What values best distinguish the world's cultures? The machine learning-based cultural values inventory.

Scientists studying culture typically focus on a small number of theoretical constructs, such as individualism-collectivism, when seeking to explain cultural differences in psychological tendencies and behaviors. However, existing theories of culture could have missed out on important constructs that are useful for explaining cross-cultural differences. We used an abductive approach combining prediction and explanation to uncover important cultural values. In the prediction phase, based on 594 attitudes, values, and beliefs included in the World Values Survey, a neural network could classify respondents' nationalities with 90% accuracy in out-of-sample data. In the explanation phase, a feature importance analysis identified the values that contributed the most to predicting individuals' countries of origin. The top 60 variables resulting from this analysis were used to create the (ML-CVI), a tool to help future researchers uncover explanations for cross-cultural differences. Four follow-up studies demonstrated ML-CVI's theoretical and practical relevance. Specifically, Americans were less likely than Mexicans to comply with COVID-19 lockdowns, and this difference was explained by Americans' stronger Christian nationalism. Moreover, Indians were more likely than Americans to engage in proenvironmental behavior, and this difference was driven by Indians' stronger perseverance. Thus, the ML-CVI broadens the range of explanatory factors available to researchers by helping them identify explanations for cultural differences that they would not have been able to identify based on traditional theories of cultural values. Overall, this research highlights that machine learning-based abductive reasoning can help expand the range of explanatory frameworks in social science research.