Dealing With Vagueness In Agent-Based Models: A Python Fuzzy Logic Abm Framework

Complex systems are everywhere; countless examples of behaviors fall into the complex systems paradigm, from physical and natural sciences to social and economic sciences. Given the nature of these systems, where the whole is greater than the sum of its constituents, scientists must have adequate tools for investigating complex systems. Recently, Agent-Based Models (ABM) have become a de facto tool for creating idealized computer simulations to investigate pattern formation, perform root-cause analysis, or simulate alternative scenarios within the domain of complex systems. This paper introduces a miniature framework for developing and analyzing agent-based models where agents and the environment can follow vague rules. The proposed tool is applied to a sample simulation, providing a proof-of-concept example of how Fuzzy Logic and Fuzzy Inference can model complex systems with vague rules.