This article proposes a novel and comprehensive framework on how to describe the probabilistic nature of decision-making process. We suggest extending the quantum-like Bayesian network formalism to incorporate the notion of maximum expected utility to model human paradoxical, sub-optimal and irrational decisions. What distinguishes this work is that we take advantage of the quantum interference effects produced in quantum-like Bayesian Networks during the inference process to influence the probabilities used to compute the maximum expected utility of some decision.

The proposed quantum-like decision model is able to (1) predict the probability distributions found in different experiments reported in the literature by modelling uncertainty through quantum interference, (2) to identify decisions that the decision-makers perceive to be optimal within their belief space, but that are actually irrational with respect to expected utility theory, (3) gain an understanding of how the decision-maker’s beliefs evolve within a decision-making scenario. The proposed model has the potential to provide new insights in decision science, as well as having direct implications for decision support systems that deal with human data, such as in the fields of economics, finance, psychology, etc.

本文提出了一个新颖且全面的框架来描述决策过程的概率性质。我们建议扩展类似量子的贝叶斯网络形式主义，将最大预期效用的概念纳入其中，以模拟人类矛盾的、次优的和非理性的决策。这项工作的与众不同之处在于，我们利用类量子贝叶斯网络在推理过程中产生的量子干涉效应来影响用于计算某些决策的最大预期效用的概率。

所提出的类量子决策模型能够（1）通过量子干涉对不确定性进行建模来预测文献中报道的不同实验中发现的概率分布，（2）识别决策者认为在其信念范围内最佳的决策空间，但就预期效用理论而言实际上是不合理的，（3）了解决策者的信念如何在决策场景中演变。所提出的模型有可能为决策科学提供新的见解，并对处理人类数据的决策支持系统产生直接影响，例如经济、金融、心理学等领域。