3 research outputs found
Exploring the Tradeoff between Competitive Ratio and Variance in Online-Matching Markets
In this paper, we propose an online-matching-based model to study the
assignment problems arising in a wide range of online-matching markets,
including online recommendations, ride-hailing platforms, and crowdsourcing
markets. It features that each assignment can request a random set of resources
and yield a random utility, and the two (cost and utility) can be arbitrarily
correlated with each other. We present two linear-programming-based
parameterized policies to study the tradeoff between the \emph{competitive
ratio} (CR) on the total utilities and the \emph{variance} on the total number
of matches (unweighted version). The first one (SAMP) is to sample an edge
according to the distribution extracted from the clairvoyant optimal, while the
second (ATT) features a time-adaptive attenuation framework that leads to an
improvement over the state-of-the-art competitive-ratio result. We also
consider the problem under a large-budget assumption and show that SAMP
achieves asymptotically optimal performance in terms of competitive ratio.Comment: This paper was accepted to the 18th Conference on Web and Internet
Economics (WINE), 202