From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
Summary: arXiv:2604.03350v1 Announce Type: cross
Abstract: Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the systematic design of experiments with machine learning surrogates. Using a predator-prey case study, our methodology proceeds in two steps. First, an automated model-based screening identifies dominant variables, assesses outcome variability, and segments the parameter space. Second, we train Machine Learning models to map the remaining nonlinear interaction effects. This approach automates the discovery of unstable regions where system outcomes are highly dependent on nonlinear interactions between many variables. Thus, this work provides modelers with a rigorous, hands-off framework for sensitivity analysis and policy testing, even when dealing with high-dimensional stochastic simulators.
Introduction
The exploration of Agent-Based Models (ABMs) has long been hindered by complexities such as the curse of dimensionality and the stochastic nature of these models. Traditional approaches often struggle to provide comprehensive insights into the behavior of ABMs, especially when dealing with high-dimensional parameter spaces. This article discusses a novel multi-stage workflow that integrates machine learning with systematic design experiments to facilitate effective exploration of these complex models.
Methodology Overview
Our proposed methodology consists of a two-step process that employs both model-based screening and machine learning techniques. This approach not only streamlines the exploration process but also enhances the understanding of variable interactions within ABMs.
- Step 1: Model-Based Screening
This initial phase focuses on identifying the dominant variables that significantly influence model outcomes. By assessing outcome variability, we can effectively segment the parameter space, allowing for a more targeted analysis of the model’s dynamics.
- Step 2: Machine Learning Surrogates
In the subsequent phase, we employ machine learning models to capture the remaining nonlinear interaction effects among the identified parameters. This step is crucial for mapping the complex relationships that may not be apparent through traditional analytical methods.
Case Study: Predator-Prey Model
To illustrate the effectiveness of our methodology, we applied it to a predator-prey case study. This example allowed us to demonstrate how our approach can automate the discovery of unstable regions in the model, where outcomes are highly sensitive to fluctuations in variable interactions.
Results and Implications
The findings from our case study reveal that this multi-stage workflow not only provides a clearer understanding of the model dynamics but also enhances the capacity for sensitivity analysis and policy testing. By automating the identification of critical variables and their interactions, modelers are empowered to explore high-dimensional stochastic simulators with greater efficiency and rigor.
Conclusion
In conclusion, the integration of systematic design of experiments with machine learning surrogates represents a significant advancement in the exploration of stochastic Agent-Based Models. This innovative workflow offers researchers and practitioners a powerful tool for navigating the complexities of ABMs, ultimately contributing to more informed decision-making in various fields, including ecology, economics, and social sciences.