Commutative Information Algebras: Representation and Duality Theory
Juerg Kohlas, Juerg Schmid

TL;DR
This paper develops a representation and duality theory for commutative information algebras, showing they can be represented as set algebras and extending classical duality theories to include additional algebraic operations.
Contribution
It introduces a representation theorem for abstract information algebras as set algebras and extends Stone and Priestley duality to these structures with additional operations.
Findings
Every abstract information algebra is isomorphic to a set algebra.
Extended duality theory for distributive and Boolean lattices with additional operations.
Framework for modeling logical connectives within information algebras.
Abstract
Information algebras arise from the idea that information comes in pieces which can be aggregated or combined into new pieces, that information refers to questions and that from any piece of information, the part relevant to a given question can be extracted. This leads to a certain type of algebraic structures, basically semilattices endowed with with additional unary operations. These operations essentially are (dual) existential quantifiers on the underlying semilattice. The archetypical instances of such algebras are semilattices of subsets of some universe, together with the saturation operators associated with a family of equivalence relations on this universe. Such algebras will be called {\em set algebras} in our context. Our first result is a basic representation theorem: Every abstract information algebra is isomorphic to a set algebra. When it comes to combine pieces of…
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 · Logic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference
