Top Ten Behavioral Biases in Project Management
Abstract
This wiki article describes key notions on the research on bias’ and fallacies’ influence on decision making in project management conducted by Bent Flyvbjerg. Bent Flyvbjerg is an esteemed professor within the field of project management, especially specialized in the management of megaprojects. Bent Flyvbjerg highlighted in 2021 what he considers to be the ten most important biases and fallacies influencing project management. This selection build on his extensive research within this area. In the discussion of the ten most important biases, he highlights the distinction between political biases and cognitive biases, and how the approach to mitigate the two types should differ. in This selection of biases build on his extensive research on the subject of behavioral sciences influence on project management. Flyvbjerg has developed a method that project managers can apply to mitigate political- and cognitive biases called reference class forecasting, which suggest that project managers apply an outside view on their project forecasting. This is done by identifying similar, completed projects and analyze the probability of a forecast based on the performance of these past projects. Flyvbjerg’s research can be seen as complimentary to project management guides such as the PMBOK guide, but seems to some degree to be based on a different ontological interpretation of the landscape of project management than the PMBOK guide. Application of reference class forecasting entails some limitations, for example the availability of valid data, as well as the sometimes limited autonomy of the individual project manager to change procedures.
Introduction
According to the PMBOK guide, project management can be defined as the application of knowledge, skills, tools and techniques to project activities to meet project requirements (Project Management Institute, 2021, p. 4). This definition places a large responsibility on the cognition of a project manager to ensure success. Bent Flyvbjerg’s article Top Ten Behavioral Biases in Project Management: An overview (2021) challenges the rationality of decision making, as he highlights the ten cognitive and political biases he believes influence project management the most. This wiki article presents the 10 behavioral biases identified by Flyvbjerg, the method Flyvbjerg has developed to reduce the impact of biases in project management, with a focus on the biases involved in forecasting. Limitations to the view on planning and uncertainty is also proposed in the article.
Bent Flyvbjerg is an esteemed professor, currently positioned at the Danish institute for IT Program management at ITU. He is conducting research within the realms of social sciences, including research on megaproject and their planning, as well as decision making. For more information about Bent Flyvbjerg’s collected contributions, see the wiki page for Bent Flyvbjerg. Flyvbjerg’s research is especially concerned with what is referred to as the iron law of projects: “over time, over budget, under benefits, over and over again” (Flyvbjerg, 2017). The ten biases highlighted in Flyvbjerg’s article (2021) Ten Behavioral Biases in Project Management: An Overview is an exploration of some of the underlying causes of the Iron Law.
A quick definition of bias and fallacy
Biases are according to the APA defined as “…any tendency or preference”, whereas a fallacy is defined as; “an error in reasoning or argument that leads to a conclusion that may appear valid but is actually invalid”(APA). The two psychologists Daniel Kahnemann and Amos Tversky played a significant role in pushing forward research that investigates the effect of biases and fallacies in judgment and decision making (Flyvbjerg, 2021). Their research has had a particular significant role in the field of behavioral economics, and Kahneman won in 2002 the nobel prize for their research contributions to the field of economy. For more information regarding the history of biases in behavioral economics, please see the Wikipedia articles on Daniel Kahnemann, Amos Tversky and Behavioral Economics.
Bent Flyvbjerg build on top of Kahnemann and Tversky’s research, but emphasizes a distinction between a political- and a cognitive bias. The former is a tendency which root cause is political, and where the subject is aware of the deception. The latter is a tendency which root cause is psychological, and where the bias leads to self-deception (Flyvbjerg, 2021, p. 532).
A quick definition of bias and fallacy
| Bias | Description |
|---|---|
| Strategic misrepresentation | The tendency to deliberately and systematically distort or misstate information or strategic purposes. Aka political bias. |
| Optimism bias | The tendency to be overly optimistic about the outcome of planned actions, including overestimation of the frequency and size of positive events and underestimation of the frequency and size of negative ones. |
| Uniqueness bias | The tendency to see ones project as more singular than it actually is. |
| Planning fallacy | The tendency to underestimate cost, schedule and risk and overestimate benefits and opportunities. |
| Overconfidence bias | The tendency to have excessive confidence in one’s own answers to questions. |
| Hindsight bias | The tendency to see past events as being predictable at the time these events happened. Also known as the knew-it-all-along effect. |
| Availability bias | The tendency to overestimate the likelihood of events with greater ease of retrieval (availability) in memory. |
| Base rate fallacy | The tendency to ignore generic base rate information and focus on specific information pertaining to a certain case or small sample. |
| Anchoring | The tendency to rely too heavily, or “anchor” one trait or piece of information when making decisions, typically the first piece of information acquired on the relevant subject. |
| Escalation of commitment | The tendency to justify increased investment in a decision, based on the cumulative prior investment, despite new evidence suggesting the decision may be wrong. Also known as the sunk cost fallacy. |
With what purpose is it relevant to highlight the ten most important biases in project management?
The interpretation of the root cause for phenomena such as cost overruns, delays, and benefit short-runs in investment decisions greatly affects the proper response to these phenomena, and is therefore a central question for project managers.
