Recognising Multidimensional Euclidean Preferences

Authors: Dominik Peters

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We show that, in contrast, for every other fixed dimension d > 1, the recognition problem is equivalent to the existential theory of the reals (ETR), and so in particular NP-hard. We further show that some Euclidean preference profiles require exponentially many bits in order to specify any Euclidean embedding, and prove that the domain of d-Euclidean preferences does not admit a finite forbidden minor characterisation for any d > 1. We also study dichotomous preferences and the behaviour of other metrics, and survey a variety of related work.
Researcher Affiliation Academia Dominik Peters Department of Computer Science University of Oxford, UK dominik.peters@cs.ox.ac.uk
Pseudocode No The paper does not contain pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide a statement about releasing source code or a link to a code repository.
Open Datasets Yes We have run some preliminary experiments on the PrefLib dataset (Mattei and Walsh 2013) using the nlsat solver (Jovanovi c and De Moura 2012) which appears to be the strongest ETR-solver available.
Dataset Splits No The paper mentions using the PrefLib dataset but does not specify any training, validation, or test splits for it.
Hardware Specification No The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments.
Software Dependencies No The paper mentions using the "nlsat solver" but does not specify its version number or any other software dependencies with versions.
Experiment Setup No The paper mentions attempting experiments with the nlsat solver and a time bound, but does not provide specific experimental setup details like hyperparameters or system-level training settings.