Ver Post

Prova de Doutoramento

5 Dezembro 2017, 15:54 - Fátima Sampaio


Candidate: Catarina Alexandra Pinto Moreira Nº 57783/D 

Title: Quantum Probabilistic Graphical Models for Cognition and Decision

Date: 13/12/2017

Time: 14:00

Location: Anfiteatro PA-3 (Piso -1, Pavilhão de Matemática) IST, Alameda

Advisor: Professor Andreas Miroslaus Wichert

Abstract: Cognitive scientists are mainly focused in developing models and cognitive structures that are able to represent processes of the human mind. One of these processes is concerned with human decision making. In the last decades, literature has been reporting several situations of human decisions that could not be easily modelled by classical models, because humans constantly violate the laws of probability theory in situations with high levels of uncertainty. In this sense, quantum-like models started to emerge as an alternative framework, which is based on the mathematical principles of quantum mechanics, in order to model and explain paradoxical findings that cognitive scientists were unable to explain using the laws of classical probability theory. Although quantum-like models succeeded to explain many paradoxical decision making scenarios, they still suffer from three main problems. First, they cannot scale to more complex decision scenarios, because the number of quantum parameters grows exponentially large. Second, they cannot be considered predictive, since they require that we know a priori the outcome of a decision problem in order to manually set quantum parameters. And third, the way one can set these quantum parameters is still an unexplored field and still an open research question in the Quantum Cognition literature. This work focuses on quantum-like probabilistic graphical models by surveying the most important aspects of classical probability theory, quantum-like models applied to human decision making and probabilistic graphical models. We also propose a Quantum-Like Bayesian Network that can easily scale up to more complex decision making scenarios due to its network structure. In order to address the problem of exponential quantum parameters, we also propose heuristic functions that can set an exponential number of quantum parameters without a priori knowledge of experimental outcomes. This makes the proposed model general and predictive in contrast with the current state of the art models, which cannot be generalised for more complex decision making scenarios and that can only provide an explanatory nature for the observed paradoxes.