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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... lead to a completely different dynamics. This is often called the “butterfly effect” and makes the behavior of chaotic systems unpredictable (beyond a certain time horizon), see Fig. 3. 1.2 Self-Organization, Competition, and ...
... leads to spatio-temporal pattern formation. The example of social insects like ants, bees, or termites shows that simple interactions can lead to complex structures and impressive functions. This is often called “swarm intelligence” [8] ...
... lead to the latter situation) or not (then, many efforts to change the system will be in vain). In fact, complex systems often counteract the action. In chemical systems, this is known as Le Chatelier's principle.4 Therefore, if it is ...
... leads to a “capacity drop”. Such a capacity drop occurs often unexpectedly and is a sign of inefficiencies due to dynamical friction or obstruction effects. It results from increasing coordination problems when sufficient space or time ...
... lead to a poor adaptation to changing environmental or market conditions. In contrast, a large variety of different approaches (i.e. a heterogeneous population) will imply a large innovation rate [41]. The innovation rate is actually ...
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 |
Outras edições - Ver tudo
Managing Complexity: Insights, Concepts, Applications Dirk Helbing Pré-visualização indisponível - 2007 |
Managing Complexity: Insights, Concepts, Applications Dirk Helbing Pré-visualização indisponível - 2010 |