MC2MABS: A monte carlo model checker for multiagent-based simulations

Abstract

Agent-based simulation has shown great success for the study of complex adaptive systems and could in many areas show advantages over traditional analytical methods. Due to their internal complexity, however, agent-based simulations are notoriously difficult to verify and validate. This paper presents MC2MABS, a Monte Carlo Model Checker for Multiagent-Based Simulations. It incorporates the idea of statistical runtime verification, a combination of statistical model checking and runtime verification, and is tailored to the approximate verification of complex agent-based simulations. We provide a description of the underlying theory together with design decisions, an architectural overview, and implementation details. The performance of MC2MABS in terms of both runtime consumption and memory allocation is evaluated against a set of example properties.