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Incentivizing Resource Pooling
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Resource pooling improves system efficiency drastically in large stochastic systems, but its effective implementation in decentralized systems remains relatively underexplored. This paper studies how to incentivize resource pooling when agents are self-interested, and their states are private information. Our primary motivation is applications in the design of decentralized computing markets, among others. We study a standard multiserver queueing model in which each server is associated with an [Formula: see text] queue and aims to minimize its time-average job holding and processing costs. We design a simple token-based mechanism where servers can earn tokens by offering help and spend tokens to request help from other servers, all in their self-interest. The mechanism induces a complex game among servers. We employ the fluid mean-field equilibrium (FMFE) concept to analyze the system, combining mean-field approximation with fluid relaxation. This framework enables us to derive a closed-form characterization of servers’ FMFE strategies. We show that these FMFE strategies approximate well the servers’ rational behavior. We leverage this framework to optimize the design of the mechanism and present our main results: As the number of servers increases, the proposed mechanism incentivizes complete resource pooling—that is, the system dynamics and performance under our mechanism match those under centralized control. Finally, we show that our mechanism achieves the first-best performance even when helping others incurs higher job processing costs and remains nearly optimal in settings with heterogeneous servers.
This paper was accepted by Omar Besbes, revenue management and market analytics.
Funding: Y. Chen acknowledges the support by the National Natural Science Foundation of China (NSFC) [Grants NSFC-72501250 and NSFC-72394361] and by the Guangdong Key Lab of Mathematical Foundations for Artificial Intelligence.
Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03550 .
Institute for Operations Research and the Management Sciences (INFORMS)
Title: Incentivizing Resource Pooling
Description:
Resource pooling improves system efficiency drastically in large stochastic systems, but its effective implementation in decentralized systems remains relatively underexplored.
This paper studies how to incentivize resource pooling when agents are self-interested, and their states are private information.
Our primary motivation is applications in the design of decentralized computing markets, among others.
We study a standard multiserver queueing model in which each server is associated with an [Formula: see text] queue and aims to minimize its time-average job holding and processing costs.
We design a simple token-based mechanism where servers can earn tokens by offering help and spend tokens to request help from other servers, all in their self-interest.
The mechanism induces a complex game among servers.
We employ the fluid mean-field equilibrium (FMFE) concept to analyze the system, combining mean-field approximation with fluid relaxation.
This framework enables us to derive a closed-form characterization of servers’ FMFE strategies.
We show that these FMFE strategies approximate well the servers’ rational behavior.
We leverage this framework to optimize the design of the mechanism and present our main results: As the number of servers increases, the proposed mechanism incentivizes complete resource pooling—that is, the system dynamics and performance under our mechanism match those under centralized control.
Finally, we show that our mechanism achieves the first-best performance even when helping others incurs higher job processing costs and remains nearly optimal in settings with heterogeneous servers.
This paper was accepted by Omar Besbes, revenue management and market analytics.
Funding: Y.
Chen acknowledges the support by the National Natural Science Foundation of China (NSFC) [Grants NSFC-72501250 and NSFC-72394361] and by the Guangdong Key Lab of Mathematical Foundations for Artificial Intelligence.
Supplemental Material: The online appendix and data files are available at https://doi.
org/10.
1287/mnsc.
2023.
03550 .
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