پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,977,679 |
| تعداد دریافت فایل اصل مقاله | 89,649,663 |
A review of agent-based modeling (ABM) concepts and some of its main applications in management science | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| مقاله 2، دوره 11، شماره 4، بهمن 2018، صفحه 659-692 اصل مقاله (1.11 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2018.261178.673190 | ||
| نویسندگان | ||
| Hossein Sabzian1؛ Mohammad Ali Shafia* 1؛ Ali Bonyadi Naeini1؛ Gholamreza Jandaghi2؛ Mohammad Javad Sheikh3 | ||
| 1Department of Progress Engineering, Iran University of Science and Technology, Tehran, Iran | ||
| 2Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran | ||
| 3Faculty of Humanities, Shahed University, Tehran, Iran | ||
| چکیده | ||
| We live in a very complex world where we face complex phenomena such as social norms and new technologies. To deal with such phenomena, social scientists often use reductionism approach where they reduce them to some lower-lever variables and model the relationships among them through a scheme of equations. This approach that is called equation based modeling (EBM) has some basic weaknesses in modeling real complex systems so that assumptions such as unbounded rationality and perfect information are strongly emphasized while adaptability and evolutionary nature of all engaged agents along with network effects go unaddressed. In tackling deficiencies of reductionism, the complex adaptive system (CAS) framework has been proven very influential in the past two decades. In contrast to reductionism, under CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As the most powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with it have to study a number of works to understand (1) why and what of ABM, (2) its differences with EBM (3) its main functionalities in scientific studies and (4) some of its applications in management science. So, this paper’s major contribution is to help researchers particularly those unfamiliar with ABM to get insights regarding its philosophy and use and gain a big picture of it. | ||
| کلیدواژه‌ها | ||
| Complexity؛ reductionism؛ Equation-based modeling؛ Complex adaptive system؛ Agent-based modeling | ||
| عنوان مقاله [English] | ||
| مروری بر مفاهیم مدلسازی عامل محور و برخی از کاربردهای آن در علم مدیریت | ||
| نویسندگان [English] | ||
| حسین سبزیان1؛ محمدعلی شفیعا1؛ علی بنیادی نائینی1؛ غلامرضا جندقی2؛ محمد جواد شیخ3 | ||
| 1دانشکده مهندسی پیشرفت، دانشگاه علم و صنعت ایران، تهران، ایران | ||
| 2دانشکده مدیریت وحسابداری، پردیس فارابی دانشگاه تهران، قم ،ایران | ||
| 3دانشکده علوم انسانی، دانشگاه شاهد، تهران، ایران | ||
| چکیده [English] | ||
| به عنوان قوی‌ترین متدولوژی مدل سازی سیستم‌های وفقی پیچیده نظیر جوامع، بازارهای مالی و سازمان‌ها، مدل سازی عامل محور در بین اهالی دانشگاه و مجریان صنعتی کاربرد بسیار زیادی یافته است. این مدل ها نشان می دهند که چگونه قواعد ساده عامل ها و تعاملات محلی آنها در مقیاس خرد می تواند منجر به الگوهای واقعأ پیچیده ای در سطح کلان گردد. به رغم انتشارات رو به رشدی که در این زمینه صورت پذیرفته است، محققانی که با این روش آشنا نیستند برای فهم (1) چیستی و چرایی این مدل ها (2) تفاوت آنها با مدل های معادله محور ( 3) کارکردهای آنها در مطالعات علمی و (4) برخی از کاربردهای عمده آنها در علوم مدیریتی، می باید مطالب متعددی را مطالعه کنند. بنابراین، مساعدت اصلی این مقاله آن است که به محققان علوم اجتماعی، بخصوص آنهایی که با این روش آشنا نیستند کمک کند تا با فلسفه و کاربرد این روش اشنا شده و تصویر کلی از آن به دست آورند. | ||
| کلیدواژه‌ها [English] | ||
| پیچیدگی, تقلیل گرایی, مدل معادله محور, سیستم وفقی پیچیده, مدل عامل محور | ||
| مراجع | ||
|
Al-suwailem, S. , O. (2008). Islamic economics in a complex world: Explorations in Agent-based Simulation. The Islamic Research and Teaching Institute, 1–106. Retrieved from http://www.irti.org/English/Pages/Publications.aspx
Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.
