Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming

Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/75068

DC Field	Value	Language
dc.contributor	經濟系
dc.creator	Chen, Shu-heng
dc.creator	陳樹衡	zh_TW
dc.date	2000
dc.date.accessioned	2015-05-11T06:02:36Z	-
dc.date.available	2015-05-11T06:02:36Z	-
dc.date.issued	2015-05-11T06:02:36Z	-
dc.identifier.uri	http://nccur.lib.nccu.edu.tw/handle/140.119/75068	-
dc.description.abstract	Using genetic programming, this paper proposes an agent- based computational modeling of double auction (DA) markets in the sense that a DA market is modeled as an evolving market of autonomous interacting traders (automated software agents). The specic DA market on which our modeling is based is the Santa Fe DA market ((12), (13)), which in structure, is a discrete-time version of the Arizona continuous- time experimental DA market ((14), (15)).
dc.format.extent	640448 bytes	-
dc.format.mimetype	application/pdf	-
dc.relation	Intelligent Data Engineering and Automated Learning - IDEAL , pp. 517-531
dc.title	Toward an Agent-Based Computational Modeling of Bargaining Strategies in Double Auction Markets with Genetic Programming
dc.type	book/chapter	en
dc.identifier.doi	10.1007/3-540-44491-2_76
dc.doi.uri	http://dx.doi.org/10.1007/3-540-44491-2_76
item.cerifentitytype	Publications	-
item.fulltext	With Fulltext	-
item.openairecristype	http://purl.org/coar/resource_type/c_18cf	-
item.openairetype	book/chapter	-
item.grantfulltext	open	-
Appears in Collections:	專書/專書篇章

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