One of the most significant problems in artificial intelligence is knowledge representation linked to the decision-making process in order to simultaneously consider a set of events to achieve the combination that allows the trigger of an action. This work uses a parallel and distributed design approach to represent knowledge, taking a decision-making process during a risk event in an applied engineering process as a case study, which also includes the uncertainty that underlies the process. This allows us to consider the advantages of this kind of knowledge representation. The design is based on innovative fuzzy cognitive maps and their ability to simultaneously consider the causality of all elements that comprise the behavior to be modeled. The approach used by the cognitive model includes: 1) event process; and 2) behavior of the expert in the case study. The analysis utilizes mental models, genetic graphs, and behavioral analysis of the process to identify elements, their causal relationships, and their relative weights.
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