Theoretical Systems Modeling Framework for Sustainability Using Bayesian and Dynamic Bayesian Networks

Abstract

The growth of new sustainable technologies and processes requires fundamental systems modeling methodologies to assess and predict their performance. This article describes theoretical frameworks for complex network modeling for sustainability. It presents a set of theoretical frameworks that enable probabilistic inference of complex networks. For each of these frameworks, this article gives their definitions, properties, and relevance to network modeling for sustainability. Included are both Bayesian networks and dynamic Bayesian networks. The definition and use of these probabilistic graphical frameworks for systems modeling and for sustainability and analysis of the system are described. Other probabilistic network modeling approaches are also introduced. Sustainable technologies must meet a suite of criteria to be successful, and they operate under uncertain environmental conditions. It is desired for any given system to be resilient under a range of possible scenarios. The methods presented in this article offer ways to capture complex interdependencies in sustainable systems, the interactions of components comprising any given system, and the intricate and uncertain network dynamics for sustainable and resilient systems.