Near-Optimality in Covering Games by Exposing Global Information
Abstract
Mechanism design for distributed systems is fundamentally concerned with aligning individual incentives with social welfare to avoid socially inefficient outcomes that can arise from agents acting autonomously. One simple and natural approach is to centrally broadcast nonbinding advice intended to guide the system to a socially near-optimal state while still harnessing the incentives of individual agents. The analytical challenge is proving fast convergence to near optimal states, and in this article we give the first results that carefully constructed advice vectors yield stronger guarantees.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 28, 2014
- Source ID
- 10.1145/2597890
Entities
People
- Georgios Piliouras
- Jinwoo Shin
- Maria-florina Balcan
- Sara Krehbiel
Organizations
- Air Force Office of Scientific Research
- Division of Computing and Communication Foundations
- Georgia Tech
- KAIST
- Office of Naval Research