Improving Resource Conflict Management in Communities using a Transdisciplinary Coupled Modelling Approach

Scholars have begun to use scientific knowledge to develop models for conflict management (CM). But natural resource conflicts (NRC) are “wicked problems” due to their uncertainty, multi-actors, multiple explanatory mechanisms and spatial-temporal context, rendering traditional CM strategies unsuitable. This article presents A Spatially Explicit Fuzzy Logic Adapted Models for CM (SEFLAME-CM). SEFLAME-CM develops a conflict vulnerability likeliness index (CVL), tested in communities vulnerable to NRC in the Nigerian Delta. SEFLAME-CM is a transdisciplinary coupled modelling approach, integrating the knowledge of the actors in support of decision-making in NRC for social learning. It involves three phases: (i) model design and joint problem framing, which identifies conflict drivers and operationalize the model, (ii) co-production of knowledge and (iii) scenario co-creation/results dissemination phase. A mixed method of satellite imageries, open interviews and workshops were used to collect data. When validated with Multiple Linear Regression Model (MLRM), the r2 shows better performance in SEFLAME-CM.