Cross-domain applications within the quantum framework: From the theory of infinite-dimensional operators to quantum cognitive science and quantum game theory
The theory of infinite-dimensional operators provides a powerful mathematical foundation for modern physics and cognitive science. This paper starts by exploring the differences between linear operators in finite-dimensional and infinite-dimensional spaces, and then discusses their applications in quantum field theory, quantum probabilistic decision theory, quantum cognitive science, and quantum game theory. Through concepts such as Hilbert spaces, projection operators, quantum interference, and entanglement, this paper demonstrates how the quantum framework can transcend classical models and more accurately describe uncertainty, context dependence, and policy interactions in complex systems. Finally, the latest real-world developments in this research are discussed.
The theory of infinite-dimensional operators provides a powerful mathematical foundation for modern physics and cognitive science. This paper starts by exploring the differences between linear operators in finite-dimensional and infinite-dimensional spaces, and then discusses their applications in quantum field theory, quantum probabilistic decision theory, quantum cognitive science, and quantum game theory. Through concepts such as Hilbert spaces, projection operators, quantum interference, and entanglement, this paper demonstrates how the quantum framework can transcend classical models and more accurately describe uncertainty, context dependence, and policy interactions in complex systems. Finally, the latest real-world developments in this research are discussed.
Infinite-dimensional operator theory provides a powerful mathematical foundation for modern physics and cognitive science. This paper begins with the distinction between linear operators in finite- and infinite-dimensional spaces, then explores their applications in quantum field theory, quantum probability decision theory, quantum cognition, and quantum game theory. Through concepts such as Hilbert space, projection operators, quantum interference, and entanglement, this paper demonstrates how the quantum framework surpasses classical models in accurately describing uncertainty, contextuality, and strategic interactions in complex systems. Finally, the latest real-world developments at the research frontier are discussed.
The increasing complexity of modern decision-making environments has challenged the effectiveness of classical mathematical models. Traditional probabilistic frameworks often struggle to represent uncertainty, ambiguity, contextual dependence, and nonlinear interactions among decision variables. Quantum-inspired mathematical models have emerged as a promising interdisciplinary approach that leverages mathematical principles derived from quantum theory without requiring quantum computing hardware. These models utilize concepts such as superposition, interference, entanglement, and contextuality to represent complex decision processes more accurately. This paper explores the theoretical foundations, mathematical structures, and applications of quantum-inspired decision models in complex systems. The study examines how quantum probability theory provides a more flexible representation of human cognition, organizational decision-making, financial forecasting, and artificial intelligence systems. Furthermore, the paper discusses mathematical formulations, implementation strategies, advantages, challenges, and future research directions. The findings suggest that quantum-inspired models offer significant potential for addressing uncertainty and dynamic interactions in complex decision environments, thereby contributing to the advancement of intelligent decision support systems.
The current chapter explores philosophical and mathematically inspired concepts of quantum cognition paradigm. Quantum cognition is an interdisciplinary domain of overlaps between quantum foundations or quantum information theory concepts and cognitive decision-making processes. The chapter explores frontier works which demonstrate that various quantum-like concepts can describe, explain and even predict human cognitive phenomena better than the standard cognitive science models. Again, no reductionism is implied, or we necessarily do not hold that mind is quantum physical for sure.
Quantum probability theory, originally developed in the context of quantum mechanics, has increasingly been applied to social sciences to explain decision-making, cognition, and collective behavior. Unlike classical probability models, which assume pre-existing and well-defined preferences, quantum models incorporate superposition, interference, and non-commutativity to capture the contextual dependence and measurement effects observed in human decisions. This paper systematically explores the theoretical foundations of quantum probability in social sciences, discusses model construction techniques, and reviews empirical findings supporting its application. By examining case studies in quantum cognition, quantum game theory, and quantum voting, we illustrate how quantum approaches provide alternative explanations for phenomena such as order effects, measurement-induced attitude shifts, and collective polarization. The paper also discusses the methodological challenges and limitations of quantum social science, emphasizing the need for rigorous experimental validation and interdisciplinary collaboration. Finally, we highlight the potential for integrating quantum probability models with emerging technologies such as quantum computing and artificial intelligence, opening new avenues for social science research and policy applications.
At first glance, quantum mechanics and behavioural science seem worlds apart -- one rooted in equations and particles, the other in thoughts and choices. Yet, emerging research reveals a profound and unexpected bridge between them. This chapter explores that bridge through quantum models of cognition and decision-making, showing how principles from quantum mechanics can help understand, and even predict, the complexities of human perception, behaviour and societal dynamics. We introduce a computationally accessible framework grounded in quantum theory, designed to model ambiguity, bias and choice in a way that classical logic cannot. Drawing on interdisciplinary sources, we demonstrate how this approach not only enriches our understanding of individual cognition but also extends to AI, video game design, financial behaviour and collective decision-making in society. Through vivid case studies -- from optical illusions in video games to decision biases in economic, defence and political contexts -- we show how quantum models offer new explanatory and predictive power. Thus, this chapter invites readers from all professional backgrounds to reimagine cognition and decision-making, opening new avenues for science, technology and human understanding.
Growing empirical evidence reveals that traditional set-theoretic structures cannot in general be applied to cognitive phenomena. This has raised several problems, as illustrated, for example, by probability judgement errors and decision-making (DM) errors. We propose here a unified theoretical perspective which applies the mathematical formalism of quantum theory in Hilbert space to cognitive domains. In this perspective, judgements and decisions are described as intrinsically non-deterministic processes which involve a contextual interaction between a conceptual entity and the cognitive context surrounding it. When a given phenomenon is considered, the quantum-theoretic framework identifies entities, states, contexts, properties and outcome statistics, and applies the mathematical formalism of quantum theory to model the considered phenomenon. We explain how the quantum-theoretic framework works in a variety of judgement and decision situations where systematic and significant deviations from classicality occur.