Ten Heuristics for Modeling Projects

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Ecosystems (2002) 5: 376-354 ECOSYSTEMS DOI: 10.10071s 10021-001-0081 • 5 O 2002 Springer-Verlag ORIGINAL ARTICLES Ten Heuristics for Interdisciplinary Modeling Projects Craig R. Nicolson,1* Anthony M. Starfield,2 Gary P. Kofinas,3A and john A. Kruse4 'Department of Natura! Resources Conservation, University of Massachusetis, Box 34210, Amherst, Massachusetts 01003-4210, USA;'Department of Ecology, Evolution and Behavior. University of Minnesota, 1987 Upper Buford Circle, St Paul, Minnesota 55018, USA; 3lnstitute of Arctic Studies, Dartmouih College, 6214 Fairchild, Hanover, New Hampshire 03755, USA; and'Institute of Social and Economic Research, University of Alaska-Anchorage, 3211 Providence Drive, Anchorage, Alaska 99508, USA ABSTR.AGT Complex environmental and ecological problems require collaborative, interdisciplinary efforts. A common approach to integrating disciplinary per- spectives on these problems is to develop simula- tion models in which the linkages between system components are explicitly represented. There is, however, little guidance in the literature on how such models should be developed through collabo- rative teamwork. In this paper, we offer a set of heuristics (rules of thumb) that address a range of challenges associated with this enterprise, including the selection of team members, negotiating a con- sensus view of the research problem, prototyping and refining models, the role of sensitivity analysis, and the importance of team communication. These heuristics arose from a comparison of our experi- ences with several interdisaplinary modeling projects. We use one such experience-a project in which natural scientists, social scientists, and local residents came together to investigate the sustain- ability of small indigenous communities in the Arc- tic-to illustrate the heuristics. Key Words: interdisciplinary; modeling; ecosys- tem; collaboration; sustainability; Arctic; integrated assessment; teamwork. INTRODUCTION In the past 100 years, knowledge has become in- creasingly specialized. This specialization has re- sulted in tremendous intellectual and technological gains, but it has also led to increasing fragmentation in the modern research enterprise (Nissani 1997). Many of the important issues in society simply can- not be addressed adequately by a single disciplinary perspective. This is particularly apparent for issues with an environmental component, such as water- shed protection, sustainable development, and cli- mate change. These issues demand that we take an integrated view; they are essentially systems prob- lems. To address systems problems effectively re- Received 27 April 2001; accepted 12 Novembcr 2001. •Corresyonding author, e-mail: craign@forwild.umass.edu quires us to bridge perspectives and disciplines (Gunderson and others 1995; Parson 1995) and deal with complex interacting processes that oper- ate at different temporal and spatial scales (Holling 1995; Likens 1998). By integrating and synthesizing knowledge from disparate domains, the emerging field of integrated assessment (IA) attempts to ac- complish this goal (Risbey and others 1996). Within IA, simulation models are commonly used for synthesizing disciplinary knowledge. They are by no means new tools for scientists (see, for example, the work on modeling marine ecosystems by Riley 1947), and since the early 1970s, the re- sults of such models have often been made acces- sible to the general public as well (for example, see the much-publicized Limits to Growth study by Meadows and others for the Club of Rome in 1972). Integrated system models offer three exuemely 376