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].
Agenda Separability in Judgment Aggregation
Authors: Jérôme Lang, Marija Slavkovik, Srdjan Vesic
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose here a weakening of independence, named agenda separability: a judgment aggregation rule satisfies it if, whenever the agenda is composed of several independent sub-agendas, the resulting collective judgment sets can be computed separately for each sub-agenda and then put together. We show that this property is discriminant, in the sense that among judgment aggregation rules so far studied in the literature, some satisfy it and some do not. We briefly discuss the implications of agenda separability on the computation of judgment aggregation rules. |
| Researcher Affiliation | Academia | J erˆome Lang and Marija Slavkovik and Srdjan Vesic LAMSADE, CNRS University of Paris-Dauphine, France, EMAIL University of Bergen, Norway, EMAIL CRIL, CNRS Univ. Artois, France, EMAIL |
| Pseudocode | No | The paper describes methods and procedures in narrative text, but it does not contain structured pseudocode or algorithm blocks with explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper is theoretical, defining and analyzing properties of judgment aggregation rules. It does not present a new software methodology or tool, and therefore does not provide concrete access to source code for such a methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. Therefore, it does not provide information about publicly available datasets used for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or data partitioning for training, validation, or testing. Thus, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments that would require hardware specifications. Therefore, no hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments that would necessitate specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, it does not provide specific experimental setup details like hyperparameter values or training configurations. |