Agent-based modeling; Artificial intelligence; Data science; Machine learning; Modeling agent decisions and actions; Agent behavior; Agent-based model; Convolutional neural network; Innovative approaches; Machine-learning; Model agents; Modeling agent decision and action; Reinforcement learning neural network; Research fields; Science and Technology; Software; Environmental Engineering; Ecological Modeling
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
An, Li ; Center for Complex Human-Environment Systems, San Diego State University, San Diego, United States ; Dept of Geography, San Diego State University, San Diego, United States
Grimm, Volker; Helmholtz Centre for Environmental Research – UFZ, Department of Ecological Modelling, Leipzig, Germany
Bai, Yu; Computer Engineering Program, College of Engineering and Computer Science at the California State University, Fullerton, United States
Sullivan, Abigail; Department of Earth and Environment, Boston University, Boston, United States
Turner, B.L.; School of Geographical Sciences and Urban Planning & School of Sustainability, Arizona State University, Tempe, United States
Malleson, Nicolas; Alan Turing Institute, British Library, London, United Kingdom
Heppenstall, Alison; Centre for Spatial Analysis and Policy, School of Geography, University of Leeds, Leeds, United Kingdom
VINCENOT, Christian ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Robinson, Derek; Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada
Ye, Xinyue; Department of Landscape Architecture and Urban Planning & Urban Data Science Lab, Texas A&M University, College Station, United States
Liu, Jianguo; Center for Systems Integration and Sustainability, Michigan State University, East Lansing, United States
Lindkvist, Emilie; Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
Tang, Wenwu; Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, United States
European Research Council National Science Foundation Horizon 2020 Framework Programme The Alan Turing Institute Economic and Social Research Council European Research Council
Funding text :
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 ).The RL-CNN approach, though promising and exciting, does not imply that AI, machine learning, and data science are not unbiased, nor does it exhaust the potentials that AI and machine learning can contribute to modelling agent behavior. First, we still emphasize the importance of domain knowledge and theory that are obtained elsewhere (Taghikhah et al., 2022). The mechanism specification in Panel D of Fig. 2, if employed as a starting point for RL network (Panel B), reflects this importance. The mechanisms or rules thus derived—for example, cause-effects and feedback loops in many instances—should be subject to continued examination by domain knowledge and theory. Also, as new data become available, the above RL-CNN or other approaches should be continually used to polish or revise existing rules, even establish new rules. Therefore, continuous real-time data collection is important for not only deriving, but also for validating and renewing, such rules. The concept of “Digital Twins” (DT) is based on this idea of updating, in regular intervals, the data underlying a realistic model used for forecasting. This principle is well-known from weather forecast and widely used in industry (Singh et al., 2022), but has also become the basis of large initiatives to support decision making regarding climate, ocean, and biodiversity, such as the Destination Earth program of the European Commission (Nativi et al., 2021).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).
Abar, S., Theodoropoulos, G.K., Lemarinier, P., O'Hare, G.M.P., Agent Based Modelling and Simulation tools: a review of the state-of-art software. Comput.Sci. Rev. 24 (2017), 13–33, 10.1016/j.cosrev.2017.03.001.
Abdulkareem, S.A., Mustafa, Y.T., Augustijn, E.-W., Filatova, T., Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models. GeoInformatica 23:2 (2019), 243–268.
An, L., Grimm, V., Sullivan, A., Turner, B.L.I., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., Tang, W., Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecol. Model., 457, 2021, 109685, 10.1016/j.ecolmodel.2021.109685.
An, L., Grimm, V., Sullivan, A., Turner, B.L. II, Wang, Z., Malleson, N., Huang, R., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindvist, E., Tang, W., Agent-based Complex Systems and Agent-Based Modeling. 2021, Working Paper of Center for Complex Human-Environment Systems, San Diego State University http://complexities.org/Photo&PDF/ACS-ABM-document.pdf.
Axelrod, R., The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. 1997, Princeton University Press.
Axtell, R.L., Zipf distribution of U.S. firm sizes. Science 293 (2001), 1818–1820.
Axtell, R.L., Endogenous Dynamics of Multi-Agent Firms (Working Paper No. 16-39). 2015, George Mason University https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2827059.
Balke, T., Gilbert, N., How do agents make decisions? A survey. J. Artif. Soc. Soc. Simulat., 17(4), 2014, 13, 10.18564/jasss.2687.
Bonabeau, E., Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99:Suppl. 3 (2002), 7280–7287.
Bourgais, M., Taillandier, P., Vercouter, L., Adam, C., Emotion modeling in social simulation: a survey. J. Artif. Soc. Soc. Simulat., 21(2), 2018, 5, 10.18564/jasss.3681.
Boyd, R., Gintis, H., Bowles, S., Richerson, P.J., The evolution of altruistic punishment. Proc. Natl. Acad. Sci. USA 100:6 (2003), 3531–3535, 10.1073/pnas.0630443100.
Bratman, M.E., Intention, Plans, and Practical Reason. 1999, CSLI Publications.
