Personalized Prediction of Behaviors and Experiences: An Idiographic Person–Situation Test
利用密集纵向数据和机器学习方法，从个体化视角预测孤独、拖延和学习行为，发现预测准确度高且关键特征因人而异。
A longstanding goal of psychology is to predict the things that people do and feel, but tools to accurately predict future behaviors and experiences remain elusive. In the present study, we used intensive longitudinal data ( N = 104 college-age adults at a midwestern university; total assessments = 5,971) and three machine-learning approaches to investigate the degree to which three future behaviors and experiences—loneliness, procrastination, and studying—could be predicted from past psychological (i.e., personality and affective states), situational (i.e., objective situations and psychological situation cues), and time (i.e., trends, diurnal cycles, time of day, and day of the week) phenomena from an idiographic, person-specific perspective. Rather than pitting persons against situations, such an approach allows psychological phenomena, situations, and time to jointly predict future behaviors and experiences. We found (a) a striking degree of prediction accuracy across participants, (b) that a majority of participants’ future behaviors are predicted by both person and situation features, and (c) that the most important features vary greatly across people.