Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered...

In this paper, we consider a classic periodic-review lost-sales inventory system with lead times, which is notoriously challenging to optimize with a wide range of real-world applications. We consider a joint learning and optimization problem in which the decision-maker does not know the demand...

Advancements in algorithms hold promise to enhance operations by improving users' decision-making. However, people sometimes fail to use algorithms, which could be a barrier to achieving such improvements. Using the bolus calculator (algorithm) use behavior in over 221,000 bolus insulin...

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there...

Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for solving computationally-demanding stochastic optimal stopping problems with known probability distributions. The...

There is growing concern that the rise of algorithmic decision-making can lead to discrimination against legally protected groups, but measuring such algorithmic discrimination is often hampered by a fundamental selection challenge. We develop new quasi-experimental tools to overcome this...

Since December 2019, the novel coronavirus (COVID-19) has been the cause of over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world and now under community trans-mission of the COVID-19 outbreak. This has created huge...

Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MINLP) problem. To solve this constrained...