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AbstractRobust systems can recover after a shock to a previous steady state. Thus, to make organizations robust is a frequent goal of system dynamics projects. However, in recent years, the adequacy of robustness as a design criterion for systems (and, thus, of the models that represent them) has been challenged based on the ideas of antifragility, that is, the ability of a system to recover after a shock and to achieve a higher performance level than before the shock. The purpose of this article is to propose how antifragility can be interpreted and operationalized in managerial settings and to explore what consequences result from its existence for supply chain behaviour and performance. System dynamics modelling and simulation are employed, and the insights of the analyses are used for a critique of the antifragility concept. It is demonstrated that the antifragility concept can lose its unambiguous advantage in highly dynamic situations.

AbstractThe purpose of this paper is to discuss the phenomenon of why some system dynamics projects fail to generate substantial impact in organizations—despite the fact that they are based on an apparently valid system dynamics model and are conducted by experts in the field. The approach followed in the paper is a conceptual discussion, extended by two short case studies. Findings are that the quality of the model and the expertise of the modeler are necessary but not sufficient requirements for organizational impact. Further research should concentrate on the detailed analysis of additional requirements. Practical implications are an increased embedding of system dynamics projects in organizational intervention architectures. The originality of the paper lies in its focus and discussion of failed projects that are invaluable sources for insight generation. Copyright © 2007 John Wiley & Sons, Ltd.

AbstractThe paper reviews references to bounded rationality within the system dynamics literature. It is argued that so far the relation between bounded rationality and system dynamics has primarily been discussed from a 'content' perspective, that is, that boundedly rational decision making needs to be incorporated in the detail of system dynamics models because of the methodology's aspiration to represent real, not ideal decision making. In this paper this content view is extended by a new 'process' perspective. This perspective concerns effects observed at the level of whole models. For example, during modelling the developer of a system dynamics model is prone to be influenced by the limitations of rationality, which might lead to erroneous models. In the same way, users conducting simulation experiments with models are prone to carry out these simulations in an unsystematic way and might draw wrong conclusions from the simulation outcomes. The paper then outlines the connections between the established content view and the new process view on bounded rationality in system dynamics. These connections lie in the knowledge elicitation phase of modelling and in the changes of real systems on the basis of insights gained in modelling and simulation. The paper closes with a brief statement of the research that the author is conducting with a view to exploring further these ideas. Copyright © 2004 John Wiley & Sons, Ltd.

AbstractSimulators can be used for experimentation and for teaching purposes. How far promises of useful applications of simulators in these areas are justified is, however, an open question. Evaluation research on simulators is still in its infancy, both inside and outside the system dynamics community. A reason for this is methodological issues that derive from the nature of experimenting/teaching, but which are also caused by the specific characteristics of simulators. Fifteen such issues are identified and explained in this article and classified into three categories; methods for the mitigation of these issues are discussed. Copyright © 2004 John Wiley & Sons, Ltd.

AbstractPrevious research has shown that decision‐makers contribute with individually varying intensity to the bullwhip effect, which refers to the amplification of demand variability along supply chains. This article examines whether differences in decision‐makers' holistic versus analytic thinking orientation are related to these variations. The hypothesis is put forward that a stronger holistic thinking orientation is negatively correlated with the bullwhip effect. We also hypothesize that holistic thinkers report a higher use of information and that reported use of information is negatively correlated with the bullwhip effect. While no relationship was found between the overall thinking orientation and the bullwhip effect, a negative relationship was observed between one subdomain of the holistic versus analyic thinking orientation and the bullwhip effect. Furthermore, the results show that holistic thinking is not correlated with reported use of information and that only reported use of specific information is negatively related to the bullwhip effect. © 2022 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Dynamic stock control tasks have been frequently used in laboratory experiments in behavioural research to investigate understanding of dynamic systems. In these studies, the dynamic system is often represented in the form of a simulation model, and they almost exclusively focus on how the structure of a system (i.e. the simulation model) affects human's inference of system behaviour. In doing so, these studies fail to consider that human's performance on dynamic decision making tasks might also be a function of the complexity embedded in other task components like goals, input, processes, output, time and presentation. Hence, the objective of this paper is to carve out what task complexity entails when applied to dynamic stock control tasks in order to determine its usefulness for future research on human understanding of such tasks. In this paper, task complexity is conceptualized consisting of ten complexity dimensions: (1) size; (2) variety; (3) redundancy; (4) ambiguity; (5) variability; (6) inaccuracy; (7) novelty; (8) incongruity; (9) connectivity; and (10) temporal demand. Copyright © 2016 John Wiley & Sons, Ltd.

Boom and bust patterns are omnipresent in the development of enterprises. An abundance of examples for businesses that grow at first and then collapse can be found. Although evolutionary economists tend to welcome the rising and vanishing of companies and believe in the survival of the fittest (based on competitive market forces), owners and managers of firms have a different concern: they strive for a sustainable business model. However, designing as well as implementing sustainable business strategies is a major challenge. Analysing data from a dynamic decision making experiment, we are able to show that participants struggle with successfully implementing a business growth strategy without either falling into the trap of overshoot and collapse or heavily underperforming in relation to potential success. We relate participants' sub‐optimal performance to the way they invest in and manage relevant business resources in a simulation‐based experiment. Copyright © 2012 John Wiley & Sons, Ltd.