The Egocentric Logic of Preferences
Authors: Junli Jiang, Pavel Naumov
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 walk08@swu.edu.cn, p.naumov@soton.ac.uk |
| 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. |