Competitive Online Truthful Time-Sensitive-Valued Data Auction
Shuangshuang Xue, Xiang-Yang Li

TL;DR
This paper develops online truthful auction mechanisms for trading time-sensitive valued data, achieving competitive revenue ratios under various models and assumptions, with proven bounds and extensive numerical validation.
Contribution
It introduces new online truthful auction mechanisms for time-sensitive data trading, with proven competitive ratios and relaxed assumptions, advancing the design of revenue-competitive online data auctions.
Findings
Mechanism $ ext{M}_1$ is truthful and $ ext{O}( ext{log} n)$-competitive.
Mechanism $ ext{M'}_W$ achieves $ ext{O}(n ext{log} n)$ competitiveness.
A posted-price mechanism attains a constant competitive ratio when $OPT_1$ is estimable.
Abstract
In this work, we investigate online mechanisms for trading time-sensitive valued data. We adopt a continuous function to represent the data value fluctuation over time . Our objective is to design an \emph{online} mechanism achieving \emph{truthfulness} and \emph{revenue-competitiveness}. We first prove several lower bounds on the revenue competitive ratios under various assumptions. We then propose several online truthful auction mechanisms for various adversarial models, such as a randomized observe-then-select mechanism and prove that it is \textit{truthful} and -competitive under some assumptions. Then we present an effective truthful weighted-selection mechanism by relaxing the assumptions on the sizes of the discount-classes. We prove that it achieves a competitive ratio for any known non-decreasing…
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Taxonomy
TopicsAuction Theory and Applications · Healthcare Operations and Scheduling Optimization · Financial Markets and Investment Strategies
