Integrating Consensus-Based Group Decision Making into the Graph Model for Conflict Resolution in Complex Conflict Environments

Complex conflict environments often involve composite conflicting parties composed of multiple individuals with divergent assessments, posing significant challenges for preference elicitation and conflict analysis in the Graph Model for Conflict Resolution (GMCR). Existing option prioritization methods in GMCR rely on ordinal rankings of statement importance and binary evaluations of state support, which limits their ability to capture nuanced and heterogeneous preferences. Moreover, current GMCR frameworks lack a systematic mechanism to reconcile divergent individual assessments and construct collective preferences. To address these gaps, this study integrates consensus-based group decision making into the GMCR framework through flexible assessments. Specifically, pairwise comparisons are employed to represent the relative importance of statements, while continuous values in [0,1] are used to characterize the degree of statement support for states. To reconcile heterogeneous assessments, a minimum adjustment-based consensus reaching process is developed. Furthermore, grounded in inequity aversion theory from behavioral economics, fairness concern is incorporated to model individuals’ sensitivity to adjustment differences between themselves and others. Based on this mechanism, two minimum adjustment consensus models with fairness concern are proposed. The resulting consensus-based preferences are subsequently integrated into GMCR stability analysis to identify equilibrium solutions. A supply chain carbon reduction case is used to illustrate the implementation process and applicability of the proposed framework.