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Building Optimal Macroscopic Representations of Complex Multi-agent Systems: Application to the Spatial and Temporal Analysis of International Relations through News Aggregation

Robin Lamarche-Perrin 1, 2 Yves Demazeau 2 Jean-Marc Vincent 1
1 MESCAL - Middleware efficiently scalable
LIG - Laboratoire d'Informatique de Grenoble, Inria Grenoble - Rhône-Alpes
2 MAGMA
LIG - Laboratoire d'Informatique de Grenoble
Abstract : The design and the debugging of large-scale MAS require abstraction tools in order to work at a macroscopic level of description. Agent aggregation provides such abstractions by reducing the complexity of the system’s microscopic representation. Since it leads to an information loss, such a key process may be extremely harmful for the analysis if poorly executed. This paper presents measures inherited from information theory to evaluate abstractions and to provide the experts with feedback regarding the quality of generated representations. Several evaluation techniques are applied to the spatial and temporal aggregation of an agent-based model of international relations. The information from on-line newspapers constitutes a complex microscopic representation of the agent states. Our approach is able to evaluate geographical abstractions used by the domain experts in order to provide efficient and meaningful macroscopic representations of the world global state.
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https://hal.archives-ouvertes.fr/hal-01263846
Contributor : Robin Lamarche-Perrin <>
Submitted on : Thursday, January 28, 2016 - 12:19:54 PM
Last modification on : Tuesday, February 9, 2021 - 3:18:04 PM

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  • HAL Id : hal-01263846, version 1

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Robin Lamarche-Perrin, Yves Demazeau, Jean-Marc Vincent. Building Optimal Macroscopic Representations of Complex Multi-agent Systems: Application to the Spatial and Temporal Analysis of International Relations through News Aggregation. Ngoc Thanh Nguyen; Ryszard Kowalczyk; Juan Manuel Corchado; Javier Bajo. Transactions on Computational Collective Intelligence XV, 8670, Springer, pp.1-27, 2014, Transactions on Computational Collective Intelligence, 978-3-662-44749-9. ⟨hal-01263846⟩

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