GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations

  • Edouard Amouroux
  • Thanh-Quang Chu
  • Alain Boucher
  • Alexis Drogoul
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5044)


In this paper, we introduce the GAMA (Gis & Agent-based Modelling Architecture) simulation platform, which aims at providing field experts, modellers, and computer scientists with a complete modelling and simulation development environment for building spatially explicit multi-agent simulations.

The most important requirements of spatially explicit multi-agent simulations that our platform fulfils are: (1) the ability to transparently use complex Geographical Information System (GIS) data as an environment for the agents; (2) the ability to handle a vast number of (heterogeneous) agents (3); the ability to offer a platform for automated controlled experiments (by automatically varying parameters, recording statistics, etc.); (4) the possibility to let non-computer scientists design models and interact with the agents during simulations.

While still in its implementation phase, the platform is currently used for two main applications. One is about the modelling of the spread of avian influenza in a province of North Vietnam in collaboration with CIRAD (French Agricultural Research Centre working for International Development). Its goal is to simulate the poultry value chain of a whole province using geolocalised data, and to use this to optimise a monitoring network. A second application conducted with the Institute for Marine Geology and Geophysics (VAST, Hanoi) is about using an interactive simulation for supporting decision-making during urban post-disaster situations. This application relies on geolocalised data as well, and requires facilities of interaction between users and the simulation.


Geographical Information System Multiagent System Decision System Grid Environment Simulation Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Edouard Amouroux
    • 1
    • 2
  • Thanh-Quang Chu
    • 1
    • 2
  • Alain Boucher
    • 1
  • Alexis Drogoul
    • 1
    • 2
  1. 1.AUF-IFI, MSI, ngo 42, Ta Quang BuuHa NoiViet Nam
  2. 2.IRD, GEODESBondy CedexFrance

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