Ten Heuristics for Modeling Projects

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Heuristics for Interdisciplinary Modelers 383 Web-based model interface that we call the "Possi- ble Futures Model" (G.P. Kofinas and others un- published). The model demonstrates our attempts at ongoing innovation in all areas of the interface between model and user: ease of use, hypertext documentation, graphical output, and built-in fea- tures for explaining model results and documenting users' feedback comments. An important lesson from the SAC Project is that the budget for face-to-face meetings was inade- quate (both for meetings among researchers and meetings between researchers and community partners). The proposal did include funds for an- nual project meetings of the entire team; these were of some value, but we found it far more profitable to hold work sessions involving small numbers of researchers from the various components to de- velop specific component linkages. E-mail is not an effective medium for planning and for the creative generation of ideas. Written exchanges work best once there is a common understanding of the prob- lem, common assumptions, and a negotiated set of task assignments; face-to-face meetings are indis- pensable for these groundwork decisions. Because face-to-face meetings are so much richer in com- municative content and because they allow trust to be built more easily than can be done in a series of written messages (Daft and Huber 1987), meetings with component researchers are also critical to the work of the synthesis modeler. When we began to hold these meetings, considerable momentum was gained. Face-to-face contact should be a nonnego- tiable part of any IA budget, particularly when team members are geographically dispersed. Heuristic 10. Approach the projed with humility. Even though the scientists on the team may be world-class experts in their respective component fields, they are all likely to be amateurs when it comes to the system as a whole. It is worth remem- bering that a distinguished group of component experts does not guarantee a distinguished system team. In fact, since laypeople often have a deep and holistic understanding of their local environment, we scientists may be no more "expert" than they are, even though their knowledge is not necessarily scientific. All team members must take the time to probe and query each other's approaches, assump- tions, and methods. More important, they must be willing to have their own assumptions and state- ments probed by others. This requires humility, a willingness to be challenged by team members out- side one's own area, and an openness to learning from such transactions. The excitement and chal- lenge of interdisclplinary research lies in uncover- ing together the unknown-namely, the behavior of the system. Humility and caution are especially important when scientists work on projects that are intended to inform policy, thereby affecting people's lives. Synthesis modelers bear the brunt of the responsi- bility of ensuring, first, that the assumptions behind the models are carefully spelled out and, second, that robust conclusions are shown to be robust even in the face of uncertainty. In the spirit of humility, we acknowledge that the 10 heuristics presented here are obviously not ex- haustive. Integrated assessment is a very compli- cated business, and as a form of inquiry, it is still in the early stages of development. Not only are the dynamics of large complex systems hard to under- stand, but the challenge of bringing disparate per- spectives together is a fomudable one. We offer these principles simply because we believe it is im- portant for synthesis modelers and interdisciplinar- ians alike to reflect on what they have done, in the hope of doing it better the next time around. ACKNOWLEDGMENTS Financial support was provided by the National Sci- ence Foundation (NSF OPP-95-21459). We ac- knowledge the role played by members of the Sus- tainability of Arctic Communities research team and the communities of Old Crow, Aklavik, Fort McPherson, and Arctic Village in our collaborative effort to build a shared understanding of a complex system. Ann Kinzig and an anonymous reviewer offered helpful comments on a previous version of the manuscript. REPERENCES Costanza R, Sklar FH. 1985. Articulation, accuracy and effective- ness of mathematicai models: a review of freshwater wetland applications. Ecol Model 27:45-68. Daft RL, Auber GP. 1987. How organizations learn: a communi- cation framework. Sociol Org 5:1-36. Dazriel CJ, 1993. Computer simulation models for the Porcupine Caribou Herd: user's guide. Richmond Hill: (Ontario): ESSA Ltd., for Canadian Wildlife Service, Environment Canada, Whitehorse, Yukon, 17 p. Epstein HE, Walker M, Chapin FS, Starfield AM, 2000. A tran- sient, nutrient-based model of arcdc plant community re- sponse to clunatic warming. 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