scholarly journals Top Ten Behavioral Biases in Project Management: An Overview

2021 ◽  
Vol 52 (6) ◽  
pp. 531-546
Author(s):  
Bent Flyvbjerg
Keyword(s):  
Project Management ◽  
Cognitive Bias ◽  
Behavioral Science ◽  
Hindsight Bias ◽  
Base Rate ◽  
Behavioral Biases ◽  
Project Outcomes ◽  
Base Rate Neglect ◽  
Planning Fallacy ◽  
Overconfidence Bias

Behavioral science has witnessed an explosion in the number of biases identified by behavioral scientists, to more than 200 at present. This article identifies the 10 most important behavioral biases for project management. First, we argue it is a mistake to equate behavioral bias with cognitive bias, as is common. Cognitive bias is half the story; political bias the other half. Second, we list the top 10 behavioral biases in project management: (1) strategic misrepresentation, (2) optimism bias, (3) uniqueness bias, (4) the planning fallacy, (5) overconfidence bias, (6) hindsight bias, (7) availability bias, (8) the base rate fallacy, (9) anchoring, and (10) escalation of commitment. Each bias is defined, and its impacts on project management are explained, with examples. Third, base rate neglect is identified as a primary reason that projects underperform. This is supported by presentation of the most comprehensive set of base rates that exist in project management scholarship, from 2,062 projects. Finally, recent findings of power law outcomes in project performance are identified as a possible first stage in discovering a general theory of project management, with more fundamental and more scientific explanations of project outcomes than found in conventional theory.

BASE RATE NEGLECT FOR THE WEALTH OF INTERACTING PEOPLE

2010 ◽  
Vol 13 (05) ◽  
pp. 607-619 ◽  
Author(s):  
DIEMO URBIG
Keyword(s):  
Information Processing ◽  
Social Learning ◽  
Learning Process ◽  
Base Rate ◽  
Human Information Processing ◽  
Base Rates ◽  
Base Rate Neglect ◽  
Positive Externalities ◽  
Rate Information

Previous research investigating base rate neglect as a bias in human information processing has focused on isolated individuals. This study complements this research by showing that in settings of interacting individuals, especially in settings of social learning, where individuals can learn from one another, base rate neglect can increase a population's welfare. This study further supports the research arguing that a population with members biased by neglecting base rates does not need to perform worse than a population with unbiased members. Adapting the model of social learning suggested by Bikhchandani, Hirshleifer and Welch (The Journal of Political Economy100 (1992) 992–1026) and including base rates that differ from generic cases such as 50–50, conditions are identified that make underweighting base rate information increasing the population's welfare. The base rate neglect can start a social learning process that otherwise had not been started and thus base rate neglect can generate positive externalities improving a population's welfare.

An Introduction to Project End Theory in Project Management

2017 ◽  
Vol 8 (3) ◽  
pp. 69-81 ◽  
Author(s):  
Umar A. Altahtooh ◽  
Margaret W. Emsley
Keyword(s):  
Project Management ◽  
Mixed Method ◽  
Critical Incident ◽  
Critical Incident Technique ◽  
Time Error ◽  
Management Software ◽  
Project Duration ◽  
Project Outcomes ◽  
Execution Phase ◽  
Almost All

Almost all project management software has lack of displaying the outcomes of projects as a feature. This is because there is no recognized way of calculating the actual project duration compared to the authorized project duration. Data were collected through a mixed method using a Critical Incident Technique (CIT) and a survey. The study finds that time error can occur throughout the project execution phase. Findings suggest that challenged projects could be successful or failed projects using a model of IT Project Outcomes Testing (MITPOT). Thus, this model establishes a foundation of Project End Theory (PET).

scholarly journals Model-based fMRI reveals dissimilarity processes underlying base rate neglect

