Markov Blankets for Sustainability

This paper’s aim is twofold: on the one hand, to provide an overview of the state of the art of some kind of Bayesian networks, i.e. Markov blankets (MB), focusing on their relationship with the cognitive theories of the free energy principle (FEP) and active inference. On the other hand, to sketch how these concepts can be practically applied to artificial intelligence (AI), with special regard to their use in the field of sustainable development. The proposal of this work, indeed, is that understanding exactly to what extent MBs may be framed in the context of FEP and active inference, could be useful to implement tools to support decision-making processes for addressing sustainability. Conversely, looking at these tools considering how they could be related to those theoretical frameworks, may help to shed some light on the debate about FEP, active inference and its linkages with MBs, which still seems to be clarified. For the above purposes, the paper is organized as follows: after a general introduction, Sect. 2 explains what a MB is, and how it is related to the concepts of FEP and active inference. Thus, Sect. 3 focuses on how MBs, joint with FEP and active inference, are employed in the field of AI. On these grounds, Sect. 4 explores whether MBs, FEP, and active inference can be useful to face the issues related to sustainability.