Reasons and Means to Model Preferences as Incomplete

TL;DR

This paper reviews the reasons for and methods of modeling preferences as incomplete, challenging the common assumption of complete preferences in artificial and human agent modeling.

Contribution

It provides a comprehensive review of motivations and techniques for representing preferences as incomplete, highlighting the need for more flexible models.

Findings

Preference models often need to be incomplete due to real-world complexity.

Various techniques exist to model incomplete preferences.

Incomplete preference modeling can better reflect actual decision-making processes.

Abstract

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences.