Technological Innovation of High-tech Industry and patent policy -Agent based Simulation with Double Loop Learning-

  • Hao Lee
  • Hiroshi Deguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2132)


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.


Technological Innovation Classifier System Profit Maximization Technological Level Action Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hao Lee
    • 1
  • Hiroshi Deguchi
    • 2
  1. 1.Graduate School of EconomicsKyoto UniversityKyoto-cityJapan
  2. 2.Graduate School of EconomicsKyoto UniversityKyoto-cityJapan

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