I'm not sure if this is the right place to ask this question, but I hope someone can point me in the right direction.
First, I'm a PhD student, studying in the engineering (signal processing, estimation, etc) field, so my mathematical background is limited to the field.
I have encountered a (fairly new in the field) concept called "possibilistic filters", claiming this framework enables modeling of uncertainties (imprecision, partial knowledge, etc) other than randomness. Even though I can see some correspondence between the possibilistic and the Bayesian filtering frameworks, I'm having hard time understanding some of the concepts. One of them is, as the title indicates, the non-additivity property of this possibilistic filters.
I'm trying to see an intuition (if any) how the non-additivity property leads to handling these situations. But the papers usually don't present such a perspective, which is understandable.
My question is that is there any resources, books, tutorial-like papers that discuss the topic in an engineering-friendly way?