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Why use an artificial intelligence (AI) system to determine crucial, major changes in a post-COVID-19 world? Globalisation is both a system and a process characterised by complexity, that is, a referential in which heterogeneous agents are constantly interacting. It therefore requires an integral and dynamic approach, and even more so a tool in tune with complexity. That is the case of the AI system Mileva, specifically designed for tackling complexity, highlighting the fabric of its reality, its core issues, and to forecast the probabilities of the different possible evolutions. In this chapter, the authors first briefly describe globalisation with regard to complexity, at the crossroads of computational complexity theory and sociological complexity theory (Edgar Morin). The authors then present the AI system Mileva, its key principles and the main lines of its architecture. Finally, the aforementioned points will be illustrated by two examples of analyses provided by Mileva on the issue of major changes in a post-COVID-19 world: the situation of the international organisations and that of the world of work in relation to health, environment, development, and democracy.

Economic models based on simple rules can result in complex and unpredictable deterministic dynamics with emergent features similar to those of actual economies. I present several such models ranging from cellular automaton and register machines to quantum computation. The additional benefit of such models is displayed by extending them to model political entanglement to determine the impact of allowing majority redistributive voting. In general, the insights obtained from simulating the computations of simple rules can serve as an additional way to study economics, complementing equilibrium, literary, experimental, and empirical approaches. I culminate by presenting a minimal model of economic complexity that generates complex economic growth and diminishing poverty without any parameter fitting, and which, when modified to incorporate political entanglement, generates volatile stagnation and greater poverty.

Chapter 7 synthesizes the perception research into plausible design and configuration strategies for the learning experience dimension of a psychophysical learning system. The processes used in all five senses to reduce information into a perception are again used to create learning activities and processes, which facilitate the learning and discriminate meaning from the learning objects and activities. This process attends to the interactions across the categories of content to determine the critical components of the discipline to include in the learning experience. Once again, the focus of the psychophysical learning experience is placed on the structure of the (external) discipline, which is used to configure the learning experiences.

Chapter 5 changes focus from the external stimulus to the internal sensemaking by integrating and comparing new learning with the prior learning of the individual, which is the process of sensory cognition. These processes are identified and compiled into a model of the processes the brain uses to construct a cognition from the information, including both individual and collective learning. This results in the internalization of a new individual cognition constructed from the integration of the new information with prior information.

Chapter 4 presents the research into the attributes of a stimulus, which the brain uses to construct a perception of the external stimulus. The processes used in all five senses are examined and compiled into a collective model of the processes the perceptual system uses to discriminate and understand an external stimulus. While there are many commonalities across the senses, the structure of the discipline (energy) each system processes yields unique insights into the processes of the total system.