Etmen-tabanlı modellemede belirme ve karmaşıklık: Güncel araştırmaların incelenmesi

Öz

Etmen-tabanlı sistemler, yapay zekanın önemli bir uygulama alanıdır ve karar destek sistemlerinde kullanılmaktadır. Etmen-tabanlı sistem, bir problem çözme aracı olmaktan çok, çeşitli senaryolara göre çözüm alternatiflerini geliştirmek ve test etmek için kullanılan bir araçtır. Bu bağlamda etmen-tabanlı modelleme, acil durumlarda karar vericilerin farklı risk senaryolarını değerlendirip daha sonra hızlı ve etkili kararlar almalarına destek olmak için oldukça etkili bir yöntemdir. Ayrıca etmen-tabanlı modelleme, yüksek karmaşıklık ve belirsizlik durumlarında karar vericileri desteklemek için çok yararlı bir yöntemdir. Bu çalışmanın amacı, en güncel araştırmaları incelemek ve araştırmacılara karar destek sistemleri geliştirirken etmen-tabanlı modellemenin nasıl kullanılacağına dair fikir vermektir. Bu makale, NetLogo, AnyLogic, MATSim ve Repast gibi çeşitli etmen-tabanlı modelleme araçları ve yazılım ortamları ile gerçekleştirilen güncel çalışmaları tanıtmaktadır. Bu makalede, etmen-tabanlı bir sistemin kısa bir tanımı yapıldıktan ve belirme ve karmaşıklık gibi kavramların etmen-tabanlı modelleme alanındaki önemi açıklandıktan sonra, etmen-tabanlı sistemleri kimlerin kullandığı, etmen-tabanlı modellemenin hangi amaçla, ne zaman, nerede, neden ve nasıl kullanıldığı, farklı alanlarda gerçekleştirilmiş en güncel çalışmalardan seçilmiş örnekler üzerinden açıklanmıştır. Ayrıca, mevcut çalışmaların bize ne öğrettiği ve gelecekteki çalışmaların etmen-tabanlı modellerden nasıl yararlanabileceği kısaca tartışılmıştır.

Anahtar Kelimeler

• Etmen-tabanlı modelleme
• belirme
• karmaşıklık
• belirsizlik
• benzetim

Emergence and complexity in agent-based modeling: Review of state-of-the-art research

Öz

Agent-based systems are an important application area of artificial intelligence and are used in decision support systems. Rather than being a problem-solving tool, agent-based system is a tool for developing and testing alternative solutions according to various scenarios. In this context, agent-based modeling is a very effective method to support decision makers in emergency situations to evaluate different risk scenarios and then make decisions quickly and effectively. Moreover, agent-based modeling is a very useful method to support decision makers in situations of high complexity and uncertainty. The aim of this study is to review state-of-the-art research and give researchers insights into how to use agent-based modeling while developing decision support systems. This paper introduces current studies performed with several agent-based modeling toolkits and software environments such as NetLogo, AnyLogic, MATSim and Repast. In this paper, after giving a brief definition of an agent-based system and explaining the importance of concepts such as emergence and complexity in the field of agent-based modeling, it is explained who uses the agent-based models for what purpose, when, where, why and how to use agent-based modeling through selected examples from state-of-the-art studies carried out in different research fields. Furthermore, what current studies teach us and how future studies can benefit from agent-based models are briefly discussed.

