Online Resource Allocation with Samples

TL;DR

This paper introduces an asymptotically optimal online resource allocation algorithm that leverages sampling during a test period to improve decision-making under demand and reward uncertainty, especially for new resources.

Contribution

It develops a novel algorithm that exploits sample information to achieve near-optimal competitive ratios in online resource allocation with unknown rewards.

Findings

Achieves a competitive ratio of 1 - Θ(1/(p√m))

Proves the ratio is tight for p = ω(1/√m)

Demonstrates effectiveness using COVID-19 hospitalization data

Abstract

We study an online resource allocation problem under uncertainty about demand and about the reward of each type of demand (agents) for the resource. Even though dealing with demand uncertainty in resource allocation problems has been the topic of many papers in the literature, the challenge of not knowing rewards has been barely explored. The lack of knowledge about agents' rewards is inspired by the problem of allocating units of a new resource (e.g., newly developed vaccines or drugs) with unknown effectiveness/value. For such settings, we assume that we can \emph{test} the market before the allocation period starts. During the test period, we sample each agent in the market with probability $p$. We study how to optimally exploit the \emph{sample information} in our online resource allocation problem under adversarial arrival processes. We present an asymptotically optimal algorithm that achieves $1-\Theta(1/(p\sqrt{m}))$ competitive ratio, where $m$ is the number of available units of the resource. By characterizing an upper bound on the competitive ratio of any randomized and deterministic algorithm, we show that our competitive ratio of $1-\Theta(1/(p\sqrt{m}))$ is tight for any $p =\omega(1/\sqrt{m})$. That asymptotic optimality is possible with sample information highlights the significant advantage of running a test period for new resources. We demonstrate the efficacy of our proposed algorithm using a dataset that contains the number of COVID-19 related hospitalized patients across different age groups.