The University of Southampton
University of Southampton Institutional Repository

Efficiency and fairness of resource utilisation under uncertainty: doctoral consortium

Efficiency and fairness of resource utilisation under uncertainty: doctoral consortium
Efficiency and fairness of resource utilisation under uncertainty: doctoral consortium
The problem of multi-agent resource allocation is important and well-studied within AI and economics. The general assumption is that the amount of each resource is known beforehand. However, many real-world problems, the exact amount of each resource may not be known at the time of decision making, e,g. in the case of weather dependent renewable energy production. This work considers a homogeneous divisible resource where the available amount is given by a probability distribution. In general, a model for efficient usage under fairness and the possibilities of manipulation is studied. Firstly, the notion of ex-ante envy-freeness, where, in expectation, agents weakly prefer their allocation over every other agent's allocation is introduced. For this case the tension between fairness and social welfare is considered. The price of envy-freeness is at least $\Omega(n)$, where $n$ is the number of agents and the problem of optimising ex-ante social welfare subject to ex-ante envy-freeness is strongly NP-hard. Additionally, the possibility for an integer program to calculate the optimal ex-ante envy-free allocation for linear satiable valuation functions is presented.
Fair allocation, Social choice theory, Auctions and mechanism design
Buermann, Jan
d12aad54-d71f-4eba-9510-daf0ee2c7fc3
Buermann, Jan
d12aad54-d71f-4eba-9510-daf0ee2c7fc3

Buermann, Jan (2020) Efficiency and fairness of resource utilisation under uncertainty: doctoral consortium. Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems, New Zealand. 09 - 13 May 2020. 3 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

The problem of multi-agent resource allocation is important and well-studied within AI and economics. The general assumption is that the amount of each resource is known beforehand. However, many real-world problems, the exact amount of each resource may not be known at the time of decision making, e,g. in the case of weather dependent renewable energy production. This work considers a homogeneous divisible resource where the available amount is given by a probability distribution. In general, a model for efficient usage under fairness and the possibilities of manipulation is studied. Firstly, the notion of ex-ante envy-freeness, where, in expectation, agents weakly prefer their allocation over every other agent's allocation is introduced. For this case the tension between fairness and social welfare is considered. The price of envy-freeness is at least $\Omega(n)$, where $n$ is the number of agents and the problem of optimising ex-ante social welfare subject to ex-ante envy-freeness is strongly NP-hard. Additionally, the possibility for an integer program to calculate the optimal ex-ante envy-free allocation for linear satiable valuation functions is presented.

Text
Efficiency_and_Fairness_of_Resource_Utilisation_under_Uncertainty - Accepted Manuscript
Available under License Other.
Download (629kB)

More information

Accepted/In Press date: 18 February 2020
Published date: 14 May 2020
Venue - Dates: Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems, New Zealand, 2020-05-09 - 2020-05-13
Keywords: Fair allocation, Social choice theory, Auctions and mechanism design

Identifiers

Local EPrints ID: 439156
URI: http://eprints.soton.ac.uk/id/eprint/439156
PURE UUID: 09b129e5-e3a9-4b82-a13e-df8d445b2e71
ORCID for Jan Buermann: ORCID iD orcid.org/0000-0002-4981-6137

Catalogue record

Date deposited: 06 Apr 2020 16:30
Last modified: 22 May 2020 00:39

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×