Anahtar Kelimeler

• Agent-based modeling
• emergence
• complexity
• uncertainty
• simulation

Kaynakça

1. Antonova, V. M., Grechishkina, N. A., & Kuznetsov, N. A. (2020). Analysis of the Modeling Results for Passenger Traffic at an Underground Station Using AnyLogic. Journal of Communications Technology and Electronics, 65(6), 712-715. https://doi.org/10.1134/S1064226920060029
2. AnyLogic. (2021, August 12). The AnyLogic Company. Retrieved August 12, 2021, from https://www.anylogic.com.
3. Arasteh, M. A., & Farjami, Y. (2021). New Hydro-economic System Dynamics and Agent-based Modeling for Sustainable Urban Groundwater Management: A Case Study of Dehno, Yazd Province, Iran. Sustainable Cities and Society, 1-13. https://doi.org/10.1016/j.scs.2021.103078
4. Batty, M. (2007). Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. MIT Press, Cambridge.
5. Bedau, M. A. (1997). Weak emergence. In J. Tomberlin (Ed.), Philosophical perspectives: mind, causation, and world (pp. 375-399). Vol. 11. Hoboken: Wiley.
6. Bedau, M. A. (2003). Artificial life: organization, adaptation and complexity from the bottom up. Trends in Cognitive Science, 7(11), 505-512. https://doi.org/10.1016/j.tics.2003.09.012
7. Bellemans, T., Kochan, B., Janssens, D., Wets, G., Arentze, T., & Timmermans, H. (2010). Implementation framework and development trajectory of FEATHERS activity-based simulation platform. Transportation Research Record, 2175(1), 111-119. https://doi.org/10.3141/2175-13
8. Berger, C., & Mahdavi, A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis. Building and Environment, 173, 1-9. https://doi.org/10.1016/j.buildenv.2020.106726

