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研究生:賴宜祥
研究生(外文):Lai, Yi Hsiang
論文名稱:Astudyoflearningmodelsforanalyzingprisoners'dilemmagamedata
論文名稱(外文):囚犯困境資料分析之學習模型研究
指導教授:余清祥余清祥引用關係楊春雷楊春雷引用關係
指導教授(外文):Jack Yue, Ching-SyangYang, Chun-Lei
學位類別:碩士
校院名稱:國立政治大學
系所名稱:統計研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
畢業學年度:93
語文別:英文
論文頁數:40
中文關鍵詞:Game learning modelReinforcement learningAttractionPrisoners'' dilemmaBelief learning
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  • 下載下載:25
  • 收藏至我的研究室書目清單書目收藏:1
  人們如何在重覆的囚犯困境賽局選擇策略是本文探討的議題,其中的賽局學習理論就是預測賽局的參與者(player)會選擇何種策略。本文使用的資料包括3個囚犯困境的實驗,各自有不同的實驗設定及配對程序,參加者都是政治大學的大學部學生,我們將使用這些資料比較不同的學習模型。除了常見的3個學習模型:增強學習模型(Reinforcement Learning model)、信念學習模型(Belief Learning model)及加權經驗吸引模型(Experience-Weighted Attraction model),本文也提出一個延伸的增強學習模型(Extended reinforcement learning model)。接著將分析劃為Training (in-sample)及Testing (out-sample),並比較各實驗間或模型間的結果。
  雖然延伸增強學習模型(Extended reinforcement learning model)較原始的增強學習模型(Reinforcement learning model)多了一個參數,該模型(Extended reinforcement learning model)在Training(in-sample)及Testing(out-sample)表現多較之前的模型來得些許的好。
How people choose strategies in a finite repeated prisoners’ dilemma game is of interest in Game Theory. The way to predict which strategies the people choose in a game is so-called game learning theory. The objective of this study is to find a proper learning model for the prisoners’ dilemma game data collected in National Cheng-Chi University. The game data consist of three experiments with different game and matching rules. Four learning models are considered, including Reinforcement learning model, Belief learning model, Experience Weighted Attraction learning model and a proposed model modified from reinforcement learning model. The data analysis was divided into 2 parts: training (in-sample) and testing (out-sample).
The proposed learning model is slightly better than the original reinforcement learning model no matter when in training or testing prediction although one more parameter is added. The performances of prediction by model fitting are all better than guessing the decisions with equal chance.
1. Introduction 1

2. Game Learning Models 7

3. Game, Matching Procedures, and Design of Experiment 3

4. Data Analysis 18

5. Conclusion 30

References 32

Appendix 1: Instructions 35

Appendix 2: Histograms of switches for players 40
Andreoni, J. and J. H. Miller (1993): “Rational Cooperation in the Finitely Repeated Prisoner’s Dilemma: Experimental Evidence,” Economic Journal, 103, 570-585.

Anderson, S., A. de Palma, and J. –F. Thisse (1992): Discrete Choice Theory of Product Differentiation. Cambridge: MIT Press.

Ben-Akiva, M., and S. Lerman (1985): Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge: MIT Press.

Browin, G. (1951): “Iterative solution of games by fictitious play,” in Activity Analysis of Production and Allocation. New York: John Wiley & Sons.

Bush, Robert, and Frederick Mosteller. 1995. Stochastic Models for Learning. New York: Wiley.

Camerer, C. F. and T.-H. Ho (1998): “EWA learning in games: probability form, heterogeneity, and time variation,” Journal of Mathematical Psychology, 42, 305-326.

Camerer, C. F. and T.-H. Ho (1999b). Experience-weighted attraction learning in normal-form games. Econometrica, 67, 827-74.

Cheung, Y. W., and D. Friedman (1997) “Individual learning in normal form games: som laboratory results,” Game and Economic Behavior, 19, 46-76.

Copper, R., D. Dejong, R. Forsythe, and T. Ross (1996): “Cooperation Without Reputation: Experimental Evidence from Prisoner's Dilemma Games,” Games and Economic Behavior, 12, 187-218.

Cournot, A. (1960): Recherches sur les Principles Mathematiques de la Theories des Richesses. Translated into English by N. Bacon as Researches in the Mathematical Principles of the Theory of Wealth. London: Haffner.

Erev, Ido, and Alvin E. Roth. (1998): Predicting how people play games: Reinforcement learning in experimental games with unique, mixed-strategy equilibria. American Economic Review, 88, 848-81.

Friendman, D. (1996): “Equilibrium in Evolutionary Games: Some Experimental Results,” Economic Journal, 106:434, 1-25.

Fudenberg, D., and D. K. Levine (1995): “Consistency and cautious Fictitious play,” Journal of Economic Dynamics and Control, 19, 1065-1090.

Fudenberg, D., and D. K. Levine (1998): Theory of Learning in Games. Cambridge: MIT Press.

Herrnstein, J. R. (1970): “On the law of effect,” Journal of Experimental Analysis of Behavior, 13, 342-366.

Ho, T-H., and K. Weigelt (1996): “Task complexity, equilibrium selection, and learning: an experimental study,” Management Science, 42, 659-679.

Kandori, M. (1992): “Social Norms and Community Enforcement,” Review of Economic Studies, 59, 63-80.

McAllister, P. H. (1991): “Adaptive approaches to stochastic programming,” Annals of Operations Research, 30, 45-62.

McKelvey, R. D., and T. R. Palfrey (1995): “Quantal response equilibria for normal form games,” Games and Economic Behavior, 10, 6-38.

Mookerjee, D., and B. Sopher (1994): “Learning behavior in an experimental matching pennies game,” Games and Economic Behavior, 7, 62-91.

Roth, A., and I. Erev (1995): “Learning in extensive-form games: experimental data and simple dynamic models in the intermediate term,” Games and Economic Behavior, 8, 164-212.

Selten, R. and R. Soecker (1986): “End Behavior in Sequences
of Finite Prisoners’ Dilemma Supergames,” Journal of Economic
Behavior and Organization, 7, 47-70.

Thorndike, E. L. (1911): Animal intelligence. New York: Macmillan.
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