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題名 Is genetic programming "human-competitive"? The case of experimental double auction markets
作者 Chen, Shu-Heng;Shih, Kuo-Chuan
陳樹衡
貢獻者 經濟系
關鍵詞 Best strategy; Cognitive psychology; Double auction; Experimental markets; Human subjects; Human-Competitiveness; Learning performance; Optimal solutions; Two stage; Working memories; Autonomous agents; Commerce; Competition; Integer programming; Genetic programming
日期 2011
上傳時間 22-Jun-2015 14:04:33 (UTC+8)
摘要 In this paper, the performance of human subjects is compared with genetic programming in trading. Within a kind of double auction market, we compare the learning performance between human subjects and autonomous agents whose trading behavior is driven by genetic programming (GP). To this end, a learning index based upon the optimal solution to a double auction market problem, characterized as integer programming, is developed, and criteria tailor-made for humans are proposed to evaluate the performance of both human subjects and software agents. It is found that GP robots generally fail to discover the best strategy, which is a two-stage procrastination strategy, but some human subjects are able to do so. An analysis from the point of view of cognitive psychology further shows that the minority who were able to find this best strategy tend to have higher working memory capacities than the majority who failed to do so. Therefore, even though GP can outperform most human subjects, it is not "human-competitive" from a higher standard. © 2011 Springer-Verlag.
關聯 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 6936 LNCS, 2011, Pages 116-126, 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011; Norwich; United Kingdom; 7 September 2011 到 9 September 2011; 代碼 86490
資料類型 conference
DOI http://dx.doi.org/10.1007/978-3-642-23878-9_15
dc.contributor 經濟系
dc.creator (作者) Chen, Shu-Heng;Shih, Kuo-Chuan
dc.creator (作者) 陳樹衡zh_TW
dc.date (日期) 2011
dc.date.accessioned 22-Jun-2015 14:04:33 (UTC+8)-
dc.date.available 22-Jun-2015 14:04:33 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2015 14:04:33 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75998-
dc.description.abstract (摘要) In this paper, the performance of human subjects is compared with genetic programming in trading. Within a kind of double auction market, we compare the learning performance between human subjects and autonomous agents whose trading behavior is driven by genetic programming (GP). To this end, a learning index based upon the optimal solution to a double auction market problem, characterized as integer programming, is developed, and criteria tailor-made for humans are proposed to evaluate the performance of both human subjects and software agents. It is found that GP robots generally fail to discover the best strategy, which is a two-stage procrastination strategy, but some human subjects are able to do so. An analysis from the point of view of cognitive psychology further shows that the minority who were able to find this best strategy tend to have higher working memory capacities than the majority who failed to do so. Therefore, even though GP can outperform most human subjects, it is not "human-competitive" from a higher standard. © 2011 Springer-Verlag.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 6936 LNCS, 2011, Pages 116-126, 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011; Norwich; United Kingdom; 7 September 2011 到 9 September 2011; 代碼 86490
dc.subject (關鍵詞) Best strategy; Cognitive psychology; Double auction; Experimental markets; Human subjects; Human-Competitiveness; Learning performance; Optimal solutions; Two stage; Working memories; Autonomous agents; Commerce; Competition; Integer programming; Genetic programming
dc.title (題名) Is genetic programming "human-competitive"? The case of experimental double auction markets
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1007/978-3-642-23878-9_15
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-642-23878-9_15