Purpose: To identify how price image, risk perception, and emotions influence online pharmacy customers' purchase intentions. Methodology: A Partial Least Squares Structural Equation Model (PLS-SEM) was estimated using data from a survey of 303 customers to evaluate the reliability and validity of the measures, along with the relationships between latent variables and purchase intentions. Results: Price image influences customers' risk perceptions and emotions; price-quality influences functional risk and emotions; perceptibility influences psychological and social risk and negative emotions; and processability influences functional and temporal risk, as well as emotions. Furthermore, psychological risk and positive emotions directly influence purchase intentions. Implications: This study identifies which dimensions of price perception are key to reducing the risk of online purchases and increasing customers' positive emotional experiences, thereby supporting the development of marketing tactics that build customer loyalty and satisfaction. Originality: This study aims to close a gap in the literature by specifying how price image dimensions influence the perceived risk and emotions of Venezuelan consumers, aspects little explored in both Latin America and Venezuela.
Portfolio replication, or the construction of a tradable basket of assets to match the risk-return profile of a target benchmark, is fundamentally an ill-posed inverse problem. When restricted to a subset of available assets, classical variance-minimizing models often yield unstable, over-leveraged portfolios highly vulnerable to market shocks. We propose a unified, two-stage methodology rooted in information theory to achieve robust portfolio replication. First, we model the constituent asset returns against target factors, estimating parameters within data-driven empirical bounds via an entropy minimization principle. Second, using the same entropic approach, we determine the optimal weight replication. In both cases we use an entropy function of the Fermi-Dirac type defined directly on sets of constraints of the inverse problem. We validate this Entropic Factor Model (EFM) against standard Ordinary Least Squares (OLS) across five numerical experiments, including standard equity tracking, multi-asset synthesis, and severe stress-test scenarios. Empirical results demonstrate that the EFM consistently outperforms OLS in terms of annualized turnover and net-of-fees returns. Crucially, during the COVID-19 market crash and under severe idiosyncratic data corruption, the entropic framework acts as a probabilistic “circuit breaker", defensively reducing capital allocation to compromised assets and providing a highly robust, risk-averse solution for generalized portfolio replication.
This work introduces a variation on the theme of the classical linear classification problem, extending it to the separation of data by non-linear polynomial hypersurfaces, which allows for more complex decision boundaries. The classification problem, central to machine learning since the perceptron model, is transformed into an ill-posed, linear inverse problem with convex constraints.We solve this using an entropy minimization procedure. Our approach differs from traditional setups as we do not pre-specify the measure of separation for the training data. Instead, the solution itself yields this measure, quantifying the degree of non-separation between the training classes. Crucially, the method intrinsically provides a way to define a region of undecidability (or uncertainty), where points that fall within that region cannot be classified with certainty.This entropic method offers a robust alternative (stable under small data perturbations) to traditional linear or quadratic optimization techniques like Support Vector Machines (SVMs). Furthermore, we explicitly compare our entropic method against classification using Convolutional Neural Networks (CNNs), a prominent class of gradient descent based models. Numerical experiments on diverse datasets, including linear and non-linear cases, demonstrate the efficiency and versatility of the method, plus its competitive performance against both established classical techniques and modern deep learning approaches.
The aim of this exposé is to make explicit the analogy between the classical notion of non-independent probability distribution and the quantum notion of entangled state. To bring that analogy forth, we consider a classical systems with two dependent random variables and a quantum system with two components. In the classical case, afet observing one of the random variables, the underlying sample space and the probability distribution change. In the quantum case, when and event pertaining to one of the components is observed, the post-measurement state captures, both, the change in the state of the system and implicitly the new probability distribution. The predictions after a measurement in the classical case and in the quantum case, have to be computed with the conditional distribution given the value of the observed variable.
In this work we reexamine the EPR paradox for composite systems with a finite number of levels. The analysis emphasizes the connection between measurements and conditional probabilities. This connection implies that when a measurement is performed, the microscopic states compatible with the measurement is different from the class of all possible microscopic states, therefore the new quantum state and the probability distribution change and become a function of the observable being measured. Therefore, the predictions that one can make given the knowledge of the result of a measurement change. Systems with finitely many levels are simpler to describe because the analysis is not encumbered by the mathematical technicalities of the continuous case, the underlying physical interpretations are the same and the experimental setups used to test quantum mechanics with the paradox in mind finitely many levels.e same.
