کاربست مدل هماهنگی عامل بنیان در مکان¬یابی مناطق توسعه¬ شهری نمونه موردی: فاز 7 هشتگرد و تپه امام گل همدان
محورهای موضوعی : آمایش محیط
1 - گروه معماری و شهرسازی، واحد همدان، دانشگاه آزاد اسلامی، همدان، ایران3
کلید واژه: مدل عامل بنیان, هماهنگی , توسعه شهری , وابستگیهای متقابل,
چکیده مقاله :
در ساختار مدیریت شهری ایران، سازمانهای متعدد و مستقلی در فرآیند توسعه شهری موثر می باشند. تعدد این سازمانها و ناهماهنگی بین آنها یکی از عمده ترین چالشهای پیش روی مدیریت شهری کشور می باشد. از این رو، مدیریت وابستگیهای متقابلی که بین سازمانهای مذکور وجود دارد می تواند نقش موثری درموفقیت فاز مکان یابی مناطق توسعه شهری ایفا نماید. پژوهش حاضر با هدف شناسایی وابستگیهای متقابل بین سازمانهای دخیل در فرآیند توسعه شهری و همچنین ارائه یک مدل منطقی برای مدیریت این وابستگی ها انجام گردید. به همین منظور، با انجام مطالعه موردی کیفی تعبیه شده چندگانه ابتدا وابستگیهای متقابل بین سازمان دخیل در فرآنید توسعه شهری شناسایی گردید. در گام بعدی، با عنایت به ماهیت پیچیده این وابستگیها و در نظر گرفتن مزیت های برجسته پارادایم مدلسازی عامل بنیان در درک و مدیریت سیستم ها و فرآیندهای پیچیده، مدل هماهنگی عامل بنیان بسط داده شده است. مدل پیشنهادی براساس یک مجموعه قواعد ساده "اگر-آنگاه" تصمیم سازان و تصمیمگیران حوزه شهری را قادر میسازد که چالشهای هماهنگی در فرآیند توسعه شهری در یک بستر منطقی مدیریت نمایند.
Urban development involves numerous independent organizations, and effective coordination among them is crucial to the success of site selection for new development areas. This study aims to identify the interdependencies among these organizations and to propose a logical model for managing such relationships. Using a multiple embedded qualitative case study approach, the research first identifies the key interdependencies present in the urban development process. Given the complexity of these interactions, and the strengths of the agent-based modeling paradigm in addressing complex systems, an agent-based coordination model is developed. The proposed model, built on a set of simple "if-then" rules, provides urban development decision-makers with a logical and adaptable framework to manage coordination challenges effectively.
1- Batty, M. (2016). Complexity in city systems: Understanding, evolution, and design. In A planner's encounter with complexity (pp. 99-122). Routledge.
2- Bettencourt, L. M. (2021). Introduction to urban science: evidence and theory of cities as complex systems.
3- Bioco, J., Cánovas, F., Prata, P., & Fazendeiro, P. (2022). SDSim: a generalized user friendly web ABM system to simulate spatiotemporal distribution of species under environmental scenarios. Environmental Modelling & Software, 147, 105234.
4- Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl_3), 7280-7287.
5- Buchanan, M. (2009). Economics: Meltdown modelling. Nature 460 (7256), 680–682.
6- Castiglione, F. (2020). Agent-Based Modeling and Simulation, Introduction to. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, 661-665.
7- Castiglione, F. (2020). Agent-Based Modeling and Simulation, Introduction to. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, 661-665.
8- Clarke, K. C. (2014). Cellular automata and agent-based models. Handbook of regional science, 1217-1233.
9- Crowston, K., & Osborn, C. S. 1998. A coordination theory approach to process description and redesign. In Malone, T. W., Crowston, K. & Herman, G. (Eds.) Organizing Business Knowledge: The MIT Process Handbook (pp. 335—370). Cambridge, MA: MIT Press. Available from http://ccs.mit.edu/papers/pdf/wp204.pdf.
10- Datseris, G., Vahdati, A. R., & DuBois, T. C. (2022). Agents. jl: a performant and feature-full agent-based modeling software of minimal code complexity. Simulation, 00375497211068820.
11- do Amaral, J. V. S., Montevechi, J. A. B., de Carvalho Miranda, R., & de Sousa Junior, W. T. (2022). Metamodel-based simulation optimization: A systematic literature review. Simulation Modelling Practice and Theory, 114, 102403.
12- Du, J., Jing, H., Castro-Lacouture, D., & Sugumaran, V. (2019). Multi-agent simulation for managing design changes in prefabricated construction projects. Engineering, Construction and Architectural Management, 27(1), 270-295.
