A Petri Net Formalism to Study Systems at Different Scales Exploiting Agent-Based and Stochastic Simulations

The recent technological advances in computer science have enabled the definition of new modeling paradigms that differ from the classical ones in describing the system in terms of its components or entities. Among them, Agent-Based Models (ABMs) are gaining more and more popularity thanks to their ability to capture emergent phenomena resulting from the interactions of individual entities. However, ABMs lack a formal definition and precisely defined semantics. To overcome this issue we propose a new method exploiting Petri Nets as a graphical meta-formalism for modeling a system from which an ABM model with clear and well-defined semantics can be automatically derived and simulated. We aim to define a framework, based on a PN formalism, in which a system can be efficiently studied through both Agent-Based Simulation and classical Stochastic one depending on the study goal.