Conceptualization is a critical task in the development of system dynamics models, which starts early in the modeling process, and extends to later stages in the development of any system dynamics project. The procedures and characteristics of model conceptualization have striking parallels with the process of theory building as described in many different strands of literature. Considering also that the modeling process as a whole is an iterative process of comparing and contrasting data and current theories by means of a rigorous yet intuitive process, it seems appropriate to reflect on the modeling process as a theory building effort, which is the main purpose of this paper. In order to illustrate the differences between theory building approaches, the paper presents two examples of system-dynamics-based theory building efforts. Thinking of the model development work as a theory building process has the potential of bringing new insights to the conceptualization of system dynamics models, and to the criteria used to assess the suitability of our models. The paper concludes with the introduction of a set of criteria to assess good theories and with reflections on the further development of these criteria for model validation purposes.
This paper presents the method of warehouse simulation model implementation using concepts of virtual reality. The basic idea is to compare the "Real process" controlled by operator with his/her heuristics with the "Virtual process" represented by simulation model, controlled by the optimization algorithm. Optimization algorithm is based on SD model of the warehouse and decision support module. The SD warehouse model represents the abstraction of "Virtual process" and is considered as a model of a model. The state of the "Virtual process" is controlled with help of decision support module and is compared with the state of Real process on the basis of actual information regarding the state of warehouse. This concept is useful for predictive model validation where the parallel observation of the actual system and virtual one takes place at the same time, and for the optimization algorithm migration into practice.
This paper argues that the positivist approach adopted by many studies into the relationship between customer satisfaction and revenue is limited; the ontology associated with positivism permits surface relationships only to be determined. What is required is a method grounded in a more realistic ontology that allows for a deeper investigation. The paper proposes that system dynamics is one such method. The differences between the positivist approach and a systems approach are expounded, and, though the positivist approach is not completely rejected (on the contrary, it is defended), its limitations, particularly when applied to a social environment, are apparent. In particular, the paper expands on the differences between studies in the natural sciences and those in the social sciences. It is these contrasts that make extremely suspect an effective translation of the methodology applied so successfully to the natural sciences, across to the social environment, and which demands an alternative methodology. This paper presents one such methodology and posits system dynamics clearly within that methodology.
Rising long-term care cost due to increase in senior population is a serious issue that concerns many public policy makers in the U.S. States come up with different policies to tackle the problem, and one of the policies that recently gained much support is a single point of entry system. The single point of entry is a place where people visit, have their needs assessed, and obtain information about different long term care service options. The supporters argue that by implementing the single point of entry, it is possible to reduce long term care cost by directing many potential institutional care recipients to better fitted home and community based care. This paper is about a system dynamics model built for a government agency that is about to propose the policy to key decision makers. The modeling process and insights gained from the model will be discussed in this paper.
This paper investigates psychological differences between constructors and interpreters of causal maps. This paper argues that dissipation effects and dilution effects applies to those who are to interprete causal maps not to those who construct them. Dissipation effects are psychological tendency that people perceive causal effect as weak as the number of causal links increases. Dilution effects occur when people undervalue the strength of causal relation as the number of causal variables increases. Experimental results show that concentration effects opposite to the dissipation effects and dilution effects explain more correctly the perception of constructors of causal maps. This paper points out that this asymmetric psychological tendencies between constructors and interpreters of causal maps is the psychological source of the communication problems between systems thinkers and their clients.
A number of papers have been published describing various System Dynamics (SD) models of the Information Systems Investment Appraisal Process from several academic and professional viewpoints. This paper builds on previous papers 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. This paper therefore presents an updated taxonomy of System Dynamics Models of Information Systems Investment Appraisal. This paper builds on the earlier taxonomy by widening the scope of the survey of completed SD investigations in the field. The findings from these investigations are briefly described. The taxonomy classifies the completed investigations into five specific areas of concern and six viewpoints.
A number of papers have been published describing various System Dynamics (SD) models of Higher 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 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. This paper therefore presents an updated taxonomy of System Dynamics Models in Higher Education. This paper builds on the earlier taxonomy by widening the scope of the survey of completed SD investigations in higher education management. The findings from these investigations are briefly described. The taxonomy classifies the completed investigations into six specific areas of concern and five hierarchical levels.
A policy for rapid deployment of fiber-to-the-home may be in direct conflict with the health of the transceiver component supplier industry. The interests of consumers, regulators, and even service providers are in conflict with the industry that provides a critical component necessary for the service. The industry needs to recognize this conflict and explore strategies to keep itself viable in light of these conflicts. A system dynamics model is used to explore the effects of government policy on the deployment of fiber-to-the-home as a broadband technology. Specifically this article investigates the effects of a policy for rapid broadband deployment on the component supplier that is farthest from the consumer in the value chain.
This paper proposes a model that gives deeper insights into the dynamics of interorganizational learning at the example of an alliance of two partnering firms. Current alliance research often tends to neglect a feedback-perspective which might be the reason why certain behavioral effects cannot be explained. However, we identify some major feedback-loops that influence interorganizational learning dynamics based on literature-based alliance research. Here, we focus on the concept of common and private benefits. According to literature findings the dilemma between the two kinds of benefits determines how many resources the parent companies invest in the alliance. We show how gatekeepers might lead a learning alliance to common success. We also show how short-term views of potential private benefits might not only lead to failed common goal attainment but also ruin a firms collaborative reputation in the industry.
The paper explores the application of loop eigenvalue elasticity analysis (LEEA) to three models in order
to reveal the potential of the method for generating insights about model behavior and to uncover
issues in developing the method further. The results indicate that the utility of the method depends
upon the character of the model and dynamics involved. In models where the transient behavior is of
interest, the method yields insights on par with the pathway participation method, though better tools
to link the method to time paths of particular variables is needed. In models involving near-equilibrium
oscillation, LEEA is clearly the most powerful, though more efficient computer programs are needed to
handle large-scale models. In highly non-linear models exhibiting deterministic chaos, LEEA, being
based upon linear concepts, does not appear to yield any insight because the eigenvalues may change
substantially even when the mode of behavior appears constant. The paper also describes the set of
tools and processes that we have developed and the design for a web-based toolbox to make the
methods readily available to a wider audience in the hope that others will join the efforts to develop
analytical methods for interpreting model behavior.