TY - GEN
T1 - Efficient allocation of free stuff
AU - Azar, Yossi
AU - Borodin, Allan
AU - Feldman, Michal
AU - Fiat, Amos
AU - Segal, Kineret
N1 - Publisher Copyright:
© 2019 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org) Ail rights reserved.
PY - 2019
Y1 - 2019
N2 - We study online matching settings with selfish agents when everything is free Inconsiderate agents break ties arbitrarily amongst equal maximal value available choices, even if the maximal value is equal to zero Even for the simplest case of zero/one valuations, where agents arrive online in an arbitrary order, and agents are restricted to taking at most one item, the resulting social welfare may be negligible for a deterministic algorithm This may be surprising when contrasted with the 1/2 approximation of the greedy algorithm, analogous to this setting, except that agents are considerate (i.e., they don't take zero-valued items) We overcome this challenge by introducing a new class of algorithms, which we refer to as prioritization algorithms We show that upgrading a random subset of the agents to "business class- A lready improves the approximation to a constant For more general valuations, we achieve a constant approximation using log n prior-it)' classes, when the valuations are known in advance We extend these results to settings where agents have additive valuations and are restricted to taking up to some q 1 items Our results are tight up to a constant.
AB - We study online matching settings with selfish agents when everything is free Inconsiderate agents break ties arbitrarily amongst equal maximal value available choices, even if the maximal value is equal to zero Even for the simplest case of zero/one valuations, where agents arrive online in an arbitrary order, and agents are restricted to taking at most one item, the resulting social welfare may be negligible for a deterministic algorithm This may be surprising when contrasted with the 1/2 approximation of the greedy algorithm, analogous to this setting, except that agents are considerate (i.e., they don't take zero-valued items) We overcome this challenge by introducing a new class of algorithms, which we refer to as prioritization algorithms We show that upgrading a random subset of the agents to "business class- A lready improves the approximation to a constant For more general valuations, we achieve a constant approximation using log n prior-it)' classes, when the valuations are known in advance We extend these results to settings where agents have additive valuations and are restricted to taking up to some q 1 items Our results are tight up to a constant.
KW - Online matching
KW - Selfish agents
KW - Welfare approximation
UR - http://www.scopus.com/inward/record.url?scp=85076954993&partnerID=8YFLogxK
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AN - SCOPUS:85076954993
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 918
EP - 925
BT - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Y2 - 13 May 2019 through 17 May 2019
ER -