Exchangeable choice functions
This work addresses a foundational issue in probability theory and decision-making for researchers in statistics and AI, but it appears incremental as it extends known theorems to choice functions.
The paper tackles the problem of modeling exchangeability using choice functions, showing that such assessments are a special case of indifference and leading to a counterpart of de Finetti's Representation Theorem in finite and countable contexts.
We investigate how to model exchangeability with choice functions. Exchangeability is a structural assessment on a sequence of uncertain variables. We show how such assessments are a special indifference assessment, and how that leads to a counterpart of de Finetti's Representation Theorem, both in a finite and a countable context.