Biases in Project Management

Revision as of 12:28, 19 February 2021

Abstract

The human mind is an effective and powerful tool. However, it is not without faults and has some limitations which causes biases. Biases occur in everyday activities and decisions, it is the brains' way of dealing with uncertainty and complexity, often in wrong ways. There are so many seemingly trivial decisions made on the basis of biases, whether it is choosing a cola drink or which car to buy. In this article cognitive biases are examined, with most emphasis on optimism bias since it is a very important factor in project management, specifically in risk assessments. Cognitive Bias also includes other topics such as Gender Bias, Stereotyping and Information Bias. The notion of biases has evolved through time and the understanding of them has been steadily increasing. These biases are very important in a team setting and therefore fall under the realm of project management. It can be found in project management literature when team building is discussed e.g. in Guide to the Project Body of Knowledge (PMBOK® Guide) where Interpersonal and Team Skills or Expert Skills are mentioned. ^[1] Project managers have a tendency to overestimate benefits and underestimate cost i.e., be too optimistic. This is known as “Optimism Bias” and is widely accepted as a key reason for overruns in projects, especially in large infrastructure projects. ^[2]

Being aware of these biases is crucial for all project managers in order to be able to offset them. By acknowledging biases and applying appropriate measures, it is possible to counter the effects.

In this article these biases related to Project Management are examined in more detail. How these biases can be seen in project management and measures to counter them are presented as well as how they can be applied and when. Finally, some limitations are considered and topics for further reading recommended.

The Big Idea

What is bias?

The definition of bias in the Oxford dictionary is split in four meanings, two of whom are relevant in project management and will be addressed in this article:

1. “a strong feeling in favour of or against one group of people, or one side in an argument, often not based on fair judgement.” ^[3]
2. “the fact that the results of research or an experiment are not accurate because a particular factor has not been considered when collecting the information.” ^[3]

The first definition is tied to people and communications between either team members or stakeholders. The latter can be more related to uncertainty and risk management.

Cognitive Bias

The concept of cognitive bias was coined by Amos Tversky and Daniel Kahneman in the 1970s. Cognitive biases are a result of your mind trying to simplify things when the human mind is forced to make a decision and deal with complexity or uncertainty. This can lead to incorrect judgement and as a result a miscalculation of the situation or project at hand. Kahneman and Tversky found that these errors were often systematic rather than random and therefore they are sometime referred to as Systematic Biases. Systematic Biases are frequent distortions in the human mind, often contrary to rational thought. ^[4] Cognitive biases can be an advantage when a quick respond is more valuable than an exact right solution. In some situations, it is crucial to make decisions timely for example when in life threatening situations. Project managers are usually not faced with those situations in their professional life and accuracy is often preferred over quick responses so biases are more often seen as disadvantages in project management.

Wikipedia has a page dedicated to different types of biases titled “List of cognitive biases” which contains 185 different types. Only a few will be mentioned in this article. The following section introduce these biases with some basic definitions. ^[5]

Gender Bias Gender Bias is quite self-explanatory, people are misjudged based on gender. Typically, women are not recognized as equals to men in fields that have been male dominated through the years. ^[6]

Stereotyping

Racial Bias

Confirmation Bias

Misconceptions of chance / The Gambler's Fallacy A gambler who has been on a losing streak feels he is due to win soon even though each game is independent of the other. ^[4]

Overconfidence effect Then the team or an individual team member is overconfident without any evidence supporting their belief. ^[7]

Recency illusion Too much emphasis is put on recent data, often older data is more relevant. ^[7]

Groupthink occurs then team members think alike, and they do not accept evidence that proves otherwise. ^[7]

Conservatism when team members is will not take into consideration new information or any negative feedback. ^[7]

Optimism Bias

What have we learned from the past? A large number of recent projects have had cost overruns and/or demand shortfalls. It is widely accepted that Optimism Bias is to blame for these miscalculations. Project Managers tend to be too optimistic when calculating the benefits of projects and downplay the costs. This is most evident in large infrastructure projects, especially in the public sector where politics play a big role. ^[2]

Bent Flyvbjerg of Oxford University, wrote a paper about megaprojects in 2014 where he lists many past projects with cost overruns and how large the overrun was. Five of these projects have a cost overrun of over 1000% The highest overrun is the Suez Canal with a cost overrun of 1900% These numbers are unacceptable since the methods and technology to predict these numbers is available. Project managers must put more emphasis on cost and benefit estimations.^[8]

Make and Preston documented 21 sources of error and biases in transport project appraisal e.g. double counting or interactions that are not taken into consideration in models, unclear objectives, and incorrect definitions of study area, base or assumptions to name a few. All these factors contribute to the last one, Optimism Bias. Benefits are sometimes counted more than once, quantifiable costs excluded, and the asset live is overestimated. In their paper they call it Appraisal Optimism and say it is the greatest problem of all. They suggest three solutions, an in-house group to ensure honest appraisals, more transparency to the public and extra emphasis on ex-post evaluation. ^[9]

Application

How and when these biases are applied in project management and how to combat them. Reference Class Forecasting, Uplift factors etc.

