A probabilistic analysis of selected notions of iterated conditioning under coherence
Lydia Castronovo, Giuseppe Sanfilippo

TL;DR
This paper analyzes different notions of iterated conditioning within coherence, showing that many basic probabilistic properties are not preserved, and introduces a new iterated conditional that satisfies key probabilistic and logical properties.
Contribution
It compares existing iterated conditioning notions in three-valued logics and proposes a new approach that maintains essential probabilistic properties.
Findings
Many existing iterated conditionals do not preserve the compound probability theorem.
A new iterated conditional is introduced that satisfies the compound prevision theorem.
The proposed iterated conditional aligns with recent developments by Gilio and Sanfilippo.
Abstract
It is well know that basic conditionals satisfy some desirable basic logical and probabilistic properties, such as the compound probability theorem, but checking the validity of these becomes trickier when we switch to compound and iterated conditionals. We consider de Finetti's notion of conditional as a three-valued object and as a conditional random quantity in the betting framework. We recall the notions of conjunction and disjunction among conditionals in selected trivalent logics. First, in the framework of specific three-valued logics we analyze the notions of iterated conditioning introduced by Cooper-Calabrese, de Finetti and Farrell, respectively. We show that the compound probability theorem and other basic properties are not preserved by these objects, by also computing some probability propagation rules. Then, for each trivalent logic we introduce an iterated conditional as…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Algebra and Logic · Bayesian Modeling and Causal Inference · Logic, Reasoning, and Knowledge
