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Using Dynamic Modeling to Scope Environmental Problems and Build Consensus

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Using Dynamic Modeling to Scope Environmental Problems and Build Consensus Using Dynamic Modeling to Scope Environmental Problems and Build Consensus ROBERT COSTANZA tools for problem scoping and consensus building among a Center for Environmental and Estuarine Studies broad range of stakeholders and describes four case stud- Zoology Department and Institute for Ecological Economics ies in which dynamic modeling has been used to collect and University of Maryland, Box 38 organize data, synthesize knowledge, and build consensus Solomons, Maryland 20688-0038, USA about the management of complex systems. The case stud- ies range from industrial systems (mining, smelting, and re- MATTHIAS RUTH* fining of iron and steel in the United States) to ecosystems Center for Energy and Environmental Studies (Louisiana coastal wetlands, and Fynbos ecosystems in Boston University South Africa) to linked ecological economic systems (Mary- 675 Commonwealth Ave. land’s Patuxent River basin in the United States). They illus- Boston, Massachusetts 02215, USA trate uses of dynamic modeling to include stakeholders in all stages of consensus building, ranging from initial problem ABSTRACT / This paper assesses the changing role of dy- scoping to model development. The resultant models are the namic modeling for understanding and managing complex first stage in a three-stage modeling process that includes ecological economic systems. It discusses new modeling research and management models as the later stages. Types and Uses of Models rational thought at all. For many everyday decisions, mental models are sufficiently detailed and accurate to In environmental systems, nonlinearities and spatial be reliably used. Our experiences with these models are and temporal lags prevail. However, all too often these passed on to others through verbal and written accounts system features are moved to the sidelines of scientific that frequently generate a common group understand- investigations. As a consequence, the presence of nonlin- ing of the workings of a system. earities and spatial and temporal lags significantly In building mental models, humans typically simplify reduces the ability of these investigations to provide systems in particular ways. We base most of our mental insights that are necessary to make proper decisions modeling on qualitative rather than quantitative relation- about the management of complex ecological–eco- ships, we linearize the relationships among system nomic systems. New modeling approaches are required components, disregard temporal and spatial lags, treat to effectively identify, collect, and relate the informa- systems as isolated from their surroundings or limit our tion that is relevant for understanding those systems, to investigations to the system’s equilibrium domain. When make consensus building an integral part of the model- problems become more complex, and when quantita- ing process, and to guide management decisions. tive relationships, nonlinearities, and time and space Model building is an essential prerequisite for com- lags are important, we encounter limits to our ability to prehension and for choosing among alternative actions. properly anticipate system change. In such cases, our Humans build mental models in virtually all decision mental models need to be supplemented. situations, by abstracting from observations that are Statistical approaches based on historical or cross- deemed irrelevant for understanding that situation and sectional data often are used to quantify the relation- by relating the relevant parts with each other. Language ships among system components. To be able to deal itself is an expression of mental modeling, and one with multiple feedbacks among system components and could argue that without modeling there could be no with spatial and temporal lags requires the availability of rich data sets and elaborate statistical models. Recent advances in statistical methods have significantly im- KEY WORDS: Dynamic modeling; Scoping; Consensus building; En- proved the ability to test for the goodness of fit of vironmental management; Ecosystem management; alternative model specifications and have even at- Policy making; Graphical programming languages tempted to test for causality in statistical models (Granger 1969, 1993). Typically, little attention is given *Author to whom correspondence should be addressed. to first principles in attempts to use statistical models to Environmental Management Vol. 22, No. 2, pp. 183–195 ௠ 1998 Springer-Verlag New York Inc. 184 R. Costanza and M. Ruth arrive at a better understanding of the cause-effect tendency to use such models as a means of legitimizing relationships that lead to system change. Model results rather than informing policy decisions. By cloaking a are driven by data, the convenience of estimation policy decision in the ostensibly neutral aura of scien- techniques, and statistical criteria—none of which en- tific forecasting, policy-makers can deflect attention sure that the fundamental drivers for system change are from the normative nature of that decision . .’’ (Robin- satisfactorily identified (Leontief 1982, Leamer 1983). son 1993). The misguided quest for objective model By the same token, a statistical modeling exercise can building highlights the need to recognize, and more only provide insight into the empirical relationships effectively deal with, the inherent subjectivity of the over a system’s history or at a point in time, but it is of model development process. In this paper we wish to limited use for analyses of a system’s future develop- put computer modeling in its proper perspective: as an ment path under alternative management schemes inherently subjective but absolutely essential tool useful (Allen 1988). In many cases, those alternative manage- in supplementing our existing mental modeling capabili- ment schemes include decisions that have not been ties in order to make more informed decisions, both chosen in the past, and their effects are therefore not individually and in groups. captured in the data of the system’s history or present In the case of modeling ecological and economic state. systems, purposes can range from developing simple Dynamic modeling is distinct from statistical model- conceptual models, in order to provide a general ing by building into the representation of a phenom- understanding of system behavior, to detailed realistic enon those aspects of a system that we know actually applications aimed at evaluating specific policy propos- exist—such as the physical laws of material and energy als. It is inappropriate to judge this whole range of conservation that describe input–output relationships models by the same criteria. At minimum, the three in industrial and biological processes (Hannon and criteria of realism (simulating system behavior in a Ruth 1994, 1997). Dynamic modeling therefore starts qualitatively realistic way), precision (simulating behav- with an advantage over the purely statistical or empirical ior in a quantitatively precise way), and generality modeling schema. It does not rely on historic or (representing a broad range of systems’ behaviors with cross-sectional data to reveal those relationships. This the same model) are necessary (Holling 1964, Levins advantage also allows dynamic models to be used in 1966). No single model can maximize all three of these more related applications than empirical models— goals, and the choice of which objectives to pursue dynamic models are more transferable to new applica- depends on the fundamental purposes of the model. tions because the fundamental concepts on which they In this paper we propose a three-step process for are built are present in many other systems. developing computer models of a situation that begins Computers have come to play a large role in develop- with an initial scoping and consensus-building stage ing dynamic models for decision-making support in aimed at producing simplified, high generality models, complex systems. Their ability to numerically solve for and then moving to a more realistic research modeling complex nonlinear relationships among system compo- stage, and only then coming to a high precision manage- nents and to deal with time and space lags and disequi- ment model stage. We elaborate this process further on. librium conditions make obsolete the use of linear functional relationships or restriction of the analysis to Using Models to Build Consensus equilibrium points. It is inappropriate to think of models as anything but Models of complex system behavior are frequently crude—yet in many cases absolutely essential—abstract used to support decisions on environmental invest- representations of complex interrelationships among ments and problems. To effectively use those models, system components (Levins 1966, Robinson 1991, Ruth i.e., to foster consensus about the appropriateness of and Cleveland 1996). Their usefulness can be judged by their assumptions and results and thus to promote a their ability to help solve decision problems as the high degree of compliance with the policies derived dynamics of the real system unfold (Ruth and Hannon from the models, it is not enough for groups of 1997). The dynamic models presented in this paper are academic experts to build integrated dynamic com- designed with that criterion in mind. They are interac- puter models. What is required is a new role for tive tools that reflect the processes that determine modeling as a tool in building a broad consensus not system change and respond to the choices made by a only across academic disciplines, but
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