Thrifty World Models for Applying Machine Learning in the Design of Complex Biosocial–Technical Systems
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| Publicado en: | Machine Learning and Knowledge Extraction vol. 7, no. 3 (2025), p. 83-98 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3254583161 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7030083 |2 doi | |
| 035 | |a 3254583161 | ||
| 045 | 2 | |b d20250701 |b d20250930 | |
| 100 | 1 | |a Fox, Stephen |u VTT Technical Research Centre of Finland, 02150 Espoo, Finland | |
| 245 | 1 | |a Thrifty World Models for Applying Machine Learning in the Design of Complex Biosocial–Technical Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Interactions between human behavior, legal regulations, and monitoring technology in road traffic systems provide an everyday example of complex biosocial–technical systems. In this paper, a study is reported that investigated the potential for a thrifty world model to predict consequences from choices about road traffic system design. Colloquially, the term thrifty means economical. In physics, the term thrifty is related to the principle of least action. Predictions were made with algebraic machine learning, which combines predefined embeddings with ongoing learning from data. The thrifty world model comprises three categories that encompass a total of only eight system design choice options. Results indicate that the thrifty world model is sufficient to encompass biosocial–technical complexity in predictions of where and when it is most likely that accidents will occur. Overall, it is argued that thrifty world models can provide a practical alternative to large photo-realistic world models, which can contribute to explainable artificial intelligence (AI) and to frugal AI. | |
| 653 | |a Machine learning | ||
| 653 | |a Behavior | ||
| 653 | |a Physics | ||
| 653 | |a Roads & highways | ||
| 653 | |a Systems design | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Automobile safety | ||
| 653 | |a Speed limits | ||
| 653 | |a Local government | ||
| 653 | |a Complexity | ||
| 653 | |a Ethics | ||
| 653 | |a Principle of least action | ||
| 653 | |a Explainable artificial intelligence | ||
| 700 | 1 | |a Fortes, Rey Vitor |u DFKI German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; vitor.fortes_rey@dfki.de | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 3 (2025), p. 83-98 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254583161/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254583161/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254583161/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |