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• W4312556552 startingPage "403" @default.
• W4312556552 abstract "Many scenarios where agents with restrictions compete for resources can be cast as maximum matching problems on bipartite graphs. Our focus is on resource allocation problems where agents may have restrictions that make them incompatible with some resources. We assume that a Principal chooses a maximum matching randomly so that each agent is matched to a resource with some probability. Agents would like to improve their chances of being matched by modifying their restrictions within certain limits. The Principal’s goal is to advise an unsatisfied agent to relax its restrictions so that the total cost of relaxation is within a budget (chosen by the agent) and the increase in the probability of being assigned a resource is maximized. We establish hardness results for some variants of this budget-constrained maximization problem and present algorithmic results for other variants. We experimentally evaluate our methods on synthetic datasets as well as on two novel real-world datasets: a vacation activities dataset and a classrooms dataset." @default.
• W4312556552 created "2023-01-05" @default.
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• W4312556552 date "2022-01-01" @default.
• W4312556552 modified "2023-09-25" @default.
• W4312556552 title "Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise" @default.
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• W4312556552 doi "https://doi.org/10.1007/978-3-031-20614-6_23" @default.
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