Abstract
Using an agent-based modular economic model, we study the effect of social interactions on product innovation and its further impact on competitiveness dynamics. Two firms with different intensities of social interactions are placed in a context of duopolistic competition. The macroscopic analysis based on various criteria, including the market share, profit rate, accumulated capital, waste ratio and consumers’ satisfaction level, indicates that high social interaction within the firm can lead to not only a healthy firm but also a healthy economy. However, this positive result is undermined by the catastrophic nature of the modular economy as shown in the microscopic analysis. Furthermore, the mesoscopic analysis shows that in the long run the duopoly market tends to become a monopolistic market, and there is a non-trivial probability that the low-interaction firm will drive out the high-interaction firm. The risk of innovation in this model may be greater than what the usual economic model may expect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arifovic, J. (1994) Genetic Algorithm Learning and the Cobweb Model. Journal of Economic Dynamics and Control 18 (1): 3–28
Birchenhall, C.R. (1995) Modular Technical Change and Genetic Algorithms. Computational Economics 8: 233–253
Birchenhall, C.R., Lin, J.S. (2002) Learning and Convergence to Pareto Optimality. In: Chen, S.H. (ed.), Genetic Algorithms and Genetic Programming in Computational Finance. Kluwer Academic Publishers, Boston
Brenner, T. (1998) Can Evolutionary Algorithms Describe Learning Processes? Journal of Evolutionary Economics, 8: 271–283
Callebaut, W., Rasskin-Gutman, D.(eds.) (2005) Modularity: Understanding the Development and Evolution of Natural Complex Systems. The MIT Press, Cambridge (MA)
Chen, S.-H, Chie, B.-T. (2005) A Functional Modularity Approach to Agent-Based Modeling of the Evolution of Technology. In: Namatame, A., Kaizouji, T., Aruka, Y. (eds.), The Complex Networks of Economic Interactions: Essays in Agent-Based Economics and Econophysics, LNMES 567, Springer, 165–178
Chen, S.-H, Chie, B.-T. (2007) Modularity, product innovation, and consumer satisfaction: An agent-based approach. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.), Intelligent Data Engineering and Automated Learning, LNCS 4881, Springer, 1053–1062
Chen, S.-H., Ni, C.-C. (2000) Simulating the Ecology of Oligopoly Games with Genetic Algorithms. Knowledge and Information Systems: An International Journal 2: 310–339
Chen, S.-H., Kuo, T.-W. (2003) Overfitting or Poor Learning: A Critique of Current Financial Applications of GP. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.), Genetic Programming, LNCS 2610, Springer, 34–46
Curzon Price, T. (1997) Using Co-Evolutionary Programming to Simulate Strategic Behaviour in Markets. Journal of Evolutionary Economics 7(3): 219–254
Dawid, H. (1999) Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models, Springer
Dawid, H. (2006) Agent-Based Models of Innovation and Technological Change. In: Tesfatsion, L., Judd, K.L. (eds.), Handbook of Computational Economics vol. 2. Elsevier, Amsterdam, 438–475
Goldberg, D.E. (2002) The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston
Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by the Means of Natural Selection. The MIT Press, Cambridge (MA)
Koza, J.R. (1994) Genetic Programming II–Automatic Discovery of Reusable Programs, The MIT Press, Cambridge (MA)
Marks, R.E., Midgley, D.F., Cooper, L.G. (2006) Co-Evolving Better Strategies in Oligopolistic Price Wars. In Rennard, J.-P. (ed.), Handbook of Research on Nature-Inspired Computing for Economy and Management, Ch. 52. Idea Group Inc., Hershey (PA)
Simon, H. (1965) The Architecture of Complexity. General Systems 10: 63–76
Vriend, N.J. (2000) An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses. Journal of Economic Dynamics and Control 24: 1–19
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Berlin Heidelberg
About this paper
Cite this paper
Chie, BT., Chen, SH. (2010). Social Interactions and Innovation: Simulation Based on an Agent-Based Modular Economy. In: Li Calzi, M., Milone, L., Pellizzari, P. (eds) Progress in Artificial Economics. Lecture Notes in Economics and Mathematical Systems, vol 645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13947-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-13947-5_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13946-8
Online ISBN: 978-3-642-13947-5
eBook Packages: Business and EconomicsEconomics and Finance (R0)