JACIII Vol.15 No.2 pp. 212-219
doi: 10.20965/jaciii.2011.p0212


Bounded Rationality on Consumer Purchase Decisions and Product Diffusion Under Network Externalities: A Study Using Agent-Based Simulation and Experiments with Human Subjects

Nariaki Nishino*1, Sobei H. Oda*2, and Kanji Ueda*3,*4

*1The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

*2Kyoto Sangyo University, Motoyama, Kamigamo, Kyoto 603-8555, Japan

*3National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8561, Japan

*4The University of Tokyom, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan

August 3, 2010
December 31, 2010
March 20, 2011
agent-based simulation, game theory, experimental economics, bounded rationality, product diffusion

This study addresses mechanisms of purchase decision-making under network externalities. In particular, we examine how bounded rationality affects product diffusion in markets where network externalities are present. In response to rapid development of information communication technology, markets with network externalities have been expanding recently. If network externalities are present, then a consumer’s utility depends on the number of same product users. In other words, the product value is determined through consumers’ interaction in society. Therefore, the elucidation of the mechanism is required. For this purpose, this study adopts an integral approach with agent-based simulations and experiments with human subjects. We use a simple decision-making model constructed by Ueda et al. [1] to conduct experiments with human subjects based on the method of experimental economics. Additionally, we construct agents who make the bounded rational actions observed in the experiments and examine how product diffusion emerges through interaction of such bounded rational agents. Results show that bounded rationality of subsequent decision-makers affects the purchase behavior of preceding consumers. Accordingly, the output of product diffusion is varied.

Cite this article as:
Nariaki Nishino, Sobei H. Oda, and Kanji Ueda, “Bounded Rationality on Consumer Purchase Decisions and Product Diffusion Under Network Externalities: A Study Using Agent-Based Simulation and Experiments with Human Subjects,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 212-219, 2011.
Data files:
  1. [1] K. Ueda, N. Nishino, and T. Takenaka, “Producer Decision-making in Markets with Network Externalities,” CIRP Annals – Manufacturing Technology, Vol.58, No.1, pp. 413-416, 2009.
  2. [2] M. L. Katz and C. Shapiro, “Network Externalities, Competition, and Compatibility,” The American Economic Review, Vol.75, No.3, pp. 424-440, 1985.
  3. [3] J. Farrell and G. Saloner, “Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation,” The American Economic Review, Vol.76, No.5, pp. 940-955, 1986.
  4. [4] S. A. Delre, W. Jager, T. H. A. Bijmolt, and M. A. Janssen, “Targeting and timing promotional activities: an agent-based model for the takeoff of new products,” J. of Business Research, Vol.60, No.8, pp. 826-835, 2007.
  5. [5] R. Garcia, “Uses of agent-based modeling in innovation – new product development research,” J. of Product Innovation Management, Vol.22, No.5, pp. 380-398, 2005.
  6. [6] J. Goldenberg, B. Libai, and E. Muller, “The chilling effects of network externalities,” Int. J. of Research in Marketing, Vol.27, No.1, pp. 4-15, 2010.
  7. [7] B. Libai, E. Muller, and R. Peres, “The role of seeding in multimarket entry,” Int. J. of Research in Marketing, Vol.22, No.4, pp. 375-393, 2005.
  8. [8] C. Ruebeck, S. Stafford, N. Tynan, W. Alpert, G. Ball, and B. Butkevich, “Network Externalities and Standardization: A Classroom Demonstration,” Southern Economic J., Vol.69, No.4, pp. 1000-1008, 2002.
  9. [9] S. Chakravarty, “Experimental evidence on product adoption in the presence of network externalities,” Review of Industrial Organization, Vol.23, pp. 233-254, 2003.
  10. [10] E. Brynjolfsson and C. F. Kemerer, “Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market,” Management Science, Vol.42, pp. 1627-1647, 1996.
  11. [11] J. Church and N. Gandal, “Complementary Network Externalities and Technological Adoption,” Int. J. of Industrial Organization, Vol.11, pp. 239-260, 1993.
  12. [12] G. Madden, G. Coble-Neal, and B. Dalzell, “A dynamic model of mobile telephony subscription incorporating a network effect,” Telecommunication Policy, Vol.28, pp. 133-144, 2004.
  13. [13] N. Economides and C. Himmelberg, “Critical Mass and Network Size with Application to the US FAX Market,” Working Paper, 1995.
  14. [14] V. Shankar and B. L. Bayus, “Network Effects and Competition: An Empirical Analysis of the Home Video Game Industry,” Strategic Management J., Vol.24, pp. 375-384, 2003.
  15. [15] D. Friedman and S. Sunder, “Experimental Methods: A Primer for Economics,” Cambridge University Press, 1994.
  16. [16] J. Kagel and A. Roth, “The Handbook of Experimental Economics,” Princeton University Press, 1995.
  17. [17] V. L. Smith, “Experimental Economics – Induced Value Theory,” American Economic Review, Vol.66, No.2, pp. 274-279, 1976.
  18. [18] V. L. Smith, “Microeconomic Systems as an Experimental Science,” American Economic Review, Vol.72, No.5, pp. 923-955, 1982.
  19. [19] U. Fischbacher, “z-Tree: Zurich toolbox for ready-made economic experiments,” Experimental Economics, Vol.10, No.2, pp. 171-178, 2007.
  20. [20] A. Rubinstein, “Modeling Bounded Rationality,” MIT Press, 1998.
  21. [21] T. Saijo and H. Nakamura, “The ‘spite’ dilemma in voluntary contribution mechanism experiments,” J. of Conflict Resolution, Vol.39, No.3, pp. 535-560, 1995.

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