Limitations

When is it impossible to offset biases.

Annotated bibliography

The following resources are the key resources used for this article, and can provide basis for further and deeper studies on the topic.

1. Leleur, S., Salling, K.B., Pilkauskiene, I. and Nicolaisen, M.S. (2015). Combining Reference Class Forecasting with Overconfidence Theory for Better Risk Assessment of Transport Infrastructure. The European Journal of Transport and Infrastructure Research, 15(3), 362-375.

This article highlights the importance of risk assessment in infrastructure projects since large number of projects have has cost overruns. It explains the concept of Optimism Bias and provides measures to combat it. The article recommends using Reference Class Forecasting, overconfidence theory and to interpret expert judgements about benefit and cost estimation, and provides instructions on these methods.

2. Tversky, A. and Kahneman, D. (1974) Judgement under Uncertainty: Heuristics and Biases. Science, New Series, 185(4157), 1124-1131.

This article was written by two psychologists in 1974 and was revolutionary within the field of psychology. It introduced the idea of Heuristics and Cognitive Biases and provided basic examples. Tversky and Kahneman wrote many other articles about this subject, all of whom are interesting and relevant to biases. The article has many examples but focuses on three heuristics employed in decisions under uncertainty, namely Representativeness, Availability of instances and Adjustments from an anchor. This article explains these heuristics and biases in very simple terms and is therefore a good reading for those interested in the psychology aspect of biases.

References

1. ↑ Project Management Institute, Inc.(PMI). (2017). Guide to the Project Management Body of Knowledge (PMBOK® Guide) (6th Edition). Retrieved on February 9th 2021 from https://app.knovel.com/hotlink/toc/id:kpGPMBKP02/guide-project-management/guide-project-management.
2. ↑ ^{2.0 2.1} Leleur, S., Salling, K.B., Pilkauskiene, I. and Nicolaisen, M.S. (2015). Combining Reference Class Forecasting with Overconfidence Theory for Better Risk Assessment of Transport Infrastructure. The European Journal of Transport and Infrastructure Research (EJTIR), 15(3), 362-375. Retrieved on February 10th 2021 from https://www.researchgate.net/publication/275213953_Combining_Reference_Class_Forecasting_with_Overconfidence_Theory_for_Better_Risk_Assessment_of_Transport_Infrastructure_Investments .
3. ↑ ^{3.0 3.1} Oxford University Press. (2021). bias noun. Retrieved from https://www.oxfordlearnersdictionaries.com/definition/english/bias_1?q=bias on February 9th 2021.
4. ↑ ^{4.0 4.1} Tversky, A. and Kahneman, D. (1974) Judgement under Uncertainty: Heuristics and Biases. Science, New Series, 185(4157), 1124-1131. Retrieved on February 10th from http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=4F92E2FFA38970D381524DF81AF1D10F?doi=10.1.1.207.2148&rep=rep1&type=pdf
5. ↑ Wikipedia. List of cognitive biases. Retrieved on February 10th from https://en.wikipedia.org/wiki/List_of_cognitive_biases
6. ↑ Pinto, J. K., Taranakul, P. and Pinto, M. B. (2017). “The aura of capability”: Gender bias in selection for a project manager job. International Journal of Project Management, 35(3), 420-431. Retrieved on February 11th from https://www.sciencedirect.com/science/article/pii/S0263786317300297?casa_token=eTCIzCrkRNcAAAAA:fNB7WE_DQurrkYOPS6EukGS3VC7Uelk63TKAOgGiuEtawgXtKMq4mZacbm8nvoq9178g0MnnvA#bb0225
7. ↑ ^{7.0 7.1 7.2 7.3} Shore, B. (2008). Systematic Biases and Culture in Project Failures. Project Management Journal, 39(4), 5–16. Retrieved on 14th of February from https://journals.sagepub.com/doi/pdf/10.1002/pmj.20082
8. ↑ Flyvbjerg, B. (2014). What You Should Know About Megaprojects and Why: An Overview. Prjoect Management Journal 45(2), 6-19. Retrieved on February 12th from https://journals-sagepub-com.proxy.findit.dtu.dk/doi/abs/10.1002/pmj.21409
9. ↑ Make, P. and Preston, J. (1998). Twenty-one sources of error and bias in transport project appraisal. Transport Policy, 5(1), 1-7. Retrieved on February 10th from https://www.sciencedirect.com/science/article/pii/S0967070X98000043