Levels of uncertainty

Abstract

Uncertainty is a common challenge in project, program, and portfolio management. It can arise from a variety of sources, including unforeseen risks and challenges in project scope, budget, or timeline. In the understanding by Knight that uncertainty diverts from risk, by being non-quantifiable, the level of uncertainty can be used by managers to prepare and adapt to the unforeseen events.

This article will explore various parameters for classifying levels of uncertainty, such as knowledge, severity, and source, and provide guidance on when to appropriately utilize each. Additionally, effective strategies for managing uncertainty at different levels in project, program, and portfolio management will be provided.

The article concludes by highlighting the importance of adopting a systematic approach to uncertainty management and will provide valuable insights for project-, program-, and portfolio managers, as well as anyone involved in the planning and delivery of projects, programs, and portfolios.

Introduction

Uncertainty is a fact of life that we all must contend with. From personal decisions to corporate strategies, uncertainty is an ever-present challenge that can affect the outcomes of our choices. When it comes to managing uncertainty, there are various levels of uncertainty that managers can use to identify the threats, that lie up ahead. Poor uncertainty management at any of these levels can lead to consequences that range from minor inconveniences to severe financial losses or even personal harm. In this wiki-article, we will explore the different levels of uncertainty, as well as strategies for effectively managing uncertainty at each level.

The right indentation of uncertainty levels has many different roots within different fields. Therefore, there is no “one size fits all” way to assess uncertainty but uncertainty management must be seen as a collection of methods that can be applied to different situations (Riesch, 2012). Levels of uncertainty can, thus, be described based on knowledge, severity, or the source of uncertainty. When each is applicable and how to mitigate the uncertainty, will be described further in this article.

Nature of uncertainty

Key challenges with managing uncertainty When uncertainty is mismanaged project performance is at great risk. One of the most common mistakes, when it comes to uncertainty, is to underestimate or ignore what cannot be measured. This is most often done either because of a lack of awareness or because of an optimism bias in the project. In the way that unmeasurable challenges probably not are that big a deal, and through the idea that “Of course this team can overcome any obstacle”. Lastly, uncertainties can be downplayed and used as a deliberate way of overselling projects (Geraldi et al., 2017).

Uncertainty management vs. risk management When managing uncertainty, there is an important distinction to be made between risk and uncertainty. Risk refers to specific events rather than general sources of uncertainty (Petit, 2012) and is thereby measurable. Efforts to do this is included in general project risk management (Wiki: PRM). Examples of PRM are easy to grasp as most have tried to estimate the odds that some process fails and planned what to do in the case that it does. Uncertainty management must also consider non-quantifiable uncertainties as well as unforeseeable events. In this sense, uncertainty simply means "a lack of certainty" (Chapman & Ward, 2012). This definition help show, that uncertainty management is a ubiquitous aspect of managing projects, programs, and portfolios. Uncertainty management is, thereby, about being adaptive in the face of unforeseen events, as too much rigidity in project planning can cause fragility in the project (Geraldi et al., 2017).

Uncertainty management also diverts from risk management by dealing with more than just possible disruptions to the project, as uncertainty contains a big personal and emotional aspect as well. This is to say, that stakeholder engagement in the project can vary on the specific stakeholder’s perception of the risk at hand (Geraldi et al., 2017). Managing expectations and minimizing threats in an unforeseeable landscape is exactly what levels of uncertainty is for.

Uncertainty based on knowledge (2 levels) – Why are we uncertain?

One way of creating levels of uncertainty is to consider the knowledgebase for the uncertainty. This reveals two inherent levels of uncertainty, as some concerns can be diminished by doing more research, and some are caused by internal randomness of the phenomena (Wiki: Epistemic vs. aleatory uncertainty)

Epistemic uncertainty

Epistemic uncertainty refers to uncertainty arising from a lack of knowledge or information about a particular system, process, or event. It is a type of uncertainty that can be reduced through gathering more data, improving models or theories, and enhancing our understanding of the underlying mechanisms (Kiureghian & Ditlevsen, 2009).

Effective management of epistemic uncertainty involves identifying and addressing knowledge gaps, building robust models and decision-making frameworks, and continuously monitoring and updating assumptions as new information becomes available (van Asselt & Rotmans, 2002).

Aleatoric uncertainty

Aleatory uncertainty refers to inherent randomness or variability in a particular system, process, or event that cannot be reduced or eliminated through additional information or analysis. This type of uncertainty is often associated with natural phenomena, such as weather patterns, seismic activity, or the behavior of complex systems. A simple dice is also a perfect example of aleatory uncertainty, in which the internal randomness of the outcome cannot be altered, even though the odds are known (Kiureghian & Ditlevsen, 2009).

Effective management of aleatory uncertainty involves understanding and quantifying the range of potential outcomes, building robust systems that can withstand a range of potential scenarios, and implementing appropriate risk management strategies to minimize the impact of adverse events (van Asselt & Rotmans, 2002).

Uncertainty based on severity (4 levels) – How critical is the uncertainty?

Authors such as Meyer et al. (2002) propose a different framework for assessing levels of uncertainty. They propose four levels based on the severity of the situation (Meyer et al., 2002). The four levels and how to manage them are as follows:

Variation Activities such as worker sickness, that push the critical path of a project (Wiki: CPM) and similar occurrences can be seen as variation in a project (Meyer et al., 2002). Events on this level can further be compared to what Millet (2002) describes as uncertainty within a near-term future (Millett, 2002).

To manage variation, including buffers in a Critical Path strategy to catch up on delays, can be recommended.