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Sean R O'Bryan ◽  
Darrell A Worthy ◽  
Evan J Livesey ◽  
Tyler Davis
Keyword(s):  
Computational Models ◽  
Human Subjects ◽  
Base Rate ◽  
Imaging Data ◽  
Multivoxel Pattern Analysis ◽  
Activation Patterns ◽  
Model Based ◽  
Base Rate Neglect ◽  
Rate Information ◽  
Rare Category

Extensive evidence suggests that people use base rate information inconsistently in decision making. A classic example is the inverse base rate effect (IBRE), whereby participants classify ambiguous stimuli sharing features of both common and rare categories as members of the rare category. Computational models of the IBRE have either posited that it arises from associative similarity-based mechanisms or dissimilarity-based processes that may depend upon higher-level inference. Here we develop a hybrid model, which posits that similarity- and dissimilarity-based evidence both contribute to the IBRE, and test it using functional magnetic resonance imaging data collected from human subjects completing an IBRE task. Consistent with our model, multivoxel pattern analysis reveals that activation patterns on ambiguous test trials contain information consistent with dissimilarity-based processing. Further, trial-by-trial activation in left rostrolateral prefrontal cortex tracks model-based predictions for dissimilarity-based processing, consistent with theories positing a role for higher-level symbolic processing in the IBRE.

scholarly journals Expanding the Weighted Updating Model

2016 ◽  
Author(s):  
Jesse Aaron Zinn
Keyword(s):  
Cognitive Biases ◽  
Bayesian Updating ◽  
Irrelevant Information ◽  
Base Rate ◽  
Order Effects ◽  
Availability Heuristic ◽  
Base Rate Neglect ◽  
Attribution Bias

This work casts light upon a pair of restrictions inherent to the basic weighted updating model, which is a generalization of Bayesian updating that allows for biased learning. Relaxing the restrictions allows for the study of individuals who discriminate between observations or who treat information in a dynamically inconsistent manner. These generalizations augment the set of cognitive biases that can be studied using new versions of the weighted updating model to include the availability heuristic, order effects, self-attribution bias, and base-rate neglect in light of irrelevant information.

The Effect of Organizational Commitment on Project Outcomes under Common Agency

2015 ◽  
Vol 6 (2) ◽  
pp. 50-71
Author(s):  
Bruce E. Hurst ◽  
Michael H. McGivern
Keyword(s):  
Information Technology ◽  
Project Management ◽  
Organizational Commitment ◽  
Project Success ◽  
The United States ◽  
Common Agency ◽  
Organizational Goals ◽  
Project Outcomes ◽  
Information Technology Project ◽  
Definition Of

This research was undertaken to look at possible ways to broaden the definition of project management success beyond the tradition measures of time, cost, and scope. The research was designed to determine if the human organization comprised of layers of relationships and governance would or could change and reduce the high failure rate attributed to IT projects. The human aspect of conflicting organizational goals, those established by the information technology principal and the business unit principal with the project manager acting as agent, was the foundation for the study. The study expanded project management theory as it related to three variables: organizational commitment, project governance, and complex agency relationships. This was accomplished through a survey distributed to 403 information technology project managers in the United States designed to measure the three variables against the dependent variable project success. The results indicated that governance, organizational commitment, and the effects of common agency on a project manager's performance in meeting what might be conflicting success criteria do have a positive correlation. This research could provide new directions for researchers, project management practitioners, and strategic corporate planners when establishing project success criterion.

Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process

2019 ◽  
Vol 12 (3) ◽  
pp. 297-314 ◽  
Author(s):  
Jinesh Jain ◽  
Nidhi Walia ◽  
Sanjay Gupta
Keyword(s):  
Decision Making ◽  
Behavioral Finance ◽  
Investment Decision ◽  
Investment Decisions ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Behavioral Biases ◽  
Investment Decision Making ◽  
Overconfidence Bias ◽  
Hierarchy Process

Purpose Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions. Design/methodology/approach The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias. Findings The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).” Research limitations/implications Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study. Practical implications The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions. Originality/value The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.

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