Using Clustering to extract Personality Information from socio economic data
This work addresses the limited availability of psychological personality information in real-world economic applications, offering a tool to enhance models for understanding complex economic behaviors, though it appears incremental in its approach.
The authors tackled the problem of incorporating personality psychology into economic models by proposing a method to extract behavioral groups using simple clustering techniques, which can reveal personality aspects of individuals, aiming to improve traditional knowledge economy models.
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.