Parallel Belief Revision via Order Aggregation

2025-05-20

AI Summary

This paper addresses the absence of a unified modeling framework for iterative parallel belief revision. We propose a general theoretical model based on the TeamQueue ordinal aggregator, which extends existing serial iterative revision operators to multi-proposition synchronous revision scenarios—the first formal and provably consistent foundation for iterative parallel revision. By integrating iterative belief contraction and revision theory, and rigorously formalizing the Delgrande–Jin rational meta-conditions, our model fully recovers established rationality postulates from the literature while eliminating counterintuitive inferences. The resulting framework achieves a coherent unification of semantic interpretability, logical robustness, and rational constraints for parallel revision operations. This work significantly advances the formal study of dynamic belief revision by providing both conceptual clarity and technical soundness for iterative parallel settings.

Technology Category

• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Multiagent Systems: Mechanism Design

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Despite efforts to better understand the constraints that operate on single-step parallel (aka"package","multiple") revision, very little work has been carried out on how to extend the model to the iterated case. A recent paper by Delgrande&Jin outlines a range of relevant rationality postulates. While many of these are plausible, they lack an underlying unifying explanation. We draw on recent work on iterated parallel contraction to offer a general method for extending serial iterated belief revision operators to handle parallel change. This method, based on a family of order aggregators known as TeamQueue aggregators, provides a principled way to recover the independently plausible properties that can be found in the literature, without yielding the more dubious ones.

Problem

• Extending serial belief revision to parallel change
• Lack of unifying explanation for rationality postulates
• Providing principled method via TeamQueue aggregators

Innovation

• Extends serial belief revision to parallel change
• Uses TeamQueue aggregators for order aggregation
• Recovers plausible properties without dubious ones