Prior exploration is an instructional strategy which has improved performance and knowledge acquisition in system-dynamics based learning environments, but only to a limited degree. This study investigates whether model transparency, showing users the internal structure of models, can extend the prior exploration strategy and improve learning even more. In an experimental study, participants in a web-based simulation learned about and managed a small developing nation. All participants were provided the prior exploration strategy but only half received prior exploration embedded in a structure-behavior diagram intended to make the underlying models structure more transparent. Participants provided with the more transparent strategy demonstrated better knowledge acquisition of the underlying model on an objective measure (multiple-choice posttest) but no difference on a subjective measure (open-ended verbal protocols based on short essay questions). Furthermore, their performance (managing the nation) was the equivalent to those in the less transparent condition. Combined with our previous studies, the results suggest that while prior exploration is a beneficial strategy for both performance and knowledge acquisition, making the model structure transparent in this way (with structure-behavior diagrams) is more limited in its effect and may depend on the participants level of expertise.
When evaluating the effectiveness of interactive learning environments it is important to include measures of knowledge acquisition that complement measures of performance. In this paper we report on participants knowledge acquisition in a dynamic decision making task where participants learned about and managed a small developing nation. In the course of the experiment participants not only had to make decisions but also answer multiple-choice questions and short essay questions. The results suggest that participants had a fairly good understanding of the reinforcing nature of national development processes and of processes that are in close causal proximity to their decisions. On the other hand, participants largely failed to recognize nonlinearities, the existence of the outflows to stocks and the proper treatment of delays with different durations. Knowledge acquisition was facilitated by the intensity of participants exploration activities during a simulation-based, guided exploration phase between reading textual instructions and making actual, simulation-based decisions.
Over the past two decades, Calgary a midwestern Canadian City of approximately 1 million inhabitants has experienced periods of rapid resource-driven economic growth and attendant municipal growing pains interspersed with periods of relative stasis. Effective municipal financial planning in this environment imposes profound challenges, particularly due to the presence of feedbacks, delays, non-linearities. To facilitate improved municipal financial planning, the City of Calgary has constructed the detailed multi-sectoral Calgary Impact Assessment Model (CIAM). CIAM whose structure draws inspiration from previous peer reviewed models includes an articulated representation of population demographics, migration, the labor market, the domestic and commercial property market and taxes thereon, infrastructure, finances, and the budget, recreational land, service levels, and quality of life. CIAM was parameterized using data from City databases and reports. Model construction incorporated a variety of best practices, and underwent a through a rigorous peer review. CIAM was subsequently calibrated to dozens of time series, leading to further model structural refinement and parameter estimation; the resulting model reproduces quite well a wide variety of historical municipal dynamics. CIAM has been used to investigate several scenarios important for municipal financial planning, and offers the potential to serve as an important decision-making tool for future city financial planning.
Almost everything we use today is manufactured by a virtual enterprise composed of hundreds of companies. These large distributed systems have led to numerous problems and challenges across multiple industries. The need is great for an analytical technique to examine the performance of a large-scale virtual enterprise. System Dynamics has been successfully used to model these large enterprises and assess the impacts on system behavior of changes in demand and various parameters. These large-scale enterprise models, however, are complex and time consuming to build and are difficult to restructure. For enterprise management, the ability to reconfigure the network of companies in response to external forces is critical, and models of the enterprise must have similar flexibility and rapid re-configurability. Using System Dynamics agent models of factories, distribution centers and customers, scmBLOX uses drag and drop features that enable fast construction of enterprise models and rapid assessments of alternative enterprise structures. Replacement of a make-to-stock factory for a make-to-order factory or the addition or elimination of distribution centers can be quickly evaluated. On-going research is focusing on the interplay between enterprise structure and performance, the development of additional agent models and new features for current agent models, and the assessment of optimization strategies such as push-pull boundaries within the global virtual enterprise.
A number of papers have been published describing various pedagogic techniques for the dissemination of the System Dynamics (SD) approach at various Education institutions and academic levels ranging from schools (K-12 in the US) to higher education. This paper builds on previous papers by this author that provided a catalogue and classification of this work in order to highlight potential areas of research in this field of study and to identify system archetypes at different hierarchical levels and discover new ones. The findings from these investigations are briefly described.
A number of papers have been published describing various System Dynamics (SD) models of various Education institutions and issues, on topics including the role of SD in Corporate Governance, Planning, Resourcing & Budgeting, Teaching Quality, Teaching Practice, Microworlds and Enrolment Demand. This paper builds on previous papers by this author that provided a catalogue and classification of this work in order to highlight potential areas of research in this field of study and to identify system archetypes at different hierarchical levels and discover new ones. The findings from these investigations are briefly described.
Complex adaptive systems-of-systems are inherently multi-scale across several dimensions, including temporal, geographical, and organizational. We present a multi-model paradigm integrating a localized community-scale individual-based model (IBM) with a population scale system dynamics (SD) model to analyze long term results of potential policy interventions for obesity prevention. The IBM uses virtual agents embedded in a social network to simulate the spread of opinions relating to nutrition and physical activity (N&PA) behaviors such as dieting and exercise, and the effects of these opinions on individual behaviors. The network structure uses a mixture of scale-free and uniformly random connections to represent a social network of relationships and interactions within a local community. The N&PA related health behaviors of individuals change dynamically relative to endogenous influences within their social network and exogenous influences from industry-based advertising and public health-related educational policies. The outputs of the IBM, seen as changes in obesogenic (N&PA unhealthy) behavior prevalences, can be used as inputs to a SD model to calculate the resulting changes in mortality and morbidity over the ensuing decades. We analyze and compare effects of possible policy interventions, and illustrate a policy cocktail that addresses multiple aspects of the obesity problem, resulting in amplification of desirable results and a strong uncertainty reduction.
In order to cope with the vast range of ambiguous, multi-causal and multi-faceted potential causes for firm success, managers tend to look for critical success factors as a reduced number of essential factors that determine future business success. Although scholars have been serving this need for more than four decades, the insights derived from empirical research on critical success factors have low impact on strategy in practice. We take this phenomenon to discuss potential causes and propose to complement empirical methods with the dynamic feedback perspective of System Dynamics modeling.