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..
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. |