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
Atiyah Elsheikh
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.