QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision
This work addresses the problem of explaining irrational decision-making in cognitive science, offering a novel explanatory analysis that goes beyond existing quantum approaches, though it appears incremental in building on prior quantum models.
The paper tackled the challenge of modeling paradoxical and irrational human decision-making by introducing QuLBIT, a unified cognitive framework derived from quantum theory, which uses quantum interference effects to quantify and explain decision-maker uncertainty, demonstrating its application in situations that violate the Sure Thing Principle.
This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.