The philosophy of constructing models requires that the models be sufficiently detailed in order for them to have a significant impact on the development of detailed corporate plans. Although dynamic behavior may adequately be captured by a “simple” model, our experience in preparing models for a number of corporations indicates that detail is useful to facilitate initial acceptance of the model, and is often essential in assuring the model’s continued use by the client.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Stochastic aspects of systems have generally been ignored in most system dynamics studies except for purposes of sensitivity testing. Yet any model that claims to be more than simply an empirical description of a system must treat the underlying stochasticity explicitly in terms of its contribution to the dynamics. Recent work in chemical, biological, and hydrodynamic systems has shown that the aggregation of stochastic effects can lead to novel behavior (self-organization in dissipative systems). In this paper, an analogy between models of these physical and system dynamics models is developed, in which system dynamics models are seen to be an approximation (to lowest order in an expansion in system size) to a stochastic model for the system. The implications of theoretical results derived for the physical system models are evaluated for their application to the system dynamics models. A research strategy to elaborate this to analyzing systems is proposed.
The oil tanker market is interesting from a system dynamics point of view. The market exhibits regularities which appear to be caused by an underlying structure which has been stable for at least 30 years, and probably longer. This seemingly stable structure is primarily the result of the systematic, but not particularly rational, behaviour of the main actor in the oil tanker market: the community of shipowners. The collective effect of their individualistic actions, I believe, is a rather violent and rhythmic development in the market- on a timescale of years to decades. The regularity is, of course, superimposed on a non-recurring pattern of developments caused by events entirely outside the control of the oil tanker community. In this paper I describe the stable structure and discuss what it means for the likely development of the oil tanker market over the next decade.
Civil Engineer curricula are made up courses. Curricula also lead to degrees and most engineering curricula provide rather narrow time allocation to fundamental categories of course offering. It is usually a tight curricula, designed to be achieved in four calendar years by the good student, five by the average. It is sequestial in nature. The upper limit of course hours is usually a constraint, the addition of new course material must be at the expense of older material. The present curricula are built on science, math, chemistry physics, tools (drafting, surveying, computer programming, statistics), mechanics, dynamics, thermo and materials followed by general engineering and then the various components of civil engineering, such as hydraulics, transportation, sanitary, water resources, structures, materials, etc. This sequence presently produces a B.S. degree holder, ready to emerge on the scene at $18,000 - $30,000/year.
A number of high technology firms have recently reported increasing delays in the development of computer-related hardware and software. Experiencing increasing product development times and schedule overruns, one such company commissioned a system dynamics study of the management of its product development group. The purpose of this study has been to uncover potential sources for rising product development times in the company and to identify those over which management can exercise some control.
System Dynamics models have been used extensively for depicting the dynamic behavior which arises from a given underlying feedback structure. In a typical application, a feedback structure is specified, numerical values for model parameters are specified, and then a base-run simulation is conducted. Following the establishment of a Base Case, initial conditions, table functions, constants, policy variables and exogenous inputs are altered; with the resulting impact on model behavior noted and analyzed.
With the goal of introducing system dynamics to high school students, a set of six learning packages were written during the 1979-80 academic year under grant number GOD7903439 from the US Office of Education. Co-authors of the material are Nancy Roberts, David Anderson, Ralph Deal, Michael Garet, and William Shaffer. The evaluations from pilot testing done during the grant year in six Greater Boston high schools suggest that the materials indeed can effectively accomplish this introductory role. The teachers involved generally made very positive comments about both the value of system dynamics as an exciting high school project as well as the appropriateness of particular materials.
Although the system dynamics literature covers issues of how to construct, analyze, test, validate, and implement dynamic models, surprisingly little attention has been paid to how managers react to and interpret the output from system dynamics models (see Gardiner and Ford, 1980; Rohrbaugh and Anderson, 1979). That is, system dynamicists construct feedback models that are simplifications of a complex reality and then conduct policy tests on these abridged representations. However, decision makers not trained in system dynamics may find that even these allegedly simplified models may be quite complex and difficult to evaluate, since model output typically consists of scores of variables interwoven over time.
This paper attempts to explain the causes of widespread rural poverty which has persisted in Pakistan in spite of the development effort. The paper also analyses the various rural development policies implemented and explains why these policies have had little if any impact on the income of the rural poor. The main instrument of analysis of the study is a system dynamics model incorporating income generation and disbursement processes in an agrarian economy consisting of a capitalist sector and a self-employed sector. The analysis takes into account only the economic factors arising out of the rational decisions of the capitalists and the cultivators. These factors are considered adequate for maintaining rural poverty, although, the role of social and political factors is acknowledged. The study suggests that the absence of an economic force that should encourage ownership of land by its cultivators is a key factor responsible for the poor economic condition of the working rural households. Land is easily separated from cultivators and is concentrated in the capitalist sector. This concentration significantly reduces income in self-employment and thus leaves the cultivators with very little bargaining power for negotiating compensation for labor. Thus, development policies striving to increase productivity may only serve to increase the claim to income on the basis of ownership of resources. If ownership is concentrated outside of the cultivators, such policies may worsen economic condition of the cultivators. The study proposes a general framework for rural development incorporating simultaneously fiscal instruments that should encourage transfer of land ownership to its cultivators and policies that should help increase productivity of land.
Alcohol abuse and treatment in the United States cost nearly $43 billion in 1975- including $19.64 billion in lost production, $12.74 billion in health and medical costs, $5.4 billion in motor vehicle accidents, $2.86 billion in violent crimes, $1.94 billion in social responses, and $0.43 billion in fire losses. There are about 13 million problem drinkers (including alcoholics) in the United States. Of these, less than 10 percent seek treatment. For those receiving treatment, the overall improvement rate ranges from 30 to 70 percent, depending on how broadly improvement is defined.
Dramatic declines in harvests strengthen the assumption that Long Island’s hard clam fishery may be heading for collapse. A family of predator-prey models has been developed to test and evaluate alternative strategies to reverse the decline in hard clam harvests and/or stabilize the clam population. Harvesting is simulated as a fixed percent of standing stock and the behavior of the baymen in response to price and supply of clams is not included in the models. Five types of policies are evaluated: closed season, maximum size limit, hatchery seeding, bounty on predators, and nursery sanctuaries (closed areas). Effectiveness is judged for both the short term (ten years) and the long term (eleven to twenty years after the policy was instituted). While seeding options produce modest short term improvement in the annual value (8.0 to 10.8 percent), only the two bounty policies produce significant improvement in both the short term (17.0 and 72.6 percent) and the long term (20.4 and 66.4 percent). The results of this model reflect the influence of specific management policies on the biological system alone. A later version, incorporating the behavior of the baymen, will introduce key social and economic factors.
This paper explores the following questions: 1.What are the economic consequences of escalations in unconventional energy costs on terms of economic growth, inflation, real energy prices, energy production, consumption, and imports? 2. To what extent are escalations internally generated by interactions between the energy sector and the rest of the economy?
There has been a dramatic upheaval in our conception of science in recent years. The old notion that science is a logical, rational enterprise continually adding to the stockpile of knowledge has been challenged; many now recognize that the evolution of science is punctuated by violent disruptions. During such crises, or scientific revolutions, a tried and true theory is abandoned for an untested and often heretical alternative. The new theory destroys the old rather than building upon it, and thought he successor may flourish for centuries eventually another crisis develops and another revolution occurs. Some even claim that science is completely anarchic, more a no-holds-barred brawl than a calm, reasoned investigation of reality.
As a novice System Dynamicist one learns all the textbook rules and advice of system dynamics modeling. As a practicing System Dynamicist one learns the many shades of rules and advice. The latter ones are only occasionally spoken about and seldom written down. The experience gained from applying formal modeling technique to a diffuse ambiguous reality, often exist as vague mental models of the various roles a formal simulation model and a policy analyst can play in the public policy formation process.
In the first lecture of the first system dynamics course I ever took the professor presented a list of the steps of a modeling project. During the rest of the semester it became apparent to all of us that actual projects never follow the list very closely. But it also became apparent that the list was useful anyway. It helped organize effort, gave direction to a stalled modeler, and provided a checklist of activities to be addressed (if not always accomplished).