13- Fagiolo, G., Guerini, M., Lamperti, F., Moneta, A., & Roventini, A. (2019). Validation of agent-based models in economics and finance. Computer simulation validation: fundamental concepts, methodological frameworks, and philosophical perspectives, 763-787.
14- Gan, L., Shi, H., Hu, Y., Lev, B., & Lan, H. (2020). Coupling coordination degree for urbanization city-industry integration level: Sichuan case. Sustainable Cities and Society, 58, 102136.
15- Gargiulo, C., & Papa, R. (2021). Chaos and chaos: the city as a complex phenomenon. TeMA-Journal of Land Use, Mobility and Environment, 14(2), 261-270.
16- Glen, C. M., Kemp, M. L., & Voit, E. O. (2019). Agent-based modeling of morphogenetic systems: Advantages and challenges. PLoS computational biology, 15(3), e1006577.
17- Hedborg, S., & Rosander, L. (2023). Self-organizing in urban development: developers coordinating between construction projects. Construction Management and Economics, 1-15.
18- Hedborg, S., Eriksson, P. E., & Gustavsson, T. K. (2020). Organisational routines in multi-project contexts: Coordinating in an urban development project ecology. International journal of project management, 38(7), 394-404.
19- Holland, J. H. (1988). “The global economy as an adaptive process.” The economy as a an evolving complex system, P.W. Anderson, K. J. Arrow, and D. Pines eds., Addison–Wesley, Redwood City, Calif.
20- Holland, J. H. (1988). “The global economy as an adaptive process.” The economy as a an evolving complex system, P.W. Anderson, K. J. Arrow, and D. Pines eds., Addison–Wesley, Redwood City, Calif.
21- Jacobson, M. J., Levin, J. A., & Kapur, M. (2019). Education as a complex system: Conceptual and methodological implications. Educational researcher, 48(2), 112-119.
22- Kavak, H. (2019). A data-driven approach for modeling agents.
23- Kieu, M., Nguyen, H., Ward, J. A., & Malleson, N. (2022). Towards real-time predictions using emulators of agent-based models. Journal of Simulation, 1-18.
24- Khodabandelu, A., & Park, J. W. (2022). Applications of agent-based modeling (ABM) in enhancing facility operation and management.
25- Kraff, N. J., Wurm, M., & Taubenböck, H. (2020). Uncertainties of human perception in visual image interpretation in complex urban environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4229-4241.
26- Macal, C.M. & North, M.J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation. 4, 151–162.
27- Malik, J., Mahdavi, A., Azar, E., Putra, H. C., Berger, C., Andrews, C., & Hong, T. (2022). Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings. Building and Environment, 217, 109016.
28- Malone, T.V. & Crowston, K. (1994). The Interdisciplinary Study of Coordination. ACM Computing Surveys, Vol. 26, No. 1, March 1994.
29- Marvuglia, A., Bayram, A., Baustert, P., Gutiérrez, T. N., & Igos, E. (2022). Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution. Journal of Cleaner Production, 332, 129847.
30- McComb, C., & Jablokow, K. (2022). A conceptual framework for multidisciplinary design research with example application to agent-based modeling. Design Studies, 78, 101074.
31- Obergh, A. (2023). Discussion of Game Design and Construction of a Videogame Utilizing PCG, CA, and ABM.
32- Olsen, M., Raunak, M. S., & Kuhn, D. R. (2023, June). Predicting ABM Results with Covering Arrays and Random Forests. In International Conference on Computational Science (pp. 237-252). Cham: Springer Nature Switzerland.
33- Rahman, M. S. (2020). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review.
34- Saeedi, S. (2018). Integrating macro and micro scale approaches in the agent-based modeling of residential dynamics. International Journal of Applied Earth Observation and Geoinformation, 68, 214-229.
35- Sanford Bernhardt, K. L. (2004). “An agent-based framework for modeling civil infrastructure systems management.” Proc., Information Technology in Civil Engineering Symp., ASCE, Baltimore.
36- Shaharuddin, R. A., & Misro, M. Y. (2021, November). Traffic simulation using agent based modelling. In AIP Conference Proceedings (Vol. 2423, No. 1). AIP Publishing.
37- Westman, L., & Castan Broto, V. (2022). Urban transformations to keep all the same: The power of ivy discourses. Antipode, 54(4), 1320-1343.
38- Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Mit Press.
39- Wooldridge, M. (2002). Introduction to MultiAgent Systems, Wiley.
40- Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Mit Press.
41- Zhao, N., Chong, H. Y., & Li, Q. (2023). Agent-based modelling of helping behaviour diffusion in project teams as an evolutionary process. Journal of Simulation, 17(3), 279-296.
42- Yin, R. K. (2011). Applications of case study research. sage.