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].
Ranked Voting on Social Networks
Authors: Ariel D. Procaccia, Nisarg Shah, Eric Sodomka
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish a general framework based on random utility theory for ranked voting on a social network with arbitrarily many alternatives... We identify a family of voting rules which... are guaranteed to recover the ground truth with high probability in large networks... We analyze the performance of two disjoint families of voting rules PM-c rules and PD-c rules... Under a mild condition... we show that all PM-c rules, an important subset of PD-c rules, and the modal ranking rule are accurate in the limit... We now use Lemma 1 to derive our main result. Theorem 1. If there exists a universal constant D N such that... all PM-c rules, the modal ranking rule, and all strict positional scoring rules are accurate in the limit... Computer-based simulations provided non-trivial counterexamples (presented in the full version). |
| Researcher Affiliation | Collaboration | Ariel D. Procaccia Carnegie Mellon University EMAIL Nisarg Shah Carnegie Mellon University EMAIL Eric Sodomka Facebook EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with specific datasets, therefore no public dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset usage, thus no training/test/validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameter values or training configurations. |