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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' schedules, efforts, and reliability. However, these organizations can invest in volunteer engagement activities to strive for a steady and adequate supply of volunteer labor. The paper, "A Dynamic Model for Managing Volunteer Engagement", models the volunteer management process. The paper derives a dynamic control policy that judiciously uses the non-profit's resources for volunteer engagement activities depending on explicit congestion thresholds, thereby lowering the overall cost of the non-profit operation. 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 reliability. However, these organizations can invest in volunteer engagement activities to ensure a steady and adequate supply of volunteer labor. We study a key operational question of how a nonprofit organization can manage its volunteer workforce capacity to ensure consistent provision of services. In particular, we formulate a multiclass queueing network model to characterize the optimal engagement activities for the nonprofit organization to minimize the costs of enhancing volunteer engagement, while maximizing productive work done by volunteers. Because this problem appears intractable, we formulate an approximating Brownian control problem in the heavy traffic limit and study the dynamic control of that system. Our solution is a nested threshold policy with explicit congestion thresholds that indicate when the nonprofit should optimally pursue various types of volunteer engagement activities. A numerical example calibrated using data from a large food bank shows that our dynamic policy for deploying engagement activities can significantly reduce the food bank's total annual cost of its volunteer operations while still maintaining almost the same level of social impact. This improvement in performance does not require any additional resources—it only requires that the food bank strategically deploy its engagement activities based on the number of volunteers signed up to work volunteer shifts. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2021.0419. [ABSTRACT FROM AUTHOR]

Latent Agents in Networks: Estimation and Targeting In "Latent Agents in Networks: Estimation and Targeting," Baris Ata, Alexandre Belloni, and Ozan Candogan address the problem of estimating network effects in a setting in which data only on a subset of agents is available. In this setting, the observable agents influence each other's outcomes both directly and indirectly through their influence on the latent agents. Even in sparse networks, the combination of direct and indirect network effects yields a nonsparse influence structure that makes estimation challenging. The authors overcome this challenge and provide an estimation algorithm that performs well in high-dimensional settings. They also establish convergence rates for their proposed estimator and show that their performance guarantees are valid for a large class of networks. Finally, the authors demonstrate the application of their algorithm to a targeted advertising problem, in which it can be used to obtain asymptotically optimal advertising decisions despite the presence of latent agents. We consider a platform that serves (observable) agents, who belong to a larger network that also includes additional agents who are not served by the platform. We refer to the latter group of agents as latent agents. Associated with each agent are the agent's covariate and outcome. The platform has access to past covariates and outcomes of the observable agents, but no data for the latent agents is available to the platform. Crucially, the agents influence each other's outcome through a certain influence structure. In particular, observable agents influence each other both directly and indirectly through the influence they exert on the latent agents. The platform doesn't know the inference structure of either the observable or the latent parts of the network. We investigate how the platform can estimate the dependence of the observable agents' outcomes on their covariates, taking the presence of the latent agents into account. First, we show that a certain matrix succinctly captures the relationship between the outcomes and the covariates. We provide an algorithm that estimates this matrix using historical data of covariates and outcomes for the observable agents under a suitable approximate sparsity condition. We also establish convergence rates for the proposed estimator despite the high dimensionality that allows more agents than observations. Second, we show that the approximate sparsity condition holds under the standard conditions used in the literature. Hence, our results apply to a large class of networks. Finally, we illustrate the applications to a targeted advertising problem. We show that, by using the available historical data with our estimator, it is possible to obtain asymptotically optimal advertising decisions despite the presence of latent agents. Funding: O. Candogan acknowledges NSF award 2216912 for "Institute for Data, Econometrics, Algorithms and Learning". Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2485. [ABSTRACT FROM AUTHOR]