Combining theoretical modelling and machine learning approaches: The case of teamwork effects on individual effort expenditure

Abstract

Machine learning modelling of psychological processes is often considered as competing alternative to theoretical modelling. In contrast, the current study explores potential synergetic effects of these two general approaches both for predictive accuracy and theoretical understanding. Theoretical models have high explanatory value but can have weak predictive power. Machine learning models have high predictive power but low transparency and require large amounts of data and computational power. The combination of machine learning and theoretical models may yield both higher predictive accuracy as well as higher explanatory value and lower requirements of data and computational power as compared to either of the two approaches alone. We examine our assumptions in the field of team motivation, using archival performance data from 1,425,926 individual and relay races of swimming competitions. While the results revealed better prediction of the machine learning model, an exploration of the machine learning model with explainable artificial intelligence methods offered new insights also for the theoretical model. Finally, the combination of machine learning and theoretical modelling required less computational power than the machine learning approach alone, but not less data for building the model.