Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Sybil-Resilient Reality-Aware Social Choice
Authors: Gal Shahaf, Ehud Shapiro, Nimrod Talmon
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is theoretical, focusing on defining models, proving theorems (Lemma 1, Theorem 1, Theorem 2, Theorem 3, Theorem 4), and presenting algorithms without empirical evaluation, datasets, or experimental results. For example, Section 3 and 4 are filled with formal definitions, lemmas, theorems, and proofs, and there are no sections related to experiments, data collection, or performance metrics. |
| Researcher Affiliation | Academia | Gal Shahaf1 , Ehud Shapiro1 , Nimrod Talmon2 1Weizmann Institute of Science 2Ben-Gurion University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 (Conservative δ-Supermajority Amendment Agenda). Let A be the set of alternatives with r A and let δ [0, 1/2]. If Aδ r = , elect r. Else, perform an Amendment Agenda vote on Aδ r starting with r and employing δ-supermajorities, and let w Aδ r be the winner. Then, vote w against all members of Aδ r not previously voted against w, if any. If w wins all these votes by a δ-supermajority then elect w. Else elect r. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use any datasets for training or evaluation. Therefore, there is no information about publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Thus, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not discuss any experimental setup that would require hardware specifications. No hardware details are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers needed for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments. Therefore, there are no details about experimental setup, hyperparameters, or training settings. |