# Can we forget how we learned? Doxastic redundancy in iterated belief revision

arXiv: https://arxiv.org/abs/2402.15445 · HTML: https://mlsanity.com/paper/2402.15445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [When You Must Forget: beyond strong persistence when forgetting in answer set programming](https://mlsanity.com/paper/1707.05152/) (2017)
- [Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach](https://mlsanity.com/paper/1902.06178/) (2019)
- [Iterated Belief Revision Under Resource Constraints: Logic as Geometry](https://mlsanity.com/paper/1812.08313/) (2018)
- [A Conditional Perspective on the Logic of Iterated Belief Contraction](https://mlsanity.com/paper/2202.03196/) (2022)
- [Representing states in iterated belief revision](https://mlsanity.com/paper/2305.09200/) (2023)
- [Natural revision is contingently-conditionalized revision](https://mlsanity.com/paper/2309.12655/) (2023)

## Abstract (arXiv)

Forgetting a belief acquisition episode may not cause information loss because of the others. Checking whether it does is not obvious, as the contribution of each belief revision is not isolated from the others, and the same information may be given not directly but by deduction. An algorithm for checking whether forgetting reduces information is given for a number of iterated belief revision operators: lexicographic, natural, severe, plain severe, moderate severe, restrained, very radical and full meet revisions. It may take exponential time in the worst case, which is expected given that the problem is coNP-hard, even in the Horn restriction. It is in coNP for homogeneous sequences of lexicographic revisions.