Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/76053


Title: Neural network models of learning and categorization in multigame experiments
Authors: Marchiori, Davide;wargline, M.
馬大衛
Contributors: 經濟系
Keywords: article;controlled study;experimental study;game;human;human experiment;learning;nerve cell network;normal human;theory
Date: 2011
Issue Date: 2015-06-22 16:08:02 (UTC+8)
Abstract: 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.
Relation: Frontiers in Neuroscience, Issue DEC, 論文編號 Article 139
Data Type: article
DOI 連結: http://dx.doi.org/10.3389/fnins.2011.00139
Appears in Collections:[經濟學系] 期刊論文

Files in This Item:

File Description SizeFormat
Neural.pdf1155KbAdobe PDF555View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing