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Whereas Volume 1 introduced the NetLogo platform as a means of prototyping simple models, this second volume focuses on the advanced use of NetLogo to connect both data and theories, making it ideal for the majority of scientific communities.
The authors focus on agent-based modeling of spatialized phenomena with a methodological and practical orientation, demonstrating how advanced agent-based spatial simulation methods and technics can be implemented.
This book provides theoretical and conceptual backgrounds, as well as algorithmic and technical insights, including code and applets, so that readers can test and re-use most of its content.
- Illustrates advanced concepts and methods in agent-based spatial simulation
- Features practical examples developed, and commented on, in a unique platform
- Provides theoretical and conceptual backgrounds, as well as algorithmic and technical insights, including code and applets, so that readers can test and re-use most of its content
Advanced practitioners from industry and academia, and graduate students in the field of multi-agent systems, focusing or not on its spatial component
- 1: NetLogo, an Open Simulation Environment
- 1.1 Introduction to extensions in NetLogo
- 1.2 Designing and developing extensions
- 1.3 Using NetLogo from other platforms
- 1.4 Deploying NetLogo models online
- 1.5 Conclusion
- 2: Multiscale Modeling: Application to Traffic Flow
- 2.1 Introduction
- 2.2 Two agent-based models: NaSch and Underwood
- 2.3 An equation-based LWR model
- 2.4 Hybrid traffic model
- 2.5 Conclusion and outlook
- 3: Macro Models, Micro Models and Network-based Coupling
- 3.1 Introduction
- 3.2 Description of the equation-based SIR model
- 3.3 Equation-based and agent-based propagation model: EpiSim
- 3.4 Coupling SIR models based on networks
- 3.5 SIR coupling without scaling: Metapop model
- 3.6 SIR coupling with scaling: MicMac model
- 3.7 Conclusion and outlook
- 4: Networking, Networks and Dynamic Graphs
- 4.1 Networking
- 4.2 Networks and graphs in NetLogo
- 4.2.2 Search for the largest connected component
- 4.2.3 Searching for the shortest path
- 4.2.4 Modularity metric
- 4.2.5 Communities
- 5: Swarm Problem-Solving
- 5.1 Introduction
- 5.2 Collective approaches
- 5.3 Collective sorting
- 5.4 From food sourcing to finding the shortest path
- 5.5 The intentions of a swarm
- 5.6 Conclusion
- 6: Exploring Complex Models in NetLogo
- 6.1 Introduction
- 6.2 Complex models and simulators
- 6.3 Using NetLogo with OpenMOLE
- 6.4 Analysis and interpretation of results
- 6.5 Conclusion
- List of Authors
- No. of pages:
- © ISTE Press - Elsevier 2017
- 22nd November 2016
- ISTE Press - Elsevier
- Hardcover ISBN:
- eBook ISBN:
Arnaud Banos is CNRS research director and head of the UMR Géographie-cités (CNRS –Panthéon-Sorbonne University – Paris Diderot University, France). As a geographer, he favors an interdisciplinary approach to complex spatial systems.
CNRS Research Director and Head, UMR Géographie-cités, CNRS –Panthéon-Sorbonne University – Paris Diderot University, France
Christophe Lang is Associate Professor of Computer Science at the University of Franche-Comté in France, within the FEMTO-ST, UMR 6174 laboratory. He conducts research in the field of distributed systems and more specifically in the field of multi-agent systems.
Associate Professor of Computer Science, University of Franche-Comté, France
Nicolas Marilleau is a research engineer at UMI 209 UMMSICO of the Institut de Recherche pour le Développement in France. He conducts research in the field of modeling-simulation agents for complex systems, which he applies to real problems together with researchers in other disciplines.
Research Engineer, UMI 209 UMMSICO, Institut de Recherche pour le Développement, France