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.

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference leads to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that they all can generate sustained exponential growth but differ in performance: even policies criticized in the literature for provoking overshoot and collapse can drive sustained growth. This leads to questions for further research regarding (1) the set of generic policies and its structure and (2) concerning the reasoning of human decision-makers when choosing between such policies and the salience of important but easily overlooked features of the decision situation.

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference has been reported to lead to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that all three policies can generate sustained exponential growth but differ in performance. These results suggest that even policies which risk generating overshoot and collapse can avoid it. This poses two questions for further research: (1) what is the reasoning of human decision-makers when choosing between these policies and (2) how can the important but easily overlooked features of such decision situations be made sufficiently salient to be accounted for?

This article addresses a challenge modeling novices face in model conceptualization: the recognition of behavior modes. Despite behavior modes being at the heart of system dynamics modeling, there is no unifying taxonomy for them, making them harder to learn for beginners. The article proposes criteria a taxonomy should satisfy, critically reviews previous taxonomies in the literature, and then introduces a new consistent taxonomy based on slope and curvature with mode names that refer only to visual cues and free of references to prior mathematical or domain-specific knowledge. Evidence from an exploratory experiment suggests that novices actually have difficulties classifying curves when using previous taxonomies. Links from these elementary modes to analogous terms in other taxonomies in diverse disciplines allow this taxonomy to be related to other ones. The article concludes by mentioning relevant limitations and future steps.

This article contributes to research on mental models and how they underpin decision policies. It proposes a framework for the joint use of mental models of dynamic systems and the theory of mental models initiated by Johnson–Laird and defines two types of errors: (1) misrepresentation of the system’s structure, and (2) failure to deploy relevant mental models of possibilities. We use a dynamic decision task based on Moxnes’ “reindeer experiment” to formulate three intuitive policies, their underlying mental models, and the reasoning, and evaluate the policies under varying initial conditions. Each of the policies generates problematic behaviors like dependance on initial conditions, underperformance because of flawed goal setting and oscillation due to leaving the delay in a feedback loop out of account. We identify errors of both types in the mental models and relate them to the behavioral problems. Limitations and questions for further research conclude the paper.