1976 Proceedings – Geilo, Norway
RESEARCH PAPER INDEX
The following research papers were presented at the 1976 Conference of the System Dynamics Society in Geilo, Norway. The original printed proceedings, edited by Jorgen Randers and Leif K. Ervik were printed in hardcopy and distributed after the conference. Papers are listed alphabetically by the first name of the first author.
Historical PROCEEDINGS INFORMATION
PAPER INDEX – listed alphabetically by the first author:
CONFERENCE ABSTRACTS
I. Broad Policy Analysis: The Need and One Response
Introduction
Social Difficulties Versus Social Problems Finn Lied Abstract: The opening address at the 1976 International Conference on System Dynamics points out that today’s social ills are diffuse difficulties rather than clear-cut problems. Remedial action must start with attempts to clarify the problem, and develop alternative comprehensive strategies that consider a wide segment of society and also the long-term future in an open minded fashion. System Dynamics may serve as a tool for broad policy analysis of this kind. |
Abstract: 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. |
Examples of system dynamics applications
B. C. Dangerfield Abstract: The paper describes a system dynamics model of the consumer durables manufacturing industry in the United Kingdom. The model purpose is to analyse the causes and effects of cyclical fluctuations in the industry with a view to encouraging government or operational policies that might improve industry stability. The paper extensively examines the consumer durables industry and explains the model in detail, each equation being accompanied by an account of its construction. The results of the simulation experiments conducted on the model using various test inputs are described. The paper appraises the technique of spectral analysis, which has served as one means of assessing model validity. The model, once validated, should form part of a larger model which will also represent the steel stockholding and steel manufacturing industries. Work on the larger model is in progress. |
Dale Runge Abstract: This paper presents a system dynamics model of worker mobility and wage determination in a multi-sector economy. The paper reviews the background and structure of the model, illustrates the model validation process, and sheds light on the dynamics of the labor market. |
A System Dynamics Study of the Transition from Ample to Scarce Wood Resources Jørgen Randers Abstract: The Scandinavian countries are approaching full utilization of the regrowth in domestic forests, and the forest industry is facing a period of a 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. |
II. Objectives: The System Dynamics Perspective
Paradigms
Donella H. Meadows Abstract: 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. |
Abstract: This paper presents a conceptual framework for understanding the influence of alternative paradigms on policy conclusions. Two types of assumptions are associated with mathematical models–meta-assumptions or methodological priors and specification assumptions. Because two different paradigms must assume two different sets of methodological priors, the possibility exists that different problem definitions and hence policy conclusions may emerge from two parallel studies of the same area. In each of two cases presented here, a single problem area has been analyzed with two different methodologies. In each case, different policy conclusions have been reached as a result of the different methodological priors of the two paradigms. The first case involves two models used to analyze changes in retirement policies within the military enlisted system of the United States Armed Services. The second case involves two models used to analyze the determinants of equal educational opportunity in the United States. The dependence of the policy conclusions upon the analytic paradigm employed in a given study has important practical implications for the use of quantitative models in the analysis of social policy situations. |
Prediction versus understanding
Views of Knowledge and System Dynamics: A Historical Perspective and Commentary James A. Bell, James F. Bell Abstract: Views of knowledge contain methodological theories–theories of how knowledge progresses– and epistemological theories– theories about the nature of knowledge. The former serve four particularly important functions: providing formulas for the generation of knowledge, criteria for the legitimation of knowledge, reasons to suspect other ideas, and rules for the propagation of ideas. |
A Framework for Understanding Social Phenomena Jan-Evert Nilsson Abstract: 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. |
Applied Principles
The Principle of Conservation and the Multiplier-Accelerator Theory of Business Cycles Gilbert W. Low Abstract: The principle of conservation states that physical quantities are confined to their own identifiable channels and can enter, circulate within, or depart from a system only by explicit processes. This paper applies the conservation principle to an analysis of the multiplier-accelerator theory of business cycles. Section I describes and critiques a well-known model of the multiplier-accelerator interaction. By ignoring accumulations of inventory and fixed capital investment, the model fails to observe the conservation of important physical flows. Section II proposes a system dynamics model that incorporates the multiplier and accelerator processes within a closed, conserved-flow framework. Section III uses computer simulation to portray the impact of conservation on the multiplier-accelerator interaction. Simulations of the system dynamics model reveal plausible long-term cycles, rather than the short-term fluctuation associated with traditional multiplier-accelerator models. At the end of Section III, the model is modified to account explicitly for labor, as well as capital, in the production process. This revised model produces both short-term and long-term oscillation when submitted to a noise input. The short-term oscillations, averaging about 5 years, reflect the attempt to adjust inventories by varying the labor input to production. The longer fluctuations in capital stock, averaging 19 years, reflect the management of investment in fixed capital. In all of the tests, the incorporation of conserved flows considerably reduces the sensitivity of system behavior to changes in parameter values. The simulations provide theoretical evidence for divorcing short-term business cycles from the interaction of the multiplier and accelerator. |
