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The Peter principle revisited: A computational study

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  • Pluchino, Alessandro
  • Rapisarda, Andrea
  • Garofalo, Cesare

Abstract

In the late sixties the Canadian psychologist Laurence J. Peter advanced an apparently paradoxical principle, named since then after him, which can be summarized as follows: ‘Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence’. Despite its apparent unreasonableness, such a principle would realistically act in any organization where the mechanism of promotion rewards the best members and where the competence at their new level in the hierarchical structure does not depend on the competence they had at the previous level, usually because the tasks of the levels are very different to each other. Here we show, by means of agent based simulations, that if the latter two features actually hold in a given model of an organization with a hierarchical structure, then not only is the Peter principle unavoidable, but also it yields in turn a significant reduction of the global efficiency of the organization. Within a game theory-like approach, we explore different promotion strategies and we find, counterintuitively, that in order to avoid such an effect the best ways for improving the efficiency of a given organization are either to promote each time an agent at random or to promote randomly the best and the worst members in terms of competence.

Suggested Citation

  • Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2010. "The Peter principle revisited: A computational study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 467-472.
  • Handle: RePEc:eee:phsmap:v:389:y:2010:i:3:p:467-472
    DOI: 10.1016/j.physa.2009.09.045
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    Cited by:

    1. L. S. Di Mauro & A. Pluchino & A. E. Biondo, 2018. "A Game of Tax Evasion: evidences from an agent-based model," Papers 1809.08146, arXiv.org.
    2. Amanda Goodall & Margit Osterloh & Mandy Fong, 2020. "Women Shy Away From Competition – How To Overcome It," CREMA Working Paper Series 2020-21, Center for Research in Economics, Management and the Arts (CREMA).
    3. Biondo, A.E. & Pluchino, A. & Rapisarda, A., 2018. "Modeling surveys effects in political competitions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 714-726.
    4. Alessandro Pluchino & Alessio Emanuele Biondo & Andrea Rapisarda, 2018. "Talent Versus Luck: The Role Of Randomness In Success And Failure," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-31, May.
    5. Inturri, Giuseppe & Le Pira, Michela & Giuffrida, Nadia & Ignaccolo, Matteo & Pluchino, Alessandro & Rapisarda, Andrea & D'Angelo, Riccardo, 2019. "Multi-agent simulation for planning and designing new shared mobility services," Research in Transportation Economics, Elsevier, vol. 73(C), pages 34-44.
    6. A. E. Biondo & G. Burgio & A. Pluchino & D. Puglisi, 2022. "Taxation and evasion: a dynamic model," Journal of Evolutionary Economics, Springer, vol. 32(3), pages 797-826, July.
    7. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    8. Javarone, Marco Alberto, 2014. "Social influences in opinion dynamics: The role of conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 19-30.
    9. Alessandro Pluchino & Giulio Burgio & Andrea Rapisarda & Alessio Emanuele Biondo & Alfredo Pulvirenti & Alfredo Ferro & Toni Giorgino, 2019. "Exploring the role of interdisciplinarity in physics: Success, talent and luck," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    10. Fetta, A.G. & Harper, P.R. & Knight, V.A. & Vieira, I.T. & Williams, J.E., 2012. "On the Peter Principle: An agent based investigation into the consequential effects of social networks and behavioural factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(9), pages 2898-2910.
    11. Pluchino, Alessandro & Garofalo, Cesare & Rapisarda, Andrea & Spagano, Salvatore & Caserta, Maurizio, 2011. "Accidental politicians: How randomly selected legislators can improve parliament efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3944-3954.
    12. , Aisdl, 2020. "Becoming Attuned," OSF Preprints j7f8y, Center for Open Science.
    13. A. E. Biondo & A. Pluchino & A. Rapisarda & D. Helbing, 2013. "Are random trading strategies more successful than technical ones?," Papers 1303.4351, arXiv.org, revised Jul 2013.
    14. Farias, B. & Rapôso, O. & Penna, T.J.P. & Girardi, D., 2021. "The Peter Principle and learning: A safer way to promote workers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    15. Nadia Giuffrida & Michela Le Pira & Giuseppe Inturri & Matteo Ignaccolo & Giovanni Calabrò & Blochin Cuius & Riccardo D’Angelo & Alessandro Pluchino, 2020. "On-Demand Flexible Transit in Fast-Growing Cities: The Case of Dubai," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    16. Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2011. "Efficient promotion strategies in hierarchical organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3496-3511.
    17. Inturri, Giuseppe & Giuffrida, Nadia & Ignaccolo, Matteo & Le Pira, Michela & Pluchino, Alessandro & Rapisarda, Andrea & D'Angelo, Riccardo, 2021. "Taxi vs. demand responsive shared transport systems: An agent-based simulation approach," Transport Policy, Elsevier, vol. 103(C), pages 116-126.
    18. João Ricardo Faria & Franklin G. Mixon, 2020. "The Peter and Dilbert Principles applied to academe," Economics of Governance, Springer, vol. 21(2), pages 115-132, June.
    19. , Aisdl, 2020. "The Serendipity Mindset," OSF Preprints w52y9, Center for Open Science.
    20. Cheng, Yuan & Chang, Meng & Xue, Yanbo, 2020. "A computational study of promotion dynamics and organizational efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    21. Pawel Sobkowicz, 2010. "Dilbert-Peter Model of Organization Effectiveness: Computer Simulations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-4.
    22. Sobkowicz, Pawel, 2016. "Agent based model of effects of task allocation strategies in flat organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 17-30.
    23. Caserta, Maurizio & Pluchino, Alessandro & Rapisarda, Andrea & Spagano, Salvatore, 2021. "Why lot? How sortition could help representative democracy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    24. Udhayanan, Prateksha & Mishra, Swasti S. & Rao, Shrisha, 2021. "Firm dynamics and employee performance management in duopoly markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    25. Alessio Emanuele Biondo & Alfio Giarlotta & Alessandro Pluchino & Andrea Rapisarda, 2016. "Perfect Information vs Random Investigation: Safety Guidelines for a Consumer in the Jungle of Product Differentiation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-26, January.

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