AMS Review

, Volume 8, Issue 3–4, pp 111–127 | Cite as

Complex systems: marketing’s new frontier

  • William RandEmail author
  • Roland T. Rust
  • Min Kim


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.


Complex systems Agent-based models Network science System dynamics Chaos theory Machine learning 


  1. Anthes, G. (2003). P&G saves millions with supply network. Retrieved July 22, 2016 from:
  2. Arthur, B. W. (1994). Increasing returns and path dependency in the economy. Ann Arbor: University of Michigan Press.Google Scholar
  3. Bankes, S. C. (2002). Agent-based modeling: a revolution? Proceedings of the National Academy of Sciences of the United States of America, 99(Suppl 3), 7199–7200.Google Scholar
  4. Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.Google Scholar
  5. Bazghandi, A. (2012). Techniques, advantages and problems of agent based modeling for traffic simulation. International Journal of Computer Science Issues, 9(1), 115–119.Google Scholar
  6. Berger, J. (2014). Word of mouth and interpersonal communication: a review and directions for future research. Journal of Consumer Psychology, 24(4), 586–607.Google Scholar
  7. 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.Google Scholar
  8. Bondi, B. A. (2000). Characteristics of scalability and their impact on performance. Proceedings of the 2nd international workshop on Software and performance, ACM.Google Scholar
  9. Borkovsky, R. N., Ellickson, P. B., Gordon, B. R., Aguirregabiria, V., Gardete, P., Grieco, P., et al. (2015). Multiplicity of equilibria and information structures in empirical games: challenges and prospects. Marketing Letters, 26(2), 115–125.Google Scholar
  10. Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. Proceedings of the 22nd international conference of the system dynamics society (Vol. 22).Google Scholar
  11. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345354.Google Scholar
  12. Chiang, M. (2015). Chaos Theory in Business Change. Retrieved August 21, 2017 from:
  13. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.Google Scholar
  14. Cooper, K. (2016). System Dynamics Case Repository: MasterCard. Retrieved August 21, 2017 from:
  15. Costa, L. d. F., Rodrigues, F. A., Travieso, G., & Boas, P. R. V. (2007). Characterization of complex networks: a survey of measurements. Advances in Physics, 56(1), 167–242.Google Scholar
  16. Cui, G., Wong, M. L., & Lui, H. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science 52(4), 597–612.Google Scholar
  17. 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, 60(8), 826–835.Google Scholar
  18. Delre, S. A., Jager, W., Bijmolt, T. H. A., & Janssen, M. A. (2010). Will it spread or not? the effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management, 27(2), 267–282.Google Scholar
  19. Dickson, P. R. (1995). Review of “Increasing returns and path dependency in the economy”. Journal of Marketing, 59(3), 97–99.Google Scholar
  20. Dickson, P. R. (1996). The static and Dynamic Mechanics of Competition: a comment on Hunt and Morgan’s comparative theory of competition. Journal of Marketing, 60(4), 102–106.Google Scholar
  21. Dickson, P. R., Farris, W. P., & Verbeke, J. W. (2001). Dynamic strategic thinking. Journal of the Academy of Marketing Science, 29(3), 216–237.Google Scholar
  22. Ding, M. (2007). A theory of intraperson games. Journal of Marketing, 71(2), 1–11.Google Scholar
  23. Dover, Y., Goldenberg, J., & Shapira, D. (2012). Network traces on penetration: uncovering degree distribution from adoption data. Marketing Science, 31(4), 689–712.Google Scholar
  24. Ericson, R., & Pakes, A. (1995). Markov-perfect industry dynamics: A framework for empirical work. Review of Economic Studies, 62(1), 53–82.Google Scholar
  25. Farris, P., Verbeke, W., Dickson, P. R., & Nierop, V. E. (1998). Path dependencies and the long-term effects of routinized marketing decisions. Marketing Letters, 9(3), 247–268.Google Scholar
  26. Forrester, J. W. (1971). Counterintuitive behavior of social systems. Theory and Decision, 2(2), 109–140.Google Scholar
  27. Fudenberg, D., & Levine, D. K. (1998). The theory of learning in games. Cambridge: MIT Press.Google Scholar
  28. Fuentes, M. (2015). Methods and methodologies of complex systems. In B. A. Furtado, P. A. M. Sakowski, & M. H. Tóvoli (Eds.), Modeling complex systems for public policies (pp. 55–71). Brasília: IPEA.Google Scholar
  29. Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. Journal of Product Innovation Management, 22(5), 380–398.Google Scholar
  30. Garcia, R., & Jager, W. (2011). Agent-based modeling of innovation diffusion. Journal of Product Innovation Management, 28(2), 148–151.Google Scholar
