How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study ( N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment ( N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

What attitudes, values, and beliefs serve as key markers of cultural change? To answer this question, we examined 221,485 respondents from the World Values Survey, a multi-wave cross-country survey of people's attitudes, values and beliefs. We trained a machine learning model to classify respondents into seven waves (i.e., periods). Once trained, the machine learning model identified a separate group of 24,611 respondents' wave with a balanced accuracy of 77%. We then queried the model to identify the attitudes, values, and beliefs that contributed the most to its classification decisions, and therefore, served as markers of cultural change. These included religiosity, social attitudes, political attitudes, independence, life satisfaction, Protestant work ethic, and prosociality. Although past research in cultural change has discussed decreasing religiosity and increasing liberalism and independence, it has not yet identified Protestant work ethic, political orientation, and prosociality as values relevant to cultural change. Thus, the current research points to new directions for future research on cultural change that might not be evident from either a deductive or an inductive approach. This research illustrates that the abductive approach of machine learning, which focuses on the most likely explanations for an outcome, can provide novel insights. Public SignificanceStatement. This research found that in recent years, people around the world have been becoming less religious and more liberal in their social attitudes and political orientation. People have been valuing independence more, although there appears to be a decline in independence in the last few years. The extent to which people emphasize hard work, thrift, and acting in a prosocial manner has also declined in recent years.

This article introduces the research community to the power of machine learning over traditional approaches when analyzing longitudinal data. Although traditional approaches work well with small to medium datasets, machine learning models are more appropriate as the available data becomes larger and more complex. Additionally, machine learning methods are ideal for analyzing longitudinal data because they do not make any assumptions about the distribution of the dependent and independent variables or the homogeneity of the underlying population. They can also analyze cases with partial information. In this article, we use the Household, Income, and Labour Dynamics in Australia (HILDA) survey to illustrate the benefits of machine learning. Using a machine learning algorithm, we analyze the relationship between job-related variables and neuroticism across 13 years of the HILDA survey. We suggest that the results produced by machine learning can be used to generate generalizable rules from the data to augment our theoretical understanding of the domain. With a technical guide, this article offers critical information and best-practice recommendations that can assist social science researchers in conducting machine learning analysis with longitudinal data.

High levels of income inequality can persist in society only if people accept the inequality as justified. To identify psychological predictors of people’s tendency to justify inequality, we retrained a pre-existing deep learning model to predict the extent to which World Values Survey respondents believed that income inequality is necessary. A feature importance analysis revealed multiple items associated with the importance of hard work as top predictors. As an emphasis on hard work is a key component of the Protestant Work Ethic, we formulated the hypothesis that the PWE increases acceptance of inequality. A correlational study found that the more people endorsed PWE, the less disturbed they were about factual statistics about wealth equality in the US. Two experiments found that exposing people to PWE items decreased their disturbance with income inequality. The findings indicate that machine learning models can be reused to generate viable hypotheses.