  The diffusion of AI and big data is reshaping decision-making processes by
increasing the amount of information that supports decisions while reducing
direct interaction with data and empirical evidence. This paradigm shift
introduces new sources of uncertainty, as limited data observability results in
ambiguity and a lack of interpretability. The need for the proper analysis of
data-driven strategies motivates the search for new models that can describe
this type of bounded access to knowledge. This contribution presents a novel
theoretical model for uncertainty in knowledge representation and its transfer
mediated by agents. We provide a dynamical description of knowledge states by
endowing our model with a structure to compare and combine them. Specifically,
an update is represented through combinations, and its explainability is based
on its consistency in different dimensional representations. We look at
inequivalent knowledge representations in terms of multiplicity of inferences,
preference relations, and information measures. Furthermore, we define a formal
analogy with two scenarios that illustrate non-classical uncertainty in terms
of ambiguity (Ellsberg's model) and reasoning about knowledge mediated by other
agents observing data (Wigner's friend). Finally, we discuss some implications
of the proposed model for data-driven strategies, with special attention to
reasoning under uncertainty about business value dimensions and the design of
measurement tools for their assessment.
