Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences

TL;DR

This paper reviews promising surrogate modeling techniques to reduce computational costs in large-scale agent-based models within social and health sciences, highlighting potential future research directions.

Contribution

It identifies and discusses surrogate methods suitable for ABMs in social health sciences, an area where such approaches are not yet extensively applied.

Findings

Surrogate models can significantly reduce ABM simulation times.

Potential for applying surrogate methods to improve ABM efficiency in SHCS.

Future research directions for surrogate modeling in ABMs are proposed.

Abstract

The execution and runtime performance of model-based analysis tools for realistic large-scale ABMs (Agent-Based Models) can be excessively long. This due to the computational demand exponentially proportional to the model size (e.g. Population size) and the number of model parameters. Even the runtime of a single simulation of a realistic ABM may demand huge computational resources when attempting to employ realistic population size. The main aim of this ad-hoc brief report is to highlight some of surrogate models that were adequate and computationally less demanding for nonlinear dynamical models in various modeling application areas.To the author knowledge, these methods have been not, at least extensively, employed for ABMs within the field of (SHCS) Social Health Computational Sciences, yet. Thus, they might be, but not necessarily, useful in progressing state of the art for establishing surrogate models for ABMs in the field of SHCS.