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Heuristics for Interdisciplinary Modelers 377
<br />useful advantages for interdisciplinary researchers.
<br />First, systems models provide a way to codify
<br />knowledge from different disciplines into a unified
<br />and coherent framework. Second, they encourage
<br />focused and disciplined thinking about the causal
<br />relationships in a system. Third, they allow re-
<br />searchers, ecosystem managers, and stakeholders to
<br />explore how their system may respond to a variety
<br />of scenarios so that responses can be formulated
<br />and management actions can be implemented.
<br />However, system models can only achieve these
<br />advantages if they are developed and used deliber-
<br />ately and thoughtfully.
<br />Developing simulation models is part science and
<br />pan craft; there are no general, infallible rules. Dif-
<br />ferent practitioners process their experiences in dif-
<br />ferent ways. In this paper, we offer 10 heuristics for
<br />interdisciplinary modeling that we have developed
<br />over a period of several years through our experi-
<br />ences in a variety of integrated research projects.
<br />The primary audience we have in mind is people
<br />who are not presently engaged in interdisciplinary
<br />research but are interested in moving in this direc-
<br />tion in the future. However, we also hope to stim-
<br />ulate thinking, discussing, and writing about meth-
<br />odology among cunent modeling practitioners, and
<br />we believe that our emphasis on rapid prototyping
<br />and sensitivity analysis will be of interest to them.
<br />What do we mean by "heuristic"? Polya (1945)
<br />defined this term as °the name of a certain branch
<br />of study" whose aim is "to understand the methods
<br />and rules of discovery and invention." However, in
<br />this essay, the word is used in the sense defined by
<br />Starfield and others (1994): "a heuristic is a plausi-
<br />ble or reasonable approach that has often proved to
<br />be useful, a rule of thumb."
<br />In other words, this is a paper in which the find-
<br />ings have been generated inductively from our col-
<br />lective experiences on a range of interdisciplinary
<br />projects rather than a deductive literature review
<br />that investigates the success or failure of other
<br />projects based on whether they did or did not use
<br />these heuristics.
<br />To illustrate our 10 heuristics, we give examples
<br />of lessons we have learned from developing inte-
<br />grated interdisciplinary models for a recent project
<br />investigating the Sustainability of Arctic Communi-
<br />ties (SAC). This project involved a team of 25 sci-
<br />entists (representing eight different disciplines in
<br />both the natural and the social sciences) and resi-
<br />dents from four indigenous Arctic communities in
<br />the Yukon Territory, Northwest Territories, and
<br />Alaska. Research team members came from several
<br />universities and from govemment agencies. The
<br />goal of the project was to investigate how changes
<br />in climate, tourism, oiI development, and govem-
<br />ment funding could affect the sustainability of our
<br />partner communities. The communities themselves
<br />defined their goals for sustainability in the early
<br />part of the project (G.P. Kofinas and others unpub-
<br />lished). These goals included (a) maintaining a
<br />strong relationship with the land and the animals,
<br />(b) developing healthy mixed economies (that is, a
<br />subsistence harvesting economy in parallel with a
<br />cash economy), (c) exercising local control over
<br />land use and resource use in their homelands, (d)
<br />educating their young people in both traditional
<br />knowledge and Westem science while also educat-
<br />ing outsiders about their way of life; and (e) main-
<br />taining a thriving native culture (evidenced, for
<br />example, by the use of indigenous language, respect
<br />for community elders, and spending time on the
<br />land). In other words, the communities saw sus-
<br />tainability not simply in terms of sustainable re-
<br />source use, but also in economic, political, and so-
<br />ciocultural terms. To address this holistic set of
<br />community goals, it was obviously essential to take
<br />an interdisciplinary view of the system. An inte-
<br />grated approach was in any case implicit in the
<br />framing of the original project proposal and in the
<br />range of disciplinary scientists included in the re-
<br />search team. Their expertise covered the fields of
<br />vegetation ecology, caribou biology, caribou behav-
<br />ior, household economies, cultural ecology, social
<br />anthropology, policy analysis, Arctic tourism, and
<br />natural resource modeling.
<br />The emphasis of this paper is not on the SAC
<br />Project itself, although examples will be drawn
<br />from that project to illustrate our heuristics. Also,
<br />the heuristics given here relate primarily to scien-
<br />tists working with other scientists on interdiscipli-
<br />nary projects rather than to scientists working with
<br />stakeholding. The SAC Project not only served to
<br />bring scientists together, but also involved residents
<br />of indigenous Arctic communities. A companion
<br />paper to this one (G.P. Kofinas and others unpub-
<br />lished) offers heuristics for researcher-stakeholder
<br />interactions and for synthesizing local knowledge
<br />and science. Finally, although we discuss various
<br />aspects of teamwork and collaboration, our focus is
<br />not on collaboration generally (as in, for example,
<br />Gray 1985, 1991 or Kofinas and Griggs 1996) but
<br />on the process of the collaborative development of
<br />synthesis models.
<br />Heurisfic 1. Know what skills to lnok for when recruit-V j-
<br />ing an interdisciplinary team. It is not a foregone
<br />conclusion that any given team of specialists will
<br />work together effectively to produce a tightly inte-
<br />grated view of a system. Indeed, there are many
<br />challenges and obstacles that must be addressed
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