How to Build the Best Macroscopic Description of your Multi-agent System? Application to News Analysis of International Relations
type: Dataset
description: The design and 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 microscopic description complexity. Since it leads to an information loss, such a key process may be extremely harmful if poorly executed. This research report presents measures inherited from information theory (Kullback-Leibler divergence and Shannon entropy) to evaluate ab- stractions and to provide the experts with feedbacks regarding the generated descriptions. Several evaluation techniques are applied to the spatial aggregation of an agent-based model of international rela- tions. The information from on-line newspapers constitutes a complex microscopic description of agent states. Our approach is able to evalu- ate geographical abstractions used by experts and to deliver them with e cient and meaningful macroscopic descriptions of the world state.
identifier: Report N°: RR-LIG-035
issued: 2026-05-06T08:46:25.717371
language: en
modified: 2026-05-06T08:46:25.717375
contact point: Organization
  fn: CCSD
keyword: agent-aggregation
keyword: geographical-and-news-analysis
keyword: infoeu-reposemanticsreport
keyword: infoinfo-macomputer-science-csmultiagent-systems-csma
keyword: information-theory
keyword: large-scale-multi-agent-systems
keyword: macroscopic-description
keyword: reports
landing page: https://inria.hal.science/hal-00947933

How to Build the Best Macroscopic Description of your Multi-agent System? Application to News Analysis of International Relations
type: Distribution
format: HTML
issued: 2026-05-06T08:46:25.728445
modified: 2026-05-06T08:46:25.694403
access URL: https://inria.hal.science/hal-00947933

test_moissonnage_selune
type: Agent