Teacher: TBA

Content: Unlike classical probability theory, which deals with crisp probabilities, imprecise probability acknowledges the limitations of perfect knowledge. It provides a robust and versatile approach to situations where information is scarce, incomplete, or unreliable. We will begin by examining the motivations behind imprecise previsions and probabilities and contrasting them with classical probability theory. We will explore the necessary mathematical tools to represent imprecise probabilities and we will explore how this framework can be used in artificial intelligence and decision theory.

Over the course we will delve into the following topics:

• Coherent upper and lower conditional previsions;
• Models of coherent upper conditional previsions based on fractal measures;
• Applications of the models to explain how human beings make decision and to represent unconscious activity of human brain in artificial intelligence.