From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
📰 ArXiv cs.AI
A multi-stage workflow for exploring stochastic Agent-Based Models using model-based screening and data-driven surrogates
Action Steps
- Identify dominant variables using automated model-based screening
- Assess outcome variability and segment the parameter space
- Develop data-driven surrogates using machine learning
- Integrate surrogates into the workflow for efficient exploration
Who Needs to Know This
Data scientists and AI engineers on a team can benefit from this workflow to efficiently explore complex Agent-Based Models, while product managers can apply the insights gained to inform decision-making
Key Insight
💡 Integrating model-based screening with data-driven surrogates can overcome the challenges of exploring complex Agent-Based Models
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🤖 Explore stochastic Agent-Based Models efficiently with a multi-stage workflow! 📈
Key Takeaways
A multi-stage workflow for exploring stochastic Agent-Based Models using model-based screening and data-driven surrogates
Full Article
Title: From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
Abstract:
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
Abstract:
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
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