Demo Data from EDBT 2026 / reviewers in the wild / expert
Joseph M. Golden
dblp:190/1096
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Theory of computation · 1
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational finance and economics · 50% Computational social science and digital humanities · 50% | |
| 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 |
|---|---|---|---|---|
Computational finance and economics › electronic commerce
online marketplace |
0.3 | 1 | 2018 | Reputation Inflation · EC 2018 |
Computational social science and digital humanities › social computing
reputation system |
0.3 | 1 | 2018 | Reputation Inflation · EC 2018 |
Algorithmic game theory and mechanism design › mechanism design
information asymmetry |
0.1 | 1 | 2018 | Reputation Inflation · EC 2018 |
Methods — techniques the papers use, named apart from their topics
empirical analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Reputation InflationabstractA solution to marketplace information asymmetries is to have trading partners publicly rate each other post-transaction. Many have shown these ratings are effective; we show that their effectiveness deteriorates over time. The problem is that ratings are prone to inflation, with raters feeling pressure to leave "above average" ratings, which in turn pushes the average higher. This pressure stems from raters' desire to not harm the rated seller. As the potential to harm is what makes ratings effective, reputation systems, as currently designed, sow the seeds of their own irrelevance. Apostolos Filippas, John Joseph Horton, Joseph M. Golden |
EC | 3 |