
A Few Queries Go a Long Way: InformationDistortion Tradeoffs in Matching
We consider the onesided matching problem, where n agents have preferen...
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Investigating the Characteristics of OneSided Matching Mechanisms Under Various Preferences and Risk Attitudes
Onesided matching mechanisms are fundamental for assigning a set of ind...
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Utilitarians Without Utilities: Maximizing Social Welfare for Graph Problems using only Ordinal Preferences  Full Version
We consider ordinal approximation algorithms for a broad class of utilit...
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Designing Approximately Optimal Search on Matching Platforms
We study the design of a decentralized twosided matching market in whic...
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Computing the Shapley Value in Allocation Problems: Approximations and Bounds, with an Application to the Italian VQR Research Assessment Program
In allocation problems, a given set of goods are assigned to agents in s...
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Incentives in Twosided Matching Markets with Predictionenhanced Preferenceformation
Twosided matching markets have long existed to pair agents in the absen...
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A New Approach to Fair Distribution of Welfare
We consider transferableutility profitsharing games that arise from se...
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Improving Welfare in Onesided Matching using Simple Threshold Queries
We study onesided matching problems where n agents have preferences over m objects and each of them need to be assigned to at most one object. Most work on such problems assume that the agents only have ordinal preferences and usually the goal in them is to compute a matching that satisfies some notion of economic efficiency. However, in reality, agents may have some preference intensities or cardinal utilities that, e.g., indicate that they like an an object much more than another object, and not taking these into account can result in a loss in welfare. While one way to potentially account for these is to directly ask the agents for this information, such an elicitation process is cognitively demanding. Therefore, we focus on learning more about their cardinal preferences using simple threshold queries which ask an agent if they value an object greater than a certain value, and use this in turn to come up with algorithms that produce a matching that, for a particular economic notion X, satisfies X and also achieves a good approximation to the optimal welfare among all matchings that satisfy X. We focus on several notions of economic efficiency, and look at both adaptive and nonadaptive algorithms. Overall, our results show how one can improve welfare by even nonadaptively asking the agents for just one bit of extra information per object.
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