EDBT 2026 Demo / reviewers in the wild / expert
Orestis Vravosinos
dblp:410/3745
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › mechanism design › information design
information disclosure |
0.9 | 1 | 2025 | Multidimensional screening of strategic candidates · EC 2025 |
Algorithmic game theory and mechanism design
mechanism design |
0.9 | 1 | 2025 | Multidimensional screening of strategic candidates · EC 2025 |
Algorithmic game theory and mechanism design › mechanism design › optimal mechanism design
multidimensional screening |
0.9 | 1 | 2025 | Multidimensional screening of strategic candidates · EC 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Multidimensional screening of strategic candidatesabstractThis 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 |
EC | 1 |