Artificial agents for discovering business strategies for network industries

作     者：Wu, DJ 

作者机构：Abstract This study explores the use of artificial agents to discover "good" pricing investment and operating strategies for network industries. It models the first-best pricing investment and operating problems for general network industries applies this theoretical framework to the electric power industry and uses artificial agents to obtain computational results on realistic problems. Artificial agents can discover optimal or near-optimal pricing investment and operating strategies when the optimal solution is known. For problems with unknown optimal solutions they can match the "best-known solutions." The near-optimal solutions provided by artificial agents can sometimes only be tested by pushing the limits of currently available nonlinear optimization software. Artificial agents if carefully designed and controlled seem very promising for solving difficult problems that are intractable by traditional analytic methods such as discovering business strategies for network industries. Keywords ARTIFICIAL AGENTS ELECTRIC POWER NETWORKS FIRST-BEST MODEL GENETIC ALGORITHMS NETWORK INDUSTRIES Institutional LoginWelcome! To use the personalized features of this site please log in or register. If you have forgotten your username or password we can help.advanced search FindQuery BuilderClose|Clear Fields Title (ti)Summary (su)Author (au)ISSN (issn)ISBN (isbn)DOI (doi)Keyword (kw)Operators And Or Not ( ) * (wildcard) Within all contentWithin this journalWithin this issueExport Citation RIS | Text ® 2015 M.E.Sharpe Metapress Privacy Policy Remote Address:183.67.53.225?Server:MPSHQWBRDR02PHTTP User Agent:Mozilla/4.0 (compatible MSIE 8.0 Windows NT 5.0 .NET CLR 1.1.4322 .NET CLR 2.0.50215 ) 

出 版 物：《INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE》 (国际电子商务杂志)

年 卷 期：2000年第5卷第1期

页      面：9-36页

核心收录：

学科分类：1202[管理学-工商管理] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：artificial agents electric power networks first-best model genetic algorithms network industries 

摘      要：This study explores the use of artificial agents to discover good pricing, investment, and operating strategies for network industries. It models the first-best pricing, investment, and operating problems for general network industries, applies this theoretical Framework to the electric power industry and uses artificial agents to obtain computational results on realistic problems. Artificial agents can discover optimal or near-optimal pricing, investment, and operating strategies when the optimal solution is known. For problems with unknown optima I solutions, they can match the best-known solutions. The near-optimal solutions provided by artificial agents can sometimes only be tested by pushing the limits of currently available nonlinear optimization software. Artificial agents, if carefully designed and controlled, seem very promising for solving difficult problems that are intractable by traditional analytic methods, such as discovering business strategies for network industries.