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Atila Abdulkadiroglu

dblp:48/11408 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0003-0450-2967ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 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 · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
distributional objectives
0.912025
Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025
Algorithmic game theory and mechanism design
fair division
0.912025
Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025
Algorithmic game theory and mechanism design
market design
0.912025
Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025
Algorithmic game theory and mechanism design › matching
matching and assignment
0.912025
Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025
Algorithmic game theory and mechanism design › market design › matching markets
school choice
0.912025
Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach · EC 2025

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

priority-based assignment · 0.9axiomatic analysis · 0.9
YearPublicationVenuePosition
2025 Market Design for Distributional Objectives in Allocation Problems: An Axiomatic Approach
abstract
We study a many-to-one assignment problem, where a set of applicants need to be assigned to a set of schools with limited seats. We formulate distributional objectives by a set of standard axioms. Axiom 1 has two parts. First, the number of applicants of each type at each school should be less than the corresponding quota. Second, whenever the number of applicants of a given type assigned to a school is less than the corresponding reserve, then it should be that no other applicant of that type prefers the school to their own assignment. Axiom 2 requires that whenever a school has available seats, it should be that there is no applicant that prefers that school to their own assignment. Finally, we say that an applicant a violates the priority of another applicant b at school s, if b prefers s to their own assignment, and b has a higher priority at that school than a. In this case, the triplet (a, b, s) is called a priority violation instance. Axiom 3 requires that there are no priority violation instances including two applicants of the same type.
Atila Abdulkadiroglu, Aram Grigoryan
EC1