Mechanism Design Simulation for Healthcare Reform in China

  • Guanqun Liang
  • Hirofumi Yamaki
  • Huanye Sheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5925)


We present a new insurance system for healthcare reform to meet the medical demand and alleviate the cost burden in China. China healthcare reform is complex where unlike most countries’ uniform system; it has two branches: urban health insurance and new rural cooperative medical. The equity and efficiency of the two medical healthcare systems are discussed in this paper. We use multi-agent based computational mechanism design simulation to analyze the healthcare insurance’s coverage, service and treatment cost of the people. A summary of the recent medical healthcare reforms undertaken in China is also discussed. Research results indicate that our novel hybrid healthcare insurance system formed by merging parts of the two branches can improve equity without compromising efficiency.


Multi-agent system Computational mechanism design Healthcare system 


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  1. 1.
    Ministry of Health: An analysis report of national health services survey in 2008. Center for Health Statistics and Information, Beijing (2008)Google Scholar
  2. 2.
    National Bureau of Statistics of China: China Health Yearbook 2006. National Bureau of Statistics of China, Beijing (2006)Google Scholar
  3. 3.
    Ward, S.: Demographic: Factors in the Chinese Healthcare Market. Nature Reviews Drug Discovery (May 2008)Google Scholar
  4. 4.
    Kwon, S.: Payment system reform for healthcare providers in Korea. Health Policy and Planning 18(1), 84–92 (2003)CrossRefGoogle Scholar
  5. 5.
    Eggleston, K., Hsieh, C.: Healthcare payment incentives: a comparative analysis of reforms in Taiwan, Korea and China. Alied Health Economics and Health Policy, 31-14 (2004)Google Scholar
  6. 6.
    Erev, I., Barron, G.: On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological review 112(4), 912–931 (2005)CrossRefGoogle Scholar
  7. 7.
    Parkes, D.C.: Computational mechanism design. In: Lecture notes of Tutorials at 10th Conf. on Theoretical Aspects of Rationality and Knowledge (TARK-2005), Institute of Mathematical Sciences, University of Singapore (2008)Google Scholar
  8. 8.
    Parkes, D.C., Lyle, H.: Ungar: Learning and adaption in multi-agent systems. In: Proc. AAAI 1997 Multi-agent Learning Workshop, Providence, USA (1997)Google Scholar
  9. 9.
    Nisan, N., Ronen, A.: Algorithmic Mechanism Design. In: Proceedings of the 31st ACM Symposium on Theory of Computing, pp. 129–140 (1999)Google Scholar
  10. 10.
    Aumann, R.J.: Rationality and Bounded Rationality. Games And Economic Behavior 21, 2–14 (1997)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Rubinstein, A.: Modeling Bounded Rationality. MIT Press, Cambridge (1998)Google Scholar
  12. 12.
    Parkes, D.C.: On Learnable Mechanism Design. In: Collectives and the Design of Complex Systems, pp. 107–131. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Dutta, P.K.: Strategies and games. MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Friedman, E.J., Shenker, S.: Learning and implementation on the internet (1998),
  15. 15.
    Kraus, S.: Negotiation and cooperation in multi-agent environments. Artificial Intelligence 94, 79–97 (1997)MATHCrossRefGoogle Scholar
  16. 16.
    National Bureau of Statistics: China statistical yearbook 2008. National Bureau of Statistics of China, Beijing (2008)Google Scholar
  17. 17.
    Ministry of Health: China health statistical yearbook 2008. Ministry of Health, Beijing (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guanqun Liang
    • 1
  • Hirofumi Yamaki
    • 2
  • Huanye Sheng
    • 1
  1. 1.Department of Computer Science and Engineering of Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Information Technology Center Nagoya UniversityNagoyaJapan

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