Heuristics-based modelling of human decision process

Document Type : Research Paper


1 School of Artificial Intelligence and Data Science, IIT Jodhpur, Jodhpur, India

2 Digital Humanities, IIT Jodhpur, Jodhpur, India

3 Hof University of Applied Sciences, Hof, Germany


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 $\mathcal{ACI}$, and test their validity
in the real world datasets, against the backdrop of preference learning.


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