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Pareto optimal allocation under uncertain preferences: Uncertainty models, algorithms, and complexity

Pareto optimal allocation under uncertain preferences: Uncertainty models, algorithms, and complexity
Pareto optimal allocation under uncertain preferences: Uncertainty models, algorithms, and complexity
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Agents express preferences over indivisible items and then the items are allocated based on these preferences. Pareto optimality is regarded as a desirable property for the chosen allocation, requiring that no other allocation exists in which no agent is worse off and at least one agent is better off. We consider the assignment problem with the additional feature that agents’ preferences involve uncertainty. The setting with uncertainty leads to a number of interesting questions including the following ones. How to compute an assignment with the highest probability of being Pareto optimal? What is the complexity of computing the probability that a given assignment is Pareto optimal? Does there exist an assignment that is Pareto optimal with probability one? We consider these problems under five natural uncertainty models. For all of the models, we present a number of algorithmic and complexity results highlighting the differences and similarities in the complexity of the models. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.
Fair division, Resource allocation, Pareto optimality, Uncertain preferences
0004-3702
57-78
Aziz, Haris
ca1da602-f9b6-4d08-bd30-c822a1e2e54b
Biró, Péter
ec1199ef-3603-4075-ba20-2f388270894f
de Haan, Ronald
2d29a3e0-f335-4cdf-a62b-c1c293283839
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1
Aziz, Haris
ca1da602-f9b6-4d08-bd30-c822a1e2e54b
Biró, Péter
ec1199ef-3603-4075-ba20-2f388270894f
de Haan, Ronald
2d29a3e0-f335-4cdf-a62b-c1c293283839
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1

Aziz, Haris, Biró, Péter, de Haan, Ronald and Rastegari, Baharak (2019) Pareto optimal allocation under uncertain preferences: Uncertainty models, algorithms, and complexity. Artificial Intelligence, 276, 57-78. (doi:10.1016/j.artint.2019.08.002).

Record type: Article

Abstract

The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Agents express preferences over indivisible items and then the items are allocated based on these preferences. Pareto optimality is regarded as a desirable property for the chosen allocation, requiring that no other allocation exists in which no agent is worse off and at least one agent is better off. We consider the assignment problem with the additional feature that agents’ preferences involve uncertainty. The setting with uncertainty leads to a number of interesting questions including the following ones. How to compute an assignment with the highest probability of being Pareto optimal? What is the complexity of computing the probability that a given assignment is Pareto optimal? Does there exist an assignment that is Pareto optimal with probability one? We consider these problems under five natural uncertainty models. For all of the models, we present a number of algorithmic and complexity results highlighting the differences and similarities in the complexity of the models. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.

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Accepted/In Press date: 3 August 2019
e-pub ahead of print date: 9 August 2019
Published date: November 2019
Keywords: Fair division, Resource allocation, Pareto optimality, Uncertain preferences

Identifiers

Local EPrints ID: 433512
URI: http://eprints.soton.ac.uk/id/eprint/433512
ISSN: 0004-3702
PURE UUID: a8a2eaae-51da-4bf6-8964-1fb74b9ac7a9
ORCID for Baharak Rastegari: ORCID iD orcid.org/0000-0002-0985-573X

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Date deposited: 23 Aug 2019 16:30
Last modified: 26 Nov 2021 05:53

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Contributors

Author: Haris Aziz
Author: Péter Biró
Author: Ronald de Haan
Author: Baharak Rastegari ORCID iD

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