We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used; we deployed the...

This paper provides an overview of behaviors and learning of heterogeneous economic agents -whether individuals or firms- within complex evolving economies. It aims to explore the microfoundation of agency and learning, two fundamental aspects that constitutively contribute to the emergence of...

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about...

Obesity disease is a significant health issue and has affected millions of people worldwide. Identifying underlying reasons for the onset of obesity risk in its early stage has become challenging for medical practitioners. The growing volume of lifestyle data related to obesity has made it...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

Identifying sensitive periods in which the returns to investments into skills are especially high is challenging, but crucial for an effective and efficient timing of parental or public investments aimed at fostering children’s skills. We can detect sensitive periods with a novel design by...

The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published...

This paper introduces a framework for selecting policies that maximize expected welfare under estimation uncertainty. The proposed method explicitly balances the size of the estimated welfare against the uncertainty inherent in its estimation, ensuring that chosen policies meet a reporting...

This study evaluates the impact of incorporating mixed reality-including both augmented and virtual reality-into auto-mechanics training for students enrolled in selected public technical technological institutes in Ecuador. The intervention aims to enhance students' understanding of automotive...

This paper develops a novel gradient-based reinforcement learning algorithm for solving dynamic quantile models with uncertainty. Unlike traditional approaches that rely on expected utility maximization, we focus on agents who evaluate outcomes based on specific quantiles of the utility...

This paper estimates a New Keynesian model with a nonlinearity in the monetary policy rule to capture the practice of inflation targeting with target zones or tolerance bands. Private-sector agents form subjective expectations, update their beliefs over time using a perceived model of the...

We demonstrate that racial biases against tutors hinder learning. In e-learning experiments, U.S. conservatives are more likely to disregard advice from Black tutors, resulting in reduced performance compared to learners taught by white tutors. We show that the bias is unconscious and,...

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative...