Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms....

The uncapacitated facility location problem (UFLP) is a real-world binary optimization problem that aims to find the number of opened facilities that minimizes the total cost of exchange between customer and facilities and opening cost of these facilities. UFLP is in class of NP-Hard and, there...

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of...

The judgments of human beings can be biased; they can also be noisy. Across a wide range of settings, use of algorithms is likely to improve accuracy, because algorithms will reduce both bias and noise. Indeed, algorithms can help identify the role of human biases; they might even identify biases...

We propose an actor-critic reinforcement learning (RL) algorithm for the optimal execution problem. We consider the celebrated Almgren-Chriss model in continuous time and formulate a relaxed stochastic control problem for execution under an entropy regularized mean-quadratic variation objective....

We propose a novel methodology for modeling and forecasting multivariate realized volatilities using graph neural networks. This approach extends the work of Zhang et al. [2022] (Graph-based methods for forecasting realized covariances) and explicitly incorporates the spillover effects from...