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Mariagrazia Cavallo

dblp:392/7846 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-5132-4716ORCID · 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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › matching › matching mechanism
deferred acceptance
0.812024
When Geography Shapes Preferences: Redesigning Teacher Assignment in Italy · EC 2024
Algorithmic game theory and mechanism design › matching
matching theory
0.812024
When Geography Shapes Preferences: Redesigning Teacher Assignment in Italy · EC 2024
Algorithmic game theory and mechanism design › matching
stable matching
0.812024
When Geography Shapes Preferences: Redesigning Teacher Assignment in Italy · EC 2024
Algorithmic game theory and mechanism design › social choice › voting
tie-breaking rule
0.812024
When Geography Shapes Preferences: Redesigning Teacher Assignment in Italy · EC 2024

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

mechanism design · 0.8empirical analysis · 0.8
YearPublicationVenuePosition
2024 When Geography Shapes Preferences: Redesigning Teacher Assignment in Italy
abstract
We investigate Italy's centralized teacher assignment system where teachers can rank "geographical regions", leading to ties in their rank order lists (ROLs). We show that the way ties in teachers' ROLs are resolved in the current assignment mechanism systematically violates teachers' priority rights and results in justified envy. We provide a practical solution to this problem. We introduce a new assignment mechanism called Deferred Acceptance with Hierarchical Choice (DA-HC), which is strategy-proof, eliminates justified envy, and Pareto improves over the benchmark deferred acceptance mechanism with simple tie-breaking (DA-STB). We show that two commonly used efficiency notions from the literature, Pareto efficiency subject to eliminating justified envy and size efficiency subject to eliminating justified envy, do not apply effectively in this context as they are not compatible with strategy-proofness. We introduce a new efficiency concept and show that the DA-HC mechanism is optimal in efficiency terms within the class of strategy-proof mechanisms that eliminate justified envy. Using administrative data, we provide evidence that our proposed mechanism can potentially bring significant welfare improvements over the benchmark DA-STB.
Mariagrazia Cavallo, Battal Dogan
EC1