According to an implicit “start simple” principle widely accepted by system dynamics practioners, model’s complexity must be progressively increased during the modeling process. How this increase in complexity should come about has yet to be explained. In this paper, two strategies are discussed and evaluated. Since a top-down strategy starts with a high level of aggregation but includes in the model all the main variables since the first formulation, it is to be preferred to a bottom-up scheme. Moreover, the top-down strategy ensures the global coherence of the model at any stage of its conception and appears to be much more consistent with the system dynamics philosophy. This paper emphasizes the need for an adequate computer modeling language and briefly describes a first attempt. The main property of such a language is to allow a hierarchical description of models, where any composing unit can be altered without the need for a complete recompiling of the whole.
The process of attaining a useful model embraces the conceptualization, formulation, and testing stages. This paper argues that effective conceptualization can be achieved through a dynamic hypothesis (that is, a chosen time development of interest and hypotheses about the underlying mechanisms). The resulting rough, conceptual model should then be improved gradually through a recursive procedure where the model is tested, redesigned and tested again, in as many ways as possible and as long as is feasible. The paper attempts to structure the hazy topic of model construction by defining a number of terms and presents lists of dysfunctional tendencies in and guidelines for model construction.
The Scandinavian countries are approaching full utilization of the regrowth in domestic forests, and the forest industry is facing a period of much slower expansion in volume than in the past. Slower growth implies problems for the industry, forestry, and society at large. The “transition” from ample to scarce wood resources could take several forms, depending on actions taken both inside and outside the forest sector. A system dynamics simulation model has been constructed to describe different possible transition paths, and to highlight potential problems. The model purpose is not to predict what will actually happen in the future, but to describe possible futures in an internally consistent way. Such insights about the consequences of various management strategies are useful to interest groups as a basis for discussing how to reach their goals. Within the industry, there is a tendency toward temporary overexpansion of capacity. The forest sector's ability to survive under slow growth conditions could be enhanced by technological and organizational remedies. The necessary remedies will be less traumatic the earlier one accepts and acts upon the problems of finite wood supply.
Industrialized societies are presently characterized by rapid change, strong interactions and an abundance of new phenomena. To increase the likelihood of policies having the intended effects, there is a need for policy analysis with a broader perspective and longer time horizon. The main task in such broad policy analysis should be to integrate the vast amount of available information into a useful conceptual structure of the problem area. System Dynamics (SD) –relying heavily on descriptive information for a data base, on a theory of the structure of social systems for theory formation, and on computer simulation for relating structure to behaviour—offers one method of attaining such broad policy analysis. This paper reviews the historical development of the field and examines the major system dynamics literature. The impatient questions of “what is?”, “why does one do?”, “when should one do?”, and “how does one do SD?” are all answered in summary fashion. Within the system dynamics profession, intense conflicts abound as to what constitutes “proper procedure” for the policy analysis process, particularly concerning model conceptualization and testing. Much disagreement arises from implicit differences in modeling objectives. Explicit recognition of objectives and procedures could reduce much of the conflict.
This paper focuses on the aggregation that is implicit in the use of distributed delays in dynamic models. The aggregation process relates the continuous time-dependent response of a delay structure to the underlying distribution of delay times of the disaggregated events which constitute the delay. The discussion covers in particular the special case of exponential delays used in system dynamics models.
Conclusions derived from world models have little value if they do no include an estimate of the uncertainty associated with the outputs. This paper describes the System Analysis Research Unit World Model and gives an account of the application of Monte Carlo techniques to testing the model. Samples of uncertain data encoded in probability densities are used as input for model runs. The model output is analysed statistically and the contribution to total uncertainty by the variance of the inputs is determined. The output is also to be additive over a limited range. Due to the strong negative feedback loops in the model, the model usually attenuates any variation in inputs. The cost of Monte Carlo methods is justified by the quality of the results obtained.
In this report we discuss our possibilities to attain insight about social phenomena. In the first part of the report we argue the nature of social phenomena is different from natural phenomena. Therefore there is a danger connected with the fact that social science for so long time has been dominated by techniques and goals which were successfully developed for the purpose of natural science. In the second part of the report we identify and discuss four essential problems in the study of social phenomena. The problems are: (a) the definition problem, (b) the issue of limitation, (c) the problem of causality and (d) the problem of stability. In the last part of the report we discuss in what way social phenomena can be understood. Six conditions for a successful paradigm in social science are presented and we can conclude that used in a proper way System Dynamics can be one paradigm that fulfill these conditions.
This paper is a summary of the major assumptions underlying the field of computer modeling and the specific assumptions that differentiate four modeling methods used to represent social systems: system dynamics, econometrics, input-output analysis, and optimization. The primary conclusions are: 1. Each modeling method is based on a set of techniques and priors that suit it well to some sorts of policy problems and poorly to others. 2. Misunderstandings between different kinds of modelers and between modelers and clients often arise from failures to recognize these implicit priors and the various strengths and weaknesses of the various modeling schools. 3. Some modeling schools, especially system dynamics and econometrics, are based on such different basic world views and assumptions about the nature of human knowledge that communication from one school to another is almost impossible.
This paper contrasts two approaches to testing the importance of model variables: single-equation statistical tests and model-behavior tests. The paper demonstrates that, both theoretically and operationally, tests which analyze the impact of individual variables on model behavior are better suited to the task of selecting model variables. Conversely, the statistical tests should not be viewed as tests of model specification per se, but as tests of a particular type of data usefulness. When viewed as tests of data usefulness, the statistical tests have a clear, albeit quite narrow, role in model validation: they warn the modeler when available data do not permit accurate estimation of model parameter. However, as a detailed example illustrates, a model relationship may be difficult to estimate yet extremely important for overall model behavior.
This paper contrast two viewpoints for analyzing the concepts of supply and demand. The first viewpoint, which dominates most economic thinking, treats supply and demand as rates of flow. For example, supply in economic models tends to be measured by a rate of production, while demand is measured by a flow of consumption or purchases. The second viewpoint sees supply and demand primarily as stock variables or integrations. According to this viewpoint, for example, supply would be measure by the available inventory of a commodity while demand would be measured by a backlog of unfilled orders. The central point of the paper is that stock-variable concepts of supply and demand must be incorporated explicitly in economic models in order to capture the full range of disequilibrium behaviour characteristics of real socio-economic systems. More specifically, the paper shows that consideration of stock-variable measures of supply and demand is necessary to describe the price- and quantity-adjustment mechanisms linking supply and demand; to analyze properly the stability characteristics of an economic systems; to analyze short-run and long-run disequilibrium behaviour; and to assess the desirability of economic policies intended to influence such disequilibrium modes behaviour as economic growth and fluctuation.