Automated modeling and analysis of agent-based simulations using the CASE framework

We present a modular evolutionary framework, coined CASE for "complex adaptive system evolver", to automate the modeling and analysis of agent-based simulations (ABSs). The field of agent-based modeling is rapidly growing due to its capabilities to expose the emerging complex phenomena occurring in a wide range of natural and artificial systems such as biological cells, societies, battlefields, stock markets, etc. Nevertheless, studying agent-based simulations is a complicated, interdisciplinary and time-consuming process. Indeed, a large number of simulation parameters has to be considered to identify and fully understand the conditions leading to the emerging phenomena of interest. To tackle this difficulty, the study of ABSs is thus typically conducted in an iterative manner, where each iteration includes the successive and manual modeling of ABSs and analysis of simulation outcomes. To automate this iterative and time-consuming process, we propose CASE, a platform-independent framework capable of evolving ABSs to exhibit the desired emerging behaviors. Through this evolutionary approach, the examination, i.e., the modeling, execution and analysis, of ABSs is automated. This process automation significantly facilitates the examination of complex systems using ABSs. In this paper, we present in detail this modular evolutionary framework which is illustrated with an example experiment. In this experiment, CASE is utilized for Automated Red Teaming, a simulation-based vulnerability assessment technique commonly employed by defense analysts. The aim of this paper is to introduce this flexible computational framework which may potentially benefit related fields involving agent-based simulations such as the gaming or financial industries.