Experimental economics for machine learning-a methodological contribution on lie detection.
In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experim...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314806 |
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| author | Dmitri Bershadskyy Laslo Dinges Marc-André Fiedler Ayoub Al-Hamadi Nina Ostermaier Joachim Weimann |
| author_facet | Dmitri Bershadskyy Laslo Dinges Marc-André Fiedler Ayoub Al-Hamadi Nina Ostermaier Joachim Weimann |
| author_sort | Dmitri Bershadskyy |
| collection | DOAJ |
| description | In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations. To demonstrate this, we focus on algorithms trained to detect lies. Such algorithms are of high relevance for research in economics as they deal with the ability to retrieve otherwise private information. We deduce that most of the commonly applied data sets for the training of lie detection algorithms could be improved by applying the toolbox of experimental economics. To illustrate this, we replicate the "lies in disguise-experiment" by Fischbacher and Föllmi-Heusi with a modification regarding monitoring. The modified setup guarantees a certain level of privacy from the experimenter yet allows to record the subjects as they lie to the camera. Despite monitoring, our results indicate the same lying behavior as in the original experiment. Yet, our experiment allows an individual-level analysis of experimental data and the generation of a lie detection algorithm with an accuracy rate of 67%, which we present in this article. |
| format | Article |
| id | doaj-art-6d8794f5b5d14ae5acb98c948d972dd6 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-6d8794f5b5d14ae5acb98c948d972dd62025-01-08T05:32:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031480610.1371/journal.pone.0314806Experimental economics for machine learning-a methodological contribution on lie detection.Dmitri BershadskyyLaslo DingesMarc-André FiedlerAyoub Al-HamadiNina OstermaierJoachim WeimannIn this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations. To demonstrate this, we focus on algorithms trained to detect lies. Such algorithms are of high relevance for research in economics as they deal with the ability to retrieve otherwise private information. We deduce that most of the commonly applied data sets for the training of lie detection algorithms could be improved by applying the toolbox of experimental economics. To illustrate this, we replicate the "lies in disguise-experiment" by Fischbacher and Föllmi-Heusi with a modification regarding monitoring. The modified setup guarantees a certain level of privacy from the experimenter yet allows to record the subjects as they lie to the camera. Despite monitoring, our results indicate the same lying behavior as in the original experiment. Yet, our experiment allows an individual-level analysis of experimental data and the generation of a lie detection algorithm with an accuracy rate of 67%, which we present in this article.https://doi.org/10.1371/journal.pone.0314806 |
| spellingShingle | Dmitri Bershadskyy Laslo Dinges Marc-André Fiedler Ayoub Al-Hamadi Nina Ostermaier Joachim Weimann Experimental economics for machine learning-a methodological contribution on lie detection. PLoS ONE |
| title | Experimental economics for machine learning-a methodological contribution on lie detection. |
| title_full | Experimental economics for machine learning-a methodological contribution on lie detection. |
| title_fullStr | Experimental economics for machine learning-a methodological contribution on lie detection. |
| title_full_unstemmed | Experimental economics for machine learning-a methodological contribution on lie detection. |
| title_short | Experimental economics for machine learning-a methodological contribution on lie detection. |
| title_sort | experimental economics for machine learning a methodological contribution on lie detection |
| url | https://doi.org/10.1371/journal.pone.0314806 |
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