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Competing Bandits in Non-Stationary Matching Markets
Avishek Ghosh · Abishek Sankararaman · Kannan Ramchandran · Tara Javidi · Arya Mazumdar

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in The Many Facets of Preference-Based Learning »
Event URL: https://openreview.net/forum?id=OPejczTnRO »

Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial interest. To that end, in this paper, we introduce the framework of decentralized two-sided matching market under non stationary (dynamic) environments. We adhere to the serial dictatorship setting, where the demand-side agents have unknown and different preferences over the supply-side (arms), but the arms have fixed and known preference over the agents. We propose and analyze an asynchronous and decentralized learning algorithm, namely Non-Stationary Competing Bandits (\texttt{NSCB}), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms. The complexity in understanding such a system stems from the fact that the competing bandits choose their actions in an asynchronous fashion, and the lower ranked agents only get to learn from a set of arms, not \emph{dominated} by the higher ranked agents, which leads to \emph{forced exploration}. With carefully defined complexity parameters, we characterize this \emph{forced exploration} and obtain sub-linear (logarithmic) regret of \texttt{NSCB}. Furthermore, we validate our theoretical findings via experiments.

Author Information

Avishek Ghosh (University of California, San Diego)
Abishek Sankararaman (Amazon Web Services)
Kannan Ramchandran (UC Berkeley)
Tara Javidi (University of California, San Diego)
Arya Mazumdar (University of California, San Diego)

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