Cultural association based on machine learning for team formation

TL;DR

This paper introduces a machine learning-based method to measure cultural association through graphical representations of expressions, aiming to improve team formation by understanding cultural similarities.

Contribution

It proposes the Graphical Association Method (GAM) to quantify cultural association based on behavioral expressions, linking culture to successful team collaboration.

Findings

GAM effectively captures cultural similarities among individuals.

The method shows promise for enhancing team formation processes.

Results indicate a strong correlation between cultural association and cooperative success.

Abstract

Culture is core to human civilization, and is essential for human intellectual achievements in social context. Culture also influences how humans work together, perform particular task and overall lifestyle and dealing with other groups of civilization. Thus, culture is concerned with establishing shared ideas, particularly those playing a key role in success. Does it impact on how two individuals can work together in achieving certain goals? In this paper, we establish a means to derive cultural association and map it to culturally mediated success. Human interactions with the environment are typically in the form of expressions. Association between culture and behavior produce similar beliefs which lead to common principles and actions, while cultural similarity as a set of common expressions and responses. To measure cultural association among different candidates, we propose the use of a Graphical Association Method (GAM). The behaviors of candidates are captured through series of expressions and represented in the graphical form. The association among corresponding node and core nodes is used for the same. Our approach provides a number of interesting results and promising avenues for future applications.