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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Responsibility Gap in Collective Decision Making
Authors: Pavel Naumov, Jia Tao
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper proposes a concept of an elected dictatorship. It shows that, in a perfect information setting, the gap is empty if and only if the mechanism is an elected dictatorship. It also proves that in an imperfect information setting, the class of gap-free mechanisms is positioned strictly between two variations of the class of elected dictatorships. |
| Researcher Affiliation | Academia | 1University of Southampton, United Kingdom 2Lafayette College, United States EMAIL, EMAIL |
| Pseudocode | No | The paper describes definitions, theorems, and proofs using mathematical notation and conceptual examples, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | There is no explicit mention of open-source code being provided for the methodology described in this paper. The paper refers to an arXiv preprint for the full version and missing proofs, but not for source code. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any empirical datasets. It uses conceptual examples like the "Two-person Rule" and "Drawing Straws" mechanisms to illustrate concepts. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset split information is applicable or provided. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and conceptual models. It does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and conceptual models. It does not describe any implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and conceptual models. It does not describe any experimental setup, hyperparameters, or training configurations. |