Mod\`eles de Substitution pour les Mod\`eles \`a base d'Agents : Enjeux, M\'ethodes et Applications

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 models, neural networks, random forests and Gaussian processes, have been applied to construct robust surrogates. Moreover, uncertainty quantification and sensitivity analysis play a crucial role in enhancing model reliability and interpretability. This article explores the motivations, methods, and applications of surrogate modeling for ABM, emphasizing the trade-offs between accuracy, computational efficiency, and interpretability. Through a case study on a segregation model, we highlight the challenges associated with building and validating surrogate models, comparing different approaches and evaluating their performance. Finally, we discuss future perspectives on integrating surrogate models within ABM to improve scalability, explainability, and real-time decision support across various fields such as ecology, urban planning and economics.