Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. This study examines the incorporation of artificial intelligence (AI) and machine...

The natural interest rate is the real rate that would prevail in the long-run. The standard view in macroeconomics is that the natural rate depends exclusively on structural factors such as productivity growth and demographics. This paper challenges this view by discussing three alternative, and...

This paper examines the role of anti-money laundering (AML) regulations and compliance in combating money laundering and terrorist financing (ML/TF) in Canada. AML regulations establish guidelines for financial institutions to identify, prevent, and report suspicious activities. However,...

"The objective of this book is to serve as a tool and an essential guide with an innovative educational approach tailored for practitioners and students seeking to on-board a modern workplace and start contributing at higher effective and efficient level. Book is also designed to be used for...

Technological change co‐determines agri‐environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm‐level models that are rich in technology details and environmental indicators, integrated with agent‐based...

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

We develop a novel approach to understand the dynamic diversification of decision makers with quantile preferences. Due to unavailability of analytical solutions to such complex problems, we suggest to approximate the behavior of agents with a Quantile Deep Reinforcement Learning (Q-DRL)...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

This paper presents the experience and lessons learned in a pilot project aimed at integrating artificial intelligence (AI) technologies in sturgeon aquaculture. The project used convolutional neural networks and visual intelligence for the evaluation of fish biomass and the optimisation of...

We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents' decisions and the aggregate...

This paper studies monetary policy in a New Keynesian model with persistent supply shocks, that is, sustained increases in production costs due to factors such as wars or geopolitical fragmentation. First, we demonstrate that Taylor rules fail to stabilize long-term inflation due to endogenous...

Recently, e-commerce platforms have gained significant attention in the media. As the number of customers using these platforms continues to grow, they face challenges such as limited support, making it increasingly difficult for customers to find answers to their inquiries, leading to a high...

We develop a novel approach to understand the dynamic diversification of decision makers with quantile preferences. Due to unavailability of analytical solutions to such complex problems, we suggest to approximate the behavior of agents with a Quantile Deep Reinforcement Learning (Q-DRL)...

Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the model; then, the inference procedure is done automatically. In contrast to MCMC, BBVI scales to many observations, is faster...

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has...

Reducing waste through automated quality control (AQC) has both positive economical and ecological effects. In order to incorporate AQC in packaging, multiple quality factor types (visual, informational, etc.) of a packaged artifact need to be evaluated. Thus, this work proposes an end-to-end...

The current Special Issue brought out the newest trends in Econophysics that have made use of the most recent available tools, such as Big Data. The emphasis of the reprint is on deciphering the effects of current world events, such as the repercussions of the recent war conflicts, the oil and...

Far-reaching transformations in the world of work are being discussed vividly under the term 'artificial intelligence'. Questions are arising about the changing role of humans in work processes and their freedom to determine work content and conditions. This book explores these potential areas...

In recent years, there has been a fast growth in the application of long-memory processes to underlying assets including stock, volatility index, exchange rate, etc. The fractional Brownian motion is the most popular of the long-memory processes and was introduced by Kolmogorov in 1940 and later...

Evaluating the recognizability of street built environments provides crucial support for urban planning, security monitoring and navigation. Although street view images (SVIs) are widely used in urban studies, it overlooks the interconnection among different locations, which can also affect...

Supply chain management (SCM) is crucial in establishing long-term partnerships that are pivotal for achieving sustained business success. Effective SCM demands rigorous criteria and decision-making processes, which significantly impact the overall outcomes. Recent studies highlight cloud-based...

This paper presents the experience and lessons learned in a pilot project aimed at integrating artificial intelligence (AI) technologies in sturgeon aquaculture. The project used convolutional neural networks and visual intelligence for the evaluation of fish biomass and the optimisation of...