Epistemic vs. Aleatory uncertainty
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[3]. 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[4]. There is still ongoing research for increasing the accuracy of a result and include more parameters in calculating an outcome.
What is Uncertainty
Different definitions have been given for uncertainty in project management, but their common denominator is “not knowing for sure”. There is information that are known to be true and other known to be false, but for a large portion of information there is not knowledge whether they are true or false, and therefore they are mentioned as uncertain[1]. According to Lindley[5] uncertainty can be considered as subjective between individuals and this is attributed to the fact that the set of information obtained from an individual can be different from another. Two facts that apply are: a) the degree of uncertainty between individuals may also differ, meaning that one person may think that an event is more likely to happen that another person, b) The number of uncertain information is vastly greater than the number of information each individual is sure that are true or false[5]. The two aforementioned facts deeply affect decision making by taking into consideration that uncertainty creates the contingency for occurrence of risky events may lead to potential damage or loss.
Epistemic vs. Aleatory uncertainty
Uncertainty is categorized into two types: Epistemic uncertainty (also known as systematic uncertainty or reducible uncertainty) and aleatory uncertainty (also known as statistical uncertainty or irreducible uncertainty)[6].
- Epistemic Uncertainty: derives its name from the Greek word “επιστήμη” (episteme) which can be roughly translated as knowledge. Therefore, epistemic uncertainty is presumed to derive from the lack of knowledge of information regarding the phenomena that dictate how a system should behave, ultimately affecting the outcome of an event[2][6].
- Aleatory Uncertainty: derives its name from the Latin word “alea” which is translated as “the roll of the dice”. Therefore, aleatory uncertainty can be defined as the internal randomness of a phenomena[2].
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
References
- ↑ 1.0 1.1 G. Grote, Management of Uncertainty - Theory and application in the design of systems and organizations, London: Springer, 2009.
- ↑ 2.0 2.1 2.2 A. D. Kiureghiana and O. Ditlevsen, "Aleatory or epistemic? Does it matter?," Structural Safety, vol. 31, no. 2, p. 105–112, March 2009.
- ↑ S. Basu, "Chapter 2: Evaluation of Hazard and Risk Analysis," in Plant Hazard Analysis and Safety Instrumentation Systems, London, Elsevier, 2017, p. 152.
- ↑ T. Aven and E. Zio, "Some considerations on the treatment of uncertainties in risk assessment for practical decision making," Reliability Engineering & System Safety, vol. 96, no. 1, pp. 64-74, 2011.
- ↑ 5.0 5.1 D. V. Lindley, "Uncertainty," in Understanding Uncertainty, New Jersey, John Wiley & Sons, Inc., 2006, pp. 1-2.
- ↑ 6.0 6.1 E. Zio and N. Pedroni, "Causes of uncertainty," in Uncertainty characterization in risk analysis for decision-making practice, number 2012-07 of the Cahiers de la Sécurité Industrielle, Toulouse, France, Foundation for an Industrial Safety Culture, 2012, pp. 8-9.
