TY - GEN
T1 - Social welfare in one-sided matchings: random priority and beyond
AU - Filos-Ratsikas, Aris
AU - Frederiksen, Soren Kristoffer Stiil
AU - Zhang, Jie
PY - 2014
Y1 - 2014
N2 - We study the problem of approximate social welfare maximization (without money) in one-sided matching problems when agents have unrestricted cardinal preferences over a finite set of items. Random priority is a very well-known truthful-in-expectation mechanism for the problem. We prove that the approximation ratio of random priority is O(n -1/2) while no truthful-in-expectation mechanism can achieve an approximation ratio better than O(n -1/2), where n is the number of agents and items. Furthermore, we prove that the approximation ratio of all ordinal (not necessarily truthful-in-expectation) mechanisms is upper bounded by O(n -1/2), indicating that random priority is asymptotically the best truthful-in-expectation mechanism and the best ordinal mechanism for the problem.
AB - We study the problem of approximate social welfare maximization (without money) in one-sided matching problems when agents have unrestricted cardinal preferences over a finite set of items. Random priority is a very well-known truthful-in-expectation mechanism for the problem. We prove that the approximation ratio of random priority is O(n -1/2) while no truthful-in-expectation mechanism can achieve an approximation ratio better than O(n -1/2), where n is the number of agents and items. Furthermore, we prove that the approximation ratio of all ordinal (not necessarily truthful-in-expectation) mechanisms is upper bounded by O(n -1/2), indicating that random priority is asymptotically the best truthful-in-expectation mechanism and the best ordinal mechanism for the problem.
U2 - 10.1007/978-3-662-44803-8_1
DO - 10.1007/978-3-662-44803-8_1
M3 - Chapter in a published conference proceeding
SN - 978-3-662-44802-1
VL - 8768
T3 - Lecture Notes in Computer Science
SP - 1
EP - 12
BT - Algorithmic Game Theory
A2 - Lavi, R
PB - Springer
CY - Switzerland
ER -