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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Uncommon Belief in Rationality
Authors: Qi Shi, Pavel Naumov
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main contributions are a solution concept that captures the reasoning process based on a given belief structure and an efficient algorithm for compressing any belief structure into a unique minimal form. |
| Researcher Affiliation | Academia | University of Southampton, UK EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1: Minimise an RBR graph |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses a 'simplified guess 2/3 of the average game' as an example to illustrate its theoretical concepts. This is a conceptual game, not an external, publicly available dataset. |
| Dataset Splits | No | The paper does not use any datasets or perform empirical experiments that would require dataset splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper describes a theoretical framework and an algorithm but does not list any specific software or library dependencies with version numbers. |
| Experiment Setup | No | The paper illustrates its theoretical concepts using a conceptual game example and an iterative rationalisation process. It does not describe an empirical experimental setup with hyperparameters, training configurations, or other system-level settings typically found in experimental papers. |