From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
arXiv:2604.03350v1 Announce Type: new 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.
arXiv:2604.03350v1 Announce Type: new 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.
Executive Summary
This article presents a multi-stage workflow for exploring stochastic Agent-Based Models (ABMs) by integrating systematic design of experiments with machine learning surrogates. The proposed methodology addresses the challenges of ABM exploration, including the curse of dimensionality and inherent stochasticity. The approach consists of two stages: automated model-based screening to identify dominant variables and segment the parameter space, and training machine learning models to map nonlinear interaction effects. This workflow enables modelers to perform sensitivity analysis and policy testing in high-dimensional stochastic simulators without manual intervention. The methodology is demonstrated using a predator-prey case study, showcasing its effectiveness in identifying unstable regions and nonlinear interactions. This work provides a rigorous, hands-off framework for exploring complex systems and has significant implications for fields such as ecology, economics, and social sciences.
Key Points
- ▸ The article proposes a multi-stage workflow for exploring stochastic ABMs.
- ▸ The methodology integrates systematic design of experiments with machine learning surrogates.
- ▸ The approach addresses the challenges of ABM exploration, including the curse of dimensionality and stochasticity.
Merits
Strength in Addressing Complexity
The proposed methodology effectively addresses the challenges of ABM exploration, making it a significant contribution to the field.
Flexibility and Scalability
The approach is flexible and scalable, enabling its application to a wide range of complex systems and high-dimensional stochastic simulators.
Demerits
Limited Case Studies
The article primarily relies on a single case study (predator-prey) to demonstrate the methodology, which may limit its generalizability to other domains.
Interpretability of Machine Learning Models
The article acknowledges the potential limitations of machine learning models in terms of interpretability, which may be a concern for some applications.
Expert Commentary
This article presents a timely and innovative contribution to the field of complex systems modeling, particularly in the context of stochastic ABMs. The proposed multi-stage workflow effectively addresses the challenges of ABM exploration, making it a valuable tool for researchers and practitioners. However, the article could benefit from more extensive case studies and a discussion on the interpretability of machine learning models. Nevertheless, the implications of this work are significant, and it has the potential to inform policy decisions and explore complex systems in various domains.
Recommendations
- ✓ Future research should focus on applying the methodology to diverse case studies and exploring the limitations of machine learning models in terms of interpretability.
- ✓ The article's findings and methodology should be disseminated to a broader audience, including policymakers and practitioners, to facilitate its adoption and application in various domains.
Sources
Original: arXiv - cs.LG