• DocumentCode
    3005995
  • Title

    How to Make the Quantitative Analysis in Evolutionary Game Theory? A Forecasting Analysis of Chinese Rural Labor Transfer

  • Author

    De-Hai Liu ; Wei-Guo Wang

  • Author_Institution
    Sch. of Math. & Quantitative Econ., Dongbei Univ. of Finance & Econ., Dalian
  • fYear
    2008
  • fDate
    25-26 Sept. 2008
  • Firstpage
    83
  • Lastpage
    87
  • Abstract
    Based on the perfect rationality and common knowledge theoretically hypotheses, the out-of-equilibrium outcome or out of subgame perfect equilibrium path couldn´t achieve in traditional game theory. Evolutionary game theory analyzes the population´s dispersive behaviors under the bounded rational hypothesis. The theoretical payoffs of different strategies are decided by the practical observed outcome, so we can make the quantitative analysis. The paper puts forward the basic method for the evolutionary game quantitative analysis in social economy system, including bounded rationality derived from individual local information, imitation derived from local search decision-making and the social statistical investigating data reflected the total interactional outcome of social populations. At last, as a case of quantitative forecasting analysis, Chinese rural labor transferring course should be discussed.
  • Keywords
    decision making; economic forecasting; evolutionary computation; game theory; labour resources; macroeconomics; search problems; Chinese rural labor transfer; bounded rational hypothesis; evolutionary game quantitative analysis; local search decision-making; quantitative forecasting analysis; social economy system; social statistical analysis; Bayesian methods; Biological system modeling; Dispersion; Economic forecasting; Environmental economics; Game theory; Genetics; Mathematics; Robustness; Uncertainty; Chinese rural labor transfer; evolutionary game; long-term forecasting; quantitative analysis method;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on
  • Conference_Location
    Hubei
  • Print_ISBN
    978-0-7695-3334-6
  • Type

    conf

  • DOI
    10.1109/WGEC.2008.59
  • Filename
    4637400