Collective Belief Revision
Authors: Theofanis I. Aravanis
JAIR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this article, we study the dynamics of collective beliefs. As a first step, we formulate David Westlund s Principle of Collective Change (PCC) a criterion that characterizes the evolution of collective knowledge in the realm of belief revision. Thereafter, we establish a number of unsatisfiability results pointing out that the widely-accepted revision operators of Alchourr on, G ardenfors and Makinson, combined with fundamental types of merging operations including the ones proposed by Konieczny and Pino P erez as well as Baral et al. collide with the PCC. These impossibility results essentially extend in the context of belief revision the negative results established by Westlund for the operations of contraction and expansion. At the opposite of the impossibility results, we also establish a number of satisfiability results, proving that, under certain (rather strict) requirements, the PCC is indeed respected for specific merging operators. |
| Researcher Affiliation | Academia | Theofanis I. Aravanis EMAIL Department of Mechanical Engineering School of Engineering University of the Peloponnese Patras 263 34, Greece |
| Pseudocode | No | The paper defines axiomatic postulates for belief revision (K1-K8) and merging functions (IC0-IC8) and discusses theoretical concepts and proofs. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code, repository links, or statements about code availability in supplementary materials or elsewhere. This is a theoretical paper and does not involve software implementation for experiments. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available or open datasets. Examples 24 and 25 illustrate concepts with constructed belief sets and propositional variables, not real-world data. |
| Dataset Splits | No | The paper does not use any datasets for experiments, so there is no information regarding dataset splits (e.g., training, test, validation splits). |
| Hardware Specification | No | The paper describes theoretical research and does not involve computational experiments, hence no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or experiments that would require specific software dependencies or version numbers. |
| Experiment Setup | No | The paper presents theoretical results and logical proofs, not experimental results. Therefore, there are no experimental setup details, hyperparameters, or training configurations described. |