Manipulative Elicitation — A New Attack on Elections with Incomplete Preferences
Authors: Palash Dey
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution in this paper is the discovery of the manipulative elicitation attack in regret based partial preferential setting. We also show that the corresponding computational problem for manipulative elicitation is polynomial time solvable for every monotone voting rule which includes all commonly used score based voting rules [Theorem 1 and Corollary 1]. ... We establish success of our approach by showing that the new constraints make the corresponding computational task of manipulative elicitation NP-complete for a large class of scoring rules [Theorem 2] which includes the plurality [Theorem 3], veto [Theorem 4], k-approval for any k, and Borda voting rules [Corollary 2], maximin [Theorem 5], Copelandα for every α [0, 1] [Theorem 6], and simplified Bucklin voting rules. |
| Researcher Affiliation | Academia | Palash Dey palash@tifr.res.in Tata Institute of Fundamental Research, Mumbai |
| Pseudocode | No | The paper presents theoretical proofs and problem definitions but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve the use of datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not involve the use of datasets or splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe any experimental setup that would require software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |