An, L. et al. (2023) Modeling agent decision and behavior in the light of data science and artificial intelligence. Environmental Modelling and Software, 166, 105713. (doi: 10.1016/j.envsoft.2023.105713)
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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.
| Item Type: | Articles |
|---|---|
| Additional Information: | We are indebted to financial support from the National Science Foundation (NSF) through the Method, Measure & Statistics and Geography and Spatial Sciences (BCS #1638446) and the Dynamics of Integrated Socio-Environmental Systems programs (BCS 1826839 and DEB 1212183). We thank the participants of the ABM 17 Symposium (sponsored by the above NSF grant; http://complexities.org/ABM17/) for input and comments. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 757455) and through an ESRC/Alan Turing Joint Fellowship (ES/R007918/1). |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Heppenstall, Professor Alison |
| Authors: | An, L., Grimm, V., Bai, Y., Sullivan, A., Turner, B.L., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., and Tang, W. |
| College/School: | College of Social Sciences > School of Social and Political Sciences > Urban Studies |
| Journal Name: | Environmental Modelling and Software |
| Publisher: | Elsevier |
| ISSN: | 1364-8152 |
| ISSN (Online): | 1873-6726 |
| Published Online: | 16 May 2023 |
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