Lexicographic choice functions
This work addresses theoretical foundations in decision theory and imprecise probability, offering incremental extensions to existing frameworks.
The paper generalizes coherent choice functions by relaxing the Archimedeanity condition while maintaining convexity, showing that such functions based on sets of desirable options correspond to lexicographic probabilities and can derive the most conservative convex choice function from a binary relation.
We investigate a generalisation of the coherent choice functions considered by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no Archimedeanity condition. We define our choice functions on vector spaces of options, which allows us to incorporate as special cases both Seidenfeld et al.'s (2010) choice functions on horse lotteries and sets of desirable gambles (Quaeghebeur, 2014), and to investigate their connections. We show that choice functions based on sets of desirable options (gambles) satisfy Seidenfeld's convexity axiom only for very particular types of sets of desirable options, which are in a one-to-one relationship with the lexicographic probabilities. We call them lexicographic choice functions. Finally, we prove that these choice functions can be used to determine the most conservative convex choice function associated with a given binary relation.