Machine learning in the prediction of human wellbeing
Abstract Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a be...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84137-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544819818627072 |
|---|---|
| author | Ekaterina Oparina Caspar Kaiser Niccolò Gentile Alexandre Tkatchenko Andrew E. Clark Jan-Emmanuel De Neve Conchita D’Ambrosio |
| author_facet | Ekaterina Oparina Caspar Kaiser Niccolò Gentile Alexandre Tkatchenko Andrew E. Clark Jan-Emmanuel De Neve Conchita D’Ambrosio |
| author_sort | Ekaterina Oparina |
| collection | DOAJ |
| description | Abstract Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing. |
| format | Article |
| id | doaj-art-6aa65cbc3edf408aa35dd4813c230439 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6aa65cbc3edf408aa35dd4813c2304392025-01-12T12:14:38ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-84137-1Machine learning in the prediction of human wellbeingEkaterina Oparina0Caspar Kaiser1Niccolò Gentile2Alexandre Tkatchenko3Andrew E. Clark4Jan-Emmanuel De Neve5Conchita D’Ambrosio6London School of EconomicsWarwick Business SchoolUniversity of LuxembourgUniversity of LuxembourgUniversity of LuxembourgUniversity of OxfordUniversity of LuxembourgAbstract Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.https://doi.org/10.1038/s41598-024-84137-1Subjective wellbeingPrediction methodsMachine learning |
| spellingShingle | Ekaterina Oparina Caspar Kaiser Niccolò Gentile Alexandre Tkatchenko Andrew E. Clark Jan-Emmanuel De Neve Conchita D’Ambrosio Machine learning in the prediction of human wellbeing Scientific Reports Subjective wellbeing Prediction methods Machine learning |
| title | Machine learning in the prediction of human wellbeing |
| title_full | Machine learning in the prediction of human wellbeing |
| title_fullStr | Machine learning in the prediction of human wellbeing |
| title_full_unstemmed | Machine learning in the prediction of human wellbeing |
| title_short | Machine learning in the prediction of human wellbeing |
| title_sort | machine learning in the prediction of human wellbeing |
| topic | Subjective wellbeing Prediction methods Machine learning |
| url | https://doi.org/10.1038/s41598-024-84137-1 |
| work_keys_str_mv | AT ekaterinaoparina machinelearninginthepredictionofhumanwellbeing AT casparkaiser machinelearninginthepredictionofhumanwellbeing AT niccologentile machinelearninginthepredictionofhumanwellbeing AT alexandretkatchenko machinelearninginthepredictionofhumanwellbeing AT andreweclark machinelearninginthepredictionofhumanwellbeing AT janemmanueldeneve machinelearninginthepredictionofhumanwellbeing AT conchitadambrosio machinelearninginthepredictionofhumanwellbeing |