Learning from Mistakes Within Organizations: An Adaptive Network-Oriented Model for a Double Bias Perspective for Safety and Security Through Cyberspace

Mojgan Hosseini, Jan Treur*, Wioleta Kucharska

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Although making mistakes is a crucial part of learning, it is still often being avoided in companies as it is considered as a shameful incident. This goes hand in hand with a mindset of a boss who dominantly believes that mistakes usually have negative consequences and therefore avoids them by only accepting simple tasks. Thus, there is no mechanism to learn from mistakes. Employees working for and being influenced by such a boss also strongly believe that mistakes usually have negative consequences but in addition they believe that the boss never makes mistakes, it is often believed that only those who never make mistakes can be bosses and hold power. That's the problem, such kinds of bosses do not learn. So, on the one hand, we have bosses who select simple tasks to be always seen as perfect. Therefore, also they believe they should avoid mistakes. On the other hand, there exists a mindset of a boss who is not limited to simple tasks, he/she accepts more complex tasks and therefore in the end has better general performance by learning from mistakes. This then also affects the mindset and actions of employees in the same direction. This chapter investigates the consequences of both attitudes for the organizations. It does so by computational analysis based on an adaptive dynamical systems modeling approach represented in a network format using the self-modeling network modeling principle.

Original languageEnglish
Title of host publicationUsing Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach
EditorsPeter H.M.P. Roelofsma, Fakhra Jabeen, H. Rob Taal, Jan Treur
PublisherSpringer Nature Switzerland AG
Pages337-350
Number of pages14
ISBN (Electronic)9783031720758
ISBN (Print)9783031720741, 9783031720772
DOIs
Publication statusPublished - 2024

Publication series

NameStudies in Systems, Decision and Control
PublisherSpringer
Volume570
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Bibliographical note

First Online: 03 January 2025.

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Adaptive network model
  • Double bias
  • Learning from mistakes

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