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This study introduces an improved sensitivity analysis method for Agent-Based Models (ABMs) to enhance decision-making in UK Children's Services. The novel approach aims to overcome limitations in current methods, improving model utility and policy effectiveness.

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Area of Science:

  • Computational Social Science
  • Public Policy Analysis
  • Machine Learning Applications

Background:

  • English Children's Services face economic pressures necessitating data-driven interventions and policy decisions.
  • Agent-Based Modelling (ABM) offers potential but faces challenges in complexity, transparency, and validation.
  • Existing sensitivity analysis methods have limitations when applied to ABMs.

Purpose of the Study:

  • To develop and present an improved method for sensitivity analysis specifically tailored for Agent-Based Models.
  • To enhance the utilization of ABMs within Children's Services for more effective policy and intervention planning.
  • To address the limitations of current sensitivity analysis techniques in the context of complex ABMs.

Main Methods:

  • Integration of machine learning-based regression with the Nomadic Peoples Optimiser (NPO).
  • Development of a novel sensitivity analysis technique optimized for Agent-Based Models.
  • Comparative analysis against existing sensitivity analysis methods.

Main Results:

  • Demonstration of an effective sensitivity analysis approach for ABMs.
  • Validation of the improved method through comparison with established techniques.
  • Presentation of an enhanced ABM design for Children's Services applications.

Conclusions:

  • The proposed sensitivity analysis method enhances the practical application of ABMs in Children's Services.
  • This approach facilitates more robust model analysis, leading to better-informed policy and interventions.
  • The study contributes to overcoming key challenges in ABM implementation and validation.