Cognitive bias is an objective existence in cognitive electronic warfare. In this paper, the cognitive bias caused by the measurement error of the electronic reconnaissance system and the cognitive bias of the commander and operator are regarded as quantum superposition states, and the mathematical model of the cognitive bias is established by using quantum cognitive theory, i.e., based on the quantum-like Bayesian network, a framework for inferencing the influence of cognitive bias on situation decision is established, wherein the quantum entanglement factor is defined for describing the relationship of human-machine cognitive biases. By taken the time domain parameter measurement of radar pulse as an example, the cognitive bias is quantitatively analyzed. The experimental results verify the effectiveness of the proposed method. The literature search shows that the proposed method belongs to the first in the field of cognitive electronic warfare, and has a distinct methodological innovation.
Cognitive bias is an objective existence in cognitive electronic warfare. In this paper, the cognitive bias caused by the measurement error of the electronic reconnaissance system and the cognitive bias of the commander and operator are regarded as quantum superposition states, and the mathematical model of the cognitive bias is established by using quantum cognitive theory, i.e., based on the quantum-like Bayesian network, a framework for inferencing the influence of cognitive bias on situation decision is established, wherein the quantum entanglement factor is defined for describing the relationship of human-machine cognitive biases. By taken the time domain parameter measurement of radar pulse as an example, the cognitive bias is quantitatively analyzed. The experimental results verify the effectiveness of the proposed method. The literature search shows that the proposed method belongs to the first in the field of cognitive electronic warfare, and has a distinct methodological innovation.
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.
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.
The emergence of quantum computing has prompted researchers to examine potential parallels between quantum mechanical principles and human cognitive processes. This article explores the theoretical framework of quantum cognition, examining how concepts from quantum mechanics, including superposition, entanglement, and measurement, might inform our understanding of human decision-making, memory, and consciousness. While quantum computing operates on fundamentally different physical principles than biological neural networks, the mathematical formalisms of quantum mechanics offer novel perspectives on cognitive phenomena that classical probability theory struggles to explain. This review critically examines the current state of quantum cognition research, its applications in computational modeling, and the ongoing debates regarding the validity of quantum mechanical analogies in psychological theory. The analysis reveals both promising theoretical insights and significant limitations in applying quantum principles to human cognition, highlighting the need for empirical validation and careful distinction between mathematical analogy and physical reality.
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.
The article presents a review of foreign scientific literature on the quantum-like approach to understanding the modeling of cognition and decision-making processes. It is shown that the current application of quantum physics approaches to consciousness, psychological approaches such as perception, cognition, emotions, consciousness and learning, involves the search for a single objective mathematical model of what underlies the experience of the brain. The use of quantum physics algorithms can help in understanding the mechanisms of brain malfunctions and memory impairments that are found in pathologies such as dementia caused by Alzheimer's disease, schizophrenia, etc. Quantum physics approaches are applicable to issues of modeling cognition and decision-making processes, as well as in cognitive research, information processing by the human brain, artificial intelligence development, psychology, social and political sciences. Application of the abstract mathematical principles of quantum theory can help in understanding human consciousness and behavior. Quantum probability theory is applied to modeling human behavior due to the prevalence of uncertainty in human interaction with the world. Quantum cognitive models are especially useful in research in such areas as errors in decision making, order effects of questions, conceptual combinations, accumulation of evidence, perception, etc. Of particular interest to researchers are issues of quantum-like modeling of cognition and decision-making processes, the apparatus of quantum information and probability theory, useful for such modeling, as well as the use of such apparatus in cognitive research, the development of artificial intelligence, psychology, social and political sciences.
Quantum cognition is a new research program that uses mathematical principles from quantum theory as a framework to explain human cognition, including judgment and decision making, concepts, reasoning, memory, and perception. This research is not concerned with whether the brain is a quantum computer. Instead, it uses quantum theory as a fresh conceptual framework and a coherent set of formal tools for explaining puzzling empirical findings in psychology. In this introduction, we focus on two quantum principles as examples to show why quantum cognition is an appealing new theoretical direction for psychology: complementarity, which suggests that some psychological measures have to be made sequentially and that the context generated by the first measure can influence responses to the next one, producing measurement order effects, and superposition, which suggests that some psychological states cannot be defined with respect to definite values but, instead, that all possible values within the superposition have some potential for being expressed. We present evidence showing how these two principles work together to provide a coherent explanation for many divergent and puzzling phenomena in psychology.