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Agent-based modeling is a flexible and intuitive approach that is close to both data and theories, which gives it a special position in the majority of scientific communities. Agent models are as much tools of understanding, exploration and adaptation as they are media for interdisciplinary exchange. It is in this kind of framework that this book is situated, beginning with agent-based modeling of spatialized phenomena with a methodological and practical orientation.
Through a governing example, taking inspiration from a real problem in epidemiology, this book proposes, with pedagogy and economy, a guide to good practices of agent modeling. The reader will thus be able to understand and put the modeling into practice and acquire a certain amount of autonomy.
- Featuring the following well-known techniques and tools: Modeling, such as UML, Simulation, such as the NetLogo platform, Exploration methods, Adaptation using participative simulation
Advanced practitioners from industry and academia, and graduate students in the field of multi-agent systems
- 1. Introduction to the Agent Approach
- 1.1 Introduction
- 1.2 Two different MAS shown through examples
- 1.3 Agents and the major trends within spatial modeling
- 1.4 The agent paradigm
- 1.5 Observing a phenomenon through agents
- 1.6 Summary
- 2. Description Formalisms in Agent Models
- 2.1 Introduction
- 2.2 Recurrent example
- 2.3 Formalization of agent models
- 2.4 Description and documentation of agent models
- 2.5 Discussion on documentation
- 3. Introduction to NetLogo
- 3.1 Introduction
- 3.2 Metamodel of NetLogo
- 3.3 The NetLogo software interface
- 3.4 Step-by-step creation of a simple model
- 3.5 Agent–agent and agent–environment interactions
- 3.6 Introduction to NetLogo’s additional functionalities
- 3.7 Conclusion
- 4. Agent-Based Model Exploration
- 4.1 Introduction
- 4.2 Exploring a simulation
- 4.3 Exploring several simulations
- 4.4 Conclusion
- 5. Dynamical Systems with NetLogo
- 5.1 Introduction
- 5.2 Aggregate model versus agent-based model
- 5.3 Aggregate representation of the spread of panic
- 5.4 Agent-based panic propagation model
- 5.5 Dynamic system version of our running example model
- 6. How to Involve Stakeholders in the Modeling Process
- 6.1 Introduction
- 6.2 Diversity of multiagent approaches in modeling
- 6.3 Simulating stakeholder games and learning about others: NetLogo's HubNet system
- 6.4 Exchanging and questioning knowledge: the PAMS collaborative portal
- 6.5 The issues to which multiagent models may provide answers
- List of Authors
- No. of pages:
- © ISTE Press - Elsevier 2015
- 19th August 2015
- 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