The quest to unravel what contributes to happiness continues to captivate interest in both everyday experiences and academic discourse. Nonetheless, empirical research on the relative importance of possible candidates and their associations with two key aspects of well-being—eudaimonia (the good life) and hedonia (pleasure)—is limited. This study addresses this gap by exploring the relative strength of 32 predictors from multiple domains on psychological well-being (PWB) and subjective well-being (SWB). Using a machine learning approach on a dataset of 559 Korean adults, we identified distinct primary determinants for each well-being aspect. For PWB, meaning in life, self-esteem, and essentialist beliefs about happiness emerged as the strongest predictors requiring careful consideration. For SWB, depressive symptoms, subjective socioeconomic status, and emotional stability were salient predictors. Our findings highlight potential cultural nuances in the prioritization of happiness and offer valuable insights for policymakers and decision-makers in tailoring interventions and strategies to optimize individual well-being.

探究幸福的根源的探索继续引起人们对日常经历和学术讨论的兴趣。尽管如此，关于可能的候选者的相对重要性及其与幸福的两个关键方面——eudaimonia（美好生活）和hedonia（快乐）——的关联的实证研究是有限的。本研究通过探索来自多个领域的 32 个心理幸福感 (PWB) 和主观幸福感 (SWB) 预测因子的相对强度来解决这一差距。我们对 559 名韩国成年人的数据集使用机器学习方法，确定了每个福祉方面的不同主要决定因素。对于 PWB 来说，生命的意义、自尊和关于幸福的本质主义信念成为需要仔细考虑的最强预测因素。对于SWB来说，抑郁症状、主观社会经济状况和情绪稳定性是显着的预测因素。我们的研究结果强调了幸福优先顺序中潜在的文化差异，并为政策制定者和决策者制定干预措施和策略以优化个人福祉提供了宝贵的见解。