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..
The Egocentric Logic of Preferences
Authors: Junli Jiang, Pavel Naumov
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper studies preferences of agents about other agents in a social network. It proposes a logical system that captures the properties of such preferences, called likes . The system can express nested constructions agent likes humbled people , agent likes those who like humbled people , etc. The main technical results are a model checking algorithm and a sound, complete, and decidable axiomatization of the proposed system. |
| Researcher Affiliation | Academia | Junli Jiang1 and Pavel Naumov2 1Institute of Logic and Intelligence, Southwest University, China 2University of Southampton, the United Kingdom EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model checking algorithm in text in Section 5 ('Model Checking') but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link, explicit code release statement, or code in supplementary materials. |
| Open Datasets | No | This paper is theoretical and does not use datasets for training. It describes abstract 'social networks' for its logical system without providing access to specific datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical, focusing on a logical system and algorithm. It does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |