Learning to Represent Individual Differences for Choice Decision Making
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908287327272960 |
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| author | Chen, Yan-Ying Weng, Yue Filipowicz, Alexandre Iliev, Rumen Chen, Francine Hakimi, Shabnam Zhang, Yanxia Lee, Matthew Lyons, Kent Wu, Charlene |
| author_facet | Chen, Yan-Ying Weng, Yue Filipowicz, Alexandre Iliev, Rumen Chen, Francine Hakimi, Shabnam Zhang, Yanxia Lee, Matthew Lyons, Kent Wu, Charlene |
| contents | Human decision making can be challenging to predict because decisions are affected by a number of complex factors. Adding to this complexity, decision-making processes can differ considerably between individuals, and methods aimed at predicting human decisions need to take individual differences into account. Behavioral science offers methods by which to measure individual differences (e.g., questionnaires, behavioral models), but these are often narrowed down to low dimensions and not tailored to specific prediction tasks. This paper investigates the use of representation learning to measure individual differences from behavioral experiment data. Representation learning offers a flexible approach to create individual embeddings from data that are both structured (e.g., demographic information) and unstructured (e.g., free text), where the flexibility provides more options for individual difference measures for personalization, e.g., free text responses may allow for open-ended questions that are less privacy-sensitive. In the current paper we use representation learning to characterize individual differences in human performance on an economic decision-making task. We demonstrate that models using representation learning to capture individual differences consistently improve decision predictions over models without representation learning, and even outperform well-known theory-based behavioral models used in these environments. Our results propose that representation learning offers a useful and flexible tool to capture individual differences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Represent Individual Differences for Choice Decision Making Chen, Yan-Ying Weng, Yue Filipowicz, Alexandre Iliev, Rumen Chen, Francine Hakimi, Shabnam Zhang, Yanxia Lee, Matthew Lyons, Kent Wu, Charlene Machine Learning Computation and Language Human decision making can be challenging to predict because decisions are affected by a number of complex factors. Adding to this complexity, decision-making processes can differ considerably between individuals, and methods aimed at predicting human decisions need to take individual differences into account. Behavioral science offers methods by which to measure individual differences (e.g., questionnaires, behavioral models), but these are often narrowed down to low dimensions and not tailored to specific prediction tasks. This paper investigates the use of representation learning to measure individual differences from behavioral experiment data. Representation learning offers a flexible approach to create individual embeddings from data that are both structured (e.g., demographic information) and unstructured (e.g., free text), where the flexibility provides more options for individual difference measures for personalization, e.g., free text responses may allow for open-ended questions that are less privacy-sensitive. In the current paper we use representation learning to characterize individual differences in human performance on an economic decision-making task. We demonstrate that models using representation learning to capture individual differences consistently improve decision predictions over models without representation learning, and even outperform well-known theory-based behavioral models used in these environments. Our results propose that representation learning offers a useful and flexible tool to capture individual differences. |
| title | Learning to Represent Individual Differences for Choice Decision Making |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2503.21704 |