Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges

By David J. Abraham, Avrim Blum, Tuomas Sandholm. 2007

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Public Health, Environmental and Occupational Health

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Citations in the corpus

Learning to Run Heuristics in Tree Search (2017, background): “…Programming (MIP) have thus spanned domains as diverse as aircraft routing [Barnhart et al., 1998], wildlife conservation [Dilkina and Gomes, 2010], sports scheduling [Nemhauser and Trick, 1998], dose distribution [Lee et al., 1999] and kidney exchange [Abraham et al., 2007], to mention a few.”
Computational thinking and thinking about computing (2008, background): “Computational thinking is transforming economics, spawning a new ﬁeld of computational microeconomics, with applications such as advertisement placement, online auctions, reputation services and even ﬁnding optimal donors for n -way kidney exchange (Abraham et al .”
Matching, Allocation, and Exchange of Discrete Resources (2009, positive): “In the computer science literature, Abraham, Blum, and Sandholm (2007) introduced an integer programming algorithm that can compute the maximal multi-way exchanges with size-limit in a fast fashion exploiting the special structure of the multi-way kidney exchange problem.”
Atomic Cross-Chain Swaps (2018, background): “ps arise when matching kidney donors and recipients. A transplant recipient with an incompatible donor can swap donors to ensure that each recipient obtains a compatible organ. A number of algorithms [1, 8, 11] have been proposed for matching donors and recipients. Shapley and Scarf [19] consider th”
Algorithmics of Matching Under Preferences (2013, background): “These include problems involving identifying optimal sets of kidney “exchanges” (in such an exchange, a patient with a willing but incompatible donor can “swap” her donor with that of another patient in a similar position) where edge weights are derived from cardinal utilities [511, 512, 17, 513, 42”
Handbook of Computational Social Choice (2016, methodology): “It involves a reduction from the NP-hard satisﬁability problem for propositional logic to our manipulation problem.”