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Problem definition: We study settings where agents sequentially search among different options under competition. Motivated by labor markets and the allocation of kidneys from deceased donors, we focus on the effect of (i) the mechanism to collect decisions, that is, whether all agents make their decisions simultaneously or sequentially, and (ii) competition, that is, the number of agents who are searching from a shared pool of options. Methodology/results: We introduce a model of sequential search under competition, in which agents are exogenously prioritized and must decide when to stop their search to maximize the chosen option's value. We characterize the optimal policy, which defines a sequence of thresholds that dictates when each agent should accept an option based on their priority relative to others still searching and the number of remaining options. Our analysis reveals that neither the mechanism for collecting agents' decisions nor the number of lower-priority agents influences the optimal policy. To test these predictions, we designed and conducted a laboratory experiment replicating our theoretical model. The results indicate significant deviations from the optimal policy. Moreover, we find that the mechanism significantly affects agents' decisions due to primarily two drivers: (i) saliency of competition and (ii) frustration. Finally, we identify an "illusion of competition" effect, whereby agents use significantly lower thresholds when the number of agents with lower priority increases. Managerial implications: Our results show that a higher perception of competition and using a simultaneous mechanism (i.e., batch offering) significantly decrease the thresholds that agents use to guide their search, making them stop their search earlier. Thus, clearinghouses that suffer from inefficient discard of options should increase the saliency of competition and use batch offerings to reduce agents' selectivity and mitigate waste. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0621. [ABSTRACT FROM AUTHOR]

Problem definition: Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Academic/practical relevance: Increasing match rates is a prevalent objective of online platforms. We provide insights into how to leverage users' preferences and behavior toward this end. Our proposed algorithm was piloted by our collaborator, a major online dating company in the United States. Methodology: Our work combines several methodologies. We model the platform's problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company's algorithm in order to estimate the users' preferences and the causal effect of previous matches on the like behavior of users, as well as other parameters of interest. Leveraging our data findings, we propose a family of heuristics to solve the platform's problem and use simulations and field experiments to assess their benefits. Results: We find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. We propose a family of heuristics to decide the profiles to show to each user on each day that accounts for this finding. Two field experiments show that our algorithm yields at least 27% more matches relative to our industry partner's algorithm. Managerial implications: Our results highlight the importance of correctly accounting for the preferences, behavior, and activity metrics of users on both ends of a transaction to improve the operational efficiency of matching platforms. In addition, we propose a novel identification strategy to measure the effect of previous matches on the users' preferences in a two-sided matching market, the result of which is leveraged by our algorithm. Our methodology may also be applied to online matching platforms in other domains. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1107. [ABSTRACT FROM AUTHOR]

Motivated by recent initiatives to increase transparency in procurement, we study the effects of disclosing information about previous purchases in a setting where an organization delegates its purchasing decisions to its employees. When employees can use their own discretion, which may be influenced by personal preferences, to select a supplier, the incentives of the employees and the organization may be misaligned. Disclosing information about previous purchasing decisions made by other employees can reduce or exacerbate this misalignment, as peer effects may come into play. To understand the effects of transparency, we introduce a theoretical model that compares employees' actions in two settings: one where employees cannot observe each other's choices and one where they can observe the decision previously made by a peer before making their own. Two behavioral assumptions are central to our model: that employees are heterogeneous in their reciprocity toward their employer, and that they experience peer effects in the form of income inequality aversion toward their peer. As a result, our model predicts the existence of negative spillovers as a reciprocal employee is more likely to choose the expensive supplier (which gives the employee a personal reward) when the employee observes that a peer did so. A laboratory experiment confirms the existence of negative spillovers and the main behavioral mechanisms described in our model. A surprising result not predicted by our theory is that employees whose decisions are observed by their peers are less likely to choose the expensive supplier than the employees in the no-transparency case. We show that observed employees' preferences for compliance with the social norm of appropriate purchasing behavior explain our data well. This paper was accepted by Yuval Victor Martinez de Albeniz, operations management. [ABSTRACT FROM AUTHOR]

In "Improving the Chilean College Admissions System," Rios, Larroucau, Parra, and Cominetti describe the design and implementation of a new system to solve the Chilean college admissions problem. The authors develop an algorithm that (i) obtains all applicant/program pairs that can be part of a stable allocation when preferences are not strict and when all students tied in the last seat of a program (if any) must be allocated and (ii) efficiently incorporates affirmative action, which is part of the system to correct the inefficiencies that arise from having double-assigned students. By unifying the regular admission with affirmative action, the solution proposed and implemented by the authors has improved the allocation of approximately 2.5% of students assigned every year since 2016, helping to improve the overall efficiency of the system. In this paper we present the design and implementation of a new system to solve the Chilean college admissions problem. We develop an algorithm that obtains all applicant/program pairs that can be part of a stable allocation when preferences are not strict and when all students tied in the last seat of a program (if any) must be allocated. We use this algorithm to identify which mechanism was used in the past to perform the allocation, and we propose a new method to incorporate the affirmative action that is part of the system to correct the inefficiencies that arise from having double-assigned students. By unifying the regular admission with the affirmative action, we have improved the allocation of approximately 2.5% of students assigned every year since 2016. From a theoretical standpoint, we show that some desired properties, such as strategy-proofness and monotonicity, cannot be guaranteed under flexible quotas. [ABSTRACT FROM AUTHOR]