Schaffernicht, M (Schaffernicht, Martin).Univ Talca, Fac Ciencias Empresariales, Coll Business Adm, Talca,his paper deals with the representation of the conceptual structure of mental models of dynamic systems (MMDS). Heretofore, this structure has not been consistently defined. Consequently, studies about MMDS continue to use different conceptual structures to measure mental models. Even such properties as feedback loops and delays, which lie at the core of dynamic systems, are often not considered. This situation leads to incompatible findings and stagnating research. We review the literature about mental models in the field of system dynamics. In addition, we refer to dynamic systems theory as the mathematical basis for system dynamics to complement and validate our conceptual structure. One may conclude that most of the existing mental model studies measure only parts of the structure that we propose. The paper's contribution is to elaborate the conceptual structure of an MMDS and to use this structure to operationally enhance the definition of an MMDS. Copyright (c) 2012 System Dynamics Societ

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

Causal loop diagrams (CLDs) are a qualitative diagramming language for representing feedback‐driven systems. One of their conceptual cornerstones—polarity—has been critiqued for some shortcomings. However, their use has not diminished, and polarity, as well as the relationship between behaviour and events continues to be used in a pragmatic way. A definition of how events relate to behaviour is proposed for a world of continuous behaviour. Then, several previously unconsidered limitations of CLDs are discussed, which prove to be closer to the event‐related thinking and therefore provide little help in avoiding faulty thinking about the behavioural consequences of causal structure. Also, the reasons underlying previously discussed limitations of CLDs become clear. Polarity is shown to be an interface between behaviour and structure. Inexperienced users need external help to use polarity in going from behaviour to structure and back. Such help could come from incorporating behaviour‐over‐time (BOT) graphs into the diagrams. Several areas of further research are identified. Copyright © 2010 John Wiley & Sons, Ltd.

Current teaching and learning of system dynamics are based on materials derived from the expertise of masters. However, there is little explicit reference to neither the stages which beginners go through to become proficient nor what is learned at each of these stages. We argue that this hinders cumulative research and development in teaching and learning strategies. We engaged 15 acknowledged masters in the field to take part in a three-round Delphi study to develop an operational representation of the competence development stages and what is learned at each stage. The resulting system dynamics competence framework consists of a qualified, expertevaluated, empirically based set of seven skills and 265 learning outcomes. The skills provide a common orientation, in the language of current educational research, to facilitate research, course design and certification efforts to ensure quality standards. To conclude, this paper provides avenues for future work.

System dynamics is often brought into connection with a double-loop learning process. Learning has been the object of an increasing number of studies. However, inquiry has focused on using models rather than modeling, and there are huge differences in assessment approaches. If learning changes models then it can be inferred from comparing models. Here it is argued that monitoring learning from modeling is feasible and desirable. One possibility is to conceive of a model as a series of versions and compare their structure. One possible method for comparing model versions is presented.