A Competitive Posted-Price Mechanism for Online Budget-Feasible Auctions
Andreas Charalampopoulos, Dimitris Fotakis, Panagiotis Patsilinakos, Thanos Tolias
TL;DR
The paper addresses online budget-feasible procurement with secretary arrivals, where a buyer seeks to maximize a monotone submodular function $f$ under a budget $B$ using posted prices. It introduces a linear-price posted-price mechanism with an evolving threshold $\\hat{t}$ and a TestThreshold online test to adaptively approximate $OPT$, achieving a constant competitive ratio. The LM construction uses four periods to learn $v_{max}$, refine $OPT$ via PowerTower and binary search, and exploit with adaptive pricing, while rigorous probabilistic analyses (including negative dependence) guarantee high-probability success. To remove large-market assumptions, the authors extend to a PostPrices framework that combines mechanisms suited to different market sizes into a universally truthful, $O(1)$-competitive procedure. Overall, the work demonstrates that sequential posted-price mechanisms can matched the performance of bidding-based approaches for online budget-feasible submodular procurement, with practical implications for crowdsourcing and related settings.
Abstract
We consider online procurement auctions, where the agents arrive sequentially, in random order, and have private costs for their services. The buyer aims to maximize a monotone submodular value function for the subset of agents whose services are procured, subject to a budget constraint on their payments. We consider a posted-price setting where upon each agent's arrival, the buyer decides on a payment offered to them. The agent accepts or rejects the offer, depending on whether the payment exceeds their cost, without revealing any other information about their private costs whatsoever. We present a randomized online posted-price mechanism with constant competitive ratio, thus resolving the main open question of (Badanidiyuru, Kleinberg and Singer, EC 2012). Posted-price mechanisms for online procurement typically operate by learning an estimation of the optimal value, denoted as OPT, and using it to determine the payments offered to the agents. The main challenge is to learn OPT within a constant factor from the agents' accept / reject responses to the payments offered. Our approach is based on an online test of whether our estimation is too low compared against OPT and a carefully designed adaptive search that gradually refines our estimation.