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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/69520

    Title: 有不充份情報的大動態博變理論的貝氏學習分析
    Other Titles: Bayesian Learning in Dynamic Large Games.
    Authors: 謝淑貞
    Contributors: 國際貿易研究所
    Keywords: 貝氏學習理論;動態大博奕理論;不充分情報
    Baysian learning;Dynamic large game;Incomplete information
    Date: 1994
    Issue Date: 2014-09-02
    Abstract: 本研究設立一非零和的隨機賽局,其中每位參賽者知道自己的報酬函數但不知其他參賽者的報酬矩陣。我們以Harsanyi對不充分訊息賽局的處理方式來設立模型。而每位參賽者對未知的參數訊息以機率分配來表示其認知。在每一期的開始,參賽者觀察到所有過去賽局的結果以及當期的外生資料,根據貝氏法則來修正其對未知參數的事後機率分配。如果參賽者的認知和真實的機率分配相容(compatible)的話,參賽者的認知會逼近到某一極限分配,更重要的,這個隨機賽局的貝氏Nash均衡存在,而且逼近到極限賽局的一組Nash均衡。
    This paper studies a non-zero sum stochastic game with a continuum of players in which each player is presumed to know his own payoff function but not the payoff functions of the other players at each point of time. We formulate a model, as in Harsanyi's standard theory for incomplete information games. Players express beliefs about unknown parameters in terms of distributions. The players use Bayes' rule in accordance with past endogenously generated outcome of the game and current exogenous data to infer the values of unknown payoff-relevant parameters. Under general conditions the sequence of beliefs converges to a limit distribution. The main result is under some conditions, Bayesian-Nash equilibria exist and converge to a set of Nash equilibria of the limit game.
    Relation: 行政院國家科學委員會
    Data Type: report
    Appears in Collections:[國際經營與貿易學系 ] 國科會研究計畫

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