Decision Maker Profiling Using Their Mental Behavior Pattern

Abstract

This study describes a method to assist the task of predicting the result of the decision-making process of an individual based on psychological and emotional aspects and using artificial intelligence (AI) techniques. This study presents indicators created for profile identification, which are organized in primary and circumstantial categories. These indicators are merged according to the ultimate purpose of profile identification, including the expected behavioral pattern for a person who performs a decision-making process. The person behavior hypothesis was successfully tested and can be approximated by an indicator such as mental functioning pattern, and the mental functioning pattern hypothesis can signal the most likely decisions of an individual. Four debtor decision variables were assessed in a debt negotiation process, in order to validate the method, which is applicable to other decision-making domains. The best signaling of the most likely decision of the debtor was seven times greater than that of a random prediction, while the gain of the worst decision signaling variable was 20%.