Designing Random Allocation Mechanisms: Theory and Applications

By Eric Budish, Yeon-Koo Che, Fuhito Kojima, Paul R. Milgrom. 2013

389 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4686149e4f495724d5e94c8b3d4692f95a00d778

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Citations in the corpus

School choice with controlled choice constraints: Hard bounds versus soft bounds (2011, neutral): “(cid:4)”
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts (2021, background): “In a different setting, the problem can also be seen as random fair assignment [5], with the difference being that in random fair assignment one typically is not concerned about dependence between the individual assignments.”
Almost Envy-Freeness with General Valuations (2017, background): “, [2, 9]), but this is not suitable for the applications we are most interested in, where the outcome is only used once.”
Two axiomatic approaches to the probabilistic serial mechanism (2014, background): “Budish et al. (2013) characterize the constraints on a random assignment that can also be satisfied by each of the deterministic assignments in the support of a lottery that induces it. The compelling notion of sd-efficiency is also the focus of other related papers. Abdulkadiroğlu and Sönmez (2003a”
Assignment Mechanisms under Distributional Constraints (2018, background): “Second, the set of constraints is more general than the bihierarchical structure that is necessary and sufficient for implementing a random assignment using a lottery over feasible assignments (Budish et al. 2013).”
Efficient Matching under Distributional Constraints: Theory and Applications (2015, background): “Milgrom (2009) and Budish, Che, Kojima, and Milgrom (2010) consider object allocation mechanisms with restrictions similar to the regional caps in our model.”
Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division (2017, background): “The algorithm [7, 38] guarantees equity, with each user believing their workload is identical.”
Reserve Design: Unintended Consequences and the Demise of Boston’s Walk Zones (2018, background): “…agenda, examined in a number of recent papers, that introduces diversity concerns into the literature on school choice mechanism design (see, e.g., Erdil and Kumano 2012; Kojima 2012; Budish et al. 2013; Hafalir, Yenmez, and Yildirim 2013; Kominers and Sönmez 2013, 2016; Echenique and Yenmez 2015).”
The Unreasonable Fairness of Maximum Nash Welfare (2016, neutral): “Second, it is possible to allow randomized allocations [Bogomolnaia and Moulin 2001, 2004; Budish et al. 2013]; this is hardly appropriate for the cases we find on Spliddit in which the outcome is used only once.”