IDEAS home Printed from https://ideas.repec.org/a/sgm/pzwzuw/v10i38y2012p104-123.html
   My bibliography  Save this article

Application of rough sets to identify the behavior rules of consumer for the purposes of multi-agent simulation model (Zastosowanie zbiorow przyblizonych do wykrywania regul zachowania konsumentow na potrzeby wieloagentowego modelu symulacyjnego)

Author

Listed:
  • Malgorzata Latuszynska

    (Uniwersytet Szczecinski)

  • Agata Wawrzyniak

    (Uniwersytet Szczecinski)

  • Barbara Wasikowska

    (Uniwersytet Szczecinski)

  • Fatimah Furaji

    (University of Basrah, Irak)

Abstract

This paper presents the possibility of application of the rough set theory in procedure of building a multi-agent model of consumer behavior. Discussed are: multi-agent simulation, methods of gathering and processing data and rough set theory in the context of identification of market behavior rules of consumers. In addition to these, the paper presents an example of a simulation model of consumer behavior in the electrical appliances market which was built with applying the proposed research procedure.

Suggested Citation

  • Malgorzata Latuszynska & Agata Wawrzyniak & Barbara Wasikowska & Fatimah Furaji, 2012. "Application of rough sets to identify the behavior rules of consumer for the purposes of multi-agent simulation model (Zastosowanie zbiorow przyblizonych do wykrywania regul zachowania konsumentow na ," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 10(38), pages 104-123.
    • Handle: RePEc:sgm:pzwzuw:v:10:i:38:y:2012:p:104-123
    as

    Download full text from publisher

    File URL: http://pz.wz.uw.edu.pl/sites/default/files/artykuly/latuszynska_wawrzyniak_i_in.pdf
    Download Restriction: no
    File URL: http://pz.wz.uw.edu.pl/en
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wilkinson, Ian & Young, Louise, 2002. "On cooperating: firms, relations and networks," Journal of Business Research, Elsevier, vol. 55(2), pages 123-132, February.
    2. Zhang, Tao & Zhang, David, 2007. "Agent-based simulation of consumer purchase decision-making and the decoy effect," Journal of Business Research, Elsevier, vol. 60(8), pages 912-922, August.
    3. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
    4. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    5. Tay, Nicholas S.P. & Lusch, Robert F., 2005. "A preliminary test of Hunt's General Theory of Competition: using artificial adaptive agents to study complex and ill-defined environments," Journal of Business Research, Elsevier, vol. 58(9), pages 1155-1168, September.
    6. Schenk, Tilman A. & Loffler, Gunter & Rauh, Jurgen, 2007. "Agent-based simulation of consumer behavior in grocery shopping on a regional level," Journal of Business Research, Elsevier, vol. 60(8), pages 894-903, August.
    7. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Borawska & Malgorzata Latuszynska, 2020. "Incorporating Neuroscience Data into Agent-Based Simulation Models of Buyer Behavior," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1197-1212.
    2. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    3. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
    4. Moore, Kevin & Smallman, Clive & Wilson, Jude & Simmons, David, 2012. "Dynamic in-destination decision-making: An adjustment model," Tourism Management, Elsevier, vol. 33(3), pages 635-645.
    5. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    6. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    7. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    8. Desmarchelier, Benoît & Fang, Eddy S., 2016. "National culture and innovation diffusion. Exploratory insights from agent-based modeling," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 121-128.
    9. Aura Reggiani, 2022. "The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 415-437, September.
    10. Ben Fitzpatrick & Jason Martinez, 2012. "Agent-Based Modeling of Ecological Niche Theory and Assortative Drinking," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-4.
    11. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    12. Declan Mungovan & Enda Howley & Jim Duggan, 2011. "The influence of random interactions and decision heuristics on norm evolution in social networks," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 152-178, May.
    13. Yingru Li & Ting Du & Jian Peng, 2018. "Understanding Out-of-Home Food Environment, Family Restaurant Choices, and Childhood Obesity with an Agent-Based Huff Model," Sustainability, MDPI, vol. 10(5), pages 1-15, May.
    14. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    15. Bertotti, M.L. & Brunner, J. & Modanese, G., 2016. "The Bass diffusion model on networks with correlations and inhomogeneous advertising," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 55-63.
    16. Daniel Ladley & Ian Wilkinson & Louise Young, 2013. "The Evolution Of Cooperation In Business: Individual Vs. Group Incentives," Discussion Papers in Economics 13/14, Division of Economics, School of Business, University of Leicester.
    17. Held, Fabian P. & Wilkinson, Ian F. & Marks, Robert E. & Young, Louise, 2014. "Agent-based Modelling, a new kind of research," Australasian marketing journal, Elsevier, vol. 22(1), pages 4-14.
    18. Roman Šperka, 2015. "Simulation Methods Comparison in Business Process Domain," Working Papers 0019, Silesian University, School of Business Administration.
    19. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    20. Jarratt, Denise & Ceric, Arnela, 2015. "The complexity of trust in business collaborations," Australasian marketing journal, Elsevier, vol. 23(1), pages 2-12.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sgm:pzwzuw:v:10:i:38:y:2012:p:104-123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/somuwpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.