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.

翻译：人工智能与大数据的扩散正在重塑决策过程，一方面增加了支持决策的信息量，另一方面却减少了与数据和经验证据的直接交互。这种范式转变引入了新的不确定性来源：有限的数据可观测性导致模糊性以及可解释性缺失。对数据驱动策略进行恰当分析的需求，促使我们探索能够描述这种有限知识获取方式的新模型。本文提出了一种关于知识表征不确定性及其通过智能体进行传递的新型理论模型。通过赋予模型比较与组合知识状态的结构，我们提供了知识状态的动力学描述。具体而言，更新通过组合来表示，其可解释性基于在不同维度表征中的一致性。我们从推理多样性、偏好关系和信息测度等角度考察了非等价的知识表征。此外，我们通过两个场景建立了形式类比：一是用模糊性（埃尔斯伯格模型）说明非经典不确定性，二是通过观察数据的其他智能体进行知识推理（维格纳朋友）。最后，我们讨论了所提模型对数据驱动策略的一些启示，特别关注在不确定条件下对商业价值维度的推理，以及评估工具的设计。