  31. Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2015). The structural virality of online diffusion. Management Science, 62(1), 180–196.Google Scholar
  32. Goldenberg, J., Libai, B., & Muller, E. (2001a). Using complex system analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Academy of Marketing Science Review, 9, 1–19.Google Scholar
  33. Goldenberg, J., Libai, B., & Muller, E. (2001b). Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3), 211–223.Google Scholar
  34. Goldenberg, J., Libai, B., & Muller, E. (2002). Riding the saddle: how cross-market communications can create a major slump in sales. Journal of Marketing, 66(2), 1–16.Google Scholar
  35. Goldenberg, J., Libai, B., & Muller, E. (2010). The chilling effects of network externalities. International Journal of Research in Marketing, 27(1), 4–15.Google Scholar
  36. Goldenberg, J., Oestreicher-Singer, G., Reichman, S. (2012). The quest for content: how user-generated links can facilitate online exploration. Journal of Marketing Research, 49(4), 452–468.Google Scholar
  37. Goldenberg, J., Garber, T., Muller, E., & Libai, B. (2004). From density to destiny: using spatial dimension of sales data for early prediction of new product success. Marketing Science, 23(3), 419–428.Google Scholar
  38. Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. International Journal of Research in Marketing, 24(3), 186–200.Google Scholar
  39. Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1–13.Google Scholar
  40. Haenlein, M. (2011). A social network analysis of customer-level revenue distribution. Marketing Letters, 22(1), 15–29.Google Scholar
  41. Haenlein, M. (2013). Social interactions in customer churn decisions: The impact of relationship directionality. International Journal of Research in Marketing, 30(3), 236–248.Google Scholar
  42. Haenlein, M., & Libai, B. (2013). Targeting revenue leaders for a new product. Journal of Marketing, 77(3), 65–80.Google Scholar
  43. Heinrich, T. & Grabner, C. (2017). Beyond equilibrium: Revisiting two-sided markets from an agent-based modeling perspective. Working paper.Google Scholar
  44. Heppenstall, A., Evans, A., & Birkin, M. (2006). Using hybrid agent-based systems to model spatially-influenced retail markets. Journal of Artificial Societies and Social Simulation, 9(3).Google Scholar
  45. Hibbert, B., & Wilkinson, I. F. (1994). Chaos theory and the dynamics of marketing systems. Journal of the Academy of Marketing Science, 22(3), 218.Google Scholar
  46. Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: identifying likely adopters via consumer networks. Statistical Science, 21(2), 256–276.Google Scholar
  47. Holland, J. H. (1998). Emergence: from chaos to order. Redwood City: Addison-Wesley.Google Scholar
  48. Holland, J. H. (2014). Complexity: a very short introduction. Oxford: Oxford University Press.Google Scholar
  49. Huang, M.-H., Rust, R. T., & Rand, W. (2016). Don’t do it right, do it fast? Speed and quality of innovation as an emergent process. Working paper.Google Scholar
  50. Jun, T., Kim, J.-Y., Jun Kim, B., & Choi, M. Y. (2006). Consumer referral in a small world network. Social Networks, 28(3), 232–246.Google Scholar
  51. Lambkin, M., & Day, G. S. (1989). Evolutionary processes in competitive markets: beyond the product life cycle. Journal of Marketing, 53(3), 4–20.Google Scholar
  52. Levy, D. (1994). Chaos Theory and Strategy: Theory, Application, and Managerial Implications. Strategic Management Journal, 15, 167–178.Google Scholar
  53. Lewis, T. G. (2008). Network science: theory and applications. Hoboken: Wiley.Google Scholar
  54. Libai, B., Muller, E., & Peres, R. (2005). The role of seeding in multi-market entry. International Journal of Research in Marketing, 22(4), 375–393.Google Scholar
  55. Lilien, G. L., Kotler, P., & Moorthy, K. S. (2011). Marketing models. New Delhi: PHI Learning.Google Scholar
  56. Lin, J. H., & Liu, H. C. (2008). System dynamics simulation for Internet marketing. System Integration, 2008 IEEE/SICE International Symposium on 83–88.Google Scholar
  57. Lorig, F., Dammenhayn, N., Müller, D. J., & Timm, I. J. (2015). Measuring and Comparing Scalability of Agent-Based Simulation Frameworks. German Conference on Multiagent System Technologies.Google Scholar
  58. Lucas, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19–46.Google Scholar
  59. Lyneis, J. M. (2000). System dynamics for market forecasting and structural analysis. System Dynamics Review, 16(1), 3–25.Google Scholar
  60. Midgley, D. F., Marks, R. E., & Cooper, L. C. (1997). Breeding competitive strategies. Management Science, 43(3), 257.Google Scholar
