Advice Provision in Multiple Prospect Selection Problems
Year: 2013
Type: conference-paper
Abstract: When humans face a broad spectrum of topics, where each topic consists of several options, they usually make a decision on each topic separately. Usually, a person will perform better by making a global decision, however, taking all consequences into account is extremely difficult. We present a novel computational method for advice-generation in an environment where people need to decide among multiple selection problems. This method is based on the prospect theory and uses machine learning techniques. We graphically present this advice to the users and compare it with an advice which encourages the users to always select the option with a higher expected outcome. We show that our method outperforms the expected outcome approach in terms of user happiness and satisfaction.
Authors: Amos Azaria, Sarit Kraus
Institution: Bar-Ilan University
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W1526599251