Mod\`eles de Substitution pour les Mod\`eles \`a base d'Agents : Enjeux, M\'ethodes et Applications
Paul Saves, Nicolas Verstaevel, Beno\^it Gaudou

TL;DR
This paper discusses the use of surrogate models to reduce computational costs in agent-based modeling, highlighting methods, challenges, and applications across various fields.
Contribution
It provides a comprehensive overview of surrogate modeling techniques for ABM, including case studies and future perspectives on improving scalability and interpretability.
Findings
Surrogate models significantly reduce computational costs.
Different machine learning techniques are effective for surrogate modeling.
Challenges include model validation and balancing accuracy with efficiency.
Abstract
Multi-agent simulations enables the modeling and analyses of the dynamic behaviors and interactions of autonomous entities evolving in complex environments. Agent-based models (ABM) are widely used to study emergent phenomena arising from local interactions. However, their high computational cost poses a significant challenge, particularly for large-scale simulations requiring extensive parameter exploration, optimization, or uncertainty quantification. The increasing complexity of ABM limits their feasibility for real-time decision-making and large-scale scenario analysis. To address these limitations, surrogate models offer an efficient alternative by learning approximations from sparse simulation data. These models provide cheap-to-evaluate predictions, significantly reducing computational costs while maintaining accuracy. Various machine learning techniques, including regression…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Simulation Techniques and Applications · Probabilistic and Robust Engineering Design