9. Bina, K., & Moghadas, N. (2020). BIM-ABM simulation for emergency evacuation from conference hall, considering gender segregation and architectural design. Architectural Engineering and Design Management, 1-15. https://doi.org/10.1080/17452007.2020.1761282
10. Caprioli, C., Bottero, M., & De Angelis, E. (2020). Supporting Policy Design for the Diffusion of Cleaner Technologies: A Spatial Empirical Agent-Based Model. ISPRS International Journal of Geo-Information, 9(10), 581. https://doi.org/10.3390/ijgi9100581
11. Carta, S., St Loe, S., Turchi, T., & Simon, J. (2020). Self-organising floor plans in care homes. Sustainability, 12(11), 1-16. https://doi.org/10.3390/su12114393
12. Chen, L. (2012). Agent-based modeling in urban and architectural research: A brief literature review, Frontiers of Architectural Research, 1(2), 166-177. https://doi.org/10.1016/j.foar.2012.03.003
13. Cheng, J. C., & Gan, V. J. (2013). Integrating agent-based human behavior simulation with building information modeling for building design. International Journal of Engineering and Technology, 5(4), 473-477. http://doi.org/10.7763/IJET.2013.V5.600
14. Chennoufi, M., Bendella, F., & Bouzid, M. (2018). Multi-agent simulation collision avoidance of complex system: application to evacuation crowd behavior. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 43-59. http://doi.org/10.4018/IJACI.2018010103
15. Collier, N. T., Ozik, J., & Tatara, E. R. (2020). Experiences in developing a distributed agent-based modeling toolkit with Python. In 2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC) (pp. 1-12). IEEE. https://doi.org/10.1109/PyHPC51966.2020.00006
16. Costa, R., Haukaas, T., & Chang, S. E. (2021). Agent-based model for post-earthquake housing recovery. Earthquake Spectra, 37(1), 46-72. https://doi.org/10.1177/8755293020944175
17. Dam, K., Nikolic, I., & Lukszo, Z. (2013). Agent-based modelling of socio-technical systems. Springer.
18. Dogaroglu, B., Caliskanelli, S.P., & Tanyel, S. (2021). Comparison of intelligent parking guidance system and conventional system with regard to capacity utilisation. Sustainable Cities and Society, 1-13. https://doi.org/10.1016/j.scs.2021.103152
19. Esposito, D., Schaumann, D., Camarda, D., & Kalay, Y. E. (2020). Multi-agent modelling and simulation of hospital acquired infection propagation dynamics by contact transmission in hospital wards. In Y. Demazeau, T. Holvoet, J. Corchado, S. Costantini (Eds.) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness (pp. 118-133). Lecture Notes in Computer Science, vol 12092. Springer. https://doi.org/10.1007/978-3-030-49778-1_10
20. Farhan, M., Göhre, B., & Junprung, E. (2020). Reinforcement learning in anylogic simulation models: a guiding example using pathmind. In 2020 Winter Simulation Conference (WSC) (pp. 3212-3223). IEEE. https://doi.org/10.1109/WSC48552.2020.9383916
21. Giorgione, G., Bolzani, L., & Viti, F. (2021). Assessing two-way and one-way carsharing: an agent-based simulation approach. Transportation Research Procedia, 52, 541-548. https://doi.org/10.1016/j.trpro.2021.01.064
22. Glavatskiy, K. S., Prokopenko, M., Carro, A., Ormerod, P., & Harre, M. (2021). Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model. SN Business & Economics, 1(6), 1-21. https://doi.org/10.1007/s43546-021-00077-2
23. Google (2021, 12 August). Google trends. Retrieved August 12, 2021, from https://trends.google.com/trends/explore?q=%2Fm%2F02tfsj,%2Fm%2F0bs8jwz,%2Fm%2F0b42hj,%2Fm%2F04n3936
24. Graur, D., Bruno, R., Bischoff, J., Rieser, M., Scherr, W., Hoefler, T., & Alonso, G. (2021). Hermes: Enabling efficient large-scale simulation in MATSim. Procedia Computer Science, 184, 635-641. https://doi.org/10.1016/j.procs.2021.03.079
25. Guo, Z. & Li, B. (2017). Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system. Frontiers of Architectural Research, 6, 53–62. https://doi.org/10.1016/j.foar.2016.11.003
26. Haer, T., Husby, T. G., Botzen, W. W., & Aerts, J. C. (2020). The safe development paradox: An agent-based model for flood risk under climate change in the European Union. Global Environmental Change, 60, 1-12. https://doi.org/10.1016/j.gloenvcha.2019.102009
27. Hager, K., Rauh, J., & Rid, W. (2015). Agent-based modeling of traffic behavior in growing metropolitan areas, Transportation Research Procedia, 10, 306-315. https://doi.org/10.1016/j.trpro.2015.09.080