Foreseen uncertainty Meyer’s take on foreseen uncertainty is very similar to what have previously been described as risk management in this article. Foreseen uncertainties are identifiable influences that may or may not occur.

To manage foreseen uncertainty, the tools in Project Risk Management can be applied as well as doing contingency plans for the project [Standard].

Unforeseen uncertainty This level of uncertainty can also be described as "unknown unknowns," as famously stated by former US Secretary of Defense, Donald Rumsfeld. Unknown uncertainties refer to risks that are not accounted for in any plans or strategies because they are completely unexpected and are near-impossible to plan against.

To manage unforeseen uncertainty, an organisation must be both agile and continuously learning (Meyer et al., 2002). Too rigid a project plan is vulnerable to unforeseen uncertainty and, therefore, agility is key. Robustness of projects and companies can also be achieved through diversifying assets, relying on multiple partnerships, and having diversity in the project portfolio [Standard].

Chaos The final level in Meyer’s ontology is managing chaos. In chaos, the outcome of a project ends up being so far from the initial project scope that it has little to no application for it. This can mean project failure, or a completely unforeseen secondary benefit. An example being the invention and market introduction of Viagra, which was firstly intended as medication for high blood pressure (Meyer et al., 2002).

Managing chaos can seem like a paradox, but to do it, nonetheless, requires a high degree of autonomy for project managers and an iterative approach to the project work. R&D are often prone to work in chaotic environments and rapid prototyping is an effective way to do this. To allow working in chaos, companies must, however, also be ruthless in killing projects when the chances for success become too small.

Uncertainty based on source (5 levels) – What are we uncertain about?

Uncertainty can be classified based on its source, and there are five levels of uncertainty according to Riesch (2013). The source of uncertainty can also be formulated as the answer to the question: “What are we uncertain about?”.

Level 1: Uncertainty of the outcome The first level is the uncertainty of the outcome, which is often expressed as a measurable probability. This means that we know what we have done, we know the model, and we know the likelihood of a particular outcome occurring (Riesch, 2013). The uncertainty of the outcome is limited to an aleatoric uncertainty in Riesch’s model, as all is known, and the epistemic uncertainties thereby have been removed. An example here is again the rolling of a dice.

Managing the uncertainty of outcome in this sense, should refer to business decisions: Is the risk worth it based on cost, time, quality, etc.?

Level 2: Uncertainty of the parameters The second level is the uncertainty of the parameters, which can result from a lack of information. The idea is, to say “once we retrieve the necessary information, we can predict the outcome with a probability, P”. This level is thus, a sort of epistemic uncertainty that refers to the external factors to the project.

Level 3: Uncertainty about the model itself The third level is uncertainty about the model itself. An example of this can be the trust that the client has in the experts who are delivering the risk assessment. Different risk assessment models exist, but the client's level of trust in the process can determine how much they trust the model. This is uncertainty of the internal factors in the organisation and of the project work. This uncertainty is, thus, unquantifiable and relates to the human and emotional aspects of managing uncertainty.

Level 4: Uncertainty about acknowledged inadequacies and implicitly made assumptions The fourth level is uncertainty about acknowledged inadequacies and implicitly made assumptions. These assumptions are often based on past experiences or established conventions and can lead to blind spots in our risk assessments. For example, a company might assume that a certain market trend will continue, without considering the possibility of disruptive innovations or changes in consumer behavior. [Source]

One example of acknowledged inadequacies is the challenge of dealing with uncertainties associated with extreme events, also known as tail risk. These events are rare and have a low probability of occurrence but can have a significant impact on outcomes. Many risk models are not designed to handle tail risk effectively, and as a result, they may underestimate the potential impact of such events. [Source]

This level of uncertainty is complex to handle and relates heavily to the human and emotional aspect of uncertainty management. Further, human biases can cause that these uncertainties are overlooked until execution has begone. So far, the levels of uncertainty proposed by Riesch (2013), has referred to an analysis stage, in which a decision is yet to be made. Now, they appear as unpleasant surprises during project execution.

Level 5: Unknown inadequacies The fifth level of uncertainty in Riesch’s (2013) framework also refers to the ‘unknown unknowns’ in Rumsfeld’s terminology. Like Meyer et al. (2002), Riesch (2013) describes this level of uncertainty as the most difficult to manage. Because we cannot predict what we do not know, we must focus on building resilient systems that can withstand a range of potential scenarios. This involves designing systems that are flexible and adaptable, with redundancies and fallback options built-in. It also means fostering a culture of innovation and continuous learning, where new ideas and approaches can be tested and evaluated (Riesch, 2013).

Discussion on the merits of each ontology

Each of the proposed frameworks clearly has merit and addresses different types of uncertainty. As the ontologies overlap, they can expand on the understanding of each other as well. The two inherent levels of epistemic vs. aleatory uncertainty are, for example, present verbatim in levels one, two, and three of Riesch’s ontology, and similarly are Meyer and Riesch not mutually exclusive. Thus, uncertainty can refer to the external parameters (Riesch Level 2) and be caused by variation in the project (Meyer level 1). This view of combining the frameworks are also present within the ontologies themselves. Riesch and fellow uncertainty researcher Wynne [source] both argue, that the levels can be applied ‘on top of each other’, rather than be assessed in a stepwise approach.

Limitations

- Of this article - Of the uncertainty theory used

Further reading

- Other paradigms - Other wiki-articles

Bibliography

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https://archive.org/details/riskuncertainty00knig_0

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