Competitive Contexts Reduce Children's Motivation to Tell White Lies Based on Big Data Analysis

Abstract

There is a growing interest in the wireless technology to complement the traditional model-driven design approaches with data-driven machine learning- (ML-) based solutions. Telling a white lie is a distinct type of prosocial behavior, because in terms of the nature of lies, it is a lie but its motivation is to benefit someone else. It is unclear how children behave when they are caught in a conflict between prosocial motivation and the psychological cost of losing in a competition. Big data analysis can improve work efficiency, make analysis work more organized, and make analysis results more accurate. So the purpose of this study was to investigate the motivation of children to tell white lies by using big data analysis to examine the effects of different competitive situations on white lie behavior among 6- to 11-year olds. A final-round-of-game paradigm was used to elicit prosocial white lies in children under varying competitive conditions. These were explored in two studies. In the study, two groups of children (N=177, Mage=104.41 months, SD=1.74, 50.8% boys) participated in either baseline conditions or a competition against others. More children tended to tell the truth in the others-competition context group, and boys tended to be more truthful. These findings show that a decision of whether to tell a white lie is influenced by the psychological cost to children.