Buşoniu, L., Babuška, R., De Schutter, B., Multi-agent reinforcement learning: an overview. Srinivasan, D., Jain, L.C., (eds.) Innovations in Multi-Agent Systems and Applications—1, 2010, Springer Berlin Heidelberg, 183–221, 10.1007/978-3-642-14435-6_7.
Conte, R., Paolucci, M., On agent-based modeling and computational social science. Front. Psychol., 5, 2014, 000668, 10.3389/fpsyg.2014.00668.
Couclelis, H., Why I no longer work with agents: a challenge for abms of human-environment interactions. Parker, D.C., Berger, T., Manson, S.M., (eds.) Meeting the Challenge of Complexity, vol. 1, 2002, Center for Spatially Integrated Social Science University of California at Santa Barbara, 14–16 1 http://www.csiss.org/events/other/agent-based/additional/proceedings.pdf.
Cranmer, M., Sanchez-Gonzalez, A., Battaglia, P., Xu, R., Cranmer, K., Spergel, D., Ho, S., Discovering symbolic models from deep learning with inductive biases. ArXiv:2006.11287 [Cs.LG] https://arxiv.org/abs/2006.11287, 2020.
Crooks, A., Castle, C., Batty, M., Key challenges in agent-based modelling for geo-spatial simulation. Comput. Environ. Urban Syst. 32:6 (2008), 417–430, 10.1016/j.compenvurbsys.2008.09.004.
De Mauro, A., Greco, M., Grimaldi, M., A formal definition of Big Data based on its essential features. Libr. Rev., 2016, 10.1108/LR-06-2015-0061.
Di Tosto, G., Paolucci, M., Conte, R., Altruism among simple and smart vampires. Int. J. Cooper. Inf. Syst. 16:1 (2007), 51–66.
Edwards, B., New Go-Playing Trick Defeats World-Class Go AI—But Loses to Human Amateurs. 2022, ARS TECHNICA https://arstechnica.com/information-technology/2022/11/new-go-playing-trick-defeats-world-class-go-ai-but-loses-to-human-amateurs/.
Filatova, T., Verburg, P.H., Parker, D.C., Stannard, C.A., Spatial agent-based models for socio-ecological systems: challenges and prospects. Thematic Issue on Spatial Agent-Based Models for Socio-Ecological Systems 45 (2013), 1–7, 10.1016/j.envsoft.2013.03.017.
Gil, Y., Selman, B., A 20-year Community Roadmap for Artificial Intelligence Research in the US. 2019, ArXiv.Org arXiv:1908.02624.
Grimm, V., Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?. Ecol. Model. 115:2–3 (1999), 129–148.
Grimm, V., Berger, U., Structural realism, emergence, and predictions in next-generation ecological modelling: synthesis from a special issue. Ecol. Model. 326 (2016), 177–187.
Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F., The ODD protocol: a review and first update. Ecol. Model. 221:23 (2010), 2760–2768.
Grimm, V., Railsback, S.F., Vincenot, C., Berger, U., Gallagher, C., DeAngelis, D., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A.S.A., Miles, A., Nabe-Nielson, J., Polhill, J.G., Radchuk, V., Rohwader, M.-S., Stillman, R.A., Theile, J., Ayllon, D., The ODD protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. J. Artif. Soc. Soc. Simulat., 23(2), 2020, 10.18564/jasss.4259 USGS Publications Warehouse.
Groeneveld, J.J., Müller, B., Buchmann, C.M., Dressler, G., Guo, C., Hase, N., Hoffmann, F., John, F., Klassert, C.J., Lauf, T., Liebelt, V., Nolzen, H., Pannicke, N., Schulze, J., Weise, H., Schwarz, N., Theoretical foundations of human decision-making in agent-based land use models – a review. Environ. Model. Software 87 (2017), 39–48.
Huber, R., Bakker, M., Balmann, A., Berger, T., Bithell, M., Brown, C., Grêt-Regamey, A., Xiong, H., Le, Q.B., Mack, G., Meyfroidt, P., Millington, J., Müller, B., Polhill, J.G., Sun, Z., Seidl, R., Troost, C., Finger, R., Representation of decision-making in European agricultural agent-based models. Agric. Syst. 167 (2018), 143–160, 10.1016/j.agsy.2018.09.007.
Kipf, T.N., Welling, M., Semi-supervised Classification with Graph Convolutional Networks. 2016 ArXiv:1609.02907 [Cs.LG].
Lorscheid, I., Berger, U., Grimm, V., Meyer, M., From cases to general principles: a call for theory development through agent-based modeling. Ecol. Model. 393 (2019), 153–156, 10.1016/j.ecolmodel.2018.10.006.
Marcus, G., Deep Learning: A Critical Appraisal. 2018, ArXiv.Org arXiv:1801.00631.
McDowall, W., Geels, F.W., Ten challenges for computer models in transitions research: commentary on Holtz et al. Environ. Innov. Soc. Transit. 22 (2017), 41–49, 10.1016/j.eist.2016.07.001.
