Heuristics-based modelling of human decision process

Attitudinal Choquet integral (ACI) is a recent aggregation operator that considers in the aggregation process the criteria interaction and the DM’s attitude, both of which are specific to the decision-maker. However, this capability comes at the cost of increased complexity that hinders its applicability in big data analytics. To address the same, in this paper, we explore some heuristics-based forms of the ACI operator, so as to somehow overcome its complexity. We devise new and efficient forms of ACI, and test their validity in the real world datasets, against the backdrop of preference learning.