This paper is a case study on the introduction of systems thinking tools into a research group within a large information service company. The central dynamics involved in this learning process was a continuous goal shift. We address the realities of trying to develop a shared dynamic problem definition, and show how would-be practitioners internalize the material in unexpected and often paradoxical ways.
The paper proposes a methodology, of building system dynamics models for queuing systems. The methodology is applied to a variety of queuing systems and it is observed that, the models so developed are more transparent than conventional state-transition diagram and incorporation of real life complexities are easier. In effect working out the transient and steady state behaviour of a wide variety of queuing system becomes easy without going into much mathematical tedium.
The paper review the experience of a consultancy in the company called BETA. Two goals are pursued: cognitive and methodological. Cognitive goal refers to the System Dynamics methodology applied to a concrete case of the company growth and strategy making within a traditionally dominated accounting framework. Based on symbolic (though keeping similarity to real) data, the article presents the ithink™ model construction and simulation within 3 strategic scenarios: optimistic, realistic, and pessimistic. The methodological objective contains the use of the Partitioning and Tearing Method in the problem conceptualization and model preparation. Although the scope of the paper excluded a possibility of its detailed description, it is argued that this method has proved to be very useful in working with complex problems containing many variables.
Periodically, at different times of its history, the Argentine economy has been dominated by a vicious circle, well known among developing countries. The Central Bank pays interest on money and such interest is financed through emission of more money thus, causing inflation. In one of these periods: the corresponding to February 1981-July 1982, the accumulated inflation increased to 250 per cent. In 1982, the government decided to reduce the interest rate abruptly, in order to achieve a quick reduction of the inflation rate. However, the year 1982 witnessed the failure of the application of this financial reform. Although the growth rate of liquid assets declined, the inflation rate of July 1982 duplicated the precious month rate. This article reformulates a small economic model, in the Cagan tradition, due to Rodriguez (1986). It was conceived to explain the historic dynamics of the financial indicators, after the reform. Hopefully, the readability of the model should improve, when compared with the original version. And, instead of attributing the dynamics globally to the complex behavior of the system, the paper identifies the cause of this dynamics throughout the causal structure that produced it.
The purpose of this paper is fourfold: 1) to survey the literature on evolutionary economics in general; 2) to survey the literature on evolutionary economics modeling in particular; 3) to outline the contribution that system dynamics can make to evolutionary economic modeling; and 4) to present two original, evolutionary, system dynamics models.
Innovation is a topic that has received much attention in the literature in recent years. For the most part, these articles have not solved an important problem facing the managers in today’s large organizations -- how to manage a portfolio of interactive product- and process- innovations, addressing the interrelated forces, including monetary constraints, manpower planning & technology capability, to a dynamic environment. By systems thinking of these problems, the author first set up a generic S.D. model as a Microcosm for portfolio analysis of technological innovations. Based on this Microcosm, an experiment aimed at pattern selection of product-& process- innovations was conducted, drawing the conclusion different from the famous Abernathy/Utterback’s. Finally, the mechanism of group decision on project selection of innovation portfolio using the Microcosm was explained, and the group decision support system was constructed.
By the thought of coordinative development between Science & Technology, economy, education and finance, this paper first concerns the problems facing China on the resource allocation of Scientific Research. A comparative study on both developed and developing countries is made. In the meantime, the mechanism of the coordinative development between Science & Technology, economy, education and finance, the coordinative development between Scientific Research (Basic Research), Applied Research & Development as well as the priority of Scientific Research in different stages of social & economic development, a system dynamic model is constructed, focusing the analysis of scale & speed of resource allocation for Scientific Research in China.
This paper describes what is meant by modelling at Sunderland and how System Dynamics fits into this ethos. The teaching and the examples covered in this System dynamics module are different the usual course and the paper deals with our experience in these areas. The reaction of Eastern European ( Bulgarian ) students to this type of teaching is discussed. Students must complete a project in a work placement to obtain a masters qualification. The reaction of companies to the use of System Dynamics ( a new experience for most ) is discussed and examples of the type of projects that have been completed are given. The paper concludes with a description of a Hypercard project which extends the use of System dynamics to Engineering students.
The development and diffusion of innovations is a highly dynamic phenomenon. It is influenced by various factors like price, product quality, and market entry time. The paper discusses the impact of pricing strategies on R&D performance and the diffusion of innovations. It is based on a comprehensive decision support model in the field of innovation management. The model consists of two components: (1) an evolution algorithm modeling the processes of corporate R&D, and (2) a DYNAMO-based modul mapping corporate policy making and the structural fundamentals of market dynamics. The integrated model is used to analyze the dynamic consequences of different pricing strategies on research and development, the readiness for market entry and the resulting competitive advantages.