Pareto Optimal Allocation under Compact Uncertain Preferences
Authors: Haris Aziz, Peter Biro, Ronald de Haan, Baharak Rastegari1740-1747
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models. Our technical results are summarized in Table 1. Note that our most interesting technical results are computational hardness results which therefore carry over to any settings in which an agent may find certain items unacceptable and/or an agent may be genuinely indifferent between two or more items. |
| Researcher Affiliation | Academia | Haris Aziz UNSW Sydney and Data61 Sydney, Australia haziz@cse.unsw.edu.au Peter Biro Hungarian Academy of Sciences Budapest, Hungary birop@econ.core.hu Ronald de Haan ILLC, University of Amsterdam Amsterdam, the Netherlands R.de Haan@uva.nl Baharak Rastegari University of Southampton, UK b.rastegari@soton.ac.uk |
| Pseudocode | No | The paper describes algorithmic procedures in prose (e.g., in the proofs of Theorem 1 and Theorem 3) but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper presents theoretical results and does not use or refer to any publicly available datasets for training purposes. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not involve data splitting for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper presents theoretical results and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical results and does not describe any experimental setup details such as hyperparameters or training configurations. |