On the Peter Principle: An agent based investigation into the consequential effects of social networks and behavioural factors

Harper, Paul Robert; Knight, Vincent A.; Vieira, Israel Teixeira; Williams, Janet E.

doi:10.1016/j.physa.2011.12.053

Cited by 9 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One can find other examples on more general topics such as organisational structures (e.g. Fetta et al, 2012).…”

Section: Motivating Empirical Studymentioning

confidence: 99%

Server behaviours in healthcare queueing systems

Harper

2019

Journal of the Operational Research Society

View full text Add to dashboard Cite

In the classical queueing theory literature, a server is commonly assumed to work at a constant speed. Motivated by observations from healthcare applications, a study is made to explore the nature of the relationship between service times and workload in order to assess and quantify any workforce (server) behaviours. Consequently, an initial analytical queueing model is considered with switching thresholds to allow for two-speed service. In this model service time depends on queue length, which for example captures the congestion in the waiting room and the resulting change in speed of the workforce to try and cope with the backlog of patients. Furthermore, related behavioural characteristics resulting from workload fatigue and service breakdown are considered. A developed analytical model with 'catastrophic' service failure is proposed to examine the consequences on patient service levels. The research helps to demonstrate the importance of more accurately capturing server behaviours in workload-dependent environments, and the impact this has on the overall system performance. It is hoped that this paper might help spawn an emergence of behavioural queueing systems literature, retaining the role that queueing theory plays within our field but with enhanced consideration of the role of behaviours when constructing such models.

show abstract

“…One can find other examples on more general topics such as organisational structures (e.g. Fetta et al, 2012).…”

Section: Motivating Empirical Studymentioning

confidence: 99%

Server behaviours in healthcare queueing systems

Harper

2019

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…These considerations, despite the problems mentioned above, have prompted researchers to apply the ABM approach to describe specific workplace environments and the related issues. An example of such modeling may be provided by the series of papers devoted to the effects of various promotion strategies on company productivity [4][5][6][7][8][9][10]. While based on rather simplified assumptions, these works have illuminated some real world effects, and were cited beyond the research literature, in the professional Human Resource management press, as well as receiving the prestigious Ig-Nobel prize for demonstrating mathematically that organizations would become more efficient if promotions were made at random!…”

Section: Introductionmentioning

confidence: 99%

Agent based model of effects of task allocation strategies in flat organizations

Sobkowicz

2016

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

“…A graphical example of a simple regression decision tree is shown in Figure 1. It demonstrates values being predicted, a number of training samples, features chosen for split (X[2], X [4]), and the split error value (MSE, mean squared error).…”

mentioning

confidence: 99%

Using Machine Learning to Overcome the Peter Principle in Modern Hierarchical Organizations

Yurchenko¹

2021

Efektyvna Ekonomika

View full text Add to dashboard Cite

According to the Peter principle, in hierarchical organizations all workers sooner or later become incompetent due to being promoted to positions that don't suit them. That is caused by traditional promotion strategies that favor the most successful employees while it does not always happen to be the best approach. On the other hand, applying unconventional promotion policies might result in negative psychological effects, including drop in personnel motivation. General promotion rules also ignore the possibility that the Peter principle may hold for some hierarchical pathways while remaining inactive for other types of promotions. The purpose of this article is to justify the new way of preventing the Peter principle effect by employing machine learning (ML) models to predict workers' potential competence level they would acquire in case of promotion. The article proposes the algorithm of organizational promotions based on the ML approach. The set of employees' characteristics for the models to be trained on, as well as specific ML algorithms, namely linear-based and tree-based are suggested. Inner working specifics of the algorithms are detailed from the mathematical point of view. The principles of the modern ML training process are described and the training data requirements are highlighted. The advantages of employing a dynamic personalized promotion strategy based on machine learning methods are described. Such a strategy allows treating separate promotions individually, choosing from candidates solely on the basis of the model prediction, thus avoiding the unnecessary usage of unconventional policies and subsequent motivational drops. In addition, ML-based approach opens up possibilities to substantiate promotion choices, as those were derived from actual historical data used for training the model. This excludes a potential sense of prejudice while using multiple features for prediction eliminates the risk of unscrupulous employees' behavior aimed at manipulating their own performance with the purpose of receiving a promotion. Properly implemented, the proposed algorithm can help to eliminate the negative effect of the Peter principle while mostly preserving the motivational component of promotions.

show abstract

On the Peter Principle: An agent based investigation into the consequential effects of social networks and behavioural factors

Cited by 9 publications

References 47 publications

Server behaviours in healthcare queueing systems

Server behaviours in healthcare queueing systems

Agent based model of effects of task allocation strategies in flat organizations

Using Machine Learning to Overcome the Peter Principle in Modern Hierarchical Organizations

Contact Info

Product

Resources

About