Navarro L. auteur aut IRD Corruble V. auteur aut IRD Flacher F. auteur aut IRD Zucker Jean-Daniel auteur aut IRD text bookSection eng text/pdf reformatted digital access Large-scale simulations often use multiple agent representations to permit the study of specific multi-agent phenomena, and to find a balance between run-time performance and level of detail of the simulation. Although these approaches are effective, they do not always offer the desired level of analysis, especially when this level is between the resolutions of the models available. In this paper, we aim at offering a finer method in exploring this tradeoff by introducing an intermediate level between two given resolutions, which can apply to all agent models and allows a more progressive transition to offer the desired level of analysis. We introduce a framework for such a methodology and evaluate it through the extension of an existing approach, along two criteria: its impact on computational resources, and an estimate of the dissimilarity between a simulation using our methodology and one without. Initial experiments show that consistency is almost maintained while CPU gain varies from low to significant depending on the context. specialized 122 Ito T. ed. edt IRD Jonker C. ed. edt IRD Gini M. ed. edt IRD Shehory O. ed. edt IRD 159-166 New York ACM 2013 AAMAS'13.International Conference on Autonomous Agents and Multiagent Systems, 12., Saint-Paul, Minnesota (USA), 2013/05/06-10 https://www.documentation.ird.fr/hor/fdi:010079286 978-1-4503-1993-5 [F B010079286] https://www.documentation.ird.fr/hor/fdi:010079286 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-08/010079286.pdf IRD - Base Horizon / Pleins textes 2020-09-08 2023-11-16 fdi:010079286 fre