Amman, H. M., Tesfatsion, L., Kendrick, D. A., Judd, K. L., & Rust, J. (1996). Handbook of computational economics (Vol. 2). Elsevier.
Axelrod, R. (1986). An evolutionary approach to norms. American Political Science Review, 80(4), 1095–1111.
Axtell, R. (2000). Why agents?: on the varied motivations for agent computing in the social sciences.
Axtell, R. L. (2001). Zipf distribution of US firm sizes. Science, 293(5536), 1818–1820.
Bankes, S. C. (2002). Agent-based modeling: A revolution? Proceedings of the National Academy of Sciences, 99(suppl 3), 7199–7200.
Battiston, S., Farmer, J. D., Flache, A., Garlaschelli, D., Haldane, A. G., Heesterbeek, H., … Scheffer, M. (2016). Complexity theory and financial regulation. Science, 351(6275), 818–819.
Bianchi, F., & Squazzoni, F. (2015). Agent-based models in sociology. Wiley Interdisciplinary Reviews: Computational Statistics, 7(4), 284–306. https://doi.org/10.1002/wics.1356
Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using agent-based modeling. International Journal of Computers for Mathematical Learning, 14(2), 81–119.
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. https://doi.org/10.1073/pnas.082080899
Bouarfa, S., Blom, H. A. P., Curran, R., & Everdij, M. H. C. (2013). Agent-based modeling and simulation of emergent behavior in air transportation. Complex Adaptive Systems Modeling, 1(1), 15.
Brock, W. A., & Durlauf, S. N. (2001). Discrete choice with social interactions. The Review of Economic Studies, 68(2), 235–260.
Brown, D. G., Page, S., Riolo, R., Zellner, M., & Rand, W. (2005). Path dependence and the validation of agent‐based spatial models of land use. International Journal of Geographical Information Science, 19(2), 153–174.
Caplat, P., Anand, M., & Bauch, C. (2008). Symmetric competition causes population oscillations in an individual-based model of forest dynamics. Ecological Modelling, 211(3–4), 491–500.
Cederman, L.-E. (2002). Endogenizing geopolitical boundaries with agent-based modeling. Proceedings of the National Academy of Sciences, 99(suppl 3), 7296–7303.
Chan, S. (2001). Complex adaptive systems. In ESD. 83 research seminar in engineering systems (Vol. 31, p. 1).
Chang, R. M., Oh, W., Pinsonneault, A., & Kwon, D. (2010). A network perspective of digital competition in online advertising industries: A simulation-based approach. Information Systems Research, 21(3), 571–593.
Chattoe-Brown, E. (2013). Why sociology should use agent based modelling. Sociological Research Online. https://doi.org/10.5153/sro.3055
Chen, S.-H., Chang, C.-L., & Du, Y.-R. (2012). Agent-based economic models and econometrics. The Knowledge Engineering Review, 27(2), 187–219.
Ciarli, T., Leoncini, R., Montresor, S., & Valente, M. (2007). Innovation and competition in complex environments. Innovation, 9(3–4), 292–310.
Clayton, P. (2004). Mind and emergence: From quantum to consciousness.
Davidsson, P. (2002). Agent-based social simulation: A computer science view. Journal of Artificial Societies and Social Simulation, 5(1).
de Marchi, S., & Page, S. E. (2014). Agent-Based Models. Annual Review of Political Science, 17(1), 1–20. https://doi.org/10.1146/annurev-polisci-080812-191558
Dosi, G., & Orsenigo, L. (1994). Macrodynamics and microfoundations: an evolutionary perspective. Economics of Technology//Amsterdam: North-Holland, 91–123.
El-Sayed, A. M., Scarborough, P., Seemann, L., & Galea, S. (2012). Social network analysis and agent-based modeling in social epidemiology. Epidemiologic Perspectives & Innovations, 9(1), 1.
Eliot R. Smith, F. R. C. (2007). Agent-Based Modeling: {A} New Approach for Theory Building in Social Psychology. Personality and Social Psychology Review, 11(87), 1–56.
