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    题名: 認知能力與認知階層 : 選美賽局的實驗分析
    Cognitive Capacity and Cognitive Hierarchy: Experimental Evidence from Keynes`s Beauty Contest
    作者: 杜業榮
    Ye-Rong Du
    贡献者: 陳樹衡
    Shu-Heng Chen
    杜業榮
    Ye-Rong Du
    关键词: 選美賽局
    智力
    工作記憶
    k層次思考
    認知階層
    強化學習
    信念學習
    經驗加權吸引力學習
    Beauty contest experiment
    Intelligence
    Working memory capacity
    Level-k reasoning
    Cognitive hierarchies
    Reinforcement learning
    Belief learning
    Experience-weighted attraction learning
    日期: 2012
    上传时间: 2013-09-02 17:19:22 (UTC+8)
    摘要: 晚近行為實驗的發展, 特別是在賽局實驗的研究中, 認知所扮演的角色漸受重視。認知階層與認知能力, 是文獻上兩個相關並且廣被討論的概念。 雖然這兩者往往出現在同樣的實驗中, 但仍少有研究正式地探討兩者之間的關係。 在本研究中, 我們透過15至20人為一組的重複選美賽局觀察受試者的認知階層, 並以工作記憶測驗測量其認知能力, 試圖檢驗認知能力對於認知階層的影響。 總的來說, 我們發現認知能力對於認知階層有正向的影響, 即認知能力較高的受試者, 所觀察到的認知階層也較高。 在最初幾個回合中, 認知能力的影響顯著。 接下來的回合中雖然效果漸弱, 但並不會完全消失。 這意謂著認知能力可能進一步影響其學習行為, 因此透過認知階層的馬可夫轉移動態與經驗加權吸引力學習模型, 我們檢驗此一可能性。 證據顯示認知能力不同反映學習行為的差異, 尤其相較於強化學習, 認知能力較高的受試者可能更傾向信念學習。
    Recent developments in behavioral experiments, in particular game experiments, have placed human cognition in a pivotal place. Two related ideas are proposed and are popularly used in the literature, namely, cognitive hierarchy and cognitive capacity. While these two often meet in the same set of experiments and observations, few studies have formally addressed their relationship. In this study, based on six series of 15- to 20-person beauty contest experiments and the associated working memory tests, we examine the effect of cognitive capacity on the observed cognitive hierarchy. It is found that cognitive capacity has a positive effect on the observed cognitive hierarchy. This effect is strong in the initial rounds, and may become weaker, but without disappearing, in subsequent rounds, which suggests the possibility that cognitive capacity may further impact learning. We examine this possibility using the Markov transition dynamics of cognitive hierarchy and experience-weighted attraction learning. There is evidence to show that subjects with different cognitive capacities may learn differently, which may cause strong convergence to be difficult to observe.
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