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- An, Li, et al.
(författare)
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Challenges, tasks, and opportunities in modeling agent-based complex systems
- 2021
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Ingår i: Ecological Modelling. - : Elsevier BV. - 0304-3800 .- 1872-7026. ; 457
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Forskningsöversikt (refereegranskat)abstract
- Humanity is facing many grand challenges at unprecedented rates, nearly everywhere, and at all levels. Yet virtually all these challenges can be traced back to the decision and behavior of autonomous agents that constitute the complex systems under such challenges. Agent-based modeling has been developed and employed to address such challenges for a few decades with great achievements and caveats. This article reviews the advances of ABM in social, ecological, and socio-ecological systems, compare ABM with other traditional, equation-based models, provide guidelines for ABM novice, modelers, and reviewers, and point out the challenges and impending tasks that need to be addressed for the ABM community. We further point out great opportunities arising from new forms of data, data science and artificial intelligence, showing that agent behavioral rules can be derived through data mining and machine learning. Towards the end, we call for a new science of Agent-based Complex Systems (ACS) that can pave an effective way to tackle the grand challenges.
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| 2. |
- An, Li, et al.
(författare)
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Corrigendum to “Challenges, tasks, and opportunities in modeling agent-based complex systems”, Ecological Modeling, 2021, 457 : 109,685, page 1–15 (Ecological Modelling (2021) 457, (S030438002100243X), (10.1016/j.ecolmodel.2021.109685))
- 2022
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Ingår i: Ecological Modelling. - : Elsevier BV. - 0304-3800 .- 1872-7026. ; 471
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Tidskriftsartikel (refereegranskat)abstract
- DOI of original article: 10.1016/j.ecolmodel.2021.109685. The authors regret that the following references are missing from the original article's online supplementary information: Box, P., 2001. Kenge GIS-CA class template for Swarm. Nat. Resour. Environ. Issues 8, 31–35. Minar, A., Burkhart, R., Langton, C.G., Manor, A., 1996. The Swarm simulation system: A toolkit for building multi-agent simulations (SFI Working paper No. 96- 06–042). Santa Fe Institute, Santa Fe, NM. Wilensky, U., Rand, W., 2015. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press, Cambridge, MA. The authors would like to apologize for any inconvenience caused.
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| 3. |
- An, Li, et al.
(författare)
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Modeling agent decision and behavior in the light of data science and artificial intelligence
- 2023
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Ingår i: Environmental Modelling & Software. - 1364-8152 .- 1873-6726. ; 166
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Tidskriftsartikel (refereegranskat)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.
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