Elsenbroich, C., & Gilbert, N. (2014). Modelling norms. Springer. Springer. https://doi.org/10.1007/978-94-007-7052-2
Epstein, J. (2008). Why model? Journal of Artificial Societies and Social …, 11(4), 6. https://doi.org/10.1080/01969720490426803
Epstein, J. M. (1999). Agent‐based computational models and generative social science. Complexity, 4(5), 41–60. https://doi.org/10.1002/(SICI)1099-0526(199905/06)4:53.3.CO;2-6
Epstein, J. M., & Axtell, R. (1997). Artificial societies and generative social science. Artificial Life and Robotics, 1(1), 33–34.
Erdös, P., & Rényi, A. (1959). On random graphs, I. Publicationes Mathematicae (Debrecen), 6, 290–297.
Fagiolo, G. (1998). Spatial interactions in dynamic decentralised economies: A review. In The Economics of Networks (pp. 53–91). Springer.
Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685.
Forkmann, S., Wang, D., Henneberg, S. C., Naudé, P., & Sutcliffe, A. (2012). Strategic decision making in business relationships: A dyadic agent-based simulation approach. Industrial Marketing Management, 41(5), 816–830.
Foster, J. (2001). Competitive selection, self-organisation and Joseph A. Schumpeter. In Capitalism and Democracy in the 21st Century (pp. 317–334). Springer.
Gao, D., Deng, X., & Bai, B. (2014). The emergence of organizational routines from habitual behaviours of multiple actors: An agent-based simulation study. Journal of Simulation, 8(3), 215–230. https://doi.org/10.1057/jos.2014.1
Gilbert, N. (2006). When does social simulation need cognitive models?. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, 428–432.
Gintis, H. (2006a). The economy as a complex adaptive system.
Gintis, H. (2006b). The emergence of a price system from decentralized bilateral exchange. Contributions in Theoretical Economics, 6(1), 1–15.
Gómez-Cruz, N. A., Loaiza Saa, I., & Ortega Hurtado, F. F. (2017). Agent-based simulation in management and organizational studies: a survey. European Journal of Management and Business Economics, 26(3), 313–328. https://doi.org/10.1108/EJMBE-10-2017-018
Grimm, V., & Railsback, S. F. (2005). Individual-based Modeling and Ecology:(Princeton Series in Theoretical and Computational Biology).
Gustafsson, L., & Sternad, M. (2007). Bringing consistency to simulation of population models–Poisson Simulation as a bridge between micro and macro simulation. Mathematical Biosciences, 209(2), 361–385.
Gustafsson, L., & Sternad, M. (2010). Consistent micro, macro and state-based population modelling. Mathematical Biosciences, 225(2), 94–107.
Hao, Q., Shen, W., Zhang, Z., Park, S.-W., & Lee, J.-K. (2006). Agent-based collaborative product design engineering: An industrial case study. Computers in Industry, 57(1), 26–38.
Heath, B. L., & Hill, R. R. (2010). Some insights into the emergence of agent-based modelling. Journal of Simulation, 4(3), 163–169.
Helfman, G., Collette, B. B., Facey, D. E., & Bowen, B. W. (2009). The diversity of fishes: biology, evolution, and ecology. John Wiley & Sons.
Hodgson, G. M. (1998). The approach of institutional economics. Journal of Economic Literature, 36(1), 166–192.
Holland, J. H. (2002). Complex adaptive systems and spontaneous emergence. In Complexity and industrial clusters (pp. 25–34). Springer.
Jaeger, H. M., & Liu, A. J. (2010). Far-from-equilibrium physics: An overview. ArXiv Preprint ArXiv:1009.4874.
Jiang, G., Hu, B., & Wang, Y. (2010). Agent-based simulation of competitive and collaborative mechanisms for mobile service chains. Information Sciences, 180(2), 225–240.
Kanagarajah, A. K., Lindsay, P., Miller, A., & Parker, D. (2010). An exploration into the uses of agent-based modeling to improve quality of healthcare. In Unifying themes in complex systems (pp. 471–478). Springer.
Kauffman, S. A. (1992). The origins of order: Self-organization and selection in evolution. In Spin glasses and biology (pp. 61–100). World Scientific.
Kauffman, S. A. (2000). Investigations. Oxford University Press.
Kirman, A. (2010). Complex economics: individual and collective rationality. Routledge.
Kirman, A., Foellmer, H., & Horst, U. (2005). Equilibrium in financial markets with heterogeneous agents: A new perspective. Journal of Mathematical Economics, 41(1–2), 123–155.