A System Dynamics project for a corporate client generally has three objectives: creation of an analytical tool, transfer of a new analysis technology into client organization, and managerial development. In many ways, the first two objectives are means toward the third. Development of new—and shared- perspectives, attitudes, and behaviors among the senior executives can be the most significant benefit from a System Dynamics project. This paper discusses how the process of System Dynamics has been used to draw out diverse points of view, to test and evaluate the differences, to build a consensus regarding key assumptions, to create confidence in the analytical tool which was developed, and, ultimately, to forge a managerial commitment to a new business strategy. The author draws upon several recent applications in the United States and Europe to illustrate the role of System Dynamics in effecting strategy change, and comments on how the process is affected by differences in organizational “culture”.
This paper introduces an aggregate view of factors and policies that can influence the development of military forces in two international alliances which see each other as potential adversaries. The growth of forces observed in the NATO and Warsaw Pact alliances is taken as a reference mode. A conceptual System Dynamics Model is described which can accommodate a number of different perspectives on this issue.
There is a growing interest in energy and energy policy analysis because of the gap between the United States’ energy consumption and energy production. Numerous policies for dealing with America’s “energy crisis” have been discussed and evaluated. Underlying these policy investigations have been a variety of simulation models designed to analyze energy demand, energy supply, and the interaction between the two. Several of the models used for energy policy analysis do not couple the energy sector to the rest of the economy. Some modeling efforts even assume that there is no causality from energy to GNP. The purpose of this study is to examine the structural relationships that govern the interaction between the energy sector and the rest of the energy policies, so as to contribute to the development of more effective national energy policies. A computer simulation model that illuminates the feedback coupling between the energy sector and the rest of the U.S. economy is used in the analysis. The model is used to analyze the effects of increasing capital intensity of the energy sector on the level of economic output and the efficiency of a general class of conservation initiatives in mitigating those effects. When conservation initiatives are introduced, cumulative energy consumption is reduced and sales and profits of the producing sectors are lower. Average GNP is lower and average general unemployment is higher when conservation is introduced.
This paper describes the application of System Dynamics in what is traditionally a hard engineering area, but where the application of analytical techniques are limited by the stochastic nature of the system driving forces (coalface output rates) and the need for highly credible, management orientated results. Methods and analysis have thus centered on using discrete simulation techniques based on open system models, primarily to assist in capacity design. The use of System Dynamics in this context is based on two premises. The first of these is that System Dynamics has, in addition to it s softer areas of application, considerable potential to both supplement and enhance Operational Research approaches in the analysis of such systems. Secondly, it is the author’s belief that the key to further development and acceptance of System Dynamics lies in bridging the gap between itself and associated subject fields, such as Operational Research, by direct demonstration of the approach within these fields. Recent technological advances within the coal clearance field have provided an excellent form for such a demonstration.
Corporate modeling in general and System Dynamics modeling in particular have now a history of more than two decades. Despite this fact impacts on the corporate planning process have not been very satisfactory. The reason is that in many cases system dynamics models (as well as other types of corporate models) had not been constructed, validated and implemented adequately for managerial use. They did not provide the information support which is needed in order to make the necessary decision in the various phases of a complex planning process that has a lot to do with major changes in markets, products, production processes, technologies, governmental regulations etc. Here, formal decisions rules as used in operation planning are impractical in most cases.
The paper reviews, briefly, the development of system dynamics (SD) and presents a modern control approach. It formulates and solves the SD policy design problem as a model-following control system design problem in an adaptive control framework. A computationally simple policy algorithm based on variable-structure system theory is used as an illustrative example of the stabilization of the dynamic characteristics of a production/raw materials system. Computer simulation results are given for the modern control approach as well as the classical SD techniques. Directions in which the modern control approach could be developed are indicated.
This paper describes the development of a limited resource, backward scheduling, network model for an assembly department using DYNAMO. The model evolved in three stages: a calculation device, a policy exploration tool and a planning and scheduling system. An interesting feature of the model is the representation of the complex flow through various disassembly operations. Graphics and report interfaces with DYNAMO are discussed. The enclosed programs are provided on an as-is basis, without warranty either express or implied. No assurance of successful installation can be given.
An exemplary model has been formulated using a methodology which casts a modified version of input-output analysis into system dynamics format. The intent is to utilize the methodology for further study of the concept of a geeignet (appropriate) population for a society. The exemplary model represents a highly aggregated socio-economic system with six sectors. Evaluation of the quality of the society is an important issue in the geeignet population study, and to that end the technique of multi-attribute utility measurement (MAUM) has been included in the model. In order to study a mechanism that can minimize the marginal production cost during the time evolution of the system, a Cobb-Douglas production function that permits substitution between two factors has been incorporated into the agricultural sector. Model runs are shown which demonstrate the approach to equilibrium for the society and the time evolution of the society as the agricultural sector changes from a labor intensive to a capital intensive configuration.
:We present a technico-economical simulation model focusing on enhances recovery in oil fields. The model simulates several assumptions on the quantity of injected fluids, the operation’s start date, as well as the incidence on recovery. It is also possible to place one’s interest on financial and economical parameters. It can also be used for any oil field for which precise physical data may be obtained. Paper: N/A
A preliminary mathematical model of fluid dynamics in acute large area burns presently incorporates plasma water, urine output, burn water loss, insensible losses via the non-burned skin, lungs, and G-I tract, as well as inputs of maintenance water and theraputic (Brooke Formula) fluids. The model is an initial step in a longer-term project to identify the pathogenetic mechanisms that control fluid shifts and to evaluate the effects of crystalloid (sodium ion), colloid (albumin), and other guidelines for fluid resuscitation. The model is initialized in homeodynamic equilibrium for a standard 70 KG person, and gives reasonable, realistic responses to a wide range of parameter variations (body sizes, burn wound loss factors), step functions (burn size, discontinuation of maintenance water), and rates of therapeutic fluid administration, given its present structure. The addition of burn and nonburn interstitial and intracellular spaces and their constituents (water, sodium, albumin and potassium) will: 1) permit validation against a wide body of clinical and experimental data, 2) suggest refinements of current resuscitation guidelines, 3) suggest more incisive research on pathogenetic mechanisms and treatment modalities, and 4) permit comparison of System Dynamics with alternative modeling and simulation approaches.
This study has illustrated that simulating an aggregate model, using the same data set at the same level of aggregation, can lead to different model conclusions when different aggregation criteria are applied. This study's conclusion to the effect of aggregation of individuals can have significant influence on the results of the model is expected to have different implications for system-dynamics modeling. For the field of system-dynamics modeling, the study has identified a kind of model sensitivity that can not be tested by the methods of sensitivity testing presently used. For future research in the field, the concept of aggregation of individuals has to be clearly established and differentiated from the concept of aggregation of variables before general rules for this type of sensitivity testing can be identified. Similar sensitivity testing should be adopted in the system-dynamics modeling technique. If this has not been done, this simulation approach should be interpreted conservatively. This paper also discusses the problem of whether a universal aggregation scheme is the only highest aggregation scheme.
In this paper we present a formal system S∆, in order to characterise the evolution of knowledge. In addition to the connectors of classical logic, we introduce two dynamic connectors- the mediate future and the immediate future-expressing the transformations that may affect data in the course of time. The axiomatisation of these connectors and their semantic characterisation lead us to define a model of interpretation for the formal system which is comparable to that of Kripke for modal logic. With this model we prove the intrinsic consistence and the validity of S∆. Similarly we demonstrate completeness and other propositions connecting the immediate future and the mediate future.The formal system S∆ is one of the component modules of the ARCHES system, a symbolic system for the representation and treatment of knowledge whose objective is to produce new knowledge through two modes of reasoning-deduction and analogy-based upon specific processes of inference.
The U.S. Navy's need for better long-range planning is discussed in light of recent dynamic increases to force plans. The difficulties embedded in the current planning and programming process, and the problems they cause in developing valid approaches are reviewed. The ongoing “Navy Resource Dynamics” project at The George Washington University is then presented as a means of overcoming the difficulties, and providing a timely planning model. The basis of the model is a lagged feedback analysis linking budget “flows” over time to weapon system asset “stocks.” The trade-off between naval force levels and the cost of owning the forces is emphasized with force readiness being a relevant measure.
The research reported in this paper was directed toward understanding and modeling acquisition policy within the DOD. The acquisition model presented was developed at the departmental level and primarily is intended to portray the strategic policy structure of the acquisition system. Lower levels of aggregation were used only where the detail involved was required to capture a major concept. The model parameters and outputs were designated to show what trends would be associated with the implementation of various policy alternatives. Emphasis was placed on the dynamic nature of the relationships within the acquisition system and how they are affected by the policies and external pressures. Exogenous factors input to the model include broad representations of the United States and Soviet economic conditions. The Soviet threat, so key to many of the political battles surrounding weapon acquisition, is generated in the model as a response to threat perceived by them, subject to the economic and political constraints. Incorporation of these and other key relationships was controlled through careful application of a design methodology.
Just as the cobbler's children are the last to have their shoes repaired, simulators—individuals who spend careers structuring the world into systematic models—have not developed tools and techniques to systematize and structure their own procedures. As a result, quick and easy communication among all interested parties during modek development is often made extremely difficult. Inputs which might be helpful to the modeller are consequently lost. It is also typically impossible for a client to maintain control over the implementation of his or her model design especially when the client and model builder are physically separated. Finally, when a version of the model is completed, there can be a considerable delay while an entirely new model description, in laymans terms, is prepared. Unfortunately, as a result of time constraints such as a description is sometimes never completed. Based on the work done at Purdue University and the frustration of designing and overseeing the implementation of a system dynamics simulation model at the Department of Energy, this paper describes a structured development and documentation approach to modelling. A systematic approach of this type forces the analyst to think out the implications of a given representation of the world before sitting down at a terminal. It provides a living (continually updated), standardized, written document which not only helps improve the quality of the work but allows for efficient communication between the client and the implementor and eliminates the need for most post model development documentation efforts.
The present paper carries these converging strands of work forward to address the problem of parameter selection for a given control structure and the comparison of the resultant performance of two competing policy designs.
The Argentine energy authorities elaborated a Plan for the Electricity National Sector, made known in 1979, where the policies to be followed for the period 1979-2000, were established. The Plan proposed basically a dramatic change in the structure of the present generating capacity configuration at the national level, toward a scheme predominantly hydro based. Apparently, the idea of an Electricity Sector less oil dependent, together with the utilization of a huge hydro-potential, which had been neglected until that moment, appeared promising. However, the study of the robustness of such plan, that is, its capability to perform well under different scenarios, became indispensible. The System Dynamics technique provided the possibility of analysing such robustness, by means of a continuous-time simulation model of the Argentine Electricity Sector. This paper presents the results of experimenting with that model, in order to determine the soundness of the policies proposed.
Recent developments in mathematics show that more-or-less random behavior and spontaneously evolving structures can be given analytical and deterministic representations. Both empirical simulation and theoretical models have been developed in economics that have similar capacities. This suggests that we are entering a new period when structural change and inherently unpredictable events can be explained or understood in terms of endogenous economic forces.
The need for grand unifying principles of the evolution of societal systems is stressed. Examples of such principles from other sciences are given. The economic long-wave or Kondratieff cycle is taken as a reference basis for the study fo the evolution of contemporary technological societies. A number of qualifications to the basic paradigm are made. Several areas of recent structural stability theory are discussed in terms their relevance to societal evolution. Particular stress is placed on the nonequilibrium and bifurcation situations. Structure-function-behavior interrelationships at and near critical points are considered the most important features pertinent to system change or reconfiguration. Attempts are made to provide a fuller integrated theory of societal evolution and structural change. A number of problems relative to system dynamics theory and modeling, and to the use of models in societal management, are introduced and suggestions for improvements are made.
The aircraft survivability model developed is comprised of five submodels: 1) Economy Submodel, 2) Budget Submodel, 3) Procurement Submodel, 4) Attrition Submodel, and 5) Survivability Submodel. The economy submodel generates the annual “Gross National Product” of the United States and “Federal Government Budget”. The budget Submodel uses the military output of the economy Submodel to determine the “Department of Defense Military Budget”. The DOD budget is broken down by service and function (Procurement, Operations and Maintenance, and RDT&E). In the Procurement Submodel, the “Procurement Budget for combat Aircraft” determined in the Budget Submodel is used to generate the parameters: “Acquisition Budget for Combat Aircraft” and “Modification Budget for Combat Aircraft”. The outputs of this submodel are the “Procurement Rate for Combat Aircraft”, and the “Modification Rate for Combat Aircraft”. The Attrition Submodel acts on the inventory of “Combat Aircraft” in the event of war. The number of combat aircraft increased by the outputs of the Procurement Submodel over years of peacetime are reduced in wartime through the “Attrition rate for Combat Aircraft”, which depends on the number of “Combat Aircraft”, the “Sortie Rate for Combat Aircraft”, “Mission Survivability for Combat Aircraft”, and the “Availability of Combat Aircraft”. The Survivability Submodel outputs are the “Mission Survivability for Combat Aircraft” and the “Availability of Combat Aircraft”. The former is the product of the “Susceptibility of Combat Aircraft” and “Vulnerability of Combat Aircraft”, both of which depend on the magnitude of the “Aircraft Survivability RDT&E Budget” outputed from the Budget Submodel. Reductions in the “Susceptibility of Combat Aircraft” and “Vulnerability of Combat Aircraft” affect the “Acquisition Cost of combat Aircraft” and “Modification Cost of Combat Aircraft” used in the Procurement Submodel. Additional feedback lops between the submodels are generated by monitoring the “Relative Strengths of U.S.S.R./U.S. Airpower” and incorporating the effects of this perception on the Economy Submodel, the Budget Submodel, the Procurement Submodel, and the Survivability Submodel. Thus the five submodels interact to form a series of interacting positive and negative feedback loops. The positive loops reinforce themselves leading to increased air power over time. The negative loops act through such constraints as resource availability and spiraling procurement costs to suppress the growth of air power.
In this paper we outline and evaluate a simple technique for analyzing the ability of a model to reproduce an oscillatory behavior mode. The technique consists of using a model as a predictor, and then performing spectral analysis on the prediction errors. The technique is referred to as the spectral analysis of residuals or SAR test. The paper motivates the use of prediction residuals and illustrates the technique with a simple model of inventory oscillation. The SAR test appears to yield a substantial amount of information about the performance of a model. However, the technique breaks down if the observed behavior is a result of the system being subjected to shocks with similar dynamic characteristics to the system output or if the system has more than one set of mechanisms generating the behavior of interest. The SAR test is not capable of distinguishing between models which can explain the behavior equally well using different state space representations.
As a step towards increasing our understanding of the dynamics of growth in solar markets, a simlpe generic System Dynamics model describing market penetration by a characteristic renewable energy technology is employed. The analysis demonstrates that for some classes of renewable energy, incentives are now adequate to provide for the necessary rates of growth. Technologies with slightly different features in our model are never able to sustain themselves in the market, no matter what federal subsidies they receive. A third group of solar technologies still needs support, even though it will evolve to become very competitive in the market without any subsidies as little as a decade from now. Relatively modest federal supports of these technologies now can bring them quickly to levels where they are economically, environmentally, and socially attractive energy options that provide significant oil savings. For these technologies federal support through initial stages of commercialization would be appropriate.
Sensitivity testing, according to the glossary of terms in a Congressional manual on simulation modeling, is defined as the “running of a simulation model by successively changing the states of the system…and comparing the model outputs to determine the effects of these changes” (Congress 1975, p. 129). Sensitivity testing is generally viewed as an important part of the modeling process because it helps researchers narrow down those areas where more data gathering would be useful. In our introductory remarks, we argue that detailed sensitivity testing is particularly important in system dynamics modeling efforts, and we list several obstacles that make detailed sensitivity testing difficult. We introduce a set of testing procedures developed at the Los Alamos National Laboratory and verified by the Control Data Corporation that can help system dynamicists perform detailed sensitivity testing on a routine basis. In the body of the paper, we present an illustrative application of the testing procedures, and we list six specific uses of the procedures. We describe the availability of the testing package, and we conclude with a set of practical guidelines for investigators wishing to make use of this unique set of procedures.
Eigenvalue analysis of dominant feedback loops promises to be a powerful new tool for identifying the structural origins of behavior in system dynamics models. Traditional simulation methods for dominant loop analysis are time-consuming and error-prone. A new technique permits calculating the marginal contribution of each feedback loop to each mode of behavior in a model. The technique computes the numbers showing the percentage change in natural frequency and damping of each eigenvalue resulting from a one percent change in loop gain. The magnitude of an elasticity measures the overall importance of a loop to a mode of behavior. The magnitudes can be used to rank loops by relative dominance over each mode, or to rank; modes by relative importance to each loop. The techniques can be used to analyze both linear and some nonlinear behavior modes.