Background and Scope: The theory of stochastic networks and their control is undergoing a phase of exciting collaborative growth with modern machine learning and artificial intelligence techniques, fueled by several new applications.
On the one hand, recent years have seen a surge of interest in the application of machine learning techniques to the control of stochastic networks arising in modern communications applications like data centers, the goal being to design agile control mechanisms that operate under minimal informational assumptions and that adapt to changes in system characteristics over time.
On the other hand, stochastic networks have emerged as a powerful modeling paradigm for addressing operational questions of matching demand and supply in modern platforms and marketplaces. Examples include ride-sharing platforms, labor platforms, energy markets, etc. A key value proposition of these platforms is the ability to learn from the tremendous amount of transactional data being generated to predict and mitigate supply-demand imbalances, and to recommend and make higher value matches than would otherwise be possible without centralized recommendations and/or matchmaking. Realizing this value proposition requires network control mechanisms that seamlessly assimilate information from data while being robust to strategic concerns.
These developments have led to a growing body of literature on the control of stochastic networks with novel architectures and at the interface of these control mechanisms with machine learning and strategic behavior. The special issue invites papers addressing novel technical challenges in this domain with direct applications to practice.More details: https://www.springer.com/journal/11134/updates/17587628
Guest Editors:Rahul Jain (USC)Vijay Kamble (UIC) Sanjay Shakkottai (UT Austin)Jiaming Xu (Duke)
Date of submission: Oct 31 20201st reviews sent: April 30 2021Revision deadline: July 31 2021Final decisions: Nov 30 2021Publication date: Dec 2021
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