QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision

05/30/2020
by   Catarina Moreira, et al.
15

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.

READ FULL TEXT

page 4

page 6

research
05/11/2019

Towards a Quantum-Like Cognitive Architecture for Decision-Making

We propose an alternative and unifying framework for decision-making tha...
research
05/10/2021

Quantum Uncertainty in Decision Theory

An approach is presented treating decision theory as a probabilistic the...
research
04/04/2019

Explaining versus Describing Human Decisions. Hilbert Space Structures in Decision Theory

Despite the impressive success of quantum structures to model long-stand...
research
04/09/2020

Predicting human-generated bitstreams using classical and quantum models

A school of thought contends that human decision making exhibits quantum...
research
01/14/2021

A Subjective Model of Human Decision Making Based on Quantum Decision Theory

Computer modeling of human decision making is of large importance for, e...
research
07/15/2013

Decision Making for Inconsistent Expert Judgments Using Negative Probabilities

In this paper we provide a simple random-variable example of inconsisten...
research
05/12/2021

Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty

It is a long-standing objective to ease the computation burden incurred ...

Please sign up or login with your details

Forgot password? Click here to reset