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.
Goal-Based Collective Decisions: Axiomatics and Computational Complexity
Authors: Arianna Novaro, Umberto Grandi, Dominique Longin, Emiliano Lorini
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,336 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,567 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 study agents expressing propositional goals over a set of binary issues to reach a collective decision. We adapt properties and rules from the literature on Social Choice Theory to our setting, providing an axiomatic characterisation of a majority rule for goal-based voting. We study the computational complexity of finding the outcome of our rules (i.e., winner determination), showing that it ranges from Nondeterministic Polynomial Time (NP) to Probabilistic Polynomial Time (PP). |
| Researcher Affiliation | Academia | Arianna Novaro1, Umberto Grandi1, Dominique Longin2, Emiliano Lorini2 1 IRIT, University of Toulouse 2 IRIT, CNRS, Toulouse |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is being released. |
| Open Datasets | No | This paper is theoretical and does not involve empirical experiments with datasets, therefore no training dataset information is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, therefore no validation dataset information is provided. |
| Hardware Specification | No | This paper is theoretical and does not involve empirical experiments, therefore no specific hardware details are provided. |
| Software Dependencies | No | This paper is theoretical and does not involve empirical experiments, therefore no specific ancillary software details with version numbers are provided. |
| Experiment Setup | No | This paper is theoretical and does not involve empirical experiments, therefore no specific experimental setup details are provided. |