  61. Midgley, D., Marks, R., & Kunchamwar, D. (2007). Breeding competitive strategies. Journal of Business Research, 60(8), 884–893.Google Scholar
  62. Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: an introduction to computational models of social life. Princeton: Princeton University Press.Google Scholar
  63. Minsky, M. (1988). Society of mind. New York: Simon and Schuster.Google Scholar
  64. Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.Google Scholar
  65. Newman, M. (2010). Networks: an introduction. Oxford: Oxford University Press.Google Scholar
  66. Nicholson, C. F., & Kaiser, H. M. (2008). Dynamic market impacts of generic dairy advertising. Journal of Business Research, 61(11), 1125–1135.Google Scholar
  67. North, M. J., & Macal, C. M. (2007). Managing business complexity: discovering strategic solutions with agent-based modeling and simulation. Oxford: Oxford University Press.Google Scholar
  68. Onno, O., Trienekens, J., & Beers, G. (2001). Chain and network science: a research framework. Journal on Chain and Network Science, 1(1), 1–6.Google Scholar
  69. Ottino-Loffler, B., Stonedahl, F., Veetil, V. P., & Wilensky, U. (2015). A General Model of Spatial Competition. Working Paper.Google Scholar
  70. Pagani, M., & Fine, C. H. (2008). Value network dynamics in 3G–4G wireless communications: a systems thinking approach to strategic value assessment. Journal of Business Research, 61(11), 1102–1112.Google Scholar
  71. Page, S. E. (2006). Path dependence. Quarterly Journal of Political Science, 1(1), 87–115.Google Scholar
  72. Pavlov, O. V., Melville, N., & Plice, R. K. (2008). Toward a sustainable email marketing infrastructure. Journal of Business Research, 61(11), 1191–1199.Google Scholar
  73. Rahmandad, H., & Sterman, J. (2008). Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Management Science, 54(5), 998–1014.Google Scholar
  74. Rand, W. (2015). Complex systems: concepts, literature, possibilities and limitations. In B. A. Furtado, P. A. M. Sakowski, & M. H. Tóvoli (Eds.), Modeling complex systems for public policies (pp. 37–54). Brasília: IPEA.Google Scholar
  75. Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.Google Scholar
  76. Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029.Google Scholar
  77. Rochet, J. C., & Tirole, J. (2006). Two-sided markets: a progress report. The RAND Journal of Economics, 1(4), 990–1029.Google Scholar
  78. Rosenblatt, M. (2013). Network analysis in marketing: a test of tie strength among networks of franchise operations. Proceedings of the Northeast Business & Economics Association, 205–208.Google Scholar
  79. Rust, R. T. (2015). The ups and downs of fashion. Global Fashion Management Conference, 6, 910–910.Google Scholar
  80. Rust, R. T., & Huang, M.-H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206–221.Google Scholar
  81. Sakowski, P. M., & Tóvolli, M. H. (2015). Complex approaches for education in Brazil. In B. A. Furtado, P. A. M. Sakowski, & M. H. Tóvoli (Eds.), Modeling complex systems for public policies (pp. 315–335). Brasília: IPEA.Google Scholar
  82. Stephen, A. T., Dover, Y., & Goldenberg, J. (2010). A comparison of the effects of transmitter activity and connectivity on the diffusion of information over online social networks. Working Paper.Google Scholar
  83. Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill.Google Scholar
  84. Taleb, N. N. (2007). The black swan: the impact of the highly improbable. New York: Random House.Google Scholar
  85. Tay, N. S. P., & Lusch, R. 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, 58(9), 1155–1168.Google Scholar
  86. Teng, S. H. (2016). Scalable algorithms for data and network analysis. Foundations and Trends in Theoretical Computer Science, 12(1-2), 1–274.Google Scholar
  87. Trusov, M., Rand, W., & Joshi, Y. V. (2013). Improving prelaunch diffusion forecasts: using synthetic networks as simulated priors. Journal of Marketing Research, 50(6), 675–690.Google Scholar
  88. Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441–458.Google Scholar
  89. Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: an introduction. Cambridge: Cambridge University Press.Google Scholar
  90. Zhang, T., Gensler, S., & Garcia, R. (2011). A Study of the Diffusion of Alternative Fuel Vehicles: An Agent-Based Modeling Approach. Journal of Product Innovation Management, 28(2), 152–168.Google Scholar

Copyright information

© Academy of Marketing Science 2018

Authors and Affiliations

  1. 1.Poole College of ManagementNorth Carolina State UniversityRaleighUSA
  2. 2.Center for Excellence in Service at the Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA
  3. 3.Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA

Personalised recommendations