EPJ Web of Conferences

Demonstration of Cognitive Bias on a Noisy Intermediate-Scale Quantum Processor

Fabio Antonio Bovino, Alessandro Comi

Published Jan 1, 2022• 0 citations

Abstract

The decision-making process of the individual is analogous to the collapse of a quantum system at the moment of measurement, whereby quantum system we mean the decision-maker, his mental processes and the environmental context that influences him in his choices. Classical probability depends on the lack of information, quantum probability is instead intrinsic to the system to be studied, meaning that the system, consisting of the decision-maker and the context, is in a superposition of states, each with its own amplitude of probability. Only the measurement process (the decision) selects in the decision maker a specific state among those in which he was overlapping. We demonstrated on a Noisy Intermediate –Scale Quantum Processor the model of the “order effect”, one of the most important cognitive bias.

DOI: 10.1051/epjconf/202226609002

Related papers

Quantum Cognition Models for Decision-Making Under Uncertainty

Ada Öztürkmen

Classical models of decision-making under uncertainty, grounded in expected utility theory and Bayesian probability, often fail to account for systematic cognitive anomalies such as preference reversals, order effects, ambiguity aversion, and context-dependent choices. Quantum cognition models offer a novel theoretical framework that applies the mathematical principles of quantum probability—such as superposition, interference, and non-commutativity—to cognitive processes without assuming physical quantum mechanisms in the brain. By representing cognitive states as probabilistic superpositions that collapse through decision “measurements,” these models provide parsimonious explanations for violations of classical rationality. This paper examines how quantum cognition frameworks capture contextual influence, belief updating, and interference effects in uncertain decision environments. Drawing on findings from cognitive psychology and behavioral economics, the study highlights the relevance of quantum cognition for applications in economic choice modeling, legal decision analysis, and artificial intelligence. The analysis suggests that quantum probability offers a robust alternative to classical approaches when modeling human decision-making in complex, ambiguous, and context-sensitive situations.

Human computer interaction.Dec 25, 20251 citation

A quantum-like cognitive approach to modeling human biased selection behavior

Aghdas Meghdadi, Mohammad-R. Akbarzadeh-T, Kurosh Javidan

Cognitive biases of the human mind significantly influence the human decision-making process. However, they are often neglected in modeling selection behaviors and hence deemed irrational. Here, we introduce a cognitive quantum-like approach for modeling human biases by simulating society as a quantum system and using a Quantum-like Bayesian network (QBN) structure. More specifically, we take inspiration from the electric field to improve our recent entangled QBN approach to model the initial bias due to unequal probabilities in parent nodes. Entangled QBN structure is particularly suitable for modeling bias behavior due to changing the state of systems with each observation and considering every decision-maker an integral part of society rather than an isolated agent. Hence, biases caused by emotions between agents or past personal experiences are also modeled by the social entanglement concept motivated by entanglement in quantum physics. In this regard, we propose a bias potential function and a new quantum-like entanglement witness in Hilbert space to introduce a biased variant of the entangled QBN (BEQBN) model based on quantum probability. The predictive BEQBN is evaluated on two well-known empirical tasks. Results indicate the superiority of the BEQBN by achieving the first rank compared to classical BN and six QBN approaches and presenting more realistic predictions of human behaviors.

Scientific ReportsDec 29, 20226 citations

Quantum Explanations for Interference Effects in Engineering Decision Making

Vijitashwa Pandey, Irina Basieva

Engineering practice routinely involves decision making under uncertainty. Much of this decision making entails reconciling multiple pieces of information to form a suitable model of uncertainty. As more information is collected, one expectedly makes better and better decisions. However, conditional probability assessments made by human decision makers, as new information arrives does not always follow expected trends and instead exhibits inconsistencies. Understanding them is necessary for a better modeling of the cognitive processes taking place in their mind, whether it be the designer or the end-user. Doing so can result in better products and product features. Quantum probability has been used in the literature to explain many commonly observed deviations from the classical probability such as: question order effect, response replicability effect, Machina and Ellsberg paradoxes and the effect of positive and negative interference between events. In this work, we present results from a survey demonstrating responses that while difficult to explain using classical probability, can be explained using a quantum formulation - highlighting its potential in engineering applications. Since quantum formulism is more general and can also match the predictions of classical probability, it serves as a richer paradigm for modeling decision making behavior in engineering practice.

