The Price of Anarchy: Centralized versus Distributed Resource Allocation Trade-offs
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ICAART_2019_51.pdf
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477.74 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Guo, Jinhong
Karlovitz, Alexander
Jaillet, Patrick
Hofmann, Martin
Date Issued
February 2019
Journal
Proceedings of the 11th International Conference on Agents and Artificial Intelligence
Publisher
Scitepress
Citation
Guo, Jinhong et al. "The Price of Anarchy: Centralized versus Distributed Resource Allocation Trade-offs." Proceedings of the 11th International Conference on Agents and Artificial Intelligence, February 2019, Prague, Czech Republic, Scitepress, 2019. © 2019 Scitepress
Version
Final published version
Abstract
Optimizing decision quality in large scale, distributed, resource allocation problems requires selecting the appropriate decision network architecture. Such resource allocation problems occur in distributed sensor networks, military air campaign planning, logistics networks, energy grids, etc. Optimal solutions require that demand, resource status, and allocation decisions are shared via messaging between geographically distributed, independent decision nodes. Jamming of wireless links, cyber attacks against the network, or infrastructure damage from natural disasters interfere with messaging and, thus, the quality of the allocation decisions. Our contribution described in the paper is a decentralized resource allocation architecture and algorithm that is robust to significant message loss and to uncertain demand arrival, and provides fine-grained, many-to-many combinatorial task allocation. Most importantly, it enables a conscious choice of the best level of decentralization under the expected degree of communications denial and quantifies the benefits of approximating status of peer nodes using proxy agents during temporary communications loss.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.5220/0007345701460153
