Leveraging Sparse Data Representation Learning for Enhanced Performance in Large-Scale Agent-Based Simulations

Agent-based simulations (ABS) are increasingly utilized to model complex systems across various domains. However, the computational cost of simulating large populations with intricate individual behaviors remains a significant challenge. This paper explores the application of sparse data representation learning techniques, inspired by recent advancements in behavioral data analysis, to optimize memory usage and computational efficiency in large-scale ABS. We propose a novel approach that leverages learned representations of agent behavior to reduce the dimensionality of state space, leading to significant performance gains without sacrificing simulation fidelity. Our results demonstrate the potential of this approach for scaling ABS to unprecedented levels.