SAE International Journal of Advances and Current Practices in MobilityMar 29, 20220 citations

A quantum-like model of decision making

Emmanuel E. Haven, Andrei Yu. Khrennikov

Introduction In the previous chapter, we presented experimental data from cognitive psychology which can be considered as a strong argument in favor of the complex (or more general) Hilbert space representation of information by cognitive systems and the use of the quantum information scheme in information processing. We have seen that the classical Law of Total Probability (LTP) is violated. The interference of probabilities induces a possibility to “reconstruct a mental wave function.” An important lesson we can learn from this is that the standard Dirac-von Neumann approach based on self-adjoint operators is too restrictive to describe known data from cognitive psychology. More general quantum models should be used. Please see also Appendix 2, this chapter. In this chapter, we proceed under the assumption (which we consider as sufficiently confirmed experimentally) that in order to make decisions, cognitive systems operate with “mental wave functions.” We describe the scheme of such decision making based on the lifting formalism, see Appendix 1, this chapter. Since this book is oriented towards non-experts in quantum information theory, we try to avoid the use of the lifting concept as long as is tolerable. In principle, the reader can proceed without using the lifting concept. However, the formulation in terms of lifting is mathematically elegant and it provides a possibility for further generalizations of the model.

Cambridge University Press eBooksJan 17, 20130 citations

Quantum Decoherence and Cognitive Biases: A Modeling Framework

Jincheng Zhang

This paper proposes a novel framework for modeling cognitive biases, specifically confirmation bias and anchoring bias, utilizing the principles of quantum decoherence. The core argument posits that the inherent instability of quantum systems, as manifested through decoherence, can effectively mimic the selective information processing and biased belief updates observed in human cognition. We develop a conceptual model where cognitive processes – perception, attention, memory, and belief formation – are represented as quantum systems. The dynamics of these systems are governed by decoherence, leading to a preferential preservation of information consistent with initial states (confirmation bias) and a susceptibility to initial influences (anchoring bias). The model highlights the potential for quantum mechanics to offer a fundamentally new perspective on the origins of cognitive biases, moving beyond purely classical psychological explanations. The framework provides a tangible, albeit conceptual, mechanism for understanding how biased information processing can arise from the fundamental nature of quantum systems. This work lays the groundwork for future research exploring the quantitative aspects of this model and its potential applications in diverse cognitive domains.

Aug 31, 20260 citations

Quantum stochastic walks on networks for decision-making

Ismael Martínez-Martínez, Eduardo Sánchez-Burillo

Recent experiments report violations of the classical law of total probability and incompatibility of certain mental representations when humans process and react to information. Evidence shows promise of a more general quantum theory providing a better explanation of the dynamics and structure of real decision-making processes than classical probability theory. Inspired by this, we show how the behavioral choice-probabilities can arise as the unique stationary distribution of quantum stochastic walkers on the classical network defined from Luce's response probabilities. This work is relevant because (i) we provide a very general framework integrating the positive characteristics of both quantum and classical approaches previously in confrontation, and (ii) we define a cognitive network which can be used to bring other connectivist approaches to decision-making into the quantum stochastic realm. We model the decision-maker as an open system in contact with her surrounding environment, and the time-length of the decision-making process reveals to be also a measure of the process' degree of interplay between the unitary and irreversible dynamics. Implementing quantum coherence on classical networks may be a door to better integrate human-like reasoning biases in stochastic models for decision-making.

Scientific ReportsMar 31, 201660 citations