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010   $a1-283-91024-1
010   $a3-642-33424-5
024 7 $a10.1007/978-3-642-33424-5
035   $a(CKB)2670000000279867
035   $a(EBL)1082683
035   $a(OCoLC)820020538
035   $a(SSID)ssj0000798512
035   $a(PQKBManifestationID)11484470
035   $a(PQKBTitleCode)TC0000798512
035   $a(PQKBWorkID)10742737
035   $a(PQKB)10647866
035   $a(DE-He213)978-3-642-33424-5
035   $a(MiAaPQ)EBC1082683
035   $a(PPN)16832444X
035   $a(EXLCZ)992670000000279867
100   $a20120820d2013      uy         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aDecoding complexity $euncovering patterns in economic networks /$fJames B. Glattfelder
205   $a1st ed. 2013.
210   $aNew York $cSpringer$d2013
215   $a1 online resource (234 p.)
225  0$aSpringer theses,$x2190-5053
300   $aDescription based upon print version of record.
311   $a3-642-42663-8 
311   $a3-642-33423-7 
320   $aIncludes bibliographical references.
327   $aThe Main Methodology: Computing Control in Ownership Networks -- Backbone of Complex Networks of Corporations: The Flow of Control -- The Network of Global Corporate Control -- The Bow-Tie Model of Ownership Networks.
330   $aToday it appears that we understand more about the universe than about our interconnected socio-economic world. In order to uncover organizational structures and novel features in these systems, we present the first comprehensive complex systems analysis of real-world ownership networks. This effort lies at the interface between the realms of economics and the emerging field loosely referred to as complexity science. The structure of global economic power is reflected in the network of ownership ties of companies and the analysis of such ownership networks has possible implications for market competition and financial stability. Thus this work presents powerful new tools for the study of economic and corporate networks that are only just beginning to attract the attention of scholars.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aBusiness networks
615  0$aBusiness networks.
676   $a306.3
700   $aGlattfelder$b James B$0837314
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910438115303321
996   $aDecoding complexity$94196595
997   $aUNINA