A comprehensive method for comparing mental models of dynamic systems

Abstract

Schaffernicht, M (Schaffernicht, Martin). Univ Talca, Fac Ciencias Empresariales, Talca 3460000, ChileMental models are the basis on which managers make decisions even though external decision support systems may provide help. Research has demonstrated that more comprehensive and dynamic mental models seem to be at the foundation for improved policies and decisions. Eliciting and comparing such models can systematically explicate key variables and their main underlying structures. In addition, superior dynamic mental models can be identified. This paper reviews existing studies which measure and compare mental models. It shows that the methods used to compare such models lack to account for relevant aspects of dynamic systems, such as, time delays in causal links, feedback structures, and the polarities of feedback loops. Mental models without those properties are mostly static models. To overcome these limitations of the methods to compare mental models, we enhance the widely used distance ratio approach (Markoczy and Goldberg, 1995) so as to comprehend these dynamic characteristics and detect differences among mental models at three levels: the level of elements, the level of individual feedback loops, and the level of the complete model. Our contribution lies in a new method to compare explicated mental models, not to elicit such models. An application of the method shows that this previously non-existent information is essential for understanding differences between managers' mental models of dynamic systems. Thereby, a further path is created to critically analyze and elaborate the models managers use in real world decision making. We discuss the benefits and limitations of our approach for research about mental models and decision making and conclude by identifying directions for further research for operational researchers. (C) 2010 Elsevier B.V. All rights reserved

Complexity Management and System Dynamics Thinking

“…The mental model approach to dynamic systems has been developed to elicit managerial cognitions about dynamic situations to represent these cognitions, and to analyse the mental models with the objective of improving decision-making. The most recent methods of elicitation and comparison can be found in (Schaffernicht and Groesser 2011;Groesser and Schaffernicht 2012;Schaffernicht and Groesser 2014).…”

Critical Learning Incidents in system dynamics modelling engagements

“…Beginning with its earliest mentions in the work of Forrester (1975a), researchers have treated system dynamics methodology as a means for individuals-whether on their own (Schaffernicht & Groesser (2011)) or in groups (Scott, Cavana, & Cameron (2014))-to better understand interesting experiences by considering cause and effect differently. System dynamics methods explicitly assume that people have one or more mental models through which their experience is processed to make sense of it and that a change in how one makes sense of experience is evidence of learning (Senge (1990); Richardson, Andersen, Maxwell, and Stewart (1994); Morecroft (2004);and Kim (2009)).…”

Distance Ratio: An Exploratory Application to Compare Complex Networks

“…[8] In this context a system is represented by elements connect by causal relations with positive or negative impact within them. It is possible to identify the existence of self-loops, when an element has an impact on itself.…”