Muelder, H., Filatova, T., One theory—many formalizations: testing different code implementations of the theory of planned behaviour in energy agent-based models. J. Artif. Soc. Soc. Simulat., 21(4), 2018, 5, 10.18564/jasss.3855.
Nativi, S., Mazzetti, P., Craglia, M., Digital ecosystems for developing digital Twins of the Earth: the destination Earth case. Rem. Sens., 13(11), 2021, 10.3390/rs13112119.
Niamir, L., Ivanova, O., Filatova, T., Economy-wide impacts of behavioral climate change mitigation: linking agent-based and computable general equilibrium models. Environ. Model. Software, 134, 2020, 104839, 10.1016/j.envsoft.2020.104839.
O'Sullivan, D., Evans, T., Manson, S., Metcalf, S., Ligmann-Zielinska, A., Bone, C., Strategic directions for agent-based modeling: avoiding the YAAWN syndrome. J. Land Use Sci. 11:2 (2016), 177–187.
Parker, D.C., Entwisle, B., Rindfuss, R.R., Vanwey, L.K., Manson, S.M., Moran, E., An, L., Deadman, P., Evans, T.P., Linderman, M., Rizi, S.M.M., Malanson, G., Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions. J. Land Use Sci. 3:1 (2008), 41–72.
Roughgarden, J., Individual Based Models in Ecology: an Evaluation, or How Not to Ruin a Good Thing. 2012, Philosophy of Science Association Biennial Meeting, San Diego, CA http://philsci-archive.pitt.edu/9434/1/RoughgardenPSA2012IBMLecture.pdf.
Schelling, T.C., Dynamic models of segregation. J. Math. Sociol. 1 (1971), 143–186.
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M.A., McAllister, R.R.J., Müller, B., Orach, K., Schwarz, N., Wijermans, N., A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecol. Econ. 131 (2017), 21–35.
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M.A., McAllister, R.R.J., Müller, B., Orach, K., Schwarz, N., Wijermans, N., A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecol. Econ. 131 (2017), 21–35.
Schmidt, B., Verbraeck, A., Krug, W., (eds.) The Modelling of Human Behaviour: the PECS Reference Model SCS Europe Bvba, 2002.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D., Mastering the game of Go with deep neural networks and tree search. Nature 529:7587 (2016), 484–489, 10.1038/nature16961.
Singh, M., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, Y., Murray, N., Devine, D., Applications of digital twin across industries: a review. Appl. Sci., 12(11), 2022, 10.3390/app12115727.
Srikrishnan, V., Keller, K., Small increases in agent-based model complexity can result in large increases in required calibration data. Environ. Model. Software, 138, 2021, 104978, 10.1016/j.envsoft.2021.104978.
Su, J., Huang, J., Adams, S.C., Chang, Q., Beling, P.A., Deep multi-agent reinforcement learning for multi-level preventive maintenance in manufacturing systems. Expert Syst. Appl., 192, 2022, 116323.
Taghikhah, F., Voinov, A., Filatova, T., Polhill, J.G., Machine-assisted agent-based modeling: opening the black box. J. Comput. Sci., 64, 2022, 101854, 10.1016/j.jocs.2022.101854.
Tan, S.K., Hu, N., Cai, W., A data-driven path planning model for crowd capacity analysis. J. Comput. Sci. 34 (2019), 66–79, 10.1016/j.jocs.2019.05.003.
van der Hoog, S., Surrogate modelling in (and of) agent-based models: a prospectus. Comput. Econ. 53:3 (2019), 1245–1263, 10.1007/s10614-018-9802-0.
Vincenot, C.E., How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science. Proc. Biol. Sci., 28(1874), 2018, 20172360.
von Bertalanffy, L., General System Theory: Foundations, Development, Applications. 1968, George Braziller.
Vu, T.M., Probst, C., Epstein, J.M., Brennan, A., Strong, M., Purshouse, R.C., Toward inverse generative social science using multi-objective genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference, 2019, 1356–1363, 10.1145/3321707.3321840.
West, D.M., Allen, J.R., How artificial intelligence is transforming the world. https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/, 2018.
Zhang, C., Yao, W., Yang, Y., Huang, R., Mostafavi, A., Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters. Comput. Aided Civ. Infrastruct. Eng., 2020, 10.1111/mice.12576 n/a(n/a).
Zhang, H., Vorobeychik, Y., Letchford, J., Lakkaraju, K., Data-driven agent-based modeling, with application to rooftop solar adoption. Aut. Agents Multi-Agent Syst. 30:6 (2016), 1023–1049, 10.1007/s10458-016-9326-8.
Zhang, W., Valencia, A., Chang, N.-B., Synergistic integration between machine learning and agent-based modeling: a multidisciplinary review. IEEE Transact. Neural Networks Learn. Syst., 2021, 1–21, 10.1109/TNNLS.2021.3106777.
