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Aghaheybat Mammadov

dblp:346/0725 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 93% Computational complexity · 7%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › fair division › envy-freeness
EFX allocation
0.712023
Fairly Allocating Goods and (Terrible) Chores · IJCAI 2023
Algorithmic game theory and mechanism design
fair division
0.712023
Fairly Allocating Goods and (Terrible) Chores · IJCAI 2023
Algorithmic game theory and mechanism design › fair division
indivisible goods and chores
0.712023
Fairly Allocating Goods and (Terrible) Chores · IJCAI 2023
Algorithmic game theory and mechanism design › fair division › share-based fairness
maximin share
0.712023
Fairly Allocating Goods and (Terrible) Chores · IJCAI 2023

Methods — techniques the papers use, named apart from their topics

pareto optimality · 0.7lexicographic preferences · 0.7
YearPublicationVenuePosition
2023 Fairly Allocating Goods and (Terrible) Chores
abstract
We study the fair allocation of mixture of indivisible goods and chores under lexicographic preferences---a subdomain of additive preferences. A prominent fairness notion for allocating indivisible items is envy-freeness up to any item (EFX). Yet, its existence and computation has remained a notable open problem. By identifying a class of instances with "terrible chores", we show that determining the existence of an EFX allocation is NP-complete. This result immediately implies the intractability of EFX under additive preferences. Nonetheless, we propose a natural subclass of lexicographic preferences for which an EFX and Pareto optimal (PO) allocation is guaranteed to exist and can be computed efficiently for any mixed instance. Focusing on two weaker fairness notions, we investigate finding EF1 and Pareto optimal allocations for special instances with terrible chores, and show that MMS and PO allocations can be computed efficiently for any mixed instance with lexicographic preferences.
Hadi Hosseini, Aghaheybat Mammadov, Tomasz Was
IJCAI2