Flyvbjerg (2021) claims the role of cognitive biases is too emphasized in project management, in relation to political biases. He highlights strategic misrepresentation as the most important bias in forecasting in megaprojects, where the political-organizational pressures are high, whereas cognitive biases, such as optimism bias, are mainly explanatory when outside pressures remain low.
Application: Mitigating biases in management forecasting
One of the management tasks vulnerable to biases and fallacies is forecasting of project prize, demand and other impacts, as this is a task that typically entails a high level of uncertainty. Flyvbjerg has developed a method to mitigate both cognitive and political biases called reference class forecasting, which is described in his article From Nobel Prize to Project Management: Getting Risks Right (2006).
Flyvbjerg (2006) argues that biases are mainly to blame for inaccurate forecasting in megaprojects, as collected data shows that inaccuracies are not normally distributed around zero, and professions in which megaprojects are conducted does not seem to show increased accuracy over time.
The reference class forecasting method consists of three steps (Flyvbjerg, 2006, p. 7): 1. Identify a relevant reference class of past, similar projects. 2. Establish a probability distribution for the selected reference class. 3. Compare the specific project with the reference class distribution, in order to establish the most likely outcome for the specific project.
The purpose of this method is to incorporate an outside view on the project at hand. Flyvbjerg explains why this method works to mitigate biases: “In the outside view project managers and forecasters are not required to make scenarios, imagine events, or gauge their own and others' levels of ability and control, so they cannot get all these things wrong. Human bias is bypassed” (Flyvbjerg, 2006, p. 9).
The method requires that there is a sufficient amount of empirical data to make sufficiently robust conclusions regarding the probability distribution for the reference class. If political bias is the most dominating course of inaccurate forecasting, a more fundamental change in the forecasting climate across organizations will likely be needed, to a system where accuracy is rewarded (Flyvbjerg, 2016).
Where is the theory and tool described by Flyvbjerg usefull?
- Flyvbjerg’s highlight on biases is mostly covering the aspect of ambiguity in the uncertainty performance domain. Flyvbjerg’s reference class forecasting could be a valuable addition to the other options to solve situational ambiguity.
- Other aspects of uncertainty, such as volatility, is not explained by biases in decision making.
- Biases in decision making is also relevant to the aspect of complexity. Maylor & Turner (2016) references Cicmil et al (2009) in distinguishing between two types of complexity: Complexity in projects, and complexity of projects. Whereas complexity in projects denotes a more rationalistic approach to complexity, the complexity of projects references the subjective experience of complexity for managers. One could argue that the biases highlighted in Flyvbjergs (2021) article describes common response patterns to the complexity of projects.
- Maylor & Turner (2016) differentiates between structural complexities, socio-political complexities and emergent complexities. Flyvbjerg (2021) emphasizes the role of socio-political complexity, as he highlights the political bias of strategic misrepresentation as a core bias in project management.
Annotated bibliography
- Flyvbjerg, B. (2021). Top Ten Behavioral Biases in Project Management: An Overview. Project Management Journal, Volume 52, Issue 6, December 2021, Pages 531-546. The main article described in this wiki article, identifying the ten most important biases for project management, their impact on project management. Includes a reflection on what can be done to mitigate the effects, thus heighten the chances of successful management.
- Project Management Institute (2021). The standard for project management and a guide to the project management body of knowledge (PMBOK guide). Seventh edition. Newtown Square, Pennsylvania: Project Management Institute, Inc A collection of fundamental concepts and constructs of the project management profession, divided into 12 principles of project management and eight project performance domains.
- Tversky, A. & Kahneman, D. (1974). Judgment Under Uncertainty: Heuristics and Biases. Science, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-1131. A walkthrough of three types of heuristics employed to assess probabilities and the biases that occur in decision making as a result.
- Flyvbjerg, B. (2006) From Nobel Prize to Project Management: Getting Risk Right. Project Management Journal, vol. 37, no. 3, August 2006, pp. 5-15. An approach to mitigating risk in forecasting projects, focusing on analyzing a group of similar projects as preparation.
- Kreiner, K. (2020). Conflicting Notions of a Project: The Battle Between Albert O. Hirschman and Bent Flyvbjerg. Project Management Journal, 51(4), 400-410. https://doi.org/10.1177/8756972820930535. A discussion of Albert O. Hirshman’s notion of the Hiding Hand and Bent Flyvbjerg’s notion that it is possible to construct better forecasting in megaprojects.
Other sources
- Bent Flyvbjerg ITU: https://pure.itu.dk/da/persons/bent-flyvbjerg
- APA Dictionary of Psychology: https://dictionary.apa.org/bias
- Flyvbjerg, Bent, 2017, "Introduction: The Iron Law of Megaproject Management", in Bent Flyvbjerg, ed., The Oxford Handbook of Megaproject Management(Oxford: Oxford University Press), pp. 1-18.
- •Maylor & Turner (2016) Understand, reduce, respond: Project complexity management theory and practice. International Journal of Operations & Production Management, Vol. 37 No. 8, 2017
pp. 1076-1093, Emerald Publishing Limited 0144-3577, DOI 10.1108/IJOPM-05-2016-0263
