Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Ranked Voting on Social Networks
Authors: Ariel D. Procaccia, Nisarg Shah, Eric Sodomka
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,933 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,258 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |