AI Agents Face Fundamental Limitations in Handling Incommensurable Choices

A recent study presents the view that existing AI agents, which function as optimizers, face two primary challenges when confronted with difficult decisions involving incommensurable options due to multiple objectives. The Identification Problem hinders agents employing Multi-Objective Optimization (MOO) from recognizing incommensurability, resulting in three notable alignment challenges: blockage, unreliability, and untrustworthiness. Traditional solutions, such as Human-in-the-Loop methods, are found lacking in numerous decision-making contexts. Even if the identification issue is addressed, a further Resolution Problem persists. This paper, which takes a technology-focused perspective diverging from traditional philosophical discussions, proposes an ensemble solution as a viable alternative. It is available on arXiv with the identifier 2504.15304v2 and investigates essential design limitations in AI decision-making frameworks.