On the Incompatibility of Efficiency and Strategyproofness in Randomized Social Choice

Authors: Haris Aziz, Florian Brandl, Felix Brandt

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we give an overview of common preference extensions, propose two new ones, and show that the abovementioned incompatibility can be extended to various other notions of strategyproofness and efficiency in randomized social choice. ... We complement and strengthen these results by proving the following theorems (always assuming anonymity): ... We prove each of these results by reasoning about a set of preference profiles and deriving a contradiction.
Researcher Affiliation Academia Haris Aziz NICTA and UNSW Sydney 2033, Australia Florian Brandl Technische Universit at M unchen 85748 Garching, Germany Felix Brandt Technische Universit at M unchen 85748 Garching, Germany
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks. The methods are described through mathematical definitions and logical proofs.
Open Source Code No The paper does not provide any concrete access to source code, such as a specific repository link, an explicit code release statement, or code in supplementary materials.
Open Datasets No The paper is theoretical and does not involve experiments with datasets. Therefore, it does not provide any information about publicly available or open datasets.
Dataset Splits No The paper is theoretical and does not involve experiments with datasets, nor does it describe any dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not involve computational experiments. Therefore, it does not mention any hardware specifications.
Software Dependencies No The paper is theoretical and does not involve computational experiments that would require specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not involve empirical experiments with specific setup details, hyperparameters, or training configurations.