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Unrecognized interdependencies in mental models—The case for taking feedback loops into account

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  • Martin Schaffernicht

Abstract

This article attempts to bridge two streams of research: on the one hand, system dynamics research investigates the “misperception of feedback” focusing on decision rules rather than mental models. On the other hand, mental model research in organizational and management studies does not take interdependencies into account. The article examines data from an interview‐based study with nine vineyard executives and finds that 47 of the 219 elicited variables belong to one or several of 49 feedback loops inherent in the structure of the elicited mental models. However, 44 of these loops are “dark”: They have not been recognized by the executives. Not taking loops into account increases the likelihood of misjudging the situation. It is concluded (a) that mental model research methods should account for loops in order to address the problem of dark loops (b) and that executives and researchers need elicitation tools that automatically detect and visualize loops.

Suggested Citation

  • Martin Schaffernicht, 2019. "Unrecognized interdependencies in mental models—The case for taking feedback loops into account," Systems Research and Behavioral Science, Wiley Blackwell, vol. 36(4), pages 580-603, July.
  • Handle: RePEc:bla:srbeha:v:36:y:2019:i:4:p:580-603
    DOI: 10.1002/sres.2572
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    Cited by:

    1. Nicholas Valcourt & Jeffrey Walters & Amy Javernick‐Will & Karl Linden, 2020. "Assessing the efficacy of group model building workshops in an applied setting through purposive text analysis," System Dynamics Review, System Dynamics Society, vol. 36(2), pages 135-157, April.

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