Quantum-like influence diagrams for decision-making
Authors: Moreira, C., Tiwari, P., Pandey, H.M., Bruza, P. and Wichert, A.
Journal: Neural Networks
Volume: 132
Pages: 190-210
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.07.009
Abstract: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.
Source: Scopus
Quantum-like influence diagrams for decision-making.
Authors: Moreira, C., Tiwari, P., Pandey, H.M., Bruza, P. and Wichert, A.
Journal: Neural Netw
Volume: 132
Pages: 190-210
eISSN: 1879-2782
DOI: 10.1016/j.neunet.2020.07.009
Abstract: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.
Source: PubMed
Quantum-like influence diagrams for decision-making
Authors: Moreira, C., Tiwari, P., Pandey, H.M., Bruza, P. and Wichert, A.
Journal: NEURAL NETWORKS
Volume: 132
Pages: 190-210
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.07.009
Source: Web of Science (Lite)
Quantum-like influence diagrams for decision-making.
Authors: Moreira, C., Tiwari, P., Pandey, H.M., Bruza, P. and Wichert, A.
Journal: Neural networks : the official journal of the International Neural Network Society
Volume: 132
Pages: 190-210
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.07.009
Abstract: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.
Source: Europe PubMed Central