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.
Complexity of Deliberative Coalition Formation
Authors: Edith Elkind, Abheek Ghosh, Paul Goldberg4975-4982
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,457 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,469 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 | Our goal is to complement the analysis of Elkind et al. (2020), by exploring the complexity of the deliberation process in their model. We focus on two deliberation spaces: in the hypercube model, voters and proposals are vertices of the d-dimensional hypercube, with distances measured according to the Hamming distance, and in the Euclidean model voters and proposals are elements of the d-dimensional Euclidean space, with || ||2 being the underlying distance measure. We consider three types of questions: What is the computational complexity of identifying a proposal approved by as many voters as possible... How many transitions may be necessary... How many coalitions need to be involved... |
| Researcher Affiliation | Academia | Edith Elkind, Abheek Ghosh , Paul Goldberg Department of Computer Science, University of Oxford, UK EMAIL |
| Pseudocode | No | The paper focuses on theoretical analysis, proofs, and discussions of computational complexity; it does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to an extended version on arXiv, but it does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on complexity analysis and proofs. It does not use empirical data or datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments run on specific hardware. |
| Software Dependencies | No | This is a theoretical paper; it does not list specific software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | This is a theoretical paper and does not include details about an experimental setup, hyperparameters, or training configurations. |