@inproceedings{562b63a601694bfd9333a64f3680ccfe,
title = "Handling risk attitudes for preference learning and intelligent decision support",
abstract = "Intelligent decision support should allow integrating human knowledge with efficient algorithms for making interpretable and useful recommendations on real world decision problems. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. Here, risk attitudes are represented by means of fuzzy-linguistic structures, and an interactive methodology is proposed for learning preferences from a group of decision makers (DMs). The methodology is built on a multi-criteria framework allowing imprecise observations/measurements, where DMs reveal their attitudes in linguistic form and receive from the system their associated type, characterized by a preference order of the alternatives, together with the amount of consensus and dissention existing among the group. Following on the system's feedback, DMs can negotiate on a common attitude while searching for a satisfactory decision. ",
author = "{Franco de los R{\'i}os}, Camilo and Hougaard, {Jens Leth} and Kurt Nielsen",
year = "2015",
doi = "10.1007/978-3-319-23240-9_7",
language = "English",
isbn = "978-3-319-23239-3",
series = "Lecture notes in computer science",
publisher = "Springer Publishing Company",
pages = "78--89",
editor = "Torra, {Vicenc } and Narukawa, {Torra }",
booktitle = "Modeling Decisions for Artificial Intelligence",
}