Abstract
In this paper, we formulate a multi-agent model of virtual high-tech industry by agent-based simulation. We introduce a classifier system as a decision-making tool of agent who makes its decision depending on the rules in the classifier system. Firm agent determines how much R&D investment and product investment it will spend. We assumed three different types of firm agents in our virtual societies, in which each different agent has a different goal. Agents of different types have different evaluation functions; also agents may change their goals (evaluation functions) when they have survival problem in industry. We verify the Schumpeter Hypothesis and effect of industrial policies in our virtual high-tech industry. We found that the difference in speed at which technology increases, when comparing imitation and innovation, affects the effectiveness of patent policy.
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
Robert Alexrod, The Complexity of Cooperation, Agent-Based Models of Competition and Collaboration, Princeton University Press. 1997
Esben Sloth Andersen, Evolutionary Economics, Post-Schumpeterian Contributions. A Cassel Imprint. 1996.
W.Brian Arthur. Increasing Returns and Path Dependence in the Economy. The University of Michigan Press. 1994
W. Brian Arthus, Steven N. Durlauf, David A. Lane. The Economy as an Evolving Complex System II. Addison-Wesley 1997
Kathleen M. Carley, Michael J. Prietula. Computational Organization Theory. Lawrence Erlbaum Associates, Publishers.
M.D. Cohen and L.S. Sproull. Organizational Learning, SAGE Publications, 1995.
Hiroshi Deguchi, Agent Based Approach for Social Complex Systems-Management of Constructed Social World In Toru Ishida(Ed), Community Computing and Support Systems LNCS 1519, Springer, p. 62–77, 1998.
Richard J. Gaylord, Louis J. D’Andria. Simulation Society: A Mathematica Toolkit for Modeling Socioeconomic Behavior. Springer-Verlag, 1998.
Hao Lee, Hiroshi Deguchi, Technological Innovation of High-tech industry, JASMIN2000 Spring, 2000. (In Japanese)
Hao, Lee, H. Deguchi, Technological Innovation of High-tech industry-Agent based Simulation with Double Loop Learning-, KSS’2000 JAIST, p. 224–229
Melanie Mitchell. An Introduction to Genetic Algorithms. Massachusetts Institute of Technology. 1996.
Nelson, Richard R &Sidney G. Winter. An evolutionary theory of economic change. Harvard University Press, 1996.
F.M. Scherer, David Ross, Industrial market structure and economic performance. Houghton Mifflin, 1990.
Schumpeter J. Capitalism, Socialism, and Democracy. Harper and Row, New York. 1942
Takadama, T. Terano, K. Hori, andS. Nakasuka, “Making Organizational Learning Operational: Implication from Learning Classifier System”, Cimputational and Mathematical Organization Theory(CMOT), Kluwer Academic Publichers, Vol. 5, No. 3pp. 229–252. 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, H., Deguchi, H. (2001). Technological Innovation of High-tech Industry and patent policy -Agent based Simulation with Double Loop Learning-. In: Yuan, S.T., Yokoo, M. (eds) Intelligent Agents: Specification, Modeling, and Applications. PRIMA 2001. Lecture Notes in Computer Science, vol 2132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44637-0_12
Download citation
DOI: https://doi.org/10.1007/3-540-44637-0_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42434-5
Online ISBN: 978-3-540-44637-8
eBook Packages: Springer Book Archive