A systematic technique is proposed for assisting in the design and implementation of policy and addressing the need to minimize or resolve disputes that may arise in the enforcement of regulations. The Graph Model for Conflict Resolution is a methodology that facilitates the modeling and analysis of interactive multiple participant-multiple objective decision problems. In the problems considered here, decision makers and policy planners engaged in capacity building typically have different viewpoints over appropriate ways of developing options and enforcing policy choices. Incompatible understandings of resource potentials and limits, and disparities in utilization of these resources, exasperate stakeholders and make the capacity building process counterproductive and even conducive to conflict. A systematic conflict resolution technique is invaluable to policy makers and practitioners in defusing confrontations and reaching out for consensus among participants. In support of current approaches to policy planning and regulation, the Graph Model provides accurate predictions and strategic insights into short- and long-term opportunities in multiple participant-multiple objective decision situations. A conflict among the government of Canada, the Mi’kmaq First Nation, and commercial fishermen over the sharing of a natural resource in New Brunswick, Canada, is used to illustrate the advantages of this technique in practical problems.
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(KEY TERMS: Graph Model for Conflict Resolution; conflict analysis; decision support system; First Nations; fisheries; planning; sustainability; emotion.)

INTRODUCTION

To build community and public capacity, and to identify and meet development challenges, governments and policy makers must better understand the interests and needs of those most affected by their policies. Unfortunately, public round tables may degenerate into heated dialogues, which often escalate into a serious conflict. For instance, controversies may arise over the enforcement of rules affecting socioeconomic development and management of natural resources, especially when these rules are defied by citizens with special interests, are compromised by judicial rulings, or transgress international agreements. To achieve, effectively, stakeholders’ aspirations, policy makers and politicians need special skills and competencies to conceptualize a conflict, reconcile positions before exacerbation, and predict strategic resolutions before engagement. Thus, there is a tremendous need for decision support and conflict analysis techniques to help government officers, practitioners, and policy makers to analyze and resolve complex strategic conflicts.

In research investigating the role of modeling in resolving water resource conflicts, Lund and Palmer (1997) call for the application of formal modeling techniques for conflict resolution into the political and planning stages of the policy making processes. Likewise, Carraro et al. (2005) recommend the use of formal modeling approaches to gain valuable insights into strategies that are sustainable and acceptable to a wide range of interested groups. Jain and Singh (2003) and Nandalal and Simonovic (2003) summarize recent systems approaches to water management and conflict resolution, and cite numerous examples of controversies related to water issues where formal conflict models and procedures have been used. Formal decision models in multiple participant-multiple objective decision methodologies have been applied to environmental conflicts (see for example, Harboe, 1992; Hipel, 1992; and Cai et al., 2004), although there are many opportunities for more challenging applications in water resources. To motivate practitioners and researchers to use these formal models, numerous decision support systems have been developed for applications related to water conflicts and environmental policy planning (see for example, Thiessen and Loucks, 1992; Loucks, 1995; and Hipel et al., 2007).

Strategic conflict is omnipresent in society, in general, and in water resources management, in particular (Gleick, 1993; Wolf, 2002). It can be understood as an interactive decision problem among stakeholders who have inconsistent preferences over the outcome of a conflict, and different capacities to affect that outcome. For example, disputes over environmental pollution or shared utilization of resources are decision problems involving several stakeholders. A conflict analysis technique can furnish an effective and precise language of communication to express a specific dispute and to assist in the search for short term advice and a sustainable resolution.

Here, the Graph Model for Conflict Resolution (GMCR) (Fang et al., 1993) and its implementation in the decision support system, GMCR II (Hipel et al., 1997; Kilgour et al., 2001; Fang et al., 2003a,b), is used to study systematically the Burnt Church Conflict, a strategic conflict over lobster fishing in New Brunswick, Canada. The dispute erupted in late 1999, and involved the Burnt Church First Nation, which claimed historical and treaty rights to determine its own fishery plan, officers of the government of Canada, who were attempting to enforce regulations, and nonnative fishermen, who relied on the commercial fishery for their livelihood. This conflict turned violent on several occasions between 1999 and 2002. To the authors’ knowledge, this research project constitutes one of the first times a formal conflict resolution methodology has been used to analyze a dispute involving aboriginal groups and natural resources. It is a postmortem assessment of events that, although they took place in a small community, had far-reaching implications. In fact, they reverberated across Canada, especially for First Nations people, who are in protracted disputes with governments over natural resources management and allocation. The authors wish to show that a systematic study of a strategic dispute deepens understanding, puts the situation into better perspective, yields insights into the best short term and long term resolutions, and provides valuable strategic advice on policy making and regulation. In addition, a formal post-event evaluation provides lessons about past policy decisions: what went wrong, how to do better in the future, and what opportunities were missed.