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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... parameters (which are often slowly changing variables of the system). When system parameters come close to critical points, small fluctuations may become a dominating influence and determine the future fate of the system. Therefore, one ...
... parameters. These reflect, for example, the degree of risk aversion. 2.2 Guided Self-Organization is Better than Control The previous section questions the classical control approach, which is, for example, used to control machines. But ...
... Parameters: 33.2% more Throughput ] h 220 / 1 [ 210 t u p h 200 190 g u o r h 180 170 T 160 150 140 130 1 1 0 . 1 . 1 0 . 1 . 6 1 2 1 0 . 1 . 1 1 0 . 2 . 1 2 1 1 0 . 2 . 1 2 1 6 1 0 . 3 . 4 0 . 3 . 5 1 1 Time [Days] 0 . 3 . 3 1 0 . 4 ...
... parameters. 3. User. Activity. and. User. Networks. The activity of the users is measured via the probability mass distributions of the number of articles sold p(s), bought (auctions won) p(w), and bid on p(a). Though it is possible to bid ...
... parameters of the empirical data and the RNM. Comparing the cumulative distribution of the link weights, i.e. the number of times two bidders have met in different auctions, we find a much more prominent difference between the data and ...
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 |
Julian Sienkiewicz Agata Fronczak Piotr Fronczak Krzysztof | 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 |