An AGM Approach to Revising Preferences
This work addresses preference revision for AI and decision-making systems, but it is incremental as it extends existing AGM belief change frameworks to preferences.
The paper tackles the problem of revising an initial preference ranking when new authoritative preference information conflicts with it, aiming to align the two without unnecessary information loss. It models this using AGM belief change, proposes rationality postulates, and derives representation theorems showing such preference change can be rationalized by a choice function based on a ranking of initial comparisons.
We look at preference change arising out of an interaction between two elements: the first is an initial preference ranking encoding a pre-existing attitude; the second element is new preference information signaling input from an authoritative source, which may come into conflict with the initial preference. The aim is to adjust the initial preference and bring it in line with the new preference, without having to give up more information than necessary. We model this process using the formal machinery of belief change, along the lines of the well-known AGM approach. We propose a set of fundamental rationality postulates, and derive the main results of the paper: a set of representation theorems showing that preference change according to these postulates can be rationalized as a choice function guided by a ranking on the comparisons in the initial preference order. We conclude by presenting operators satisfying our proposed postulates. Our approach thus allows us to situate preference revision within the larger family of belief change operators.