Strategic foresight often involves navigating deeply uncertain futures through scenario-based reasoning. Traditional methods rely on static narratives or probabilistic models, which fail to capture the dynamic, interacting, and nonexclusive nature of competing futures. This paper introduces a quantum-inspired framework for scenario interaction grounded in the mathematical formalism of superposition, interference, and contextuality. Agents are modeled as epistemic learners who iteratively update their preferences across multiple narrative pathways, which are treated as basis states in a conceptual Hilbert space. The simulation combines reinforcement learning with stochastic imitation, producing emergent distributions that resemble quantum-like collapse under feedback. Central to the model is the emergence of symmetries in the scenario structure and learning dynamics. Agents begin with neutral priors, facing a balanced reward landscape, epistemic and normative symmetry that is gradually broken through adaptive behavior. However, statistical symmetries persist at the ensemble level, as agents maintain partial preferences and oscillate among futures. These layered symmetries reflect both the cognitive realism of foresight practices and the mathematical tractability of quantum-inspired systems. The proposed model bridges strategic foresight, game-theoretic interaction, and quantum cognition, and offers a novel computational lens to study how futures are constructed, selected, and stabilized under uncertainty.
Strategic foresight often involves navigating deeply uncertain futures through scenario-based reasoning. Traditional methods rely on static narratives or probabilistic models, which fail to capture the dynamic, interacting, and nonexclusive nature of competing futures. This paper introduces a quantum-inspired framework for scenario interaction grounded in the mathematical formalism of superposition, interference, and contextuality. Agents are modeled as epistemic learners who iteratively update their preferences across multiple narrative pathways, which are treated as basis states in a conceptual Hilbert space. The simulation combines reinforcement learning with stochastic imitation, producing emergent distributions that resemble quantum-like collapse under feedback. Central to the model is the emergence of symmetries in the scenario structure and learning dynamics. Agents begin with neutral priors, facing a balanced reward landscape, epistemic and normative symmetry that is gradually broken through adaptive behavior. However, statistical symmetries persist at the ensemble level, as agents maintain partial preferences and oscillate among futures. These layered symmetries reflect both the cognitive realism of foresight practices and the mathematical tractability of quantum-inspired systems. The proposed model bridges strategic foresight, game-theoretic interaction, and quantum cognition, and offers a novel computational lens to study how futures are constructed, selected, and stabilized under uncertainty.
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.
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 quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents, each assigned evolving roles as compliers, resistors, or enforcers. Within a simulated environment monitored by a central observer (the Watcher), agents interact, respond to surveillance, and adapt to periodic normative disruptions. These interactions allow the system to self-organize under external stress and reveal emergent patterns. Key findings show that quantum principles, when integrated with generative AI, enable the modeling of uncertainty, emergence, and interdependence in complex social systems. Simulations reveal patterns including convergence toward normative order, the spread of resistance, and the spontaneous emergence of new equilibria in social rules. In conclusion, this study introduces a novel computational lens that lays the groundwork for a quantum-informed social theory. It offers interdisciplinary insights into how society can be understood not just as a structure to observe but as a dynamic system to simulate and redesign through quantum technologies.
This paper investigates the application of quantum-inspired algorithms to the field of reinforcement learning (RL). Traditional RL methods often struggle with exploration challenges and can be computationally expensive, particularly in complex environments. We propose a novel RL algorithm that leverages concepts from quantum mechanics, specifically superposition and entanglement, to address these limitations. The core idea is to represent states, actions, and rewards using quantum-inspired representations, allowing the agent to explore the environment more efficiently and potentially converge to optimal policies faster. This work presents a detailed formulation of the algorithm, including the mathematical framework and key considerations for implementation. The potential benefits of this approach, including improved exploration and reduced computational complexity, are discussed. The algorithm is designed to iteratively refine its policy based on simulated quantum measurements, aiming to minimize the expected return. The exploration strategy is guided by the inherent probabilistic nature of quantum states, leading to a more diverse and potentially faster learning process. Further research is warranted to fully explore the capabilities and limitations of this approach across various RL benchmark problems.
Abstract Multiplayer quantum games have received growing attention due to the ability of quantum computational models to access enlarged strategic spaces, thereby enabling the emergence of nonclassical Nash equilibria and Pareto-efficient solutions. Concurrently, graph-theoretic representations have become a standard tool for encoding relational dependencies among interacting players. This work establishes a unified formalism that embeds arbitrary graph topologies into parametrized quantum-game circuits, providing a direct mapping between network structure and the strategic interaction space of quantum players. Within this framework, we analytically and computationally evaluate how graph-induced interpersonal couplings modulate payoff distributions over repeated quantum-game iterations. In addition, we integrate a reward-driven adaptation algorithm that allows players to optimize their local strategy parameters dynamically with respect to accumulated payoffs. Experimental results demonstrate that the equilibrium reward landscape is highly sensitive to the players’ graph-theoretic centrality and the weighted structure of their adversarial and cooperative relationships.
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.