In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.
In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.
In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.Request access from your librarian to read this chapter's full text.
Since the beginning of the 21st century, a new interdisciplinary research movement has started, which aims at developing quantum math-like (or simply quantum-like) models to provide an explanation for a variety of socio-economic processes and human behaviour. By making use of mainly the probabilistic aspects of quantum theory, this research movement has led to many important results in the areas of decision-making and finance. In this article, we introduce a novel and more exhaustive approach, to analyze the socio-economic processes and activities, than the pure quantum math-like modelling approach, by taking into account the physical foundations of quantum theory. We also provide a plausibility argument for its exhaustiveness in terms of what we can expect from such an approach, when it is applied to, for example, a generic socio-economic decision process.
Written by world experts in the foundations of quantum mechanics and its applications to social science, this book shows how elementary quantum mechanical principles can be applied to decision-making paradoxes in psychology and used in modelling information in finance and economics. The book starts with a thorough overview of some of the salient differences between classical, statistical and quantum mechanics. It presents arguments on why quantum mechanics can be applied outside of physics and defines quantum social science. The issue of the existence of quantum probabilistic effects in psychology, economics and finance is addressed and basic questions and answers are provided. Aimed at researchers in economics and psychology, as well as physics, basic mathematical preliminaries and elementary concepts from quantum mechanics are defined in a self-contained way.
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.
This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.
This paper presents a comprehensive review of the Social Laser Theory (SLT) as a natural extension of the broader framework of Quantum-Like Modeling (QLM). While QLM applies the mathematical formalism of quantum theory — such as Hilbert space representations, interference, and non-commutative observables — to model context-dependent and non-classical phenomena in cognition, decision-making, and social behavior, SLT advances this approach by integrating concepts from quantum field theory. The theory conceptualizes social systems as ensembles of “social atoms” capable of absorbing and emitting quantized units of social energy. Under conditions analogous to population inversion in physical lasers, external informational stimuli (e.g. media signals or mobilizing rhetoric) can trigger coherence across the population, resulting in large-scale, synchronized collective actions such as protests or ideological shifts. SLT thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior. Beyond its metaphorical appeal, the theory proposes measurable quantities and predictive parameters that may support empirical diagnostics of sociopolitical dynamics. By bridging micro-level psychological processes with macro-level sociological phenomena, SLT extends QLM into the domain of complex social systems, offering a mathematically grounded paradigm for interpreting rapid transformations in contemporary societies.