Demo Data from EDBT 2026 / reviewers in the wild / expert
Farzad Pourbabaee
dblp:289/7077
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0001-5259-9690ORCID · 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% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › multi-agent systems › multi-agent learning
social learning |
0.7 | 1 | 2023 | The Hazards and Benefits of Condescension in Social Learning · EC 2023 |
Methods — techniques the papers use, named apart from their topics
sequential model · 1.3bayesian inference · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | The Hazards and Benefits of Condescension in Social LearningabstractIn a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension. Itai Arieli, Yakov Babichenko, Farzad Pourbabaee, Omer Tamuz |
EC | 4 |