Managing Complexity: Insights, Concepts, ApplicationsDirk Helbing Springer, 13/10/2007 - 393 páginas Each chapter in Managing Complexity focuses on analyzing real-world complex systems and transferring knowledge from the complex-systems sciences to applications in business, industry and society. The interdisciplinary contributions range from markets and production through logistics, traffic control, and critical infrastructures, up to network design, information systems, social conflicts and building consensus. They serve to raise readers' awareness concerning the often counter-intuitive behavior of complex systems and to help them integrate insights gained in complexity research into everyday planning, decision making, strategic optimization, and policy. Intended for a broad readership, the contributions have been kept largely non-technical and address a general, scientifically literate audience involved in corporate, academic, and public institutions. |
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Página 20
... defined by the seller who lists the article such that the article can be found effectively , hence , the categorization is mainly a taxonomy . Using this to coarse grain the data would introduce a bias in the analysis . Second, eBay ...
... defined by the seller who lists the article such that the article can be found effectively , hence , the categorization is mainly a taxonomy . Using this to coarse grain the data would introduce a bias in the analysis . Second, eBay ...
Página 25
... defined by Girvan and Newman ( GN ) [ 23 ] . The definition of Q can also be written as [ 24 ] : q 1 ΜΣ ( mss - [ mss ] ) = S S Css SS q 1 ΜΣ ( Mrs - [ Mrs ] ) . s < r Ars ( 1 ) Here , the first sum runs over all group indices s , while ...
... defined by Girvan and Newman ( GN ) [ 23 ] . The definition of Q can also be written as [ 24 ] : q 1 ΜΣ ( mss - [ mss ] ) = S S Css SS q 1 ΜΣ ( Mrs - [ Mrs ] ) . s < r Ars ( 1 ) Here , the first sum runs over all group indices s , while ...
Página 26
... define a consensus ordering of the bidders from the γ = 0.5 and y = 1 ordering by reshuffling the internal order of the γ = 0.5 clusters according to the y = 1 clustering . Remember the orderings for the two values of γ were obtained ...
... define a consensus ordering of the bidders from the γ = 0.5 and y = 1 ordering by reshuffling the internal order of the γ = 0.5 clusters according to the y = 1 clustering . Remember the orderings for the two values of γ were obtained ...
Página 29
... defined as ORCs = P(bidding P(bidding in in C|not C|member member of of cluster cluster s) s) , (2) i.e. the ratio of the odds of bidding in category C, given a bidder is member of group s vs. the odds of bidding in category C given the ...
... defined as ORCs = P(bidding P(bidding in in C|not C|member member of of cluster cluster s) s) , (2) i.e. the ratio of the odds of bidding in category C, given a bidder is member of group s vs. the odds of bidding in category C given the ...
Página 31
... definition of any kind of similarity measure between articles or product categories. Rather, we solely used the taxonomic information about articles provided by eBay to interpret our results. We can classify 85% of the users into only a ...
... definition of any kind of similarity measure between articles or product categories. Rather, we solely used the taxonomic information about articles provided by eBay to interpret our results. We can classify 85% of the users into only a ...
Índice
| 1 | |
| 18 | |
Managing Autonomy and Control in Economic Systems | 37 |
The Illusion of Control | 57 |
Benefits and Drawbacks of Simple Models for Complex | 89 |
Coping with Nonlinearity and Complexity | 119 |
Repeated Auction Games and Learning Dynamics | 137 |
Decentralized Approaches to Adaptive Traffic Control | 177 |
Stefano Battiston Domenico Delli Gatti Mauro Gallegati 219 | 241 |
Bootstrapping the Long Tail in Peer to Peer Systems | 262 |
Complexity in Human Conflict | 303 |
Fostering Consensus in Multidimensional Continuous Opinion | 321 |
MultiStakeholder Governance Emergence | 335 |
Evolutionary Engineering of Complex Functional Networks | 350 |
Path Length Scaling and Discrete Effects in Complex | 369 |
Index | 389 |
Arne Kesting Martin Schönhof Stefan Lämmer Martin Treiber | 201 |
Trade Credit Networks and Systemic Risk | 218 |
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Managing Complexity: Insights, Concepts, Applications Dirk Helbing Pré-visualização indisponível - 2007 |
Managing Complexity: Insights, Concepts, Applications Dirk Helbing Pré-visualização indisponível - 2010 |
Palavras e frases frequentes
adjacency matrix agents algorithm analysis applied approach autonomy average bankruptcy behavior bidders bidding factor bounded rationality capacity carrier cluster clustering coefficient coefficient communication competitors complex systems computational connected cost customers cycle decision defined degree distribution delay demand density distribution eBay economic effect enterprise evolution example factory fictitious play FIFO Figure firm flow forecast function global graph groups Helbing increase input Intel interactions Internet Governance Forum jam front logistics lots manufacturing methods nodes operational opinion space optimization oscillations output paradigm parameter performance Phys players power law predict probability problem production profit random random graphs recommendation system reinforcement learning represents robustness scale-free networks scenario second price auction self-organization semiconductor server shipment simulation social network strategies structure suppliers tion tool topological traffic Transportation trust users vehicles
Passagens conhecidas
Página 65 - By preferring the support of domestic to that of foreign industry, he intends only his own security; and by directing that industry in such a manner as its produce may be of the greatest value, he intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was no part of his intention.
Página 137 - a market institution with an explicit set of rules determining resource allocation and prices on the basis of bids from the market participants".
Página 35 - Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 (2002) 7821-7826 [15] Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks.
Página 222 - The rest of the chapter is organised as follows: in Section 2, we analyze the role of FDI in export-oriented industrialisation.
Página 118 - Simulation methods for studying nonstationary behavior of computer networks," IEEE Journal on Selected Areas in Communications, Vol. 8, No. 9, pp. 1696-1708, December 1990 [2] JA Garay and IS Copal, "Call preemption in communication networks," Proceedings of INFOCOM '92, pp.
Página 174 - Deciding between the common and private value paradigms in empirical models of auctions.
Página 21 - We conclude with a discussion of the implications of these findings for the growth potential of online vendors.