Stock and Flow Variables and the Dynamics of Supply and Demand Nathaniel J. Mass Abstract: 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. |
III.Steps in the Process of Modeling
Conceptualization
A Framework for Discussion of Model Conceptualization Jorgen Randers Abstract: The process of attaining a useful model embraces the conceptualization, formulation, and testing stages. |
The Reference Mode as a Guide to Transparent Causal Structure Dale Runge Abstract: This paper establishes the importance and usefulness of a well-defined reference mode as a guide to developing transparent causal structures for system dynamics models. The importance of a transparent causal structure is two-fold: it enhances understanding the model dynamics, and it facilitates communicating to others the model and the insights derived from model simulations. |
Top-Down Systems Analysis and Modeling F. Rechenmann Abstract: 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. |
A Method for Initial Formulation of System Dynamics Models R.G. Coyle Abstract: Even the experienced practitioner of system dynamics can encounter serious conceptual problems in getting started on a model, and is tempted to add more and more to his model. A technique – ‘list extension’ – is described which, from the purpose of the project and the importance of feedback loops, guides the evolution of the simplest adequate model. This model is expressed as an influence, or causal loop, diagram. |
Formulation
Parameter Formulation and Estimation in System Dynamics Model Alan K. Graham Abstract: The purpose of this paper is to convey the techniques and considerations normally involved in formulating and estimating parameters in system dynamics models. Ideally, model equations should be formulated so that the associated parameters each describe some unique observable characteristic of the real system. Thereby, translating observations and measurements below the level of aggregation of model structure (estimation from disaggregate data) into specific parameter values becomes very straightforward. Fewer assumptions about the structure of the system are needed than if the parameters were set by equation estimation or model estimation from data at the level of aggregation of model structure. Making additional assumptions provides more opportunities for systematic errors to creep into the parameter-setting process. Rather than using data at or above the level of aggregation of model structure to set parameters, such information might better be reserved for validity testing. When such data are not already used to set parameter values, the validity tests become simpler and depend upon fewer assumptions. |
Delays and Aggregation in System Dynamics Model R. Joel Rahn Abstract: 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. |
Estimating Lengths and Orders of Delays in System Dynamics Models Margaret S. Hamilton Abstract: Delays are a ubiquitous feature of dynamic systems; they are present at every stage of an action. An understanding of delays is necessary if policy makers are to foresee the consequences of their actions. It is often not sufficient to rely on “expert” opinion to tell how long it will take for the repercussions of an action to be complete, because even the “experts” can seriously underestimated delay times. It is, therefore, important to have systematic methods of estimating the length of delays in system dynamics models. The time structure of delays is also important.Whether a delay is destabilizing or stabilizing will depend on whether the repercussions are concentrated or dispersed, as well as whether the time lag is long or short. Systematic methods of estimating the orders of delays are, therefore, also useful. This paper presents five statistical methods that can be used to estimate lengths and orders of delays in system dynamics models. The presentation contains a discussion of when each method is applicable and what problems may be encountered in using it. Empirical results from applying two of the methods are discussed. The empirical studies respectively involve the problem of estimating the delay between changes in export prices and changes in export market shares and the problem of estimating the delay between capital appropriations and capital expenditures.The paper also offers guidelines for choosing an estimation technique and discusses validation of the estimates obtained. |
Fred Wenstop Abstract: This paper introduces and discusses the concept of verbally formulated simulation models. Such models can operate with linguistic values as ‘high’, ‘rather high’, ‘low’ and ‘not low’, etc. as inputs. The output will be similarly verbally formulated. The stimulation procedure is based on a fuzzy set-theoretical semantical model of a fragment of English language, which converts verbal expressions into numerical quantities. The paper applies one particular semantical model in a simulation example. |
The Integration of Alternative Modelling with DYNAMO Hermann Krallmann Abstract: Often system dynamics, and particularly the DYNAMO- language, is attacked for not integrating other modelling approaches into the field. This investigation offers alternatives that will hopefully stand up against the critics. |
Behaviour Analysis
