Cultural association based on machine learning for team formation
This work addresses team formation by leveraging cultural similarity, but it appears incremental as it builds on existing concepts of cultural association without specifying broad advancements.
The paper tackles the problem of measuring cultural association among individuals to predict team success, proposing a Graphical Association Method (GAM) that captures behaviors through expressions and represents them graphically, with results described as interesting and promising for future applications.
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.