Agent-based Spatial Simulation with NetLogo, Volume 2 ：Advanced Concepts

Publication subTitle ：Advanced Concepts

Author： Banos Arnaud;Lang Christophe;Marilleau Nicolas

Publisher： Elsevier Science‎

Publication year： 2016

E-ISBN: 9780081010648

P-ISBN(Paperback): 9781785481574

Subject： TP301.6 algorithm theory;TP31 computer software;TP312 程序语言、算法语言

Keyword：计算机软件,算法理论,自动化技术、计算机技术,数据处理、数据处理系统

Language： ENG

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Description

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

Chapter

Front Cover

Agent-based Spatial Simulation with NetLogo 2: Advanced Concepts

Contents

Introduction

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

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

Conclusion

Bibliography

List of Authors

Index

Back Cover

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