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Complex systems: marketing’s new frontier


  • William Rand

    () (North Carolina State University)

  • Roland T. Rust

    () (University of Maryland)

  • Min Kim

    () (University of Maryland)


Abstract Complex systems approaches are emerging as new methods that complement conventional analytical and statistical approaches for analyzing marketing phenomena. These methods can provide researchers with tools to understand and predict marketing outcomes that emerge at the aggregate level by modeling feedback between heterogeneous agents and agent interaction with various marketing environmental variables. While the benefits of complex systems approaches often come with a high computational cost, steady advances in access to better computational resources has allowed more researchers to adopt complex systems approaches as part of their portfolio of methods. In this paper, we will provide a description of the key concepts, benefits, and tools of complex systems. The goal of this work is to encourage marketing researchers and practitioners who are not familiar with these approaches to consider the adoption of these methods. We end with a discussion of the future research opportunities that this powerful methodology enables.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:amsrev:v:8:y:2018:i:3:d:10.1007_s13162-018-0122-2
    DOI: 10.1007/s13162-018-0122-2

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    References listed on IDEAS

    1. Yaniv Dover & Jacob Goldenberg & Daniel Shapira, 2012. "Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data," Marketing Science, INFORMS, vol. 31(4), pages 689-712, July.
    2. Tal Garber & Jacob Goldenberg & Barak Libai & Eitan Muller, 2004. "From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success," Marketing Science, INFORMS, vol. 23(3), pages 419-428, August.
    3. 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.
    4. Jean‐Charles Rochet & Jean Tirole, 2006. "Two‐sided markets: a progress report," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 645-667, September.
    5. Jean-Charles Rochet & Jean Tirole, 2014. "Platform Competition in Two-Sided Markets," CPI Journal, Competition Policy International, vol. 10.
    6. Pagani, Margherita & Fine, Charles H., 2008. "Value network dynamics in 3G-4G wireless communications: A systems thinking approach to strategic value assessment," Journal of Business Research, Elsevier, vol. 61(11), pages 1102-1112, November.
    7. Ron Borkovsky & Paul Ellickson & Brett Gordon & Victor Aguirregabiria & Pedro Gardete & Paul Grieco & Todd Gureckis & Teck-Hua Ho & Laurent Mathevet & Andrew Sweeting, 2015. "Multiplicity of equilibria and information structures in empirical games: challenges and prospects," Marketing Letters, Springer, vol. 26(2), pages 115-125, June.
    8. Michael Haenlein, 2011. "A social network analysis of customer-level revenue distribution," Marketing Letters, Springer, vol. 22(1), pages 15-29, March.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    10. David F. Midgley & Robert E. Marks & Lee C. Cooper, 1997. "Breeding Competitive Strategies," Management Science, INFORMS, vol. 43(3), pages 257-275, March.
    11. 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.
    12. Haenlein, Michael, 2013. "Social interactions in customer churn decisions: The impact of relationship directionality," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 236-248.
    13. Richard Ericson & Ariel Pakes, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," Review of Economic Studies, Oxford University Press, vol. 62(1), pages 53-82.
    14. Alison Heppenstall & Andrew Evans & Mark Birkin, 2006. "Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-2.
    15. 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.
    16. Nicholson, Charles F. & Kaiser, Harry M., 2008. "Dynamic market impacts of generic dairy advertising," Journal of Business Research, Elsevier, vol. 61(11), pages 1125-1135, November.
    17. 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.
    18. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    19. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    20. David Levy, 1994. "Chaos theory and strategy: Theory, application, and managerial implications," Strategic Management Journal, Wiley Blackwell, vol. 15(S2), pages 167-178, June.
    21. Heinrich, Torsten & Gräbner, Claudius, 2015. "Beyond Equilibrium: Revisiting Two-Sided Markets from an Agent-Based Modeling Perspective," MPRA Paper 67860, University Library of Munich, Germany.
    22. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    23. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    24. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Oxford University Press, vol. 34(4), pages 441-458, May.
    25. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
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