Responsibility Gap in Collective Decision Making
Authors: Pavel Naumov, Jia Tao
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |