Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees
Authors: Zongjun Yang, Luofeng Liao, Christian Kroer
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study an online allocation problem with sequentially arriving items and adversarially chosen agent values, with the goal of balancing fairness and efficiency. Our goal is to study the performance of algorithms that achieve strong guarantees under other input models such as stochastic inputs, in order to achieve robust guarantees against a variety of inputs. To that end, we study the PACE (Pacing According to Current Estimated utility) algorithm, an existing algorithm designed for stochastic input. We show that in the equal-budgets case, PACE is equivalent to an integral greedy algorithm. We go on to show that with natural restrictions on the adversarial input model, both the greedy allocation and PACE have asymptotically bounded multiplicative envy as well as competitive ratio for Nash welfare, with the multiplicative factors either constant or with optimal order dependence on the number of agents. This completes a best-of-many-worlds guarantee for PACE, since past work showed that PACE achieves guarantees for stationary and stochastic-but-non-stationary input models. |
| Researcher Affiliation | Academia | Zongjun Yang1, Luofeng Liao2, Christian Kroer2* 1School of Electronics Engineering and Computer Science, Peking University 2Columbia University allenyzj@stu.pku.edu.cn, {ll3530, christian.kroer}@columbia.edu |
| Pseudocode | Yes | Pseudocode for PACE is shown in Algorithm 2.1. ... We focus on first-order, integral greedy algorithm, or simply greedy algorithm in short, shown in Algorithm 2.2. |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology, nor a link to a code repository, was found. The arXiv link provided is for the paper itself. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments; therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments; therefore, no experimental setup details are provided. |