Orestis Vravosinos

dblp:410/3745 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0002-4121-8052ORCID · 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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › mechanism design › information design
information disclosure
0.912025
Multidimensional screening of strategic candidates · EC 2025
Algorithmic game theory and mechanism design
mechanism design
0.912025
Multidimensional screening of strategic candidates · EC 2025
Algorithmic game theory and mechanism design › mechanism design › optimal mechanism design
multidimensional screening
0.912025
Multidimensional screening of strategic candidates · EC 2025
YearPublicationVenuePosition
2025 Multidimensional screening of strategic candidates
abstract
This paper proposes a novel model of multidimensional screening, where a candidate (she) with two attributes—training and talent—chooses how much hard evidence of training to present. Then, the evaluator (he) possibly verifies at a cost the value of a composite measure of the candidate's training and talent before deciding whether to accept or reject the candidate. The candidate cannot unilaterally provide evidence of talent. The composite measure is increasing in both training and talent, and the evaluator (weakly) values both training and talent in the candidate. If the evaluator is going to verify the value of the composite measure, the candidate may have incentives to withhold evidence of training—although the evaluator values training—to influence how the evaluator interprets the composite measure. Particularly, she may want to withhold evidence of training to make the evaluator attribute the composite measure to talent instead, thereby overestimating her talent.
Orestis Vravosinos
EC1