Combining theoretical modelling and machine learning approaches: The case of teamwork effects on individual effort expenditure
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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.
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A case study indicated that when dealing with complex social factors, integrating predictive ML models with explanatory models can enhance understanding and prediction of outcomes.
“A case study indicated that when dealing with complex social factors, integrating predictive ML models with explanatory models can enhance understanding and prediction of outcomes [57].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [57]
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- Authors
- Eisbach, S., Mai, O., & Hertel, G.
- Affiliations
- University of Münster (DE)
- Corresponding author
- Hertel, Guido
- Venue
- New Ideas in Psychology
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 73
- Pages
- 101077
- ISSN
- 0732-118X (ISSN-L)
- OpenAlex
- W4392119704
- Semantic Scholar
- 13f03c46f0247d162a36d63d575d9ac366643f90
- Cited by
- 12
- References
- 56
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- New Ideas in Psychology (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Keywords
- teamwork, team motivation, machine learning, theoretical modeling, explainable AI, prediction accuracy, computational efficiency, data requirements, cognitive processes
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