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

- Consequences of poor uncertainty management - Different definition in different fields and different take-aways. Therefore, no right or wrong, but a list of useful tools to most (or something like that)

Nature of uncertainty

- Risk vs. uncertainty

- Risk refers to events rather than general sources of uncertainty (Petit, 2012)

- Un-measurable – as opposed to risk (Link PRM or similar on wiki)

- Emotional aspect of uncertainty – show difficulty of management

Uncertainty based on knowledge (2 levels) (van Asselt & Rotmans, 2002)

- Epistemic - Aleatoric

Uncertainty based on severity (4 levels) (Meyer et al., 2002)

- Four types of uncertainty:

- Variation

- Activities such as worker sickness, that push the critical path of a project. Often combined small influences

- Foreseen uncertainty

- Identifiable influences that may or may not occur. Very risk management appropriate

- Unforeseen uncertainty

- Basically unknown-unknowns

- Chaos

- When 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.

- Briefly: four types of uncertainty (Millett, 2002)

- 1: Near-term and predictable future

- 2: A limited number of possible outcomes can be assessed

- 3: A range of possible outcomes can be assessed

- 4: Highly uncertain

- Like Meyer, but less tangible. (Mentioned as perspective and as warrant)

- Briefly Uncertainty and environmental learning (Wynne, 1992)

- Risk: Know the odds

- Uncertainty: Know the parameters

- Ignorance: Unknown unknowns

- Indeterminacy: Causal chains or networks open

- Wynne views the levels as “one on the other”, rather than “one or the other”, so that they overlap and expand

Uncertainty based on source (5 levels) ((Riesch, 2013))

1. Uncertainty of the outcome a. Measurable probability (E.g. ”We know what we have done, we know the model, and we know that there is X% chance of this outcome”) 2. Uncertainty of the parameters a. Can be a lack of information. Once information is retrieved, we can predict outcome with probability, P 3. Uncertainty about the model itself a. Different risk assessment models exist, but it can be interpreted as trust in the processes themselves. Often the client of the risk assessment will transfer how much they trust the experts delivering the assessment into how much they trust the model 4. Uncertainty about acknowledged inadequacies and implicitly made assumptions a. Pitfalls (ignoring what we cannot measure, optimism bias) 5. Unknown inadequacies a. Unknown unknowns (Rumsfeld)

Discussion on the merits of each ontology

• Levels based on severity or area of uncertainty • When to use which type of categorisation • Project, program, and portfolio management perspectives

Strategies for mitigation

o Address mode of action for each level - Rapid Knowledge Development (RKD) for Project Managers (Maybe not so great after all) (Kerzner & Saladis, 2009) - Uncertainty-based management (Meyer et al., 2002) o Managing variation:  Critical path method (CPM)  Buffers  Comes with cases o Managing foreseen uncertainty  Risk management (PRM)  Contingency plan o Managing unforeseen uncertainty  “To deal with unforeseen uncertainty, project managers must move from troubleshooting to opportunistic orchestrating and networking”  Comes with many cases o Managing chaos  The need for flexibility and iteration obliges project managers to cope with constant change.  team managers must have a high degree of autonomy.  Rapid prototyping is one way to support such an experimental approach  Companies must be ruthless in cutting projects when the chance of success becomes too small

Examples

- The lottery (Hauke Riesch) - Climate change (Hauke Riesch) - (More to come)

Limitations

- Of this article - Of the uncertainty theory used

Further reading

- Other paradigms - Other wiki-articles

Bibliography

Geraldi, J., Thuesen, C., Oehmen, J., & Stingl, V. (2017). Doing Projects. A Nordic Flavour to Managing Projects: DS-handbook 185:2017. Dansk Standard. Kerzner, H., & Saladis, F. P. (2009). Bringing the PMBOK guide to life : a companion for the practicing project manager. In Bringing the Pmbok® Guide To Life: a Companion for the Practicing Project Manager. Wiley. Meyer, A. de, Loch, C. H., & Meyer, D. (2002). Managing project uncertainty: From variation to chaos Managing project uncertainty. MIT Sloan Management Review, 43(2), 60–67. https://ink.library.smu.edu.sg/lkcsb_research Millett, S. M. (2002). Four levels of uncertainty. Strategy and Leadership, 30(2). https://doi.org/10.1108/sl.2002.26130bae.001 Petit, Y. (2012). Project portfolios in dynamic environments: Organizing for uncertainty. International Journal of Project Management, 30(5), 539–553. https://doi.org/https://doi.org/10.1016/j.ijproman.2011.11.007 Riesch, H. (2013). Levels of Uncertainty. In Springerbriefs in Philosophy (pp. 29–56). Springer Science and Business Media B.V. https://doi.org/10.1007/978-94-007-5455-3_2 van Asselt, M. B. A., & Rotmans, J. (2002). Uncertainty in Integrated Assessment Modelling. Climatic Change, 54(1), 75–105. https://doi.org/10.1023/A:1015783803445 Wynne, B. (1992). Uncertainty and environmental learning: Reconceiving science and policy in the preventive paradigm. Global Environmental Change, 2(2), 111–127. https://doi.org/https://doi.org/10.1016/0959-3780(92)90017-2