Mini-DYNAMO has been adapted for the Apple II computer operating under Apple’s PASCAL system. Working within the constraints of a micro-computer, Micro-DYNAMO offers surprising capacity and speed. Models with up to 25 active equations will run in tolerable lengths of time, and models with up to 100 active equations can be run, although the time required to simulate them is rather long.
System Dynamics modeling has been used in the formulation and implementation of strategic planning models for nearly five years within the Long Range and Strategic Studies Division of the British Telecommunications Business. This modelling has proceeded in close collaboration with the Department of Control and Management Systems of Cambridge University. The business itself is a public corporation which means that despite a certain degree of autonomy, it is still ultimately dependent upon the Government for approval of its investment plans and also its investment capital.
An experiment was conducted using DYNAMO simulation to gain an understanding of the relation between the structure and behavior for a well-defined family of nonlinear, second-order systems. The result of the empirical investigation was 1) a taxonomy of structures—a categorization of the structures that give rise to all of the possible behavior modes; and 2) a set of observations and precepts—simply stated guidelines gleaned from the taxonomy that relate structure and behavior.
Many Congressional and Executive Branch policymakers are becoming discontent with the contribution of models to the policy process. One reason the modeling process and modeling results are being questioned is because of their perceived incomprehensibility and limited utility. This discontent has intensified with the Administration’s proposed reductions in domestic programs. This new mood of austerity is forcing researchers to justify modeling as useful to government policymaking.
My paper focuses on an extension of the basic R&D model. The basic model uses the concept of an average product which the firm develops and eventually sells. The extended model used in my paper diaggregates products into products and architectures. In the extended model, products are developed and sold just as they are in the basic model. An “architecture” is a basic engineering development which, when completed, enables the firm to develop a large number of products. An investment of resources in architectural development is necessary before marketable products can be created.
This study proposes to compare two types of computer simulation techniques, namely tactical and strategic simulations. It explores the advantages and disadvantages of the two methods and stresses the importance of the insight to be gained by combining both approaches in the evaluation of public policies. A school finance reform policy is presented as a case study. More specifically, the research evaluates the implementation of a cost-of- education index (a mechanism to adjust for disparities in educational costs among school districts in a state) in the New York State aid formula. The study investigates, using two computer simulation techniques, the impact of this policy in terms of organizing per pupil expenditures.
This paper examines the linkages between system dynamics and the Carnegie school in their treatment of human decision making. It is argued that the structure of system dynamics models implicitly assumes bounded rationality in decision making and that the recognition of this assumption would aid system dynamicists in model construction and in communication to other social science disciplines. The paper begins by examining Simon’s “Principles of Bounded Rationality” which draws attention to the cognitive limitations on the information gathering and processing powers of human decision makers. Forrester’s “Market Growth Model” is used to illustrate the central theme that system dynamics models are portrayals of bounded rationality. Close examination of the model reveals that the information content of decision functions is limited and that the information is processed through simple rules of thumb. In the final part of the paper there is a discussion of the implications of Carnegie philosophy for system dynamics, as it affects communication, model structuring and analysis, and future research.
A particularly interesting area for the application of system dynamics methodology is in business management; especially the interplay of quantitative (financial, economic) and qualitative factors (motivation, morale), and the decision-making choices which confront management. When a firm has a product which can be measured in economic terms, the construction of a model can be quite straight-forward. Even in non-quantitative areas such as research and development, models have provided insight into the decision-making process. While these models have been informative from both a system dynamics and management science perspective, the practical application of the results has been too often lacking. For a businessman, simulations and models are academic exercises unless they provide some measure of practical guidance. It was from a basis of requiring that the system dynamics model provide practical decision-making guidance in real-world environments that we have attempted several studies of R&D projects.
Both in incipient and later phases of developing a model, unexpected behavior is frequently encountered—that is, behavior which is at odds with the initial expectations of the model builder or client. The appearance of such surprise behavior immediately raises two possibilities: either the behavior is implausible, and the model therefore must be revised; or the behavior withstands scrutiny and reveals previously unappreciated aspects of the system. In either instance, the process of diagnosing and interpreting surprise behavior gives a powerful basis for model for model evolution and generating policy insights. But frequently, it is quite difficult in practice to discern whether the incidence of surprise model behavior reveals errors or suggests insights. The paper is designed to contribute to the literature on model formulation, testing, and policy analysis, by discussing the criteria for diagnosing surprise model behavior. Several case examples are presented in which appropriate resolution of surprise model behavior led to significant model improvements and/or behavior insights. Moreover, operational guidelines are presented to increase the likelihood of uncovering and successfully treating surprise behavior.
The background of this paper is an analysis carried out on the occasion of an election within an academic self-administration in West Berlin in 1980/81. This analysis considers (1) papers presented during the time before the election with opposing opinions as to image and efficiency of this administration, and (2) statistical data concerning possibilities within the structure of this administration and the realization of these possibilities by members of the staff over a period of seven years.