Decision-making with E-admissibility given a finite assessment of choices
This work addresses decision-making under uncertainty for scenarios with incomplete information, but it is incremental as it builds on existing frameworks like choice functions and E-admissibility.
The paper tackles the problem of extending limited rejection information to a full decision-making model using E-admissibility, characterizing the most conservative extension and providing an algorithm based on solving linear feasibility problems.
Given information about which options a decision-maker definitely rejects from given finite sets of options, we study the implications for decision-making with E-admissibility. This means that from any finite set of options, we reject those options that no probability mass function compatible with the given information gives the highest expected utility. We use the mathematical framework of choice functions to specify choices and rejections, and specify the available information in the form of conditions on such functions. We characterise the most conservative extension of the given information to a choice function that makes choices based on E-admissibility, and provide an algorithm that computes this extension by solving linear feasibility problems.