An agent-based cognitive mapping system for sales opportunity analysis

The cognitive map (CM) is a representation of the causal relationships existing among the decision elements of a given object and/or problem, and also describes experts’ tacit knowledge. The CM has proven particularly useful in solving unstructured problems with many variables and causal relationships. Taking into consideration the amount of CM application studies thus far conducted in various fields, there has been relatively little research focused on the process of developing a CM. There have been some studies concerning the CM design process, most notably those conducted by Nelson, Nadkarni, Narayanan, and Ghods (2000) and Annibal, Tatiana, Susan, Julie, and Arthur (2006); however, these have failed to come up with a systematic approach in terms of the essential CM elements, which include: (1) the number of nodes, (2) the cause-and-effect relationships (arrows) among nodes, and (3) the strength of the cause-and-effect relationships (causality coefficients). The principal objective of this study, then, was to, first, determine the number of nodes that constitute a CM; second, to extract the cause-and-effect relationships among the nodes; and third, to devise an objective and systematic approach by which the causality coefficient within a single framework can be obtained. To accomplish this objective, our study adopts a CM-based mechanism referred to as the agent-based cognitive mapping system (ABCMS). Moreover, in order to extract effectively the three key elements of a CM, this study introduces the concepts of the multi-agent system (MAS) and particle swarm optimization (PSO), which allow for the construction of an ABCMS with a flexible and dynamic mechanism.