Guidelines and Tools for Understanding Dynamic Models Wil Thissen Abstract: Starting from the aims and difficulties of social systems modeling this paper argues that a good understanding of dynamic mathematical models is indispensible. The author’s background, and its relation to System Dynamics is elucidated, and a number of definitions are given of concepts and terms that will be employed. A set of general guidelines, and a list of strategies and tools for understanding follow. Most of the methods presented have been applied successfully in an extensive study of the World Models by Forrester and Meadows et al., and are commonly used in systems and control engineering. The main emphasis is on techniques are points of view that are generally unknown to researchers and practicians in the non-technical disciplines. |
Sensitivity Analysis in System Dynamics Carsten Tank-Nielsen Abstract: This paper describes some of the central, non-procedural aspects of sensitivity analysis in system dynamics. |
Sensitivity Analysis Methods for System Dynamics Model J.A. Sharp Abstract: System Dynamics (SD) may be viewed as a process of designing ROBUST systems. The concept of ROBUSTNESS leads to a need for analyzing the effects on SD models of both parameter changes and stochastic inputs. It is demonstrated that the effects of large parameter changes can be measured by the use of hill climbing techniques given efficient computation. The paper describes the traditional ways of assessing sensitivities in SD models, together with methods based on perturbation techniques which unify the parameter and stochastic sensitivity problems. The computational characteristics of the various methods are analysed and the factors that affect their computational efficiency are discussed. |
Testing
Alternative Tests for the Selection of Model Variables Nathaniel J. Mass, Peter M. Senge Abstract: 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. |
Statistical Tools for System Dynamics David W. Peterson Abstract: For questions of parameter choice and validity, the system dynamicist has usually relied on “manual” examination of the detailed structure of the model. Numerical data may be used in the process, but only where the implications are obvious by inspection. |
Monte Carlo Tests of Conclusion Robustness W.G.B. Phillips Abstract: 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. |
Refinement
Guidelines for Model Refinement John Stanley-Miller Abstract: Model building standards within the field of system dynamics are still evolving. This paper offers some general guidelines for development and presentation of refined models. Model refinement, the core of the modeling process, encompasses incremental structural and/or parametric changes to existing models. Development and presentation of refined models are enhanced through comparison of original and refined model behaviour and through comparison of policy response. Model comparison aids the modeler in identifying misspecification of new structure. In addition, presentation of comparison results assists the reader in evaluating the merits of the refined as compared to the original model, and helps to insure that the builder and user of the refined model is familiar with original model assumptions. |
Modeling Procedure
Managerial Sketches of the Steps of Modeling Jennifer M. Robinson Abstract: Observations of modeling efforts suggest that many models fail for managerial reasons. This paper is based on the hypothesis that 1) managerial failures occur because various facets of the modeling process are inherently hard to manage, and 2) that deliberate management can reduce or eliminate many common problems. The hypothesis is pursued by breaking the modeling procedure into a series of steps, sketching what typically does but should not happen at each of them, and putting forth some thoughts about what can be done to avoid the normal pitfalls. Particular attention is paid to mundane variables such as time allocations and finances and attitudes and emotional considerations. In general, when modeling study is not deliberately managed, the construction phase preempts the bulk of time and resources to the detriment of planning, conceptualization, testing, documentation, and client-modeler interaction. This phenomenon appears to be caused, in part, by an over-emphasis on the “harder”, more technical work of construction; by difficulty justifying work that produces no direct, tangible product; and by mental resistance to testing. |
Achieving Implemented Results from System Dynamics Projects: The Evolution of an Approach Henry Birdseye Weil Abstract: This paper documents a series of lessons that the author and his colleagues have learned about how to achieve implemented results from system dynamics projects. Through a series of three case studies, the paper illustrates the evolution of their approach to implementation over the period of 1966 to 1975. These case studies focus on: client involvement in projects; the process of model development; the nature of the models developed; and the end of the projects. The paper draws upon the case studies and earlier writing on the subject by Roberts to generalize about the factors that are most critical in achieving successful implementation. These factors include: the sharpness of the project’s problem focus; the urgency of the problem addressed; the organizational position of the clients; the degree and nature of client involvement; the size of the model developed; the demonstrable validity of the model and the nature of the project’s end-products. |
Lennart Stenberg Abstract: The basic assumption of this paper is that system dynamics in its original form was developed to suit policy-making in small organizations and that application of system dynamics in the field of public policy must be accompanied by change in research methodology and organization. To support this view, the paper describes experiences from a study of the Scandinavian forestry and forest industry. |