Long-term decision-related activities, such as bottom-up and top-down policy development, analysis, and planning, stand to benefit from the creation and application of knowledge-based cognitive multi-agent (KCM) decision support systems that would be capable of representing real-world spatiotemporal social human behavior in local contexts. However, limitations in our ability to represent the knowledge and cognition of real-world decision-makers have hindered the development of such models.
This article describes a method for acquiring and operationalizing local decision-making knowledge in social contexts, which is one of the six integral components of the SOSIEL toolkit. The method is a hybrid in that it relies on a respondent selection technique equipped for representative sampling of large and heterogeneous populations of individuals and on a knowledge elicitation technique capable of delving deep into detail with a specific individual. With this, the method is aimed at capturing both, the necessary breadth and depth in local decision-making knowledge that is required for parameterizing and initializing KCM decision support systems that represent local populations of decision-makers.
The method consists of the following five main steps: (a) preparation, (b) acquisition, (c) processing and analysis, (d) formulation, and (e) initial verification and testing. The step-by-step presentation of the method is provided in the context of a case study, which involved acquiring and operationalizing local knowledge for a human-forest-climate change model that is currently being implemented in the Ukrainian Carpathian Mountains to study human adaptation to climate change.