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政大機構典藏 > 社會科學學院 > 經濟學系 > 期刊論文 > Item 140.119/76053

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題名:	Neural network models of learning and categorization in multigame experiments
作者:	Marchiori, Davide;wargline, M. 馬大衛
貢獻者:	經濟系
關鍵詞:	article;controlled study;experimental study;game;human;human experiment;learning;nerve cell network;normal human;theory
日期:	2011
上傳時間:	2015-06-22 16:08:02 (UTC+8)
摘要:	Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2 × 2 games.Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium. © 2011 Marchiori and Warglien.
關聯:	Frontiers in Neuroscience, Issue DEC, 論文編號 Article 139
資料類型:	article
DOI 連結:	http://dx.doi.org/10.3389/fnins.2011.00139
DOI:	10.3389/fnins.2011.00139
顯示於類別:	[經濟學系] 期刊論文

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