EDBT 2026 Demo / reviewers in the wild / expert
Thomas Brzustowski
dblp:392/7794
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0000-8667-2753ORCID · 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 5 heaviest of 5, 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
bayesian persuasion |
0.8 | 1 | 2024 | Encouraging a Go-Getter · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design
contract theory |
0.8 | 1 | 2024 | Encouraging a Go-Getter · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design
information design |
0.8 | 1 | 2024 | Encouraging a Go-Getter · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design › information design
information disclosure |
0.8 | 1 | 2024 | Encouraging a Go-Getter · EC 2024 |
Algorithmic game theory and mechanism design › mechanism design › contract theory
moral hazard |
0.8 | 1 | 2024 | Encouraging a Go-Getter · EC 2024 |
Methods — techniques the papers use, named apart from their topics
optimal policy design · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Encouraging a Go-GetterabstractConsider a decision maker (he) who may receive an exogenously fixed reward if he takes a costly action above an unknown threshold. The information available to the decision maker about the threshold is controlled by a designer (she), whose utility depends on the decision maker's action, in an increasing manner. The designer can commit to disclose arbitrary information about the threshold. The goal of this paper is to solve for the designer's optimal information disclosure policy. Thomas Brzustowski |
EC | 1 |