Conditioning Accept-Desirability models in the context of AGM-like belief change
This work addresses theoretical foundations for belief change in decision-making, extending to imprecise probabilities and unifying classical and quantum contexts, but it is incremental as it builds on existing AGM and Accept-Desirability frameworks.
The paper tackles the problem of conditioning Accept-Desirability models in abstract decision-making, introducing a new conditioning rule based on event-induced indifferences and investigating its alignment with AGM belief revision axioms, with results showing that all axioms hold in classical propositional logic and full conditional probabilities cases.
We discuss conditionalisation for Accept-Desirability models in an abstract decision-making framework, where uncertain rewards live in a general linear space, and events are special projection operators on that linear space. This abstract setting allows us to unify classical and quantum probabilities, and extend them to an imprecise probabilities context. We introduce a new conditioning rule for our Accept-Desirability models, based on the idea that observing an event introduces new indifferences between options. We associate a belief revision operator with our conditioning rule, and investigate which of the AGM axioms for belief revision still hold in our more general framework. We investigate two interesting special cases where all of these axioms are shown to still hold: classical propositional logic and full conditional probabilities.