4 research outputs found
Online Fair Division: A Survey
We survey a burgeoning and promising new research area that considers the
online nature of many practical fair division problems. We identify wide
variety of such online fair division problems, as well as discuss new
mechanisms and normative properties that apply to this online setting. The
online nature of such fair division problems provides both opportunities and
challenges such as the possibility to develop new online mechanisms as well as
the difficulty of dealing with an uncertain future.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
Indecision Modeling
AI systems are often used to make or contribute to important decisions in a
growing range of applications, including criminal justice, hiring, and
medicine. Since these decisions impact human lives, it is important that the AI
systems act in ways which align with human values. Techniques for preference
modeling and social choice help researchers learn and aggregate peoples'
preferences, which are used to guide AI behavior; thus, it is imperative that
these learned preferences are accurate. These techniques often assume that
people are willing to express strict preferences over alternatives; which is
not true in practice. People are often indecisive, and especially so when their
decision has moral implications. The philosophy and psychology literature shows
that indecision is a measurable and nuanced behavior -- and that there are
several different reasons people are indecisive. This complicates the task of
both learning and aggregating preferences, since most of the relevant
literature makes restrictive assumptions on the meaning of indecision. We begin
to close this gap by formalizing several mathematical \emph{indecision} models
based on theories from philosophy, psychology, and economics; these models can
be used to describe (indecisive) agent decisions, both when they are allowed to
express indecision and when they are not. We test these models using data
collected from an online survey where participants choose how to
(hypothetically) allocate organs to patients waiting for a transplant.Comment: Accepted at AAAI 202
Online voluntary mentoring: Optimising the assignment of students and mentors
After the closure of the schools in Hungary from March 2020 due to the
pandemic, many students were left at home with no or not enough parental help
for studying, and in the meantime some people had more free time and
willingness to help others in need during the lockdown. In this paper we
describe the optimisation aspects of a joint NGO project for allocating
voluntary mentors to students using a web-based coordination mechanism. The
goal of the project has been to form optimal pairs and study groups by taking
into the preferences and the constraints of the participants. In this paper we
present the optimisation concept, and the integer programming techniques used
for solving the allocation problems. Furthermore, we conducted computation
simulations on real and generated data for evaluate the performance of this
dynamic matching scheme under different parameter settings