TY - GEN
T1 - Implementing the maximum of monotone algorithms
AU - Blumrosen, Liad
PY - 2007
Y1 - 2007
N2 - Running several sub-optimal algorithms and choosing the optimal one is a common procedure in computer science, most notably in the design of approximation algorithms. This paper deals with one significant flaw of this technique in environments where the inputs are provided by selfish agents: such protocols are not necessarily incentive compatible even when the underlying algorithms are. We characterize sufficient and necessary conditions for such best-outcome protocols to be incentive compatible in a general model for agents with one-dimensional private data. We show how our techniques apply in several settings.
AB - Running several sub-optimal algorithms and choosing the optimal one is a common procedure in computer science, most notably in the design of approximation algorithms. This paper deals with one significant flaw of this technique in environments where the inputs are provided by selfish agents: such protocols are not necessarily incentive compatible even when the underlying algorithms are. We characterize sufficient and necessary conditions for such best-outcome protocols to be incentive compatible in a general model for agents with one-dimensional private data. We show how our techniques apply in several settings.
UR - http://www.scopus.com/inward/record.url?scp=36349035155&partnerID=8YFLogxK
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:36349035155
SN - 1577353234
SN - 9781577353232
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 30
EP - 35
BT - AAAI-07/IAAI-07 Proceedings
T2 - AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Y2 - 22 July 2007 through 26 July 2007
ER -