CoVaR is an important measure of financial systemic risk due to its ability to capture tail dependence between the losses of different portfolios and its capacity to predict financial crises. Estimating CoVaR is challenging because its definition involves ...

CoVaR is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being at risk. In this paper we first develop a Monte Carlo simulation–based batching ...

The curse of dimensionality significantly restricts the use of dynamic programming methods in solving complex problems. Consequently, researchers and practitioners often resort to approximate (suboptimal) control policies that strike a balance between ...

We use the technique of information relaxation to develop a duality-driven iterative approach (DDP) to obtain and improve confidence interval estimates for the true value of finite-horizon stochastic dynamic programming problems. Each iteration of the ...

Asymptotic Optimality of Simple Heuristic Policies for Multidimensional Inventory Systems

Stochastic inventory systems with multidimensional state spaces, such as lost-sales system with positive lead time and perishable inventory system, are challenging to ...

We consider three classes of inventory systems under long-run average cost: (i) periodic-review systems with lost sales, positive lead times, and a nonstationary demand process; (ii) periodic-review systems for a perishable product with partial backorders ...

This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data. Despite a large number of prior works tackling this problem, the state-of-the-art results suffer from the curse of multiple agents in the ...

This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data. Specifically, consider a γ-discounted, infinite-horizon Markov game with S states, in which the max-player has A actions and the min-player ...

Inventory Projection and Asymptotic Optimality

van Jaarsveld and Arts propose a new policy for the canonical periodic review lost sales inventory problem. Under this policy, orders are placed in each period such that the expected inventory level at the ...

We consider the canonical periodic review lost sales inventory system with positive lead times and stochastic i.i.d. demand under the average cost criterion. We introduce a new policy that places orders such that the expected inventory level at the time ...

Frequent production rate changes are prohibitive because of high setup costs or setup times in producing such items as sugar, glass, computer displays, and ...

We consider continuous-review, single-product inventory systems with a constant replenishment rate, Lévy demand, general inventory holding cost, and general lost-sales penalty. The Lévy demand encompasses various demand dynamics used in the inventory ...

Random Consideration Set Model

We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–...

In this paper, we operationalize the random consideration set (RCS) choice model proposed by Manzini and Mariotti which assumes that consumers make purchase decisions based on a fixed preference ordering and random consideration sets drawn from ...

Using Induced Order Statistics to Construct Optimal Impact Portfolios with General Dependence and Marginals

We develop a mathematical framework for constructing optimal impact portfolios and quantifying their financial performance by characterizing the ...

We develop a mathematical framework for constructing optimal impact portfolios and quantifying their financial performance by characterizing the returns of impact-ranked assets using induced order statistics and copulas. The distribution of induced order ...

When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to ...

When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to ...

Our paper proposes an effective way to make decisions based on data for uncertain situations. In simple terms, a data-driven decision is just a choice we make by looking at the available data. We express this choice as the best one according to a model we ...

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be ...

Managing Volunteer Engagement

Non-profit organizations that provide food, shelter, and other services to people in need, rely on volunteers to deliver their services. Unlike paid labor, non-profit organizations have less control over unpaid volunteers’ ...

Nonprofit organizations that provide food, shelter, and other services to people in need, rely on volunteers to deliver their services. Unlike paid labor, nonprofit organizations have less control over unpaid volunteers’ schedules, efforts, and ...

Policy gradient methods, which have powered a lot of recent success in reinforcement learning, search for an optimal policy in a parameterized policy class by performing stochastic gradient descent on the cumulative expected cost-to-go under some initial ...

Policy gradients methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a parameterized class of polices. Unfortunately, even for simple control problems solvable by standard dynamic programming ...

Modeling and decision making for queueing systems have been one of fundamental topics in operations research. For these problems, uncertainty models are established by estimation of key parameters such as expected interarrival and service times. In ...

Observational data from queueing systems are of great practical interest in many application areas because they can be leveraged for better statistical inference of service processes. However, these observations often only provide partial information of ...

There are many ways to elicit honest probabilistic forecasts from experts. Once those forecasts are elicited, there are many ways to aggregate them into a single forecast. Should the choice of elicitation method inform the choice of aggregation method? In ...

This paper forges a strong connection between two seemingly unrelated forecasting problems: incentive-compatible forecast elicitation and forecast aggregation. Proper scoring rules are the well-known solution to the former problem. To each such rule s, we ...

Wasserstein distributionally robust optimization is a recent emerging modeling paradigm for decision making under data uncertainty. Because of its computational tractability and interpretability, it has achieved great empirical successes across several ...

Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance. Despite its recent empirical success in operations research and machine learning, ...

A generic way to verify asymptotic optimality of semi-open-loop policies for a wide class of MDPs with large lead times.

In many real-life inventory models, order lead times can result in uncertain effects of inventory decisions. However, as the lead time ...

We consider a generic Markov decision process (MDP) with two controls: one control taking effect immediately and the other control whose effect is delayed by a positive lead time. As the lead time grows, one naturally expects that the effect of the ...

A Sequential Model for High-Volume Recruitment Under Random Yields

High-volume recruiting is challenging, as it involves hiring a larger number of people in a short amount of time. In “A Sequential Model for High-Volume Recruitment Under Random Yields,” Du,...

We model a multiphase and high-volume recruitment process as a large-scale dynamic program. The success of the process is measured by a reward, which is the total assessment score of accepted candidates minus the penalty cost of the number of accepted ...

In “Demand Estimation Under Uncertain Consideration Sets,” Jagabathula, Mitrofanov, and Vulcano investigate statistical properties of the consider-then-choose (CTC) models, which gained recent attention in the operations literature as an alternative to ...

To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered but also because it was not considered. To account for this behavior, existing literature ...

This paper addresses decision making in multiple stages, where prior information is available and where consecutive and successive decisions are made. Risk measures assess the random outcome by taking various candidate probability measures into account. ...

Many decisions, in particular decisions in a managerial context, are subject to uncertainty. Risk measures cope with uncertainty by involving more than one candidate probability. The corresponding risk averse decision takes all potential candidate ...

Modeling as real options the operations of energy production companies that operate in wholesale markets gives rise to a challenging Markov decision process. In “Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production,” Yang, ...

We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. Least squares Monte Carlo combined with information relaxation and duality is a state-of-the-art reinforcement ...