Application of the symbolic regression program AI-Feynman to psychology
The discovery of hidden laws in data is the core challenge in many fields, from the natural sciences to the social sciences. However, this task has historically relied on human intuition and experience in many areas, including psychology. Therefore, discovering laws using artificial intelligence (AI...
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| Format: | Article |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1039438/full |
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| author | Masato Miyazaki Ken-Ichi Ishikawa Ken'ichiro Nakashima Hiroshi Shimizu Taiki Takahashi Nobuyuki Takahashi |
| author_facet | Masato Miyazaki Ken-Ichi Ishikawa Ken'ichiro Nakashima Hiroshi Shimizu Taiki Takahashi Nobuyuki Takahashi |
| author_sort | Masato Miyazaki |
| collection | DOAJ |
| description | The discovery of hidden laws in data is the core challenge in many fields, from the natural sciences to the social sciences. However, this task has historically relied on human intuition and experience in many areas, including psychology. Therefore, discovering laws using artificial intelligence (AI) has two significant advantages. First, it makes it possible to detect laws that humans cannot discover. Second, it will help construct more accurate theories. An AI called AI-Feynman was released in a very different field, and it performed impressively. Although AI-Feynman was initially designed to discover laws in physics, it can also work well in psychology. This research aims to examine whether AI-Feynman can be a new data analysis method for inter-temporal choice experiments by testing whether it can discover the hyperbolic discount model as a discount function. An inter-temporal choice experiment was conducted to accomplish these objectives, and the data were input into AI-Feynman. As a result, seven discount function candidates were proposed by AI-Feynman. One candidate was the hyperbolic discount model, which is currently considered the most accurate. The three functions of the root-mean-squared errors were superior to the hyperbolic discount model. Moreover, one of the three candidates was more “hyperbolic” than the standard hyperbolic discount function. These results indicate two things. One is that AI-Feynman can be a new data analysis method for inter-temporal choice experiments. The other is that AI-Feynman can discover discount functions that humans cannot find. |
| first_indexed | 2024-04-10T20:01:54Z |
| format | Article |
| id | doaj.art-d6ec97aaed134229a1126f74e645037e |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-10T20:01:54Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-d6ec97aaed134229a1126f74e645037e2023-01-27T06:02:41ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122023-01-01610.3389/frai.2023.10394381039438Application of the symbolic regression program AI-Feynman to psychologyMasato Miyazaki0Ken-Ichi Ishikawa1Ken'ichiro Nakashima2Hiroshi Shimizu3Taiki Takahashi4Nobuyuki Takahashi5Department of Behavioral Science, Graduate School of Letters, Hokkaido University, Sapporo, Hokkaido, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima, JapanDepartment of Psychology, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashi-Hiroshima, Hiroshima, JapanFaculty of Sociology, Kwansei Gakuin University, Nishinomiya, Hyogo, JapanDepartment of Behavioral Science, Graduate School of Letters, Hokkaido University, Sapporo, Hokkaido, JapanDepartment of Behavioral Science, Graduate School of Letters, Hokkaido University, Sapporo, Hokkaido, JapanThe discovery of hidden laws in data is the core challenge in many fields, from the natural sciences to the social sciences. However, this task has historically relied on human intuition and experience in many areas, including psychology. Therefore, discovering laws using artificial intelligence (AI) has two significant advantages. First, it makes it possible to detect laws that humans cannot discover. Second, it will help construct more accurate theories. An AI called AI-Feynman was released in a very different field, and it performed impressively. Although AI-Feynman was initially designed to discover laws in physics, it can also work well in psychology. This research aims to examine whether AI-Feynman can be a new data analysis method for inter-temporal choice experiments by testing whether it can discover the hyperbolic discount model as a discount function. An inter-temporal choice experiment was conducted to accomplish these objectives, and the data were input into AI-Feynman. As a result, seven discount function candidates were proposed by AI-Feynman. One candidate was the hyperbolic discount model, which is currently considered the most accurate. The three functions of the root-mean-squared errors were superior to the hyperbolic discount model. Moreover, one of the three candidates was more “hyperbolic” than the standard hyperbolic discount function. These results indicate two things. One is that AI-Feynman can be a new data analysis method for inter-temporal choice experiments. The other is that AI-Feynman can discover discount functions that humans cannot find.https://www.frontiersin.org/articles/10.3389/frai.2023.1039438/fulltime preferencesymbolic regressionAI-Feynmanhyperbolic discounting modelartificial intelligence |
| spellingShingle | Masato Miyazaki Ken-Ichi Ishikawa Ken'ichiro Nakashima Hiroshi Shimizu Taiki Takahashi Nobuyuki Takahashi Application of the symbolic regression program AI-Feynman to psychology Frontiers in Artificial Intelligence time preference symbolic regression AI-Feynman hyperbolic discounting model artificial intelligence |
| title | Application of the symbolic regression program AI-Feynman to psychology |
| title_full | Application of the symbolic regression program AI-Feynman to psychology |
| title_fullStr | Application of the symbolic regression program AI-Feynman to psychology |
| title_full_unstemmed | Application of the symbolic regression program AI-Feynman to psychology |
| title_short | Application of the symbolic regression program AI-Feynman to psychology |
| title_sort | application of the symbolic regression program ai feynman to psychology |
| topic | time preference symbolic regression AI-Feynman hyperbolic discounting model artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1039438/full |
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