Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envsoft.2023.105713 |
| Title (Primary) | Modeling agent decision and behavior in the light of data science and artificial intelligence |
| Author | An, L.; Grimm, V.; Bai, Y.; Sullivan, A.; Turner II, B.L.; Malleson, N.; Heppenstall, A.; Vincenot, C.; Robinson, D.; Ye, X.; Liu, J.; Lindkvist, E.; Tang, W. |
| Source Titel | Environmental Modelling & Software |
| Year | 2023 |
| Department | OESA |
| Volume | 166 |
| Page From | art. 105713 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Abstract | Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents’ behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=27040 |
| An, L., Grimm, V., Bai, Y., Sullivan, A., Turner II, B.L., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., Tang, W. (2023): Modeling agent decision and behavior in the light of data science and artificial intelligence Environ. Modell. Softw. 166 , art. 105713 10.1016/j.envsoft.2023.105713 |
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