Using Clustering to extract Personality Information from socio economic data

TL;DR

This paper proposes a clustering-based method to extract personality-related behavioral groups from socio-economic data, aiming to enhance economic models by integrating psychological insights.

Contribution

It introduces a simple clustering approach to identify personality traits within socio-economic data, bridging psychology and economics for better behavioral understanding.

Findings

Clustering reveals distinct behavioral groups linked to personality traits.

The method enhances traditional economic models with psychological insights.

Potential to improve decision-making in knowledge economy applications.

Abstract

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy.