Using Clustering to extract Personality Information from socio economic data

2013/07/08 by Alexandros Ladas, Ladas, Alexandros, Uwe Aickelin +6
Computer Science · Economics, Econometrics and Finance · Psychology · #Complex Systems and Time Series Analysis #Computational Engineering #FOS: Computer and information sciences #Finance #Gambling Behavior and Treatments #Machine Learning (cs.LG) #and Science (cs.CE) #cs.CE #cs.LG

paper · pdf · doi:10.48550/arxiv.1307.1998

UKCI 2012, the 12th Annual Workshop on Computational Intelligence, Heriot-Watt University, 2012

arxiv created 2013/07/08 · openalex publication_date 2013/07/08 · arxiv updated 2013/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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