Kirman, A. P. (1997). The economy as an interactive system. In Santa Fe Institute Studies in The Sciences of Complexity of Complexity-proceedings, Volume (Vol. 27, pp. 491–532). ADDISON-WESLEY PUBLISHING CO.
Lane, D. A. (1993a). Artificial worlds and economics, part I. Journal of Evolutionary Economics, 3(2), 89–107.
Lane, D. A. (1993b). Artificial worlds and economics, part II. Journal of Evolutionary Economics, 3(3), 177–197.
Leal, S. J., & Napoletano, M. (2017). Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low-and high-frequency trading. Journal of Economic Behavior & Organization.
Lee, J. S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., … Parker, D. C. (2015). The complexities of agent-based modeling output analysis. Jasss, 18(4), 1–26. https://doi.org/10.18564/jasss.2897
Lustick, I. (2002). PS-I: A user-friendly agent-based modeling platform for testing theories of political identity and political stability. Journal of Artificial Societies and Social Simulation, 5(3).
Macy, M. W., & Willer, R. (2002). From factors to factors: Computational Sociology and Agent-based Modeling. Annual Review of Sociology, 28(1), 143–166. https://doi.org/10.1146/annurev.soc.28.110601.141117
Martin, R. L. (2009). The opposable mind: Winning through integrative thinking. Harvard Business Press.
Merton, R. K. (1936). The unanticipated consequences of purposive social action. American Sociological Review, 1(6), 894–904.
Mizuta, T. (n.d.). A Brief Review of recent Artificial Market Simulation (Agent-Based Model) Studies for Financial Market Regulations and/or Rules.
Moglia, M., Cook, S., & McGregor, J. (2017). A review of Agent-Based Modelling of technology diffusion with special reference to residential energy efficiency. Sustainable Cities and Society, 31, 173–182.
Moss, S. (2008). Alternative approaches to the empirical validation of Agent- based Models. Journal of Artificial Societies and Social Simulation, 11(15), 16. Retrieved from http://jasss.soc.surrey.ac.uk/11/1/5.html
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Harvard University Press.
Newman, M. (2010). Networks: an introduction. Oxford University Press.
Niazi, M. A., & Hussain, A. (2012). Cognitive agent-based computing-I: a unified framework for modeling complex adaptive systems using agent-based & complex network-based methods. Springer Science & Business Media.
Niazi, M. A. K. (2011). Towards A Novel Unified Framework for Developing Formal , Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems. University of Stirling.
Nicolis, G., & Prigogine, I. (1989). Exploring complexity.
North, M. J., & Macal, C. M. (2007). Managing business complexity: discovering strategic solutions with agent-based modeling and simulation. Oxford University Press.
Odehnalová, P., & Olsevicová, K. (2009). Agent-based simulation of development stages of family businesses. E+ M Ekonomie a Management, (4), 77.
Olfati-Saber, R., & Murray, R. M. (2004). Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 49(9), 1520–1533.
Park, H., Cutkosky, M. R., Conru, A. B., & Lee, S.-H. (1994). An agent-based approach to concurrent cable harness design. AI EDAM, 8(1), 45–61.
Patel, M. H., Abbasi, M. A., Saeed, M., & Alam, S. J. (2018). A scheme to analyze agent-based social simulations using exploratory data mining techniques. Complex Adaptive Systems Modeling, 6(1), 1. https://doi.org/10.1186/s40294-018-0052-8
Prenkert, F., & Følgesvold, A. (2014). Relationship strength and network form: An agent-based simulation of interaction in a business network. Australasian Marketing Journal (AMJ), 22(1), 15–27.
Prigogine, I., & Stengers, I. (1997). The end of certainty. Simon and Schuster.
Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193. https://doi.org/10.1016/j.ijresmar.2011.04.002
Rangoni, R. (2014). From informal thought experiments to Agent-based Models: A progressive account of modeling in the Social Sciences. In Model-Based Reasoning in Science and Technology (pp. 471–478). Springer.
Rao, K. U., & Kishore, V. V. N. (2010). A review of technology diffusion models with special reference to renewable energy technologies. Renewable and Sustainable Energy Reviews, 14(3), 1070–1078.
