EDBT 2026 Demo / reviewers in the wild / expert
Christoph Carnehl
dblp:342/5518
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0001-5305-5388ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 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 · 100% |
Topics — the 2 heaviest of 2, 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 |
0.9 | 1 | 2025 | Inputs or Outputs: What to Test and How to Test · EC 2025 |
Algorithmic game theory and mechanism design
mechanism design |
0.9 | 1 | 2025 | Inputs or Outputs: What to Test and How to Test · EC 2025 |
Methods — techniques the papers use, named apart from their topics
mechanism design · 0.9competitive market model · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Inputs or Outputs: What to Test and How to TestabstractWe study the optimal design of tests in environments where agents can invest in inputs (e.g., effort) to enhance their individual outputs (e.g., human capital), which are then sold in a competitive market (e.g., labor market). Recognizing agents' heterogeneity in converting inputs into outputs, the designer devises a test to maximize the expected total output. Matteo Camboni, Christoph Carnehl |
EC | 2 |