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An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network

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  • Wen Zhou
  • Nikita Koptyug
  • Shutao Ye
  • Yifan Jia
  • Xiaolong Lu

Abstract

As computer science and complex network theory develop, non-cooperative games and their formation and application on complex networks have been important research topics. In the inter-firm innovation network, it is a typical game behavior for firms to invest in their alliance partners. Accounting for the possibility that firms can be resource constrained, this paper analyzes a coordination game using the Nash bargaining solution as allocation rules between firms in an inter-firm innovation network. We build an extended inter-firm n-player game based on nonidealized conditions, describe four investment strategies and simulate the strategies on an inter-firm innovation network in order to compare their performance. By analyzing the results of our experiments, we find that our proposed greedy strategy is the best-performing in most situations. We hope this study provides a theoretical insight into how firms make investment decisions.

Suggested Citation

  • Wen Zhou & Nikita Koptyug & Shutao Ye & Yifan Jia & Xiaolong Lu, 2016. "An Extended N-Player Network Game and Simulation of Four Investment Strategies on a Complex Innovation Network," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
    • Handle: RePEc:plo:pone00:0145407
    DOI: 10.1371/journal.pone.0145407
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    Cited by:

    1. Zhou, Wen & Jia, Yifan, 2017. "Predicting links based on knowledge dissemination in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 561-568.

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    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

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