Top Ten Behavioral Biases in Project Management

Revision as of 10:13, 5 April 2023

Abstract

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 cognitive and political biases that can have a great significance on project management. This wiki article presents the top ten behavioral biases identified by Flyvbjerg, the applications aimed at reducing the impact of biases in project management, and limitations to the view on planning and uncertainty proposed in the article. The content of this wiki article includes:

Big idea

• 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.
• Flyvbjerg’s research are 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 in Ten Behavioral Biases in Project Management: An Overview is an exploration of some of the underlying causes of the Iron Law.

The ten biases highlighted in the article (directly quoted from Flyvbjerg, 2021).

• "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 ones own answers to questions.
• Hindsight bias: The tendency to see past events as being predictable at the time does 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 informationwhen 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."

What is a bias?

• Biases are according to the APA defined as “…any tendency or preference”.
• Kahnemann and Tversky played a key role in promoting the role of biases in judgment and decision making.
• Their research has had a particular significant role in the field of behavioral economics, and they won a nobel prize in economy or their work.
• Destinction between a cognitive and a political bias
• Cognitive bias: a tendency which root cause is psychological. The bias leads to self-deception.
• Political bias: a tendency which root cause is political. The subject is aware of the deception.

With what purpose is it relevant to highlight the ten most important biases?

• Flyvbjerg believes 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 megaprojects.
• The relative dominance of cognitive and political biases is influenced by the degree of political-organizational pressures.
• Political bias can amplify cognitive bias.
• The biases, if not mitigated, contribute to maintaining the power of the Iron Law.

Application

Tool from Flyvbjerg: reference class forecasting (Flyvbjerg, 2006).

• A tool designed to mitigate both cognitive and political biases in decision making.
• Approach
  • Identify a relevant reference class of past, similar projects
  • Establish a probability distribution for the selected reference class.
  • Compaire the specific project with the reference class distribution, in order to establish the most likely outcome for the specific project.
• Purpose: regressing the forecasters best guess towards the average of the reference class and expanding their credible interval towards the corresponding interval for the class (Flyvbjerg, 2006, p. 7).

Limitations

How does the theory explained in Flyvbjerg’s article relate to the status quo of management standards?

• The PMBOK standard and guide (2021). A guide
• The uncertainty performance domain includes Uncertainty, ambiguity, complexity, volatility and risk. These are all relevant for the key notions in Flyvbjergs article.
• The PMBOK guide recommends to respond to uncertainty by:
• Gather information
• Prepare for multiple outcomes
• Look at trade-offs for different designs or alternatives early in the project
• Build in resilience to unexpected changes
• The differences between the PMBOK guide and Flyvbjerg’s explanation of uncertainty
• Flyvbjerg (2006) warns about adopting an inside view when planning new projects. The PMBOK guide suggests to collect relevant data to prepaire for uncertainty, but does not specifically address what kind of information is relevant. Flyvbjerg (2006) suggests that there are situations where one should not rely on an intuitive processing of information and apply a critical evaluation of the evidence.

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