Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or bad-mouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.
Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or bad-mouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.
Since the 70’s, researchers noticed that people do not follow the rules of probability theory while making decisions under risk.Just like quantum physics was created in order to accommodate paradoxical findings from classical physics, quantum cognition arose as a disciple with the promise of creating models that would accommodate and explain the violations found in human decision-making.In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences. We propose a Quantum Probabilistic Graphical Model in order to predict human decisions under situations with high levels of uncertainty.Under the proposed network, if a node (event) is unobserved, then a particle can enter in a superposition state and produce interference effects. These effects provide some explanation in terms of cognition, since they can be seen as the feeling of confusion or ambiguity.This work also discusses the problem of creating a generalized quantum probabilistic model: the number of quantum parameters grows exponentially. We will see that for a given event, several outcomes are possible through quantum probability. A final question is posed and discussed: is it possible to find a method to assign quantum parameters in order to provide accurate predictions?
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 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.
Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a state vector with different sequences of operators for different orderings of information. We demonstrate this process by fitting the quantum model to data collected in a medical diagnostic task and a jury decision-making task. To further test the quantum inference model, a new jury decision-making experiment is developed. Using the results of this experiment, we compare the quantum inference model with two versions of the belief-adjustment model, the adding model and the averaging model. We show that both the quantum model and the adding model provide good fits to the data. To distinguish the quantum model from the adding model, we develop a new experiment involving extreme evidence. The results from this new experiment suggest that the adding model faces limitations when accounting for tasks involving extreme evidence, whereas the quantum inference model does not. Ultimately, we argue that the quantum model provides a more coherent account for order effects that was not possible before.
Human reasoning is traditionally modeled through rational-order frameworks that assume stability, separability, and coherence. Yet across judgment, valuation, perception, and social decision-making, empirical work consistently reveals patterned violations of these assumptions. These deviations intensify in real-world contexts shaped by institutional constraints, identity commitments, and collective narratives, where reasoning must navigate incompatible interpretive frames and interdependent evaluative pressures. Existing theories typically treat such phenomena as noise, bias, or bounded rationality, leaving no formal account of how rational-order rules interact with the variability inherent in social domains. This article proposes a structural framework that explains why these two regimes diverge and how their interaction produces systematic mismatches. Building on quantum probability theory, not as a physical metaphor but as a representational tool, it formalizes evaluative states that remain indeterminate until elicited, transform under contextual modulation, and become relationally coupled across agents and domains. Whereas, existing quantum-cognition models primarily address task-level effects such as conjunction errors or order dependence, this framework scales quantum principles to socially and institutionally embedded reasoning. The account identifies contradiction, interference, entanglement, and resolution as recurrent properties of real-world cognition and shows how quantum formalisms provide a coherent vocabulary for capturing these phenomena. To support cumulative progress, the article outlines a research agenda with empirically testable designs for distinguishing incompatible bases, assessing inseparability, modeling context-driven transformations, and integrating multi-level reasoning environments. This program positions quantum and classical approaches within a unified architecture and advances a broader science of reasoning.
Recent work in cognitive psychology has revealed that quantum probability theory provides another method of computing probabilities without falling into the restrictions that classical probability has in regard to modeling cognitive systems and decision-making. This enables the explanation of paradoxical scenarios that are difficult, or even impossible, to explain through classical probability theory. In this work, we perform an overview of the most important quantum models in the literature that are used to make predictions under scenarios where the Sure Thing Principle is being violated (the Quantum-Like Approach, the Quantum Dynamical Model, the Quantum Prospect Theory and Quantum-Like Bayesian Networks). We evaluated these models in terms of three metrics: interference effects, parameter tuning and scalability. The first examines if the analyzed model makes use of any type of quantum interference to explain human decision-making. The second is concerned with the assignment of values to a large number of quantum parameters. The last one consists of analyzing the ability of the models to be extended and generalized to more complex scenarios. We also studied the growth of the quantum parameters when the complexity and the levels of uncertainty of the decision scenario increase. Finally, we compared these quantum models with traditional classical models from the literature. We conclude with a discussion of the manner in which the models addressed in this paper can only deal with very small decision problems and why they do not scale well to larger, more complex decision scenarios.