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普及正通过增加支撑决策的信息量，同时减少对数据和经验证据的直接交互，重塑决策过程。这种范式转变引入了新的不确定性来源，因为数据可观测性的局限性导致歧义性和可解释性缺失。对数据驱动策略进行恰当分析的需求促使人们探索能够描述这种知识获取受限的新型模型。本文提出了一种新颖的理论模型，用于表征知识表征中的不确定性及其通过智能体中介的传递过程。我们通过为模型赋予比较与组合知识状态的结构，提供了知识状态的动态描述。具体而言，更新过程通过组合操作来表征，其可解释性基于在不同维度表征中的一致性。我们从不等价推理、偏好关系及信息测度等多重角度审视了不等价的知识表征。此外，我们建立了与两个经典场景的形式化类比：分别以埃尔斯伯格模型（体现歧义性导致的非经典不确定性）和维格纳的朋友（体现通过其他观测者中介的数据推理知识）为例。最后，我们讨论了所提模型对数据驱动策略的若干启示，重点关注商业价值维度不确定性推理及其评估测量工具的设计。