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    題名: Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning
    作者: Shu-Heng Chen;Bin-Tzong Chie;Ying-Fang Kao;Ragupathy Venkatachalam
    關鍵詞: Non-tâtonnement process;Coordination;Agent-based modeling;Learning
    日期: 2019-06
    上傳時間: 2019-07-02 12:10:15 (UTC+8)
    出版者: Springer
    摘要: In this paper, we propose a meta-learning model to hierarchically integrate individual learning and social learning schemes. This meta-learning model is incorporated into an agent-based model to show that Herbert Scarf’s famous counterexample on Walrasian stability can become stable in some cases under a non-tâtonnement process when both learning schemes are involved, a result previously obtained by Herbert Gintis. However, we find that the stability of the competitive equilibrium depends on how individuals learn—whether they are innovators (individual learners) or imitators (social learners), and their switching frequency (mobility) between the two. We show that this endogenous behavior, apart from the initial population of innovators, is mainly determined by the agents’ intensity of choice. This study grounds the Walrasian competitive equilibrium based on the view of a balanced resource allocation between exploitation and exploration. This balance, achieved through a meta-learning model, is shown to be underpinned by a behavioral/psychological characteristic.
    關聯: Computational Economics 54(1), p.305-341
    DOI: 10.1007/s10614-017-9721-5
    顯示於類別:[產業經濟學系暨研究所] 期刊論文

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