Enhancing Learning Capabilities by Providing Transparency in Business Simulators

Abstract

Prefabricated computer-based simulations usually offer a user-friendly interface. This allows inexperienced users fast access to the simulation because they do not have to possess specific knowledge about simulation techniques. Thus, giving simulation models an easy-to-use interface increases the acceptance of the simulation tool and draws attention to it. Learners are not only able to examine the results of their decisions but also the causes of these results using powerful system dynamics diagramming techniques. This adds transparency to the former black-boxes, producing so-called transparent-box business simulators. This article reports on an experiment evaluating the relevance and effects of structural transparency. This experimental design also can be used to examine other types of business simulators. Hypotheses regarding the effectiveness of transparency were tested. Results show the necessity for further research and collaboration.Computer simulation models are powerful tools to support problem solving and learning processes. In particular, the iterative process of modeling and simulating-which leads to a simulation model, mapping the problem structure adequately and showing the behavior modes of reality-is important in promoting the understanding of complex systems. Partly because modeling and simulation require significant expertise, business simulators have been developed to allow easier access to a specified model and simulation through the implementation of a user-friendly interface. Although these devices are to a large extent designed as black-box simulators, they enable the user to experience the dynamics created by his or her policies and decisions and therefore facilitate learning about and understanding of complex systems. However, black-box simulators do not provide direct insight into the problem structure. There is no information about the internal feedback structure of the model available, and the users usually have not participated in the process of model development. Hence, black-box simulators are assumed to be of limited effectiveness and efficiency in supporting the learning and problem-solving capabilities.For several years, the idea of transparent-box simulation/gaming has been discussed in the system dynamics (SD) community. Adding features to provide structural AUTHORS' NOTE: Andreas Größler wishes to express his thanks to Pål Davidsen and J. Michael Spector for their help and encouragement in an earlier stage of the research project reported here.

Effects of Structural Transparency in System Dynamics Simulators on Performance and Understanding

“…Several studies by Größler and his colleagues [18][19][20] provided similar evidence, although some of the results were more mixed. Those, and other, studies are analyzed in [21] which, in addition to concluding that transparency is beneficial for only some learners and some learning objectives, also concluded that different methods of providing transparency (verbal explanations, videos, causal-loop diagrams, and stock and flow diagrams) are differentially effective.…”

The Effect of a Structured Method on Mental Model Accuracy and Performance in a Complex Task

“…Providing structural information about the underlying system in order to increase transparency seems to have a positive effect on performance [1][2][3]. Knowledge of cause-and-effect relationships supports learning and the development of mental models which are more similar to a real system.…”

“…Several methods for dealing with complexity are discussed in the literature, such as in in Größler, Maier, and Milling [1], Capelo and Dias [2], Ritchie-Dunham [3], Rouwette, Größler and Vennix [4], Sengupta and Abdel-Hamid [5], Richmond [6], Sterman [7], Sweeney and Sterman [8]. However, less attention has been paid to so-called "heuristic competence" [9].…”

Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

“…By making forecasting support systems models transparent, the users are encouraged to reflect not only on the outcomes but also on the reasons for the specific outcome. In this way individuals' and organizations' mental models are tested and the underlying model can improve (Grossler, Maier and Milling, 2000;Senge and Sterman, 1992), thus the gap between the forecasts and actual outcomes can be reduced. Furthermore, the machine learning model, built on a small set of features and supported by visualizations to support the reflection of a decision-makers, addresses important limitations of human decision-makers (Simon, 1991;Sterman, 1994and states the requirements for successful learning.…”