of these methods include simulation, statistical analysis or measurements. There is still ongoing research for increasing the accuracy of a result and include more parameters in calculating an outcome.
What is Uncertainty
In this subsection the definition of uncertainty is going to be given.
Uncertainty in Management
In this subsection, details are going to be given regarding where is uncertainty met in program, project and portfolio management.
Epistemic vs. Aleatory uncertainty
Distinguishing between epistemic and aleatory uncertainty
In this subsection the distinction between epistemic and aleatory uncertainty is going to be given.
Differences of the properties for each uncertainty type are going to be given
An risk management example is going to be provided in order to better distinguish the difference between epistemic and aleatory uncertainty
Sources of epistemic and aleatory uncertainty
Quantification of epistemic uncertainty
In this subsection, methods and models for quantifying epistemic uncertainty are going to be briefly mentioned and in certain cases further analysed
