A sheaf-theoretic framework for causal abstraction networks that represents and learns consistent collections of mixture causal models across distributed agents.
Convex Spaces I: Definition and Examples
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abstract
We propose an abstract definition of convex spaces as sets where one can take convex combinations in a consistent way. A priori, a convex space is an algebra over a finitary version of the Giry monad. We identify the corresponding Lawvere theory as the category from arXiv:0902.2554 and use the results obtained there to extract a concrete definition of convex space in terms of a family of binary operations satisfying certain compatibility conditions. After giving an extensive list of examples of convex sets as they appear throughout mathematics and theoretical physics, we find that there also exist convex spaces that cannot be embedded into a vector space: semilattices are a class of examples of purely combinatorial type. In an information-theoretic interpretation, convex subsets of vector spaces are probabilistic, while semilattices are possibilistic. Convex spaces unify these two concepts.
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Networks of Causal Abstractions: A Sheaf-theoretic Framework
A sheaf-theoretic framework for causal abstraction networks that represents and learns consistent collections of mixture causal models across distributed agents.