Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | AAAI-07/IAAI-07 Proceedings |
| Subtitle of host publication | 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference |
| Pages | 30-35 |
| Number of pages | 6 |
| State | Published - 2007 |
| Externally published | Yes |
| Event | AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference - Vancouver, BC, Canada Duration: 22 Jul 2007 → 26 Jul 2007 |
Publication series
| Name | Proceedings of the National Conference on Artificial Intelligence |
|---|---|
| Volume | 1 |
Conference
| Conference | AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference |
|---|---|
| Country/Territory | Canada |
| City | Vancouver, BC |
| Period | 22/07/07 → 26/07/07 |