The rapid expansion of cryptocurrencies and decentralized finance (DeFi) has redefined global financial systems, creating new challenges in asset pricing, risk measurement, and systemic stability. This study conducts a comprehensive review of 93 peer-reviewed articles published between 2019 and...

Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach...

Timely and accurate forecasting of Gross Domestic Product (GDP) is critical for economic policymaking, yet it is complicated by the need to integrate high-frequency financial indicators with low-frequency macroeconomic data and to capture complex nonlinear dynamics. This study introduces a novel...

The Benefit of the Doubt (BoD) is a non-parametric frontier model derived from Data Envelopment Analysis (DEA), used to construct composite indicators in various sectors of economic activity, with a particular focus on macroeconomic assessments. Based on documents published in the Web of Science...

This research acknowledges the existence of a dichotomy between the weak sustainability (WS) and strong sustainability (SST) paradigms, which can heavily influence decision-making analysis methods. Rather than entering the debate between WS and SST, we take the need for SST as a starting point...

In many developing countries, heavy reliance on centralized, non-renewable energy sources poses threats to both energy security and the environment. However, there is still a lack of structured evidence on which factors should be prioritized to accelerate the adoption of decentralized renewable...

Evaluation metrics are essential in selecting the most appropriate predictive model. Conventional metrics such as R-squared, root mean squared error (RMSE), and mean absolute percentage error (MAPE) rely on arithmetic averages, making them sensitive to outliers, undefined values, and scale...

Early and accurate diagnosis of Alzheimer's disease (AD) is crucial for managing the disease and selecting therapies to slow its progression. Machine learning (ML) has demonstrated significant potential in improving its diagnostic accuracy with structural Magnetic Resonance Imaging (sMRI) data....