28. Hassanpour, S., Rassafi, A. A., Gonzalez, V., & Liu, J. (2021). A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning. Journal of Choice Modelling, 39, 1-20. https://doi.org/10.1016/j.jocm.2021.100288
29. Hébert, G. A., Perez, L., & Harati, S. (2018). An agent-based model to identify migration pathways of refugees: the case of Syria. In L. Perez, E. K. Kim, R. Sengupta (Eds.) Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. Advances in Geographic Information Science (pp. 45-58). Springer. https://doi.org/10.1007/978-3-319-65993-0_4 Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19(1), 1-8. https://doi.org/10.1007/s11424-006-0001-z
30. Hoertel, N., Blachier, M., Blanco, C., Olfson, M., Massetti, M., Rico, M. S., Limosin, F., & Leleu, H. (2020). A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nature medicine, 26(9), 1417-1421. https://doi.org/10.1038/s41591-020-1001-6
31. Holland, J. H. (1996). Hidden order: how adaptation builds complexity. Addison-Wesley Longman Publishing Co.
32. Horni, A., Nagel, K., & Axhausen, K. W. (2012). High-resolution destination choice in agent-based models, TRB 2012 Annual Meeting Preprint, 12-1988, Transportation Research Board, Washington, D.C. https://doi.org/10.3929/ethz-b-000053944
33. Hörl, S., & Balac, M. (2021). Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transportation Research Part C: Emerging Technologies, 130, 1-16. https://doi.org/10.1016/j.trc.2021.103291
34. Kaligotla, C., Ozik, J., Collier, N., Macal, C. M., Lindau, S., Abramsohn, E., & Huang, E. (2018). Modeling an information-based community health intervention on the south side of Chicago. In 2018 Winter Simulation Conference (WSC) (pp. 2600-2611). IEEE. https://doi.org/10.1109/WSC.2018.8632525
35. Kırdar, G., Cenani, S., & Cagdas, G. (2019). Smart bicycle-sharing system design for the historical peninsula of Istanbul. İdealkent, 10(27), 630-652. https://doi.org/10.31198/idealkent.507208
36. Kochenderfer, M. J. (2015). Decision making under uncertainty: theory and application. MIT press.
37. Kono, T., & Haneda, K. (2021). Simulation-supported maintenance design and decision-making using agent-based modeling technology. CIRP Annals - Manufacturing Technology, 70, 13-16. https://doi.org/10.1016/j.cirp.2021.03.014
38. Koskela, O., Dempers, C., Kymäläinen, M., & Nummela, J. (2021). Simulating a Biorefinery EcosystemtoManage andMotivate Sustainable Regional Nutrient Circulation. Technology Innovation Management Review, 11(2), 33-43.
39. Kuklová, J. (2021). Highway modeling in Anylogic for multi-agent approach to smart city management. In 2021 Smart City Symposium Prague (SCSP) (pp. 1-6). IEEE. https://doi.org/10.1109/SCSP52043.2021.9447402
40. Li, Y., Zhang, Y., & Cao, L. (2020). Evaluation and selection of hospital layout based on an integrated simulation method. In 2020 Winter Simulation Conference (WSC) (pp. 2560-2568). IEEE. https://doi.org/10.1109/WSC48552.2020.9384091
41. Li, Z. Y., Tang, M., Liang, D., & Zhao, Z. (2016). Numerical simulation of evacuation in a subway station. Procedia Engineering, 135, 616-621. https://doi.org/10.1016/j.proeng.2016.01.126
42. Liu, R., Jiang, D., & Shi, L. (2016). Agent-based simulation of alternative classroom evacuation scenarios, Frontiers of Architectural Research. 5(1), 111-125. https://doi.org/10.1016/j.foar.2015.12.002
43. Llorca, C., & Moeckel, R. (2019). Effects of scaling down the population for agent-based traffic simulations. Procedia Computer Science, 151, 782-787. https://doi.org/10.1016/j.procs.2019.04.106
44. Lu, P., Zhang, Z., Li, M., Chen, D., & Yang, H. (2020). Agent-based modeling and simulations of terrorist attacks combined with stampedes. Knowledge-Based Systems, 205, 1-13. https://doi.org/10.1016/j.knosys.2020.106291
45. Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the 2005 Winter Simulation Conference. (pp. 14-pp). IEEE. https://doi.org/10.1109/WSC.2005.1574234
46. Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. Journal of Simulation, 10(2), 144-156. https://doi.org/10.1057/jos.2016.7
47. Macal, C. M., Collier, N. T., Ozik, J., Tatara, E. R., & Murphy, J. T. (2018). Chisim: An agent-based simulation model of social interactions in a large urban area. In 2018 Winter Simulation Conference (WSC) (pp. 810-820). IEEE. https://doi.org/10.1109/WSC.2018.8632409
48. Macal, C. M. (2020). Agent-based modeling and artificial life. In M. Sotomayor, D. Pérez-Castrillo, F. Castiglione (Eds.) Complex Social and Behavioral Systems. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-0368-0_7
