Aggregation of Continuous Preferences in One Dimension
Authors: Alberto Del Pia, DuĊĦan Knop, Alexandra Lassota, Krzysztof Sornat, Nimrod Talmon
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As our model is novel, we are mainly interested in understanding what is possible to compute in our model i.e., what realizations of the model and which special cases of it have a mathematical structure that admits efficient aggregation algorithms. Generally speaking, we observe that, while the model is computationally intractable in its most generality, it admits efficient, exact algorithms for certain aggregation functions and certain restrictions on voter preferences. In what follows, after reviewing related work (Section 2), we describe our parameterized formal model (Section 3). Then, we present our theoretical results (which are formally summarized in Section 3.2): a proof of the general computational intractability of the model is described in Section 4; algorithms for basic aggregation functions are described in Section 5; and efficient algorithms for linear inputs and output are described in Section 6. We end with a future-facing discussion. |
| Researcher Affiliation | Academia | 1University of Wisconsin-Madison, USA 2Czech Technical University in Prague, Czech Republic 3Eindhoven University of Technology, Netherlands 4AGH University, Poland 5Ben-Gurion University of the Negev, Israel |
| Pseudocode | No | The paper describes algorithms in prose and through mathematical formulations (e.g., in Section 5) but does not provide structured pseudocode blocks or explicitly labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with datasets, therefore no public dataset information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, therefore no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments with specific software implementations, therefore no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific models or hyperparameters, therefore no experimental setup details are provided. |