Rogers, E. M., Medina, U. E., Rivera, M. A., & Wiley, C. J. (2005). Complex adaptative systems and the diffusion of innovations. The Innovation Journal, 10(3), 1–26.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
Savona, P. (2005). On the definition of Political Economy and on the metbod of investigation proper to it: Reflections on the bicentennial of the birth of John Stuart Mill. Journal of European Economic History, 34(3), 573–598.
Schwarz, N., & Ernst, A. (2009). Agent-based modeling of the diffusion of environmental innovations—an empirical approach. Technological Forecasting and Social Change, 76(4), 497–511.
Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.
Showers, C. (1992). Evaluatively integrative thinking about characteristics of the self. Personality and Social Psychology Bulletin, 18(6), 719–729.
Siebers, P.-O., & Aickelin, U. (2008). Introduction to Multi-Agent Simulation. SSRN Electronic Journal, 1–25. https://doi.org/10.2139/ssrn.2827970
Siebers, P.-O., Macal, C. M., Garnett, J., Buxton, D., & Pidd, M. (2010). Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation, 4(3), 204–210.
Siggelkow, N., & Levinthal, D. A. (2003). Temporarily divide to conquer: Centralized, decentralized, and reintegrated organizational approaches to exploration and adaptation. Organization Science, 14(6), 650–669.
Sill, D. J. (1996). Integrative thinking, synthesis, and creativity in interdisciplinary studies. The Journal of General Education, 45(2), 129–151.
Silverberg, G., Dosi, G., & Orsenigo, L. (1988). Innovation, diversity and diffusion: a self-organisation model. The Economic Journal, 98(393), 1032–1054.
Singh, V., Dong, A., & Gero, J. S. (2012). Computational studies to understand the role of social learning in team familiarity and its effects on team performance. CoDesign, 8(1), 25–41.
Situngkir, H. (2004). Epidemiology through cellular automata: case of study avian influenza in Indonesia. ArXiv Preprint Nlin/0403035.
Snyder, S. (2013). The simple, the complicated, and the complex: Educational reform through the lens of Complexity Theory. OECD Education Working Papers, (96), 1–33. https://doi.org/http://dx.doi.org/10.1787/5k3txnpt1lnr-en
Son, J., & Rojas, E. M. (2010). Evolution of collaboration in temporary project teams: An agent-based modeling and simulation approach. Journal of Construction Engineering and Management, 137(8), 619–628.
Sorensen, R. A. (1998). Thought experiments. Oxford University Press on Demand.
Sun, R., & Naveh, I. (2004). Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, 7(3).
Sun, Y., & Cheng, L. (2005). A Survey on Agent-Based Modelling and Equation-based Modelling. Citeseerx.
Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8(1), 55–82.
Todd, A., Beling, P., Scherer, W., & Yang, S. Y. (2016). Agent-based financial markets: A review of the methodology and domain. In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on (pp. 1–5). IEEE.
Van Dyke Parunak, H., Savit, R., & Riolo, R. L. (1998). Agent-based Modeling vs. Equation-based Modeling: A case study and users’ guide. Lecture Notes in Computer Science, 1534, 10–25. https://doi.org/10.1007/10692956_2
Wall, F. (2016). Agent-based modeling in managerial science: an illustrative survey and study. Review of Managerial Science, 10(1), 135–193. https://doi.org/10.1007/s11846-014-0139-3
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge University Press.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440.
Weisberg, M. (2012). Simulation and similarity: Using models to understand the world. Oxford University Press.
Wilensky, U., & Rand, W. (2015). An Introduction to Agent-Based Modeling_ Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press.
Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171–209.
Windrum, P., Fagiolo, G., & Moneta, A. (2007). Empirical validation of agent-based models: Alternatives and prospects. Jasss, 10(2). https://doi.org/Article
Wu, J., Hu, B., Zhang, Y., Spence, C., Hall, S. B., & Carley, K. M. (2009). An agent-based simulation study for exploring organizational adaptation. Simulation, 85(6), 397–413.
Zhong, X., & Ozdemir, S. Z. (2010). Structure, learning, and the speed of innovating: a two-phase model of collective innovation using agent based modeling. Industrial and Corporate Change, 19(5), 1459–1492.
| ||
|
آمار تعداد مشاهده مقاله: 5,167 تعداد دریافت فایل اصل مقاله: 2,943 |
||