49. Manley, E., & Cheng, T. (2018). Exploring the role of spatial cognition in predicting urban traffic flow through agent-based modelling. Transportation Research Part A: Policy and Practice, 109, 14-23. https://doi.org/10.1016/j.tra.2018.01.020
50. Marzouk, M., & Daour, I. (2018). Planning labor evacuation for construction sites using BIM and agent-based simulation. Safety Science, 109, 174-185. https://doi.org/10.1016/j.ssci.2018.04.023
51. MATSim. (2021, August 12). Multi-Agent Transportation Simulation. Retrieved August 12, 2021, from http://www.matsim.org
52. Melnikov, V. R., Krzhizhanovskaya, V. V., Lees, M. H., & Boukhanovsky, A. V. (2016). Data-driven travel demand modelling and agent-based traffic simulation in Amsterdam urban area, Procedia Computer Science, 80, 2030-2041. https://doi.org/10.1016/j.procs.2016.05.523
53. Meyers, R. A. (Ed.). (2012). Mathematics of complexity and dynamical systems. Springer Science & Business Media. Mora-Herrera, D. Y., Huerta-Barrientos, A., & Zuniga-Escobar, O. (2021). A review of agent-based modeling for simulation of agricultural systems. DYNA, 88(217), 103-110. https://doi.org/10.15446/dyna.v88n217.89133
54. Muravev, D., Hu, H., Rakhmangulov, A., & Mishkurov, P. (2021). Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port. International Journal of Information Management, 57, 1-15. https://doi.org/10.1016/j.ijinfomgt.2020.102133
55. North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal, C. M., Bragen, M., & Sydelko, P. (2013). Complex adaptive systems modeling with Repast Simphony. Complex Adaptive Systems Modeling, 1(3), 1-26. https://doi.org/10.1186/2194-3206-1-3
56. Onggo, B. S., Yilmaz, L., Klügl, F., Terano, T., & Macal, C. M. (2019). Credible agent-based simulation - An illusion or only a step away?. In 2019 Winter Simulation Conference (WSC) (pp. 273-284). IEEE. https://doi.org/10.1109/WSC40007.2019.9004716
57. Park, B. H., Aziz, H. A., Morton, A., & Stewart, R. (2018). High performance data driven agent-based modeling framework for simulation of commute mode choices in metropolitan area. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 3779-3784). IEEE. https://doi.org/10.1109/ITSC.2018.8569232
58. Rothfeld, R., Fu, M., Balać, M., & Antoniou, C. (2021). Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco. Sustainability, 13(4), 1-20. https://doi.org/10.3390/su13042217
59. Russell S., & Norvig, P. (2021). Artificial intelligence: a modern approach. (4th edition). Pearson.
60. Salgado, M., & Gilbert, N. (2013). Agent based modelling. In Handbook of quantitative methods for educational research (pp. 247-265). Brill Sense.
61. Silverman, E., Gostoli, U., Picascia, S., Almagor, J., McCann, M., Shaw, R., & Angione, C. (2021). Situating agent-based modelling in population health research. Emerging Themes in Epidemiology, 18(1), 1-15. https://doi.org/10.1186/s12982-021-00102-7
62. Slovic, P., Fischhoff, B., & Lichtenstein, S. (1977). Behavioral decision theory. Annual review of psychology, 28(1), 1-39.
63. Vizzari, G., Crociani, L., & Bandini, S. (2020). An agent-based model for plausible wayfinding in pedestrian simulation. Engineering Applications of Artificial Intelligence, 87, 1-13. https://doi.org/10.1016/j.engappai.2019.103241
64. Vo, T. T. A., van der Waerden, P., & Wets, G. (2016). Micro-simulation of car drivers’ movements at parking lots. Procedia Engineering, 142, 100-107. https://doi.org/10.1016/j.proeng.2016.02.019
65. Wang, Z., & Jia, G. (2021). Tsunami evacuation risk assessment and probabilistic sensitivity analysis using augmented sample-based approach. International Journal of Disaster Risk Reduction, 1-12. https://doi.org/10.1016/j.ijdrr.2021.102462
66. Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/
67. Wooldridge, M. (2009). An introduction to multiagent systems. John Wiley & Sons.
68. Yıldız, B., & Çağdaş, G. (2020). Fuzzy logic in agent-based modeling of user movement in urban space: Definition and application to a case study of a square. Building and Environment, 169, 1-13. https://doi.org/10.1016/j.buildenv.2019.106597
69. Zhuo, L., & Han, D. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 1-11. https://doi.org/10.1016/j.jhydrol.2020.125600
70. Ziemke, D., Knapen, L., & Nagel, K. (2021). Expanding the analysis scope of a MATSim transport simulation by integrating the FEATHERS activity-based demand model. Procedia Computer Science, 184, 753-760. https://doi.org/